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We are the last.

The last generation to be unaugmented.

The last generation to be intellectually alone.

The last generation to be limited by our bodies.

We are the first.

The first generation to be augmented.

The first generation to be intellectually together.

The first generation to be limited only by our imaginations.

We stand both before and after, balancing on the razor edge of the Event

Horizon of the Singularity. That this sublime juxtapositional tautology

has gone unnoticed until now is itself remarkable.

We're so exquisitely privileged to be living in this time, to be born

right on the precipice of the greatest paradigm shift in human history,

the only thing that approaches the importance of that reality is finding

like minds that realize the same, and being able to make some connection

with them.

If these books have influenced you the same way that they have us, we

invite your contact at the email addresses listed below.

Enjoy,

Michael Beight, piman_314@yahoo.com

Steven Reddell, cronyx@gmail.com

Here are some new links that we’ve found interesting:

KurzweilAI.net

News articles, essays, and discussion on the latest topics in

technology and accelerating intelligence.

SingInst.org

The Singularity Institute for Artificial Intelligence: A

think tank devoted to increasing Humanity’s odds of

experiencing a safe, beneficial Singularity. Many

interesting articles on such topics as Friendly AI,

Existential Risks.

SingInst.org/Media

Videos, audio, and PowerPoints from the Singularity Summits;

and videos about SIAI’s purpose.

blinkx.com/videos/kurzweil

Videos on the internet in which the word “Kurzweil” is

spoken. Great new resource!

[Inside Jacket]:

U.S. $25.95

(continued from front flap)

C A N A D A $ 3 6 . 9 9

I

out brains along direct neural pathways;

magine a world where the difference

computers, for their part, will have read all

the world’s literature. The distinction

between man and machine blurs, where the

between us and computers will have

difference between humanity and

become sufficiently blurred that when the

technology fades, where the soul and the

machines claim to be conscious, we will

silicon chip unite. This is not science fiction.

believe them.

This is the twenty-first century according to

Ray Kurzweil, the “restless genius” (Wall

In The Age Of Spiritual Machines, the

Street Journal) and inventor of the most

“ultimate thinking machine” (Forbes) forges

innovative and compelling technology of our

the ultimate road to the next century.

era. In The Age of Spiritual Machines, the

brains behind the Kurzweil Reading

Machine, the Kurzweil Synthesizer,

advanced speech recognition, and other

technologies devises a framework for

envisioning the next century. In his inspiring

hands, life in the new millennium no longer

seems daunting. Instead, Kurzweil’s twenty-

first century promises to be age in which the

marriage of human sensitivity and artificial

Ray Kurzwe il

intelligence fundamentally alters and

is the author of The Age of Intelligent

improves the way we live.

Machines, which won the Association

American Publishers’ Award for The Most

The Age of Spiritual Machines is no mere list

Outstanding Computer Science Book of

of predictions but a prophetic blueprint for

1990. He was awarded the Dickson Prize,

the future. Kurzweil guides us through the

Carnegie Mellon’s top science prize, in 1994.

inexorable advances that will results in

The Massachusetts Institute of Technology

computers exceeding the memory capacity

named him the Inventor of the Year in 1988.

and computational ability of the human

He is the recipient of nine honorary

brain. According to Kurzweil, machines will

doctorates and honors from two U.S.

achieve all this by 2020, with human

presidents. Kurzweil lives in a suburb of

attributes not far behind. We will begin to

Boston.

have relationships with automated

JACKET DESIGN BY DAVID J. HIGH

personalities and use them as teachers,

AUTHOR PHOTOGRAPH BY JERRY BAUER

companions, and lovers. A mere ten years

later, information will be fed straight into

A Member of Penguin Putnam Inc

375 Hudson Street, New York, N.Y. 10014

http://www.penguinputnam.com

Printed in U.S.A.

(continued on back flap) 0199

THE AGE OF

SPIRITUAL

MACHINES

ALSO BY RAY KURZWEIL

The 10% Solution for a Healthy Life

The Age of Intelligent Machines

THE AGE OF

SPIRITUAL

MACHINES

WHEN COMPUTERS EXCEED HUMAN INTELLIGENCE

RAY KURZWEIL

VIKING

Published by the Penguin Group

Penguin Putnam Inc., 375 Hudson Street,

New York, New York 10014, U.S.A.

Penguin Books Ltd, 27 Wrights Lane, London W8 5TZ, England

Penguin Books Australia Ltd., Ringwood, Victoria, Australia

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Toronto, Ontario, Canada M4V 3B2

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Auckland 10, New Zealand

Penguin India, 210 Chiranjiv Tower, 43 Nehru Place,

New Delhi 11009, India

Penguin Books Ltd, Registered Offices:

Harmondsworth, Middlesex, England

First Published in 1999 by Viking Penguin,

a member of Penguin Books Inc.

1 3 5 7 9 10 8 6 4 2

Copyright © Ray Kurzweil, 1999

All rights reserved

Illustrations credits

Pages 24, 26–27, 104, 156: Concept and text by Ray Kurzweil. Illustration by Rose Russo and Robert Brun.

Page 72: © 1977 by Sidney Harris

Pages 167–168: Paintings by Aaron, a computerized robot built and programmed by Harold Cohen.

Photographed by Becky Cohen.

Page 188: Roz Chast © 1998. From The Cartoon Bank. All rights reserved.

Page 194: Danny Shanahan © 1994. From The New Yorker Collection. All rights reserved.

Page 219: Peter Steiner © 1997. From The New Yorker Collection. All rights reserved.

LIBRARY OF CONGRESS CATALOGING IN PUBLICATION DATA

Kurzweil, Ray.

The age of spiritual machines / Ray Kurzweil.

p. cm.

Includes bibliographical references and index.

ISBN 0‐670‐88217‐8

1. Artificial Intelligence. 2. Computers. 3. Title.

Q335.K88 1999

006.3—dc21 98–38804

This book is printed on acid‐free paper.

Printed in the United States of America

Set in Berkeley Oldstyle

Without limiting the rights under copyright reserved above, no part of this publication may be

reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means

(electronic, mechanical, photocopying, recording or otherwise), without the prior written

permission of both the copyright owner and the above publisher of this book.

A NOTE TO THE READER

As a photon wends its way through an arrangement of glass panes and mirrors, its path remains ambiguous. It essentially takes every possible path available to it (apparently these photons have not read Robert Frostʹs poem ʺThe Road Not Takenʺ). This ambiguity remains until observation by a conscious observer forces the particle to decide which path it had taken. Then the uncertainty is resolved—retroactively—and it is as if the selected path had been taken all along.

Like these quantum particles, you—the reader—have choices to make in your path through this book. You can read the chapters as I intended them to be read, in sequential order. Or, after reading the Prologue, you may decide that the future canʹt wait, and you wish to immediately jump to the chapters in Part III on the twenty‐first century (the table of contents on the next pages offers a description of each chapter). You may then make your way back to the earlier chapters that describe the nature and origin of the trends and forces that will manifest themselves in this coming century. Or, perhaps, your course will remain ambiguous until the end. But when you come to the Epilogue,

any remaining ambiguity will be resolved, and it will be as if you had always intended to read the book in the order that you selected.

CONTENTS

A NOTE TO THE READER

9

ACKNOWLEDGEMENTS

13

PROLOGUE: AN INEXORABLE EMERGENCE

14

Before the next century is over, human beings will no longer be the most intelligent or capable type of entity

on the planet. Actually, let me take that back. The truth of that last statement depends on how we define

human.

PART ONE: PROBING THE PAST

CHAPTER ONE: THE LAW OF TIME AND CHAOS

19

For the past forty years, in accordance with Moore's Law, the power of transistor-based computing has been

growing exponentially. But by the year 2020, transistor features will be just a few atoms thick, and Moore's

Law will have run its course. What then? To answer this critical question, we need to understand the

exponential nature of time.

CHAPTER TWO: THE INTELLIGENCE OF THE UNIVERSE

40

Can an intelligence create another intelligence more intelligent than itself? Are we more intelligent than the

evolutionary process that created us? In turn, will the intelligence that we are creating come to exceed that of

its creator?

CHAPTER THREE: OF MIND AND MACHINES

47

"I am lonely and bored, please keep me company." If your computer displayed this message on its screen, would that convince you that it is conscious and has feelings? Before you say no too quickly, we need to

consider how such a plaintive message originated.

CHAPTER FOUR: A NEW FORM OF INTELLIGENCE ON EARTH

56

Intelligence rapidly creates satisfying, sometimes surprising plans that meet an array of constraints. Clearly,

no simple formula can emulate this most powerful of phenomena. Actually, that's wrong. All that is needed to

solve a surprisingly wide range of intelligent problems is exactly this: simple methods combined with heavy

doses of computation, itself a simple process.

CHAPTER FIVE: CONTEXT AND KNOWLEDGE

71

It is sensible to remember today's insights for tomorrow's challenges. It is not fruitful to rethink every

problem that comes along. This is particularly true for humans, due to the extremely slow speed of our

computing circuitry.

PART TWO: PREPARING THE PRESENT

CHAPTER SIX: BUILDING NEW BRAINS . . .

78

Evolution has found a way around the computational limitations of neural circuitry. Cleverly, it has created

organisms who in turn invented a computational technology a million times faster than carbon-based

neurons. Ultimately, the computing conducted on extremely slow mammalian neural circuits will be ported to

a far more versatile and speedier electronic (and photonic) equivalent.

CHAPTER SEVEN: . . . AND BODIES

98

A disembodied mind will quickly get depressed. So what kind of bodies will we provide for our twenty-first-

century machines? Later on, the question will become: What sort of bodies will they provide for themselves?

CHAPTER EIGHT: 1999

114

If all the computers in 1960 stopped functioning, few people would have noticed. Circa 1999 is another matter.

Although computers still lack a sense of humor, a gift for small talk, and other endearing qualities of human

thought, they are nonetheless mastering an increasingly diverse array of tasks that previously required

human intelligence.

PART THREE: TO FACE THE FUTURE

CHAPTER NINE: 2009

137

It is now 2009. A $1,000 personal computer can perform about a trillion calculations per second. Computers

are imbedded in clothing and jewelry. Most routine business transactions take place between a human and a

virtual personality. Translating telephones are commonly used. Human musicians routinely jam with

cybernetic musicians. The neo-Luddite movement is growing.

CHAPTER TEN: 2019

146

A $1,000 computing device is now approximately equal to the computational ability of the human brain.

Computers are now largely invisible and are embedded everywhere. Three-dimensional virtual-reality

displays, embedded in glasses and contact lenses, provide the primary interface for communication with other

persons, the Web, and virtual reality. Most interaction with computing is through gestures and two-way

natural-language spoken communication. Realistic all-encompassing visual, auditory, and tactile

environments enable people to do virtually anything with anybody regardless of physical proximity. People

are beginning to have relationships with automated personalities as companions, teachers, caretakers, and

lovers.

CHAPTER ELEVEN: 2029

161

A $1,000 unit of computation has the computing capacity of approximately one thousand human brains.

Direct neural pathways have been perfected for high-bandwidth connection to the human brain. A range of

neural implants is becoming available to enhance visual and auditory perception and interpretation, memory,

and reasoning. Computers have read all available human- and machine-generated literature and multimedia

material. There is growing discussion about the legal rights of computers and what constitutes being human.

Machines claim to be conscious and these claims are largely accepted.

CHAPTER TWELVE: 2099

173

There is a strong trend toward a merger of human thinking with the world of machine intelligence that the

human species initially created. There is no longer any clear distinction between humans and computers.

Most conscious entities do not have a permanent physical presence. Machine-based intelligences derived from

extended models of human intelligence claim to be human. Most of these intelligences are not tied to a

specific computational processing unit. The number of software-based humans vastly exceeds those still using

native neuron-cell-based computation. Even among those human intelligences still using carbon-based

neurons, there is ubiquitous use of neural-implant technology that provides enormous augmentation of

human perceptual and cognitive abilities. Humans who do not utilize such implants are unable to

meaningfully participate in dialogues with those who do. Life expectancy is no longer a viable term in relation

to intelligent beings.

EPILOGUE: THE REST OF THE UNIVERSE REVISITED

191

Intelligent beings consider the fate of the universe.

TIME LINE

196

HOW TO BUILD AN INTELLIGENT MACHINE IN THREE EASY PARADIGMS

213

GLOSSARY

227

NOTES

241

SUGGESTED READINGS

263

WEB LINKS

282

INDEX [Omitted]

289

ACKNOWLEDGMENTS

I would like to express my gratitude to the many persons who have provided inspiration, patience, ideas, criticism, insight, and all manner of assistance for this project. In particular, I would like to thank:

• My wife, Sonya, for her loving patience through the twists and turns of the creative process

• My mother for long engaging walks with me when I was a child in the Woods of Queens (yes, there were

forests in Queens, New York, when I was growing up) and for her enthusiastic interest in and early

support for my not‐always‐fully‐baked ideas

• My Viking editors, Barbara Grossman and Dawn Drzal, for their insightful guidance and editorial

expertise and the dedicated team at Viking Penguin, including Susan Petersen, publisher; Ivan Held and

Paul Slovak, marketing executives; John Jusino, copy editor; Betty Lew, designer; Jariya Wanapun,

editorial assistant, and Laura Ogar, indexer

• Jerry Bauer for his patient photography

• David High for actually devising a spiritual machine for the cover My literary agent, Loretta Barrett, for

helping to shape this project

• My wonderfully capable researchers, Wendy Dennis and Nancy Mulford, for their dedicated and

resourceful efforts, and Tom Garfield for his valuable assistance

• Rose Russo and Robert Brun for turning illustration ideas into beautiful visual presentations

• Aaron Kleiner for his encouragement and support

• George Gilder for his stimulating thoughts and insights

• Harry George, Don Gonson, Larry Janowitch, Hannah Kurzweil, Rob Pressman, and Mickey Singer for

engaging and helpful discussions on these topics

• My readers: Peter Arnold, Melanie Baker‐Futorian, Loretta Barrett, Stephen Baum, Bryan Bergeron, Mike

Brown, Cheryl Cordima, Avi Coren, Wendy Dennis, Mark Dionne, Dawn Drzal, Nicholas Fabijanic, Gil

Fischman, Ozzie Frankell, Vicky Frankell, Bob Frankston, Francis Ganong, Tom Garfield, Harry George,

Audra Gerhardt, George Gilder, Don Gonson, Martin Greenberger, Barbara Grossman, Larry Janowitch,

Aaron Kleiner, Jerry Kleiner, Allen Kurzweil, Amy Kurzweil, Arielle Kurzweil, Edith Kurzweil, Ethan

Kurzweil, Hannah Kurzweil, Lenny Kurzweil, Missy Kurzweil, Nancy Kurzweil, Peter Kurzweil, Rachel

Kurzweil, Sonya Kurzweil, Jo Lernout, Jon Lieff, Elliot Lobel, Cyrus Mehta, Nancy Mulford, Nicholas

Mullendore, Rob Pressman, Vlad Sejnoha, Mickey Singer, Mike Sokol, Kim Storey, and Barbara Tyrell for

their compliments and criticisms (the latter being the most helpful) and many invaluable suggestions

• Finally, all the scientists, engineers, entrepreneurs, and artists who are busy creating the age of spiritual

machines.

PROLOGUE:

AN INEXORABLE EMERGENCE

The gambler had not expected to be here. But on reflection, he thought he had shown some kindness in his time. And

this place was even more beautiful and satisfying than he had imagined. Everywhere there were magnificent crystal

chandeliers, the finest handmade carpets, the most sumptuous foods, and, yes, the most beautiful women, who seemed intrigued with their new heaven mate. He tried his hand at roulette, and amazingly his number came up time

after time. He tried the gaming tables and his luck was nothing short of remarkable: He won game after game. Indeed

his winnings were causing quite a stir, attracting much excitement from the attentive staff, and from the beautiful women.

This continued day after day, week after week, with the gambler winning every game, accumulating bigger and

bigger earnings. Everything was going his way. He just kept on winning. And week after week, month after month,

the gamblerʹs streak of success remained unbreakable.

After a while, this started to get tedious. The gambler was getting restless; the winning was starting to lose its meaning. Yet nothing changed. He just kept on winning every game, until one day, the now anguished gambler turned to the angel who seemed to be in charge and said that he couldnʹt take it anymore. Heaven was not for him

after all. He had figured he was destined for the ʺother placeʺ nonetheless, and indeed that is where he wanted to be.

ʺBut this is the other place,ʺ came the reply.

That is my recollection of an episode of The Twilight Zone that I saw as a young child. I donʹt recall the title, but I would call it ʺBe Careful What You Wish For.ʺ [1] As this engaging series was wont to do, it illustrated one of the paradoxes of human nature: We like to solve problems, but we donʹt want them all solved, not too quickly, anyway.

We are more attached to the problems than to the solutions.

Take death, for example. A great deal of our effort goes into avoiding it. We make extraordinary efforts to delay it, and indeed often consider its intrusion a tragic event. Yet we would find it hard to live without it. Death gives meaning to our lives. It gives importance and value to time. Time would become meaningless if there were too much

of it. If death were indefinitely put off, the human psyche would end up, well, like the gambler in The Twilight Zone episode.

We do not yet have this predicament. We have no shortage today of either death or human problems. Few observers feel that the twentieth century has left us with too much of a good thing. There is growing prosperity, fueled not incidentally by information technology, but the human species is still challenged by issues and difficulties not altogether different than those with which it has struggled from the beginning of its recorded history.

The twenty‐first century will be different. The human species, along with the computational technology it created,

will be able to solve age‐old problems of need, if not desire, and will be in a position to change the nature of mortality in a post‐biological future. Do we have the psychological capacity for all the good things that await us? Probably not.

That, however, might change as well.

Before the next century is over, human beings will no longer be the most intelligent or capable type of entity on

the planet. Actually, let me take that back. The truth of that last statement depends on how we define human. And

here we see one profound difference between these two centuries: The primary political and philosophical issue of the next century will be the definition of who we are. [2]

But I am getting ahead of myself. This last century has seen enormous technological change and the social upheavals that go along with it, which few pundits circa 1899 foresaw. The pace of change is accelerating and has been since the inception of invention (as I will discuss in the first chapter, this acceleration is an inherent feature of technology). The result will be far greater transformations in the first two decades of the twenty‐first century than we saw in the entire twentieth century. However, to appreciate the inexorable logic of where the twenty‐first century will bring us, we have to go back and start with the present.

TRANSITION TO THE TWENTY‐FIRST CENTURY

Computers today exceed human intelligence in a broad variety of intelligent yet narrow domains such as playing

chess, diagnosing certain medical conditions, buying and selling stocks, and guiding cruise missiles. Yet human intelligence overall remains far more supple and flexible. Computers are still unable to describe the objects on a crowded kitchen table, write a summary of a movie, tie a pair of shoelaces, tell the difference between a dog and a cat (although this feat, I believe, is becoming feasible today with contemporary neural nets—computer simulations of human neurons), [3] recognize humor, or perform other subtle tasks in which their human creators excel.

One reason for this disparity in capabilities is that our most advanced computers are still simpler than the human

brain currently about a million times simpler (give or take one or two orders of magnitude depending on the assumptions used). But this disparity will not remain the case as we go through the early part of the next century.

Computers doubled in speed every three years at the beginning of the twentieth century, every two years in the 1950s and 1960s, and are now doubling in speed every twelve months. This trend will continue, with computers achieving

the memory capacity and computing speed of the human brain by around the year 2020.

Achieving the basic complexity and capacity of the human brain will not automatically result in computers matching the flexibility of human intelligence. The organization and content of these resources—the software of intelligence—is equally important. One approach to emulating the brainʹs software is through reverse engineering—

scanning a human brain (which will be achievable early in the next century) [4] and essentially copying its neural circuitry in a neural computer (a computer designed to simulate a massive number of human neurons) of sufficient

capacity.

There is a plethora of credible scenarios for achieving human‐level intelligence in a machine. We will be able to evolve and train a system combining massively parallel neural nets with other paradigms to understand language and model knowledge, including the ability to read and understand written documents. Although the ability of todayʹs computers to extract and learn knowledge from natural‐language documents is quite limited, their abilities in this domain are improving rapidly. Computers will be able to read on their own, understanding and modeling what

they have read, by the second decade of the twenty‐first century. We can then have our computers read all of the worldʹs literature books, magazines, scientific journals, and other available material. Ultimately, the machines will gather knowledge on their own by venturing into the physical world, drawing from the full spectrum of media and

information services, and sharing knowledge with each other (which machines can do far more easily than their human creators).

Once a computer achieves a human level of intelligence, it will necessarily roar past it. Since their inception, computers have significantly exceeded human mental dexterity in their ability to remember and process information.

A computer can remember billions or even trillions of facts perfectly while we are hard pressed to remember a handful of phone numbers. A computer can quickly search a database with billions of records in fractions of a second.

Computers can readily share their knowledge bases. The combination of human‐level intelligence in a machine with a

computerʹs inherent superiority in the speed, accuracy, and sharing ability of its memory will be formidable.

Mammalian neurons are marvelous creations, but we wouldnʹt build them the same way. Much of their

complexity is devoted to supporting their own life processes, not to their information‐handling abilities. Furthermore, neurons are extremely slow; electronic circuits are at least a million times faster. Once a computer achieves a human level of ability in understanding abstract concepts, recognizing patterns, and other attributes of human intelligence, it will be able to apply this ability to a knowledge base of all human‐acquired‐and machine‐acquired‐knowledge.

A common reaction to the proposition that computers will seriously compete with human intelligence is to dismiss this specter based primarily on an examination of contemporary capability. After all, when I interact with my personal computer, its intelligence seems limited and brittle, if it appears intelligent at all. It is hard to imagine oneʹs personal computer having a sense of humor, holding an opinion, or displaying any of the other endearing qualities of human thought.

But the state of the art in computer technology is anything but static. Computer capabilities are emerging today

that were considered impossible one or two decades ago. Examples include the ability to transcribe accurately normal continuous human speech, to understand and respond intelligently to natural language, to recognize patterns in medical procedures such as electrocardiograms and blood tests with an accuracy rivaling that of human physicians,

and, of course, to play chess at a world‐championship level. In the next decade, we will see translating telephones that provide real‐time speech translation from one human language to another, intelligent computerized personal assistants that can converse and rapidly search and understand the worldʹs knowledge bases, and a profusion of other machines with increasingly broad and flexible intelligence.

In the second decade of the next century, it will become increasingly difficult to draw any clear distinction between the capabilities of human and machine intelligence. The advantages of computer intelligence in terms of speed, accuracy, and capacity will be clear. The advantages of human intelligence, on the other hand, will become increasingly difficult to distinguish.

The skills of computer software are already better than many people realize. It is frequently my experience that when demonstrating recent advances in, say, speech or character recognition, observers are surprised at the state of the art. For example, a typical computer userʹs last experience with speech‐recognition technology may have been a

low‐end freely bundled piece of software from several years ago that recognized a limited vocabulary, required pauses between words, and did an incorrect job at that. These users are then surprised to see contemporary systems

that can recognize fully continuous speech on a 60,000‐word vocabulary, with accuracy levels comparable to a human

typist.

Also keep in mind that the progression of computer intelligence will sneak up on us. As just one example, consider Gary Kasparovʹs confidence in 1990 that a computer would never come close to defeating him. After all, he

had played the best computers, and their chess‐playing ability—compared to his—was pathetic. But computer chess

playing made steady progress, gaining forty‐five rating points each year. In 1997, a computer sailed past Kasparov, at least in chess. There has been a great deal of commentary that other human endeavors are far more difficult to emulate than chess playing. This is true. In many areas—the ability to write a book on computers, for example—

computers are still pathetic. But as computers continue to gain in capacity at an exponential rate we will have the same experience in these other areas that Kasparov had in chess. Over the next several decades, machine competence

will rival—and ultimately surpass—any particular human skill one cares to cite, including our marvelous ability to place our ideas in a broad diversity of contexts.

Evolution has been seen as a billion‐year drama that led inexorably to its grandest creation: human intelligence.

The emergence in the early twenty‐first century of a new form of intelligence on Earth that can compete with, and ultimately significantly exceed, human intelligence will be a development of greater import than any of the events that have shaped human history. It will be no less important than the creation of the intelligence that created it, and will have profound implications for all aspects of human endeavor, including the nature of work, human learning, government, warfare, the arts, and our concept of ourselves.

This specter is not yet here. But with the emergence of computers that truly rival and exceed the human brain in

complexity will come a corresponding ability of machines to understand and respond to abstractions and subtleties.

Human beings appear to be complex in part because of our competing internal goals. Values and emotions represent

goals that often conflict with each other, and are an unavoidable by‐product of the levels of abstraction that we deal with as human beings. As computers achieve a comparable—and greater—level of complexity, and as they are increasingly derived at least in part from models of human intelligence, they, too, will necessarily utilize goals with implicit values and emotions, although not necessarily the same values and emotions that humans exhibit.

A variety of philosophical issues will emerge. Are computers thinking, or are they just calculating? Conversely, are human beings thinking, or are they just calculating? The human brain presumably follows the laws of physics, so

it must be a machine, albeit a very complex one. Is there an inherent difference between human thinking and machine

thinking? To pose the question another way, once computers are as complex as the human brain, and can match the

human brain in subtlety and complexity of thought, are we to consider them conscious? This is a difficult question even to pose, and some philosophers believe it is not a meaningful question; others believe it is the only meaningful question in philosophy. This question actually goes back to Platoʹs time, but with the emergence of machines that genuinely appear to possess volition and emotion, the issue will become increasingly compelling.

For example, if a person scans his brain through a noninvasive scanning technology of the twenty‐first century (such as an advanced magnetic resonance imaging), and downloads his mind to his personal computer, is the ʺpersonʺ who emerges in the machine the same consciousness as the person who was scanned?

That ʺpersonʺ may convincingly implore you that ʺheʺ grew up in Brooklyn, went to college in Massachusetts, walked into a scanner here, and woke up in the machine there. The original person who was scanned, on the other

hand, will acknowledge that the person in the machine does indeed appear to share his history, knowledge, memory,

and personality, but is otherwise an impostor, a different person.

Even if we limit our discussion to computers that are not directly derived from a particular human brain, they will

increasingly appear to have their own personalities, evidencing reactions that we can only label as emotions and articulating their own goals and purposes. They will appear to have their own free will. They will claim to have spiritual experiences. And people—those still using carbon‐based neurons or otherwise—will believe them.

One often reads predictions of the next several decades discussing a variety of demographic, economic, and political trends that largely ignore the revolutionary impact of machines with their own opinions and agendas. Yet we need to reflect on the implications of the gradual, yet inevitable, emergence of true competition to the full range of human thought in order to comprehend the world that lies ahead.

PART ONE

PROBING THE

PAST

C H A P T E R O N E

THE LAW OF TIME AND CHAOS

A (VERY BRIEF) HISTORY OF THE UNIVERSE:

TIME SLOWING DOWN

The universe is made of stories, not of atoms.

—Muriel Rukeyser

Is the universe a great mechanism, a great computation, a great symmetry, a great accident or a great thought?

—John D. Barrow

As we start at the beginning, we will notice an unusual attribute of the nature of time, one that is critical to our passage to the twenty‐first century. Our story begins perhaps 15 billion years ago. No conscious life existed to appreciate the birth of our Universe at the time, but we appreciate it now, so retroactively it did happen. (In retrospect—from one perspective of quantum mechanics—we could say that any Universe that fails to evolve conscious life to apprehend its existence never existed in the first place.)

It was not until 10‐43 seconds (a tenth of a millionth of a trillionth of a trillionth of a trillionth of a second) after the birth of the Universe [1] that the situation had cooled off sufficiently (to 100 million trillion trillion degrees) that a distinct force—gravity—evolved.

Not much happened for another 10‐34 seconds (this is also a very tiny fraction of a second, but it is a billion times longer than 10‐43 seconds), at which point an even cooler Universe (now only a billion billion billion degrees) allowed the emergence of matter in the form of electrons and quarks. To keep things balanced, antimatter appeared as well. It was an eventful time, as new forces evolved at a rapid rate. We were now up to three: gravity, the strong force, [2]

and the electroweak force. [3]

After another 10‐10 seconds (a tenth of a billionth of a second), the electroweak force split into the electromagnetic and weak forces [4] we know so well today.

Things got complicated after another 10‐5 seconds (ten millionths of a second). With the temperature now down to

a relatively balmy trillion degrees, the quarks came together to form protons and neutrons. The antiquarks did the same, forming antiprotons.

Somehow, the matter particles achieved a slight edge. How this happened is not entirely clear. Up until then, everything had seemed so, well, even. But had everything stayed evenly balanced, it would have been a rather boring

Universe. For one thing, life never would have evolved, and thus we could conclude that the Universe would never

have existed in the first place.

For every 10 billion antiprotons, the Universe contained 10 billion and 1 protons. The protons and antiprotons collided, causing the emergence of another important phenomenon: light (photons). Thus, almost all of the antimatter was destroyed, leaving matter as dominant. (This shows you the danger of allowing a competitor to achieve even a

slight advantage.)

Of course, had antimatter won, its descendants would have called it matter and would have called matter antimatter, so we would be back where we started (perhaps that is what happened).

After another second (a second is a very long time compared to some of the earlier chapters in the Universeʹs history, so notice how the time frames are growing exponentially larger), the electrons and antielectrons (called positrons) followed the lead of the protons and antiprotons and similarly annihilated each other, leaving mostly the electrons.

After another minute, the neutrons and protons began coalescing into heavier nuclei, such as helium, lithium, and

heavy forms of hydrogen. The temperature was now only a billion degrees.

About 300,000 years later (things are slowing down now rather quickly), with the average temperature now only

3,000 degrees, the first atoms were created as the nuclei took control of nearby electrons.

After a billion years, these atoms formed large clouds that gradually swirled into galaxies.

After another two billion years, the matter within the galaxies coalesced further into distinct stars, many with their own solar systems.

Three billion years later, circling an unexceptional star on the arm of a common galaxy, an unremarkable planet

we call the Earth was born.

Now before we go any further, letʹs notice a striking feature of the passage of time. Events moved quickly at the

beginning of the Universeʹs history. We had three paradigm shifts in just the first billionth of a second. Later on, events of cosmological significance took billions of years. The nature of time is that it inherently moves in an exponential fashion either geometrically gaining in speed, or, as in the history of our Universe, geometrically slowing down. Time only seems to be linear during those eons in which not much happens. Thus most of the time, the linear

passage of time is a reasonable approximation of its passage. But thatʹs not the inherent nature of time.

Why is this significant? Itʹs not when youʹre stuck in the eons in which not much happens. But it is of great significance when you find yourself in the ʺknee of the curve,ʺ those periods in which the exponential nature of the curve of time explodes either inwardly or outwardly. Itʹs like falling into a black hole (in that case, time accelerates exponentially faster as one falls in).

The Speed of Time

But wait a second, how can we say that time is changing its ʺspeedʺ? We can talk about the rate of a process, in terms of its progress per second, but can we say that time is changing its rate? Can time start moving at, say, two seconds per second?

Einstein said exactly this—time is relative to the entities experiencing it. [5] One manʹs second can be another womanʹs forty years. Einstein gives the example of a man who travels at very close to the speed of light to a star—say, twenty light‐years away. From our Earth‐bound perspective, the trip takes slightly more than twenty years in each direction. When the man gets back, his wife has aged forty years. For him, however, the trip was rather brief. If he travels at close enough to the speed of light, it may have only taken a second or less (from a practical perspective we would have to consider some limitations, such as the time to accelerate and decelerate without crushing his body).

Whose time frame is the correct one?

Einstein says they are both correct, and exist only relative to each other.

Certain species of birds have a life span of only several years. If you observe their rapid movements, it appears that they are experiencing the passage of time on a different scale. We experience this in our own lives. A young childʹs rate of change and experience of time is different from that of an adult. Of particular note, we will see that the acceleration in the passage of time for evolution is moving in a different direction than that for the Universe from which it emerges.

It is in the nature of exponential growth that events develop extremely slowly for extremely long periods of time,

but as one glides through the knee of the curve, events erupt at an increasingly furious pace. And that is what we will experience as we enter the twenty‐first century.

EVOLUTION: TIME SPEEDING UP

In the beginning was the word. . . . And the word became flesh.

—John 1:1,14

A great deal of the universe does not need any explanation. Elephants, for instance. Once molecules have learnt to compete and create other molecules in their own image, elephants, and things resembling elephants, will in due course be found roaming through the countryside.

—Peter Atkins

The further backward you look, the further forward you can see.

—Winston Churchill

Weʹll come back to the knee of the curve, but letʹs delve further into the exponential nature of time. In the nineteenth century, a set of unifying principles called the laws of thermodynamics [6] was postulated. As the name implies, they deal with the dynamic nature of heat and were the first major refinement of the laws of classical mechanics perfected by Isaac Newton a century earlier. Whereas Newton had described a world of clockwork perfection in which particles

and objects of all sizes followed highly disciplined, predictable patterns, the laws of thermodynamics describe a world of chaos. Indeed, that is what heat is. Heat is the chaotic—unpredictable—movement of the particles that make

up the world. A corollary of the second law of thermodynamics is that in a closed system (interacting entities and forces not subject to outside influence; for example, the Universe), disorder (called ʺentropyʺ) increases. Thus, left to its own devices, a system such as the world we live in becomes increasingly chaotic. Many people find this describes their lives rather well. But in the nineteenth century, the laws of thermodynamics were considered a disturbing discovery. At the beginning of that century, it appeared that the basic principles governing the world were both understood and orderly There were a few details left to be filled in, but the basic picture was under control.

Thermodynamics was the first contradiction to this complacent picture. It would not be the last.

The second law of thermodynamics, sometimes called the Law of Increasing Entropy, would seem to imply that

the natural emergence of intelligence is impossible. Intelligent behavior is the opposite of random behavior, and any system capable of intelligent responses to its environment needs to be highly ordered. The chemistry of life, particularly of intelligent life, is comprised of exceptionally intricate designs. Out of the increasingly chaotic swirl of particles and energy in the world, extraordinary designs somehow emerged. How do we reconcile the emergence of

intelligent life with the Law of increasing Entropy?

There are two answers here. First, while the Law of Increasing Entropy would appear to contradict the thrust of

evolution, which is toward increasingly elaborate order, the two phenomena are not inherently contradictory. The order of life takes place amid great chaos, and the existence of life‐forms does not appreciably affect the measure of entropy in the larger system in which life has evolved. An organism is not a closed system. It is part of a larger system we call the environment, which remains high in entropy In other words, the order represented by the existence of life-forms is insignificant in terms of measuring overall entropy.

Thus, while chaos increases in the Universe, it is possible for evolutionary processes that create increasingly intricate, ordered patterns to exist simultaneously. [7] Evolution is a process, but it is not a closed system. It is subject to outside influence, and indeed draws upon the chaos in which it is embedded. So the Law of Increasing Entropy does not rule out the emergence of life and intelligence.

For the second answer, we need to take a closer look at evolution, as it was the original creator of intelligence.

The Exponentially Quickening Pace of Evolution

As you will recall, after billions of years, the unremarkable planet called Earth was formed. Churned by the energy of the sun, the elements formed more and more complex molecules. From physics, chemistry was born.

Two billion years later, life began. That is to say, patterns of matter and energy that could perpetuate themselves

and survive perpetuated themselves and survived. That this apparent tautology went unnoticed until a couple of centuries ago is itself remarkable.

Over time, the patterns became more complicated than mere chains of molecules. Structures of molecules performing distinct functions organized themselves into little societies of molecules. From chemistry, biology was born.

Thus, about 3.4 billion years ago, the first earthly organisms emerged: anaerobic (not requiring oxygen) prokaryotes (single‐celled creatures) with a rudimentary method for perpetuating their own designs. Early innovations that followed included a simple genetic system, the ability to swim, and photosynthesis, which set the stage for more advanced, oxygen‐consuming organisms. The most important development for the next couple of billion years was the DNA‐based genetics that would henceforth guide and record evolutionary development.

A key requirement for an evolutionary process is a ʺwrittenʺ

record of achievement, for otherwise the process would be doomed to repeat finding solutions to problems already solved. For the earliest organisms, the record was written (embodied) in their bodies, coded directly into the chemistry of their primitive cellular structures. With the invention of DNA‐based genetics, evolution had designed a digital computer to record its handiwork. This design permitted more complex experiments. The aggregations of molecules called cells organized themselves into societies of cells with the appearance of the first multicellular plants and animals about 700 million years ago. For the next 130 million years, the basic body plans of modern animals were designed, including a spinal cord‐based skeleton that provided early fish with an efficient swimming style.

So while evolution took billions of years to design the first primitive cells, salient events then began occurring in hundreds of millions of years, a distinct quickening of the pace. [8] When some calamity finished off the dinosaurs 65

million years ago, mammals inherited the Earth (although the insects might disagree). [9] With the emergence of the

primates, progress was then measured in mere tens of millions of years. [10] Humanoids emerged 15 million years ago, distinguished by walking on their hind legs, and now weʹre down to millions of years. [11]

With larger brains, particularly in the area of the highly convoluted cortex responsible for rational thought, our own species, Homo sapiens, emerged perhaps 500,000 years ago. Homo sapiens are not very different from other advanced primates in terms of their genetic heritage. Their DNA is 98.6 percent the same as the lowland gorilla, and 97.8 percent the same as the orangutans. [12] The story of evolution since that time now focuses in on a human-sponsored variant of evolution: technology.

TECHNOLOGY: EVOLUTION BY OTHER MEANS

When a scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.

The only way of discovering the limits of the possible is to venture a little way past them into the impossible.

Any sufficiently advanced technology is indistinguishable from magic.

—Arthur C. Clarkeʹs three laws of technology

A machine is as distinctively and brilliantly and expressively human as a violin sonata or a theorem in Euclid.

—Gregory Vlastos

Technology picks right up with the exponentially quickening pace of evolution. Although not the only tool‐using animal, Homo sapiens are distinguished by their creation of technology. [13] Technology goes beyond the mere fashioning and use of tools. It involves a record of tool making and a progression in the sophistication of tools. it requires invention and is itself a continuation of evolution by other means. The ʺgenetic codeʺ of the evolutionary process of technology is the record maintained by the tool‐making species. Just as the genetic code of the early life-forms was simply the chemical composition of the organisms themselves, the written record of early tools consisted of the tools themselves. Later on, the genesʺ of technological evolution evolved into records using written language and are now often stored in computer databases. Ultimately, the technology itself will create new technology. But we are getting ahead of ourselves.

Our story is now marked in tens of thousands of years. There were multiple subspecies of Homo sapiens. Homo sapiens neanderthalensis emerged about 100,000 years ago in Europe and the Middle East and then disappeared mysteriously about 35,000 to 40,000 years ago. Despite their brutish image, Neanderthals cultivated an involved culture that included elaborate funeral rituals—burying their dead with ornaments, including flowers. Weʹre not entirely sure what happened to our Homo sapiens cousins, but they apparently got into conflict with our own immediate ancestors Homo sapiens sapiens, who emerged about 90,000 years ago. Several species and subspecies of humanoids initiated the creation of technology. The most clever and aggressive of these subspecies was the only one

to survive. This established a pattern that would repeat itself throughout human history, in that the technologically more advanced group ends up becoming dominant. This trend may not bode well as intelligent machines themselves

surpass us in intelligence and technological sophistication in the twenty‐first century.

Our Homo sapiens sapiens subspecies was thus left alone among humanoids about 40,000 years ago.

Our forebears had already inherited from earlier hominid species and subspecies such innovations as the recording of events on cave walls, pictorial art, music, dance, religion, advanced language, fire, and weapons. For tens of thousands of years, humans had created tools by sharpening one side of a stone. It took our species tens of thousands of years to figure out that by sharpening both sides, the resultant sharp edge provided a far more useful

tool. One significant point, however, is that these innovations did occur, and they endured. No other tool‐using animal on Earth has demonstrated the ability to create and retain innovations in their use of tools.

The other significant point is that technology, like the evolution of life‐forms; that spawned it, is inherently an accelerating process. The foundations of technology—such as creating a sharp edge from a stone—took eons to perfect, although for human‐created technology, eons means thousands of years rather than the billions of years that the evolution of life‐forms required to get started.

Like the evolution of life‐forms, the pace of technology has greatly accelerated over time. [14] The progress of technology in the nineteenth century, for example, greatly exceeded that of earlier centuries, with the building of canals and great ships, the advent of paved roads, the spread of the railroad, the development of the telegraph, and the invention of photography, the bicycle, sewing machine, typewriter, telephone, phonograph, motion picture, automobile, and of course Thomas Edisonʹs light bulb. The continued exponential growth of technology in the first two decades of the twentieth century matched that of the entire nineteenth century. Today, we have major transformations in just a few yearsʹ time. As one of many examples, the latest revolution in communications—the World Wide Web—didnʹt exist just a few years ago.

WHAT IS TECHNOLOGY?

As technology is the continuation of evolution by other means, it shares the phenomenon of an exponentially

quickening pace. The word is derived from the Greek tekhnē, which means "craft" or "art," and logia, which means "the study of." Thus one interpretation of technology is the study of crafting, in which crafting refers to the shaping of resources for a practical purpose. I use the term resources rather than materials because

technology extends to the shaping of nonmaterial resources such as information.

Technology is often defined as the creation of tools to gain control over the environment, However, this

definition is not entirely sufficient. Humans are not alone in their use or even creation of tools. Orangutans in

Sumatra's Suaq Balimbing swamp make tools out of long sticks to break open termite nests. Crows fashion

tools from sticks and leaves. The leaf-cutter ant mixes dry leaves with its saliva to create a paste. Crocodiles

use tree roots to anchor dead prey. [15]

What is uniquely human is the application of knowledge—recorded knowledge—to the fashioning of tools.

The knowledge base represents the genetic code for the evolving technology. And as technology has evolved,

the means for recording this knowledge base has also evolved, from the oral traditions of antiquity to the

written design logs of nineteenth-century craftsmen to the computer-assisted design databases of the 1990s.

Technology also implies a transcendence of the materials used to comprise it. When the elements of an

invention are assembled in just the right way, they produce an enchanting effect that goes beyond the mere

parts. When Alexander Graham Bell accidentally wire-connected two moving drums and solenoids (metal cores

wrapped in wire) in 1875, the result transcended the materials he was working with. For the first time, a

human voice was transported, magically it seemed, to a remote location. Most assemblages are just that:

random assemblies. But when materials—and in the case of modern technology, information—are assembled

in just the right way, transcendence occurs. The assembled object becomes far greater than the sum of its

parts.

The same phenomenon of transcendence occurs in art, which may properly be regarded as another form of

human technology. When wood, varnishes, and strings are assembled in just the right way, the result is

wondrous: a violin, a piano. When such a device is manipulated in just the right way, there is magic of

another sort: music. Music goes beyond mere sound. It evokes a response—cognitive, emotional, perhaps

spiritual—in the listener, another form of transcendence. All of the arts share the same goal: communicating

from artist to audience. The communication is not of unadorned data, but of the more important items in the

phenomenological garden: feelings, ideas, experiences, longings. The Greek meaning of tekhnē logia includes

art as a key manifestation of technology.

Language is another form of human-created technology, One of the primary applications of technology is

communication, and language provides the foundation for Homo sapiens communication. Communication is a

critical survival skill. It enabled human families and tribes to develop cooperative strategies to overcome

obstacles and adversaries. Other animals communicate. Monkeys and apes use elaborate gestures and grunts

to communicate a variety of messages. Bees perform intricate dances in a figure-eight pattern to

communicate where caches of nectar may be found. Female tree frogs in Malaysia do tap dances to signal

their availability. Crabs wave their claws in one way to warn adversaries but use a different rhythm for

courtship. [16] But these methods do not appear to evolve, other than through the usual DNA-based

evolution. These species lack a way to record their means of communication, so the methods remain static

from one generation to the next. In contrast, human language does evolve, as do all forms of technology.

Along with the evolving forms of language itself, technology has provided ever-improving means for recording

and distributing human language.

Homo sapiens are unique in their use and fostering of all forms of what I regard as technology: art,

language, and machines, all representing evolution by other means. In the 1960s through 1990s, several well-

publicized primates were said to have mastered at least childlike language skills. Chimpanzees Lana and Kanzi

pressed sequences of buttons with symbols on them. Gorillas Washoe and Koko were said to be using

American Sign Language. Many linguists are skeptical, noting that many primate "sentences" were jumbles,

such as "Nim eat, Nim eat, drink eat me Nim, me gum me gum, tickle me, Nim play, you me banana me

banana you." Even if we view this phenomenon more generously, it would be the exception that proves the

rule. These primates did not evolve the languages they are credited with using, they do not appear to develop

these skills spontaneously, and their these skills is very limited. [17] They are at best participating

peripherally in what is still a uniquely human invention communicating using the recursive (self-referencing),

symbolic, evolving means called language.

The Inevitability of Technology

Once life takes hold on a planet, we can consider the emergence of technology as inevitable. The ability to expand the reach of oneʹs physical capabilities, not to mention mental facilities, through technology is clearly useful for survival.

Technology has enabled our subspecies to dominate its ecological niche. Technology requires two attributes of its creator: intelligence and the physical ability to manipulate the environment. Weʹll talk more in chapter 4, ʺA New Form of Intelligence on Earth,ʺ about the nature of intelligence, but it clearly represents an ability to use limited resources optimally, including time. This ability is inherently useful for survival, so it is favored. The ability to manipulate the environment is also useful; otherwise an organism is at the mercy of its environment for safety, food, and the satisfaction of its other needs. Sooner or later, an organism is bound to emerge with both attributes.

THE INEVITABILITY OF COMPUTATION

It is not a bad definition of man to describe him as a tool‐making animal. His earliest contrivances to support uncivilized life were tools of the simplest and rudest construction. His latest achievements in the substitution of machinery, not merely for the skill of the human hand, but for the relief of the human intellect, are founded on the use of tools of a still higher order.

—Charles Babbage

All of the fundamental processes we have examined—the development of the Universe, the evolution of life‐forms, the subsequent evolution of technology—have all progressed in an exponential fashion, some slowing down, some speeding up. What is the common thread here? Why did cosmology exponentially slow down while evolution accelerated? The answers are surprising, and fundamental to understanding the twenty‐first century.

But before I attempt to answer these questions, letʹs examine one other very relevant example of acceleration: the

exponential growth of computation.

Early in the evolution of life‐forms, specialized organs developed the ability to maintain internal states and respond differentially to external stimuli. The trend ever since has been toward more complex and capable nervous

systems with the ability to store extensive memories; recognize patterns in visual, auditory, and tactile stimuli; and engage in increasingly sophisticated levels of reasoning. The ability to remember and to solve problems—

computation—has constituted the cutting edge in the evolution of multicellular organisms.

The same value of computation holds true in the evolution of human‐created technology. Products are more useful if they can maintain internal states and respond differentially to varying conditions and situations. As machines moved beyond mere implements to extend human reach and strength, they also began to accumulate the ability to remember and perform logical manipulations. The simple cams, gears, and levers of the Middle Ages were

assembled into the elaborate automata of the European Renaissance. Mechanical calculators, which first emerged in

the seventeenth century, became increasingly complex, culminating in the first automated U.S. census in 1890.

Computers played a crucial role in at least one theater of the Second World War, and have developed in an accelerating spiral ever since.

THE LIFE CYCLE OF A TECHNOLOGY

Technologies fight for survival, evolve, and undergo their own characteristic life cycle. We can identify seven

distinct stages. During the precursor stage, the prerequisites of a technology exist, and dreamers may

contemplate these elements coming together. We do not, however, regard dreaming to be the same as

inventing, even if the dreams are written down. Leonardo da Vinci drew convincing pictures of airplanes and

automobiles, but he is not considered to have invented either.

The next stage, one highly celebrated in our culture, is invention, a very brief stage, not dissimilar in some respects to the process of birth after an extended period of labor. Here the inventor blends curiosity, scientific

skills, determination, and usually a measure of showmanship to combine methods in a new way to bring a

new technology to life.

The next stage is development, during which the invention is protected and supported by doting guardians

(which may include the original inventor). Often this stage is more crucial than invention and may involve

additional creation that can have greater significance than the original invention. Many tinkerers had

constructed finely hand-tuned horseless carriages, but it was Henry Ford's innovation of mass production that

enabled the automobile to take root and flourish.

The fourth stage is maturity. Although continuing to evolve, the technology now has a life of its own and

has become an independent and established part of the community. It may become so interwoven in the

fabric of life that it appears to many observers that it will last forever. This creates an interesting drama when

the next stage arrives, which I call the stage of the pretenders. Here an upstart threatens to eclipse the older technology. Its enthusiasts prematurely predict victory. While providing some distinct benefits, the newer

technology is found on reflection to be missing some key element of functionality or quality. When it indeed

fails to dislodge the established order, the technology conservatives take this as evidence that the original

approach will indeed live forever.

This is usually a short-lived victory for the aging technology. Shortly thereafter, another new technology

typically does succeed in rendering the original technology into the stage of obsolescence. In this part of the

life cycle, the technology lives out its senior years in gradual decline, its original purpose and functionality

now subsumed by a more spry competitor. This stage, which may comprise 5 to 10 percent of the life cycle,

finally yields to antiquity (examples today: the horse and buggy, the harpsichord, the manual typewriter, and the electromechanical calculator).

To illustrate this, consider the phonograph record. In the mid-nineteenth century, there were several

precursors, including Édouard-Léon Scott de Martinville's phonautograph, a device that recorded sound

vibrations as a printed pattern. It was Thomas Edison, however, who in 1877 brought all of the elements

together and invented the first device that could record and reproduce sound. Further refinements were

necessary for the phonograph to become commercially viable. It became a fully mature technology in 1948

when Columbia introduced the 33 revolutions-per-minute (rpm) long-playing record (LP) and RCA Victor

introduced the 45-rpm small disc. The pretender was the cassette tape, introduced in the 1960s and

popularized during the 1970s. Early enthusiasts predicted that its small size and ability to be re-recorded

would make the relatively bulky and scratchable record obsolete.

Despite these obvious benefits, cassettes lack random access (the ability to play selections in a desired

order) and are prone to their own forms of distortion and lack of fidelity. In the late 1980s and early 1990s,

the digital compact disc (CD) did deliver the mortal blow. With the CD providing both random access and a

level of quality close to the limits of the human auditory system, the phonograph record entered the stage of

obsolescence in the first half of the 1990s. Although still produced in small quantities, the technology that

Edison gave birth to more than a century ago is now approaching antiquity.

Another example is the print book, a rather mature technology today. It is now in the stage of the

pretenders, with the software-based "virtual" book as the pretender. Lacking the resolution, contrast, lack of flicker, and other visual qualities of paper and ink, the current generation of virtual book does not have the

capability of displacing paper-based publications. Yet this victory of the paper-based book will be short-lived

as future generations of computer displays succeed in providing a fully satisfactory alternative to paper.

The Emergence of Mooreʹs Law

Gordon Moore, an inventor of the integrated circuit and then chairman of Intel, noted in 1965 that the surface area of a transistor (as etched on an integrated circuit) was being reduced by approximately 50 percent every twelve months.

In 1975, he was widely reported to have revised this observation to eighteen months. Moore claims that his 1975

update was to twenty‐four months, and that does appear to be a better fit to the data.

The result is that every two years, you can pack twice as many transistors on an integrated circuit. This doubles

both the number of components on a chip as well as its speed. Since the cost of an integrated circuit is fairly constant, the implication is that every two years you can get twice as much circuitry running at twice the speed for the same

price. For many applications, thatʹs an effective quadrupling of the value. The observation holds true for every type of circuit, from memory chips to computer processors.

This insightful observation has become known as Mooreʹs Law on Integrated Circuits, and the remarkable phenomenon of the law has been driving the acceleration of computing for the past forty years. But how much longer

can this go on? The chip companies have expressed confidence in another fifteen to twenty years of Mooreʹs Law by

continuing their practice of using increasingly higher resolutions of optical lithography (an electronic process similar to photographic printing) to reduce the feature size—measured today in millionths of a meter—of transistors and other key components. [18] But then—after almost sixty years—this paradigm will break down. The transistor insulators will then be just a few atoms thick, and the conventional approach of shrinking them wonʹt work.

What then?

We first note that the exponential growth of computing did not start with Mooreʹs Law on Integrated Circuits. In

the accompanying figure, ʺThe Exponential Growth of Computing, 1900–1998,ʺ [19] I plotted forty‐nine notable computing machines spanning the twentieth century on an exponential chart, in which the vertical axis represents powers of ten in computer speed per unit cost (as measured in the number of ʺcalculations per secondʺ that can be purchased for $1,000). Each point on the graph represents one of the machines. The first five machines used mechanical technology, followed by three electromechanical (relay based) computers, followed by eleven vacuum-tube machines, followed by twelve machines using discrete transistors. Only the last eighteen computers used integrated circuits.

I then fit a curve to the points called a fourth‐order polynomial, which allows for up to four bends. In other words, I did not try to fit a straight line to the points, just the closest fourth‐order curve. Yet a straight line is close to what I got. A straight line on an exponential graph means exponential growth. A careful examination of the trend shows that the curve is actually bending slightly upward, indicating a small exponential growth in the rate of exponential growth. This may result from the interaction of two different exponential trends, as I will discuss in chapter 6, ʺBuilding New Brains.ʺ Or there may indeed be two levels of exponential growth. Yet even if we take the more conservative view that there is just one level of acceleration, we can see that the exponential growth of computing did not start with Mooreʹs Law on Integrated Circuits, but dates back to the advent of electrical computing at the beginning of the twentieth century.

Mechanical Computing Devices

1. 1900

Analytical Engine

2. 1908

Hollerith Tabulator

3. 1911

Monroe Calculator

4. 1919

IBM Tabulator

5. 1928

National Ellis 3000

Electromechanical (Relay Based) Computers

6. 1939

Zuse 2

7. 1940

Bell Calculator Model 1

8. 1941

Zuse 3

Vacuum‐Tube Computers

9. 1943

Colossus

10. 1946

ENIAC

11. 1948

IBM SSEC

12. 1949

BINAC

13. 1949

EDSAC

14. 1951

Univac 1

15. 1953

Univac 1103

16. 1953

IBM 701

17. 1954

EDVAC

18. 1955

Whirlwind

19. 1955

IBM 704

Discrete Transistor Computers

20. 1958

Datamatic 1000

21. 1958

Univac II

22. 1959

Mobidic

23. 1959

IBM 7090

24. 1960

IBM 1620

25. 1960

DEC PDP‐1

26. 1961

DEC PDP‐4

27. 1962

Univac III

28. 1964

CDC 6600

29. 1965

IBM 1130

30. 1965

DEC PDP‐8

31. 1966

IBM 360 Model 75

Integrated Circuit Computers

32. 1968

DEC PDP‐10

33. 1973

Intellec‐8

34. 1973

Data General Nova

35. 1975

Altair 8800

36. 1976

DEC PDP‐11 Model 70

37. 1977

Cray I

38. 1977

Apple II

39. 1979

DEC VAX 11Model 780

40. 1980

Sun‐1

41. 1982

IBM PC

42. 1982

Compaq Portable

43. 1983

IBM AT‐80286

44. 1984

Apple Macintosh

45. 1986

Compaq Deskpro 386

46. 1987

Apple Mac II

47. 1993

Pentium PC

48. 1996

Pentium PC

49. 1998

Pentium II PC

In the 1980s, a number of observers, including Carnegie Mellon University professor Hans Moravec, Nippon Electric Companyʹs David Waltz, and myself, noticed that computers have been growing exponentially in power, long

before the invention. of the integrated circuit in 1958 or even the transistor in 1947. [20] The speed and density of computation have been doubling every three years (at the beginning of the twentieth century) to one year (at the end of the twentieth century), regardless of the type of hardware used. Remarkably, this ʺExponential Law of Computingʺ

has held true for at least a century, from the mechanical card‐based electrical computing technology used in the 1890

U.S. census, to the relay‐based computers that cracked the Nazi Enigma code, to the vacuum‐tube‐based computers of

the 1950s, to the transistor‐based machines of the 1960s, and to all of the generations of integrated circuits of the past four decades. Computers are about one hundred million times more powerful for the same unit cost than they were a

half century ago. If the automobile industry had made as much progress in the past fifty years, a car today would cost a hundredth of a cent and go faster than the speed of light.

As with any phenomenon of exponential growth, the increases are so slow at first as to be virtually unnoticeable.

Despite many decades of progress since the first electrical calculating equipment was used in the 1890 census, it was not until the mid1960s that this phenomenon was even noticed (although Alan Turing had an inkling of it in 1950).

Even then, it was appreciated only by a small community of computer engineers and scientists. Today, you have only

to scan the personal computer ads—or the toy ads—in your local newspaper to see the dramatic improvements in the

price performance of computation that now arrive on a monthly basis.

So Mooreʹs Law on Integrated Circuits was not the first, but the fifth paradigm to continue the now one‐century‐

long exponential growth of computing. Each new paradigm came along just when needed. This suggests that exponential growth wonʹt stop with the end of Mooreʹs Law. But the answer to our question on the continuation of

the exponential growth of computing is critical to our understanding of the twenty‐first century. So to gain a deeper understanding of the true nature of this trend, we need to go back to our earlier questions on the exponential nature of time.

THE LAW OF TIME AND CHAOS

In a process, the time interval between salient events (i.e., events that change the nature of the process,

or significantly affect the future of the process) expands or contracts along with the amount of chaos

THE LAW OF INCREASING CHAOS

THE LAW OF ACCELERATING RETURNS

As chaos exponentially increases, time exponentially

As order exponentially increases, time exponentially

slows down (i.e., the time interval between salient

speeds up (i.e., the time interval between salient

events grows longer as time passes).

events grows shorter as time passes).

THE LAW OF ACCELERATING RETURNS AS

THE LAW OF

THE LAW OF

APPLIED TO AN EVOLUTIONARY PROCESS

INCREASING CHAOS

INCREASING CHAOS

An evolutionary process is not a closed system;

AS APPLIED TO THE

AS APPLIED TO THE

therefore, evolution draws upon the chaos in the

UNIVERSE

LIFE OF AN

larger system in which it takes place for its options

The Universe started as

ORGANISM

for diversity; and

a "singularity," a single

The development of an

• Evolution builds on its own increasing order.

undifferentiated point

organism from

Therefore:

with no size and no

conception as a single

chaos, so early epochal

cell through maturation

• In an evolutionary process, order increases

events were extremely

is a process moving

exponentially.

rapid. The Univers grew

toward greater diversity

Therefore:

greatly in chaos as time

and thus greater

• Time exponentially speeds up.

went on. Thus time

disorder. Thus the time

Therefore:

slowed down (i.e., the

interval between salient

•

The returns (i.e., the valuable products of the

time interval between

events grows longer

process) accelerate.

salient events grew

over time.

exponentially longer

over time).

THE LAW OF ACCELERATING RETURNS AS

APPLIED TO THE EVOLUTION OF LIFE-FORMS

The time interval between salient events (e.g., a

significant new branch) grows exponentially

shorter as time passes.

THE EVOLUTION OF LIFE-FORMS LEADS TO

THE EVOLUTION OF TECHNOLOGY

The advance of technology is inherently an

evolutionary process. Indeed, it is a continuation

of the same evolutionary process that gave rise to

technology-creating species. Therefore, in

accordance with the Law of Accelerating Returns,

the time interval between salient advances grows

exponentially shorter as time passes. The

"returns" (i.e., the value) of technology increase

over time.

TECHNOLOGY BEGETS COMPUTATION

Computation if the essence of order in

technology. In accordance with the Law of

Accelerating Returns, the value—power—of

computation increases exponentially over time.

MOORE'S LAW ON INTEGRATED CIRCUITS

Transistor die sizes are cut in half every twenty-

four months, therefore both computing capacity

(i.e., the number of transistors on a chip) and the

speed of each transistor double every twenty-four

months. This is the fifth paradigm since the

inception of computation—after mechanical,

electromechanical (i.e., relay based), vacuum

tube, and discrete transistor technology—to

provide accelerating returns to computation.

THE LAW OF TIME AND CHAOS

Is the flow of time something real, or might our sense of time passing be just an illusion that hides the fact that what is real is only a vast collection of moments?

—Lee Smolin

Time is natureʹs way of preventing everything from happening at once.

—Graffito

Things are more like they are now than they ever were before.

—Dwight Eisenhower

Consider these diverse exponential trends:

• The exponentially slowing pace that the Universe followed, with three epochs in the first billionth of a second, with later salient events taking billions of years.

• The exponentially slowing pace in the development of an organism. In the first month after conception, we

grow a body, a head, even a tail. We grow a brain in the first couple of months. After leaving our maternal

confines, our maturation both physically and mentally, is rapid at first. In the first year, we learn basic forms

of mobility and communication. We experience milestones every month or so. Later on, key events march ever more slowly, taking years and then decades.

• The exponentially quickening pace of the evolution of life‐forms on Earth.

• The exponentially quickening pace of the evolution of human‐created technology, which picked up the pace

from the evolution of life‐forms.

• The exponential growth of computing. Note that exponential growth of a process over time is just another

way of expressing an exponentially quickening pace. For example, it took about ninety years to achieve the

first MIP (Million Instructions per Second) for a thousand dollars. Now we add an additional MIP per thousand dollars every day. The overall innovation rate is clearly accelerating as well.

• Mooreʹs Law on Integrated Circuits. As I noted, this was the fifth paradigm to achieve the exponential growth of computing.

Many questions come to mind:

What is the common thread between these varied exponential trends?

Why do some of these processes speed up while others slow down?

And what does this tell us about the continuation of the exponential growth of computing when Mooreʹs Law

dies?

Is Mooreʹs Law just a set of industry expectations and goals, as Randy Isaac, head of basic science at IBM, contends? Or is it part of a deeper phenomenon that goes far beyond the photolithography of integrated circuits?

After thinking about the relationship between these apparently diverse trends for several years, the surprising common theme became apparent to me.

What determines whether time speeds up or slows down? The consistent answer is that time moves in relation to the amount of chaos. We can state the Law of Time and Chaos as follows:

The Law of Time and Chaos: In a process, the time interval between salient events (that is, events that

change the nature of the process, or significantly affect the future of the process) expands or contracts

along with the amount of chaos.

When there is a lot of chaos in a process, it takes more time for significant events to occur. Conversely, as order

increases, the time periods between salient events decrease.

We have to be careful here in our definition of chaos. It refers to the quantity of disordered (that is, random) events that are relevant to the process. If weʹre dealing with the random movement of atoms and molecules in a gas or liquid, then; heat is an appropriate measure. If weʹre dealing with the process of evolution of life‐forms, then chaos represents the unpredictable events encountered by organisms, and the random mutations that are introduced in the

genetic code.

Letʹs see how the Law of Time and Chaos applies to our examples. If chaos is increasing, the Law of Time and Chaos implies the following sublaw:

The Law of Increasing Chaos: As chaos exponentially increases, time exponentially slows down (that is,

the time interval between salient events grows longer as time passes).

This fits the Universe rather well. When the entire Universe was just a ʺnakedʺ singularity—a perfectly orderly single point in space and time—there was no chaos and conspicuous events took almost no time at all. As the Universe grew in size, chaos increased exponentially, and so did the timescale for epochal changes. Now, with billions of galaxies sprawled out over trillions of light‐years of space, the Universe contains vast reaches of chaos, and indeed requires billions of years to get everything organized for a paradigm shift to take place.

We see a similar phenomenon in the progression of an organismʹs life. We start out as a single fertilized cell, so

thereʹs only rather limited chaos there. Ending up with trillions of cells, chaos greatly expands. Finally, at the end of our lives, our designs deteriorate, engendering even greater randomness. So the time period between salient biological events grows longer as we grow older. And that is indeed what we experience.

But it is the opposite spiral of the Law of Time and Chaos that is the most important and relevant for our purposes. Consider the inverse sublaw, which I call the Law of Accelerating Returns:

The Law of Accelerating Returns: As order exponentially increases, time exponentially speeds up (that is,

the time interval between salient events grows shorter as time passes).

The Law of Accelerating Returns (to distinguish it from a better‐known law in which returns diminish) applies specifically to evolutionary processes. In an evolutionary process, it is order—the opposite of chaos—that is increasing. And, as we have seen, time speeds up.

Disdisorder

I noted above that the concept of chaos in the Law of Time and Chaos is tricky. Chaos alone is not sufficient—

disorder for our purposes requires randomness that is relevant to the process we are concerned with. The opposite of disorder—which I called ʺorderʺ in the above Law of Accelerating Returns—is even trickier.

Letʹs start with our definition of disorder and work backward. If disorder represents a random sequence of events,

then the opposite of disorder should imply ʺnot random.ʺ And if random means unpredictable, then we might conclude that order means predictable. But that would be wrong.

Borrowing a page from information theory, [21] consider the difference between information and noise.

Information is a sequence of data that is meaningful in a process, such as the DNA code of an organism, or the bits in a computer program. Noise, on the other hand, is a random sequence. Neither noise nor information is predictable.

Noise is inherently unpredictable, but carries no information. Information, however, is also unpredictable. If we can predict future data from past data, then that future data stops being information. For example, consider a sequence

which simply alternates between zero and one (01010101 . . .). Such a sequence is certainly orderly, and very predictable. Specifically because it is so predictable, we do not consider it information bearing, beyond the first couple of bits.

Thus orderliness does not constitute order because order requires information. So, perhaps I should use the word

information instead of order. However, information alone is not sufficient for our purposes either. Consider a phone book. It certainly represents a lot of information, and some order as well. Yet if we double the size of the phone book, we have increased the amount of data, but we have not achieved a deeper level of order.

Order, then, is information that fits a purpose. The measure of order is the measure of how well the information

fits the purpose. In the evolution of life‐forms, the purpose is to survive. In an evolutionary algorithm (a computer program that simulates evolution to solve a problem) applied to, say, investing in the stock market, the purpose is to make money. Simply having more information does not necessarily result in a better fit. A superior solution for a purpose may very well involve less data.

The concept of ʺcomplexityʺ has been used recently to describe the nature of the information created by an evolutionary process. Complexity is a reasonably close fit to the concept of order that I am describing. After all, the designs created by the evolution of life‐forms on Earth appear to have become more complex over time. However, complexity is not a perfect fit, either. Sometimes, a deeper order —a better fit to a purpose—is achieved through simplification rather than further increases in complexity. As Einstein said, ʺEverything should be made as simple as possible, but no simpler.ʺ For example, a new theory that ties together apparently disparate ideas into one broader, more coherent theory reduces complexity but nonetheless may increase the ʺorder for a purposeʺ that I am describing.

Evolution has shown, however, that the general trend toward greater order does generally result in greater complexity. [22]

Thus improving a solution to a problem—which may increase or decrease complexity—increases order. Now that

just leaves the issue of defining the problem. And as we will see, defining a problem well is often the key to finding its solution.

The Law of Increasing Entropy Versus the Growth of Order Another consideration is how the Law of Time and

Chaos relates to the second law of thermodynamics. Unlike the second law, the Law of Time and Chaos is not necessarily concerned with a closed system. It deals instead with a process. The Universe is a closed system (not subject to outside influence, since there is nothing outside the Universe), so in accordance with the second law of thermodynamics, chaos increases and time slows down. In contrast, evolution is precisely not a closed system. it takes place amid great chaos, and indeed depends on the disorder in its midst, from which it draws its options for diversity. And from these options, an evolutionary process continually prunes its choices to create ever greater order.

Even a crisis that appears to introduce a significant new source of chaos is likely to end up increasing—deepening—

the order created by an evolutionary process. For example, Consider the asteroid that is thought to have killed off big organisms such as the dinosaurs 65 million years ago. The crash of that asteroid suddenly created a vast increase in chaos (and lots of dust, too). Yet it appears to have hastened the rise of mammals in the niche previously dominated by large reptiles and ultimately led to the emergence of a technology‐creating species. When the dust settled (literally), the crisis of the asteroid had increased order.

As I pointed out earlier, only a tiny fraction of the stuff in the Universe, or even on a life‐ and technology‐bearing planet such as Earth, can be considered to be part of evolutionʹs inventions. Thus evolution does not contradict the Law of Increasing Entropy. Indeed, it depends on it to provide a never‐ending supply of options.

As I noted, given the emergence of life, the emergence of a technology‐creating species—and of technology—is inevitable. Technology is the continuation of evolution by other means, and is itself an evolutionary process. So it, too, speeds up.

A primary reason that evolution—of life‐forms or of technology—speeds up is that it builds on its own increasing

order. Innovations created by evolution encourage and enable faster evolution. In the case of the evolution of life-forms, the most notable example is DNA, which provides a recorded and protected transcription of lifeʹs design from

which to launch further experiments.

In the case of the evolution of technology, ever improving human methods of recording information have fostered

further technology. The first computers were designed on paper and assembled by hand. Today, they are designed on

computer workstations with the computers themselves working out many details of the next generationʹs design, and

are then produced in fully automated factories with human guidance but limited direct intervention.

The evolutionary process of technology seeks to improve capabilities in an exponential fashion. Innovators seek to

improve things by multiples. Innovation is multiplicative, not additive. Technology, like any evolutionary process, builds on itself. This aspect will continue to accelerate when the technology itself takes full control of its own progression.

We can thus conclude the following with regard to the evolution of life‐forms, and of technology:

The Law of Accelerating Returns as Applied to an Evolutionary Process:

▲ An evolutionary process is not a closed system; therefore, evolution draws upon the chaos in the larger

system in which it takes place for its options for diversity; and

▲ Evolution builds on its own increasing order.

Therefore:

▲ In an evolutionary process, order increases exponentially.

Therefore:

▲ Time exponentially speeds up.

Therefore:

▲ The returns (that is, the valuable products of the process) accelerate.

The phenomenon of time slowing down and speeding up is occurring simultaneously. Cosmologically speaking,

the Universe continues to slow down. Evolution, now most noticeably in the form of human‐created technology, continues to speed up. These are the two sides—two interleaved spirals—of the Law of Time and Chaos.

The spiral we are most interested in—the Law of Accelerating Returns—gives us ever greater order in technology,

which inevitably leads to the emergence of computation. Computation is the essence of order. It provides the ability for a technology to respond in a variable and appropriate manner to its environment to carry out its mission. Thus computational technology is also an evolutionary process, and also builds on its own progress. The time to accomplish a fixed objective gets exponentially shorter over time (for example, ninety years for the first MIP per thousand dollars versus one day for an additional MIP today). That the power of computing grows exponentially over time is just another way to say the same thing.

So Where Does That Leave Mooreʹs Law?

Well, it still leaves it dead by the year 2020. Mooreʹs law came along in 1958 just when it was needed and will have done its sixty years of service by 2018, a rather long period of time for a paradigm nowadays. Unlike Mooreʹs Law,

however, the Law of Accelerating Returns is not a temporary methodology. It is a basic attribute of the nature of time and chaos—a sublaw of the Law of Time and Chaos—and describes a wide range of apparently divergent phenomena

and trends. In accordance with the Law of Accelerating Returns, another computational technology will pick up where Mooreʹs Law will have left off, without missing a beat.

Most Exponential Trends Hit a Wall . . . but Not This One

A frequent criticism of predictions of the future is that they rely on mindless extrapolation of current trends without consideration of forces that may terminate or alter that trend. This criticism is particularly relevant in the case of exponential trends. A classic example is a species happening upon a hospitable new habitat, perhaps transplanted there by human intervention (rabbits in Australia, say). Its numbers multiply exponentially for a while, but this phenomenon is quickly terminated when the exploding population runs into a new predator or the limits of its environment. Similarly, the geometric population growth of our own species has been a source of anxiety, but changing social and economic factors, including growing prosperity, have greatly slowed this expansion in recent years, even in developing countries.

Based on this, some observers are quick to predict the demise of the exponential growth of computing.

But the growth predicted by the Law of Accelerating Returns is an exception to the frequently cited limitations to

exponential growth. Even a catastrophe, as apparently befell our reptilian cohabitants in the late Cretaceous period, only sidesteps an evolutionary process, which then picks up the pieces and continues unabated (unless the entire process is wiped out). An evolutionary process accelerates because it builds on its past achievements, which includes improvements in its own means for further evolution. In the evolution of life‐forms, in addition to DNA‐based genetic coding, the innovation of sexual reproduction provided for improved means of experimenting with diverse characteristics within an otherwise homogenous population. The establishment of basic body plans of modern animals in the ʺCambrian explosion,ʺ about 570 million years ago, allowed evolution to concentrate on higher‐level features such as expanded brain function. The inventions of evolution in one era provide the means, and often the intelligence, for innovation in the next.

The Law of Accelerating Returns applies equally to the evolutionary process of computation, which inherently will grow exponentially and essentially without limit. The two resources it needs—the growing order of the evolving technology itself and the chaos from which an evolutionary process draws its options for further diversity—are unbounded.

Ultimately, the innovation needed for further turns of the screw will come from the machines themselves.

How will the power of computing continue to accelerate after Mooreʹs Law dies? We are just beginning to explore

the third dimension in chip design. The vast majority of todayʹs chips are flat, whereas our brain is organized in three dimensions. We live in a three‐dimensional world, so why not use the third dimension? Improvements in semiconductor materials, including superconducting circuits that donʹt generate heat, will enable us to develop chips—that is, cubes—with thousands of layers of circuitry that, combined with far smaller component geometries, will improve computing power by a factor of many millions. And there are more than enough other new computing

technologies waiting in the wings—nanotube, optical, crystalline, DNA, and quantum (which weʹll visit in chapter 6,

ʺBuilding New Brainsʺ)—to keep the Law of Accelerating Returns going in the world of computation for a very long

time.

THE LEARNING CURVE: SLUG VERSUS HUMAN

The "learning curve" describes the mastery of a skill over time. As an entity—slug or human—learns a new

skill, the newly acquired ability builds on itself, and so the learning curve starts out looking like the

exponential growth we see in the Law of Accelerating Returns. Skills tend to be bounded, so as the new

expertise is mastered, the law of diminishing returns sets in, and growth in mastery levels off. So the learning

curve is what we call an S curve because exponential growth followed by a leveling off looks like an S leaning

slightly to the right:

The learning curve is remarkably universal: Most multicellular creatures do it. Slugs, for example, follow the

learning curve when learning how to ascend a new tree in search of leaves. Humans, of course, are always

learning something new.

But there's a salient difference between humans and slugs. Humans are capable of innovation, which is the

creation and retention of new skills and knowledge. Innovation is the driving force in the Law of Accelerating

Returns, and eliminates the leveling-off part of the S curve. So innovation turns the S curve into indefinite

exponential expansion.

Overcoming the S curve is another way to express the unique status of the human species. No other species

appears to do this. Why are we unique in this way, given that other primates are so close to us in terms of

genetic similarity?

The reason is that the ability to overcome the S curve defines a new ecological niche. As I pointed out, there

were indeed other humanoid species and subspecies capable of innovation, but the niche seems to have

tolerated only one surviving competitor. But we will have company in the twenty-first century as our machines

join us in this exclusive niche.

A Planetary Affair

The introduction of technology on Earth is not merely the private affair of one of the Earthʹs innumerable species. It is a pivotal event in the history of the planet. Evolutionʹs grandest creation—human intelligence—is providing the means for the next stage of evolution, which is technology. The emergence of technology is predicted by the Law of

Accelerating Returns. The Homo sapiens sapiens subspecies emerged only tens of thousands of years after its human forebears. According to the Law of Accelerating Returns, the next stage of evolution should measure its salient events in mere thousands of years, too quick for DNA‐based evolution. This next stage of evolution was necessarily created

by human intelligence itself, another example of the exponential engine of evolution using its innovations from one

period (human beings) to create the next (intelligent machines). Evolution draws upon the great chaos in its midst—

the ever increasing entropy governed by the flip side of the Law of Time and Chaos—for its options for innovation.

These two strands of the Law of Time and Chaos—time exponentially slowing down due to the increasing chaos predicted by the second law of thermodynamics; and time exponentially speeding up due to the increasing order created by evolution—coexist and progress without limit . In particular, the resources of evolution, order and chaos, are unbounded. I stress this point because it is crucial to understanding the evolutionary—and revolutionary—nature of computer technology.

The emergence of technology was a milestone in the evolution of intelligence on Earth because it represented a new means of evolution recording its designs. The next milestone will be technology creating its own next generation without human intervention. That there is only a period of tens of thousands of years between these two milestones is another example of the exponentially quickening pace that is evolution.

The Inventor of Chess and the Emperor of China

To appreciate the implications of this (or any) geometric trend, it is useful to recall the legend of the inventor of chess and his patron, the emperor of China. The emperor had so fallen in love with his new game that he offered the inventor a reward of anything he wanted in the kingdom.

ʺJust one grain of rice on the first square, Your Majestyʺ

ʺJust one grain of rice?ʺ

ʺYes, Your Majesty, just one grain of rice on the first square, and two grains of rice on the second square.ʺ

ʺThatʹs it—one and two grains of rice?ʺ

ʺWell, okay, and four grains on the third square, and so on.ʺ

The emperor immediately granted the inventorʹs seemingly humble request. One version of the story has the emperor going bankrupt because the doubling of grains of rice for each square ultimately equaled 18 million trillion grains of rice. At ten grains of rice per square inch, this requires rice fields covering twice the surface area of the Earth, oceans included.

The other version of the story has the inventor losing his head. Itʹs not yet clear which outcome weʹre headed for.

But there is one thing we should note: It was fairly uneventful as the emperor and the inventor went through the

first half of the chessboard. After thirty‐two squares, the emperor had given the inventor about 4 billion grains of rice.

Thatʹs a reasonable quantity—about one large fieldʹs worth—and the emperor did start to take notice.

But the emperor could still remain an emperor. And the inventor could still retain his head. It was as they headed

into the second half of the chessboard that at least one of them got into trouble. So where do we stand now? There

have been about thirty‐two doublings of speed and capacity since the first operating computers were built in the 1940s. Where we stand right now is that we have finished the first half of the chessboard. And, indeed, people are starting to take notice. Now, as we head into the next century, we are heading into the second half of the chessboard.

And this is where things start to get interesting.

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OKAY, LET ME GET THIS STRAIGHT, MY CONCEPTION AS A FERTILIZED EGG WAS LIKE THE UNIVERSEʹS

BIG BANG—UH, NO PUN INTENDED—THAT IS, THINGS STARTED OUT HAPPENING VERY FAST, THEN

KIND OF SLOWED DOWN, AND NOW THEYʹRE REAL SLOW?

Thatʹs a reasonable way to put it, the time interval now between milestones is a lot longer than it was when you were an infant, let alone a fetus.

YOU MENTIONED THE UNIVERSE HAD THREE PARADIGM SHIFTS IN THE FIRST BILLIONTH OF A SECOND.

WERE THINGS THAT FAST WHEN I GOT STARTED?

Not quite that fast. The Universe started as a singularity, a single point taking up no space and comprising, therefore, no chaos. So the first major event, which was the creation of the Universe, took no time at all. With the Universe still very small, events unfolded extremely quickly. We donʹt start out as a single point, but as a rather complex cell. it has order but there is a lot of random activity within a cell compared to a single point in space. So our first major event as an organism, which is the first mitosis of our fertilized egg, is measured in hours, not trillionths of a second. Things slow down from there.

BUT I FEEL LIKE TIME IS SPEEDING UP. THE YEARS JUST GO BY SO MUCH FASTER NOW THAN THEY DID

WHEN I WAS A KID. DONʹT YOU HAVE IT BACKWARD?

Yes, well, the subjective experience is the opposite of the objective reality.

OF COURSE. WHY DIDNʹT I THINK OF THAT?

Let me clarify what I mean. The objective reality is the reality of the outside observer observing the process. If we observe the development of an individual, salient events happen very quickly at first, but later on milestones are more spread out, so we say time is slowing down. The subjective experience, however, is the experience of the process itself, assuming, of course, that the process is conscious. Which in your case, it is. At least, I assume thatʹs the case.

THANK YOU.

Subjectively, our perception of time is affected by the spacing of milestones.

MILESTONES?

Yeah, like growing a body and a brain.

AND BEING BORN?

Sure, thatʹs a milestone. Then learning to sit up, walking, talking . . .

OKAY.

We can consider each subjective unit of time to be equivalent to one milestone spacing. Since our milestones are spaced further apart as we grow older, a subjective unit of time will represent a longer span of time for an adult than for a child. Thus time feels like it is passing by more quickly as we grow older. That is, an interval of a few years as an adult may be perceived as comparable to a few months to a young child. Thus a long interval to an adult and a short

interval to a child both represent the same subjective time in terms of the passage of salient events Of course, long and short intervals also represent comparable fractions of their respective past lives.

SO DOES THAT EXPLAIN WHY TIME PASSES MORE QUICKLY WHEN IʹM HAVING A GOOD TIME?

Well, it may be relevant to one phenomenon. If someone goes through an experience in which a lot of significant events occur, that experience may feel like a much longer period of time than a calmer period. Again, we measure subjective time in terms of salient experiences.

NOW IF I FIND TIME SPEEDING UP WHEN OBJECTIVELY IT IS SLOWING DOWN, THEN EVOLUTION WOULD

SUBJECTIVELY FIND TIME SLOWING DOWN AS IT OBJECTIVELY SPEEDS UP, DO I HAVE THAT STRAIGHT?

Yes, if evolution were conscious.

WELL, IS IT?

Thereʹs no way to really tell, but evolution has its time spiral going in the opposite direction from entities we generally consider to be conscious, such as humans. In other words, evolution starts out slow and speeds up over time, whereas the development of a person starts out fast and then slows down. The Universe, however, does have its

time spiral going in the same direction as us organisms, so it would make more sense to say that the Universe is conscious. And come to think of it, that does shed some light on what happened before the big bang.

I WAS JUST WONDERING ABOUT THAT.

As we look back in time and get closer to the event of the big bang, chaos is shrinking to zero. Thus from the subjective perspective, time is stretching out. indeed, as we go back in time and approach the big bang, subjective time approaches infinity. Thus it is not possible to go back past a subjective infinity of time.

THATʹS A LOAD OFF MY MIND. NOW YOU SAID THAT THE EXPONENTIAL PROGRESS OF AN

EVOLUTIONARY PROCESS GOES ON FOREVER. IS THERE ANYTHING THAT CAN STOP IT?

Only a catastrophe that wipes out the entire process.

SUCH AS AN ALL‐OUT NUCLEAR WAR?

Thatʹs one scenario, but in the next century, we will encounter a plethora of other ʺfailure modes.ʺ Weʹll talk about this in later chapters.

I CANʹT WAIT. NOW TELL ME THIS, WHAT DOES THE LAW OF ACCELERATING RETURNS HAVE TO DO

WITH THE TWENTY‐FIRST CENTURY?

Exponential trends are immensely powerful but deceptive. They linger for eons with very little effect. But once they reach the ʺknee of the curve,ʺ they explode with unrelenting fury. With regard to computer technology and its impact on human society, that knee is approaching with the new millennium. Now I have a question for you.

SHOOT.

Just who are you anyway?

WHY, IʹM THE READER.

Of course. Well, itʹs good to have you contributing to the book while thereʹs still time to do something about it.

GLAD TO. NOW, YOU NEVER DID GIVE THE ENDING TO THE EMPEROR STORY. SO DOES THE EMPEROR

LOSE HIS EMPIRE, OR DOES THE INVENTOR LOSE HIS HEAD?

I have two endings, so I just canʹt say.

MAYBE THEY REACH A COMPROMISE SOLUTION. THE INVENTOR MIGHT BE HAPPY TO SETTLE FOR, SAY,

JUST ONE PROVINCE OF CHINA.

Yes, that would be a good result. And maybe an even better parable for the twenty‐first century.

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C H A P T E R T W O

THE INTELLIGENCE OF

EVOLUTION

Hereʹs another critical question for understanding the twenty‐first century: Can an intelligence create another intelligence more intelligent than itself?

Letʹs first consider the intelligent process that created us: evolution.

Evolution is a master programmer. It has been prolific, designing millions of species of breathtaking diversity and

ingenuity. And thatʹs just here on Earth. The software programs have been all written down, recorded as digital data in the chemical structure of an ingenious molecule called deoxyribonucleic acid, or DNA. DNA was first described by

J. D. Watson and E H. C. Crick in 1953 as a double helix consisting of a twisting pair of strands of polynucleotides with two bits of information encoded at each ledge of a spiral staircase, encoded by the choice of nucleotides. [1] This master ʺread onlyʺ memory controls the vast machinery of life.

Supported by a twisting sugar‐phosphate backbone, the DNA molecule consists of between several dozen and several million rungs, each of which is coded with one nucleotide letter drawn from a four‐letter alphabet of base pairs (adenine‐thymine, thymine‐adenine, cytosine‐guanine, and guanine‐cytosine). Human DNA is a long

molecule—it would measure up to six feet in length if stretched out‐but it is packed into an elaborate coil only 1/2500 of an inch across.

The mechanism to peel off copies of the DNA code consists of other special machines: organic molecules called enzymes, which split each base pair and then assemble two identical DNA molecules by rematching the broken base

pairs. Other little chemical machines then verify the validity of the copy by checking the integrity of the base‐pair matches. The error rate of these chemical information‐processing transactions is about one error in a billion base‐pair replications. There are further redundancy and error‐correction codes built into the data itself, so meaningful mistakes are rare. Some mistakes do get through, most of which cause defects in a single cell. Mistakes in an early fetal cell may cause birth defects in the newborn organism. Once in a long while such defects offer an advantage, and this new encoding may eventually be favored through the enhanced survival of that organism and its offspring.

The DNA code controls the salient details of the construction of every cell in the organism, including the shapes

and processes of the cell, and of the organs comprised of the cells. In a process called translation, other enzymes translate the coded DNA information by building proteins. It is these proteins that define the structure, behavior, and intelligence of each cell, and of the organism. [2]

This computational machinery is at once remarkably complex and amazingly simple. Only four base pairs provide

the data storage for the complexity of all the millions of life‐forms on Earth, from primitive bacteria to human beings.

The ribosomes—little tape‐recorder molecules—read the code and build proteins from only twenty amino acids. The

synchronized flexing of muscle cells, the intricate biochemical interactions in our blood, the structure and functioning of our brains, and all of the other diverse functions of the Earthʹs creatures are programmed in this efficient code.

The genetic information‐processing appliance is an existence proof of nano‐engineering (building machines atom

by atom), because the machinery of life indeed takes place on the atomic level. Tiny bits of molecules consisting of just dozens of atoms encode each bit and perform the transcription, error detection, and correction functions. The actual building of the organic stuff is conducted atom by atom with the building of the amino acid chains.

This is our understanding of the hardware of the computational engine driving life on Earth. We are just beginning, however, to unravel the software. While prolific, evolution has been a sloppy programmer. It has left us

the object code (billions of bits of coded data), but there is no higher‐level source code (statements in a language we can understand), no explanatory comments, no ʺhelpʺ file, no documentation, and no user manual. Through the Human Genome Project, we are in the process of writing down the 6‐billion‐bit code for the human genetic code, and

are capturing the code for thousands of other species as well. [3] But reverse engineering the genome code—

understanding how it works—is a slow and laborious process that we are just beginning. As we do this, however, we

are learning the information‐processing basis of disease, maturation, and aging, and are gaining the means to correct and refine evolutionʹs unfinished invention.

In addition to evolutionʹs lack of documentation, it is also a very inefficient programmer. Most of the code—97

percent according to current estimates—does not compute; that is, most of the sequences do not produce proteins and

appear to be useless. That means that the active part of the code is only about 23 megabytes, which is less than the code for Microsoft Word. The code is also replete with redundancies. For example, an apparently meaningless sequence called Alu, comprising 300 nucleotide letters, occurs 300,000 times in the human genome, representing more

than 3 percent of our genetic program.

The theory of evolution states that programming changes are introduced essentially at random. The changes are

evaluated for retention by survival of the entire organism and its ability to reproduce. Yet the genetic program controls not just the one characteristic being ʺexperimentedʺ with, but millions of other features as well, Survival of the fittest appears to be a crude technique capable of concentrating on one or at most a few characteristics at a time.

Since the vast majority of changes make things worse, it may seem surprising that this technique works at all.

This contrasts with the conventional human approach to computer programming in which changes are designed

with a purpose in mind, multiple changes may be introduced at a time, and the changes made are tested by focusing

in on each change, rather than by overall survival of the program. If we attempted to improve our computer programs the way that evolution apparently improves its design, our programs would collapse from increasing randomness.

It is remarkable that by concentrating on one refinement at a time, such elaborate structures as the human eye could have been designed. Some observers have postulated that such intricate design is impossible through the incremental‐refinement method that evolution uses. A design as intricate as the eye or the heart would appear to require a design methodology in which it was designed all at once.

However, the fact that designs such as the eye have many interacting aspects does not rule out its creation through a design path comprising one small refinement at a time. In utero, the human fetus appears to go through a

process of evolution, although whether this is a corollary of the phases of evolution that led to our subspecies is not universally accepted. Nonetheless, most medical students learn that ontogeny (fetal development) recapitulates phylogeny (evolution of a genetically related group of organisms, such as a phylum). We appear to start out in the womb with similarities to a fish embryo, progress to an amphibian, then a mammal, and so on. Regardless of the phylogeny controversy, we can see in the history of evolution the intermediate design drafts that evolution went through in designing apparently ʺcompleteʺ mechanisms such as the human eye. Even though evolution focuses on

just one issue at a time, it is indeed capable of creating striking designs with many interacting parts.

There is a disadvantage, however, to evolutionʹs incremental method of design: It canʹt easily perform complete redesigns. It is stuck, for example, with the very slow computing speed of the mammalian neuron. But there is a way

around this, as we will explore in chapter 6, ʺBuilding New Brains.ʺ

The Evolution of Evolution

There are also certain ways in which evolution has evolved its own means for evolution. The DNA‐based coding itself

is clearly one such means. Within the code, other means have developed. Certain design elements, such as the shape

of the eye, are coded in a way that makes mutations less likely. The error detection and correction mechanisms built into the DNA‐based coding make changes in these regions very unlikely. This enforcement of design integrity for certain critical features evolved because they provide an advantage—changes to these characteristics are usually catastrophic. Other design elements, such as the number and layout of light‐sensitive rods and cones in the retina, have fewer design enforcements built into the code. If we examine the evolutionary record, we do see more recent change in the layout of the retina than in the shape of the eyeball itself. So in certain ways, the strategies of evolution have evolved. The Law of Accelerating Returns says that it should, for evolving its own strategies is the primary way that an evolutionary process builds on itself.

By simulating evolution, we can also confirm the ability of evolutionʹs ʺone step at a timeʺ design process to build ingenious designs of many interacting elements. One example is a software simulation of the evolution of life‐forms

called Network Tierra designed by Thomas Ray, a biologist and rain forest expert. [4] Rayʹs creaturesʺ are software simulations of organisms in which each ʺcellʺ has its own DNA‐like genetic code. The organisms compete with each

other for the limited simulated space and energy resources of their simulated environment.

A unique aspect of this artificial world is that the creatures have free rein of 150 computers on the Internet, like ʺislands in an archipelagoʺ according to Ray. One of the goals of this research is to understand how the explosion of diverse body plans that occurred on Earth during the Cambrian period some 570 million years ago was possible. ʺTo

watch evolution unfold is a thrill,ʺ Ray exclaimed as he watched his creatures evolve from unspecialized single‐celled organisms to multicellular organisms with at least modest increases in diversity. Ray has reportedly identified the equivalent of parasites, immunities, and crude social interaction. One of the acknowledged limitations in Rayʹs simulation is a lack of complexity in his simulated environment. One insight of this research is the need for a suitably chaotic environment as a key resource needed to push evolution along, a resource in ample supply in the real world.

A practical application of evolution is the area of evolutionary algorithms, in which millions of evolving computer

programs compete with one another in a simulated evolutionary process, thereby harnessing the inherent intelligence

of evolution to solve real‐world problems. Since the intelligence of evolution is weak, we focus and amplify it the same way a lens concentrates the sparse rays of the sun. Weʹll talk more about this powerful approach to software design in chapter 4, ʺA New Form of Intelligence on Earth.ʺ

The Intelligence Quotient of Evolution

Let us first praise evolution. It has created a plethora of designs of indescribable beauty, complexity, and elegance, not to mention effectiveness. Indeed, some theories of aesthetics define beauty as the degree of success in emulating the natural beauty that evolution has created. It created human beings with their intelligent human brains, beings smart enough to create their own intelligent technology.

Its intelligence seems vast. Or is it? It has one deficiency—evolution is very slow. While it is true that it has created some remarkable designs, it has taken an extremely long period of time to do so. It took eons for the process to get started and, for the evolution of life‐forms, eons meant billions of years. Our human forebears also took eons to get started in their creation of technology, but for us eons meant only tens of thousands of years, a distinct improvement.

Is the length of time required to solve a problem or create an intelligent design relevant to an evaluation of intelligence?

The authors of our human intelligence‐quotient tests seem to think so, which is why most IQ tests are timed. We

regard solving a problem in a few seconds better than solving it in a few hours or years. Periodically, the timed aspect of IQ tests gives rise to controversy, but it shouldnʹt. The speed of an intelligent process is a valid aspect of its evaluation. If a large, hunched, catlike animal perched on a tree limb suddenly appears out of my left cornea, designing an evasive tactic in a second or two is preferable to pondering the challenge for a few hours. If your boss asks you to design a marketing program, she probably doesnʹt want to wait a hundred years. Viking Penguin wanted

this book delivered before the end of the second, not the third, millennium. [5]

Evolution has achieved an extraordinary record of design, yet has taken an extraordinarily long period of time to

do so. If we factor its achievements by its ponderous pace, I believe we need to conclude that its intelligence quotient is only infinitesimally greater than zero. An IQ of only slightly greater than zero (defining truly arbitrary behavior as zero) is enough for evolution to beat entropy and create wonderful designs, given enough time, in the same way that

an ever so slight asymmetry in the balance between matter and antimatter was enough to allow matter to almost completely overtake its antithesis.

Evolution is thereby only a quantum smarter than completely unintelligent behavior. The reason that our human‐

created evolutionary algorithms are effective is that we speed up time a million‐ or billionfold, so as to concentrate and focus its otherwise diffuse power. in contrast, humans are a lot smarter than just a quantum greater than total stupidity (of course, your view may vary depending on the latest news reports).

THE LIFE CYCLE OF A TECHNOLOGY

What does the Law of Time and Chaos say about the end of the Universe? One theory is that the Universe

will continue its expansion forever. Alternatively, if there's enough stuff, then the force of the Universe's own

gravity will stop the expansion, resulting in a final "big crunch". Unless, of course, there's an antigravity force.

Or if the "cosmological constant" Einstein's "fudge factor," is big enough. I've had to rewrite this paragraph three times over the past several months because the physicists can't make up their minds. The latest

speculation apparently favors indefinite expansion.

Personally, I prefer the idea of the Universe closing in again on itself as more aesthetically pleasing. That

would mean that the Universe would reverse its expansion and reach a singularity again. We can speculate

that it would again expand and contract in an endless cycle. Most things in the Universe seem to move in

cycles, so why not the Universe itself? The Universe could then be regarded as a tiny wave particle in some

other really big Universe. And that big Universe would itself be a vibrating particle in yet another even bigger

Universe. Conversely, the tiny wave particles in our Universe can each be regarded as little Universes with

each of their vibrations lasting fractions of a trillionth of a second in our Universe representing billions of years of expansion and contraction in that little Universe. And each particle in those little Universes could

be. . . okay, so I'm getting a little carried away.

How to Unsmash a Cup

Let's say the Universe reverses its expansion. The phase of contraction has the opposite characteristics of

the phase of expansion that we are now in. Clearly, chaos in the Universe will be decreasing as the Universe

gets smaller. I can see that this is the case by considering the endpoint, which is again a singularity with no

size, and therefore no disorder.

We regard time as moving in one direction because processes in time are not generally reversible. If we

smash a cup, we find it difficult to unsmash it. The reason for this has to do with the second law of thermodynamics. Since overall entropy may increase but can never decrease, time has directionality.

Smashing a cup increases randomness. Unsmashing the cup would violate the second law of thermodynamics.

Yet in the contracting phase of the Universe, chaos is decreasing, so we should regard time's direction as

reversed.

This reverses all processes in time, turning evolution into devolution. Time moves backward during the

second half of the Universe's time span. So if you smash a favorite cup, try to do it as we approach the midpoint of the Universe's time span. You should find the cup coming together again as we cross over into the

Universe's contracting phase of its time span.

Now if time is moving backward during this contracting phase, what we (living in the expanding phase of

the Universe) look forward to as the big crunch is actually a big bang to the creatures living (in reverse time)

during the contracting phase. Consider the perspective of these time-reversed creatures living in what we

regard as the contracting phase of the Universe. From their perspective, what we regard as the second phase

is actually their first phase, with time going in the reverse direction. So from their perspective, the Universe

during this phase is expanding, not contracting. Thus, if the "Universe will eventually contract" theory is correct, it would be proper to say that the Universe is bounded in time by two big bangs, with events flowing

in opposite directions in time from each big bang, meeting in the middle. Creatures living in both phases can

say that they are in the first half of the Universe's history, since both phases will appear to be the first half to creatures living in those phases. And in both halves of the time span of the Universe, the Law of Entropy, the

Law of Time and Chaos, and the Law of Accelerating Returns (as applied to evolution) all hold true, but with

time moving in opposite directions. [6]

The End of Time

And what if the Universe expands indefinitely? This would mean that the stars and galaxies will eventually

exhaust their energy, leaving a Universe of dead stars expanding forever. That would leave a big mess—lots

of randomness—and no meaningful order, so according to the Law of Time and Chaos, time would gradually

come to a halt. Consistently, if a dead Universe means that there will be no conscious beings to appreciate it,

then both the Quantum Mechanical and the Eastern subjective viewpoints appear to imply that the Universe

would cease to exist.

In my view, neither conclusion is quite right. At the end of this book, I'll share with you my perspective of

what happens at the end of the Universe. But don't look ahead.

Consider the sophistication of our creations over a period of only a few thousand years. Ultimately, our machines

will match and exceed human intelligence, no matter how one cares to define or measure this elusive term. Even if my time frames are off, few serious observers who have studied the issue claim that computers will never achieve and surpass human intelligence. Humans will have vastly beaten evolution, therefore, achieving in a matter of only thousands of years as much or more than evolution achieved in billions of years. So human intelligence, a product of evolution, is far more intelligent than its creator.

And so, too, will the intelligence that we are creating come to exceed the intelligence of its creator. That is not the case today. But as the rest of this book will argue, it will take place very soon—in evolutionary terms, or even in terms of human history—and within the lifetimes of most of the readers of this book. The Law of Accelerating Returns predicts it. And furthermore, it predicts that the progression in the capabilities of human‐created machines will only continue to accelerate. The human species creating intelligent technology is another example of evolutionʹs progress building on itself. Evolution created human intelligence. Now human intelligence is designing intelligent machines at a far faster pace, Yet another example will be when our intelligent technology takes control of the creation of yet more intelligent technology than itself.

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NOW ON THIS TIME THING, WE START OUT AS A SINGLE CELL, RIGHT?

Thatʹs right.

AND THEN WE DEVELOP INTO SOMETHING RESEMBLING A FISH, THEN AN AMPHIBIAN, ULTIMATELY A

MAMMAL, AND SO ON—YOU KNOW ONTOGENY RECAPITULATES—

Phylogeny, yes.

SO THATʹS JUST LIKE EVOLUTION, RIGHT? WE GO THROUGH EVOLUTION IN OUR MOTHERʹS WOMB.

Yes, thatʹs the theory. The word phylogeny is derived from phylum . . .

BUT YOU SAID THAT IN EVOLUTION, TIME SPEEDS UP. YET IN AN ORGANISMʹS LIFE, TIME SLOWS DOWN.

Ah yes, a good catch, I can explain.

IʹM ALL EARS.

The Law of Time and Chaos states that, in a process the average time interval between salient events is proportional to the amount of chaos in the process. So we have to be careful to define precisely what constitutes the process. It is true that evolution started out with single cells. And we also start out as a single cell. Sounds similar, but from the perspective of the Law of Time and Chaos, itʹs not. We start out as just one cell. When evolution was at the point of single cells, it was not one cell, but many trillions of cells. And these cells were just swirling about; thatʹs a lot of chaos and not much order. The primary movement of evolution has been toward greater order. In the development of an

organism, however, the primary movement is toward greater chaos—the grown organism has far greater disorder than the single cell it started out as. It draws that chaos from the environment as its cells multiply, and as it has encounters with its environment. Is that clear?

UH, SURE. BUT DONʹT QUIZ ME ON IT. I THINK THE GREATEST CHAOS IN MY LIFE WAS WHEN I LEFT

HOME TO GO TO COLLEGE. THINGS ARE JUST BEGINNING TO SETTLE DOWN NOW AGAIN.

I never said the Law of Time and Chaos explains everything.

OKAY, BUT EXPLAIN THIS. YOU SAID THAT EVOLUTION WASNʹT VERY SMART, OR AT LEAST WAS RATHER

SLOW‐WITTED. BUT ARENʹT SOME OF THESE VIRUSES AND BACTERIA USING EVOLUTION TO OUTSMART

US?

Evolution operates on different timescales. If we speed it up, it can be smarter than us. Thatʹs the idea behind software programs that apply a simulated evolutionary process to solving complex problems. Pathogen evolution is

another example of the ability of evolution to amplify and focus its diffuse powers. After all, a viral generation can take place in minutes or hours compared to decades for the human race. However, I do think we will ultimately prevail against the evolutionary tactics of our disease agents.

IT WOULD BE HELPFUL IF WE STOPPED OVERUSING ANTIBIOTICS.

Yes, and that brings up another issue, which is whether the human species is more intelligent than its individual members.

AS A SPECIES, WEʹRE CERTAINLY PRETTY SELF‐DESTRUCTIVE.

Thatʹs often true. Nonetheless, we do have a profound species‐wide dialogue going on. In other species, the individuals may communicate in a small clan or colony, but there is little, if any, sharing of information beyond that, and little apparent accumulated knowledge. The human knowledge base of science, technology, art, culture, and history has no parallel in any other species.

WHAT ABOUT WHALE SONGS?

Hmmm. I guess we just donʹt know what theyʹre singing about.

AND WHAT ABOUT THOSE APES THAT YOU CAN TALK TO ON THE INTERNET?

Well, on April 27, 1998, Koko the gorilla did engage in what her mentor, Francine Patterson, called the first interspecies chat, on America Online. [8] But Kokoʹs critics intimate that Patterson is the brains behind Koko.

BUT PEOPLE WERE ABLE TO CHAT WITH KOKO ONLINE.

Yes. However, Koko is rusty on her typing skills, so questions were interpreted by Patterson into American Sign Language, which Koko observed, and then Kokoʹs signed responses were interpreted by Patterson back into typed responses. I guess the suspicion is that Patterson is like those language interpreters from the diplomatic corps—one wonders if youʹre communicating with the dignitary, in this case Koko, or the interpreter.

ISNʹT IT CLEAR IN GENERAL THAT THE APES ARE COMMUNICATING? THEYʹRE NOT THAT DIFFERENT

FROM US GENETICALLY, AS YOU SAID.

Thereʹs clearly some form of communication going on. The question being addressed by the linguistics community is

whether the apes can really deal with the levels of symbolism embodied in human language. I think that Dr. Emily

Savage‐Rumbaugh of Georgia State University, who runs a fifty‐five‐acre ape‐communication laboratory, made a fair

statement recently when she said, ʺThey. [her critics] are asking Kanzi [one of her ape subjects] to do everything that humans do, which is specious. Heʹll never do that. It still doesnʹt negate what he can do.ʺ

WELL, IʹM ROOTING FOR THE APES.

Yes, it would be nice to have someone to talk to when we get tired of other humans.

SO WHY DONʹT YOU JUST HAVE A LITTLE TALK WITH YOUR COMPUTER?

I do talk to my computer, and it dutifully takes down what I say to it. And I can give commands by speaking in natural language to Microsoft Word, [9] but itʹs still not a very engaging conversationalist. Remember, computers are still a million times simpler than the human brain, so itʹs going to be a couple of decades yet before they become comforting companions.

BACK ON THIS INDIVIDUAL‐VERSUS‐GROUP‐INTELLIGENCE ISSUE, ARENʹT MOST ACHIEVEMENTS IN ART

AND SCIENCE ACCOMPLISHED BY INDIVIDUALS? YOU KNOW, YOU CANʹT WRITE A SONG OR PAINT A

PICTURE BY COMMITTEE.

Actually, a lot of important science and technology is done in large groups.

BUT ARENʹT THE REAL BREAKTHROUGHS DONE BY INDIVIDUALS?

In many cases, thatʹs true. Even then, the critics and the technology conservatives, even the intolerant ones, do play an important screening role. Not every new and different idea is worth pursuing. Itʹs worthwhile having some barriers

to break through.

Overall, the human enterprise is clearly capable of achievements that go far beyond what we can do as individuals.

HOW ABOUT THE INTELLIGENCE OF A LYNCH MOB?

I suppose a group is not always more intelligent than its members.

WELL, I HOPE THOSE TWENTY‐FIRST‐CENTURY MACHINES DONʹT EXHIBIT OUR MOB PSYCHOLOGY.

Good point.

I MEAN, I WOULDNʹT WANT TO END UP IN A DARK ALLEY WITH A BAND OF UNRULY MACHINES.

We should keep that in mind as we design our future machines. Iʹll make a little note . . .

YES, PARTICULARLY BEFORE THE MACHINES START, AS YOU SAID, DESIGNING THEMSELVES.

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C H A P T E R T H R E E

OF MIND AND MACHINES

PHILOSOPHICAL MIND EXPERIMENTS

ʺI am lonely and bored; please keep me company.ʺ

If your computer displayed this message on its screen, would that convince you that your notebook is conscious

and has feelings?

Well, clearly no, itʹs rather trivial for a program to display such a message. The message actually comes from the

presumably human author of the program that includes the message. The computer is just a conduit for the message,

like a book or a fortune cookie.

Suppose we add speech synthesis to the program and have the computer speak its plaintive message. Have we changed anything?

While we have added technical complexity to the program, and some humanlike communication means, we still

do not regard the computer as the genuine author of the message.

Suppose now that the message is not explicitly programmed, but is produced by a game‐playing program that contains a complex model of its own situation. The specific message may never have been foreseen by the human creators of the program. It is created by the computer from the state of its own internal model as it interacts with you, the user. Are we getting closer to considering the computer as a conscious, feeling entity?

Maybe just a tad. But if we consider contemporary game software, the illusion is probably short‐lived as we gradually figure out the methods and limitations behind the computerʹs ability for small talk.

Now suppose the mechanisms behind the message grow to become a massive neural net, built from silicon but based on a reverse engineering of the human brain. Suppose we develop a learning protocol for this neural net that

enables it to learn human language and model human knowledge. Its circuits are a million times faster than human

neurons, so it has plenty of time to read all human literature and develop its own conceptions of reality, Its creators do not tell it how to respond to the world. Suppose now that it says, ʺIʹm lonely . . .ʺ

At what point do we consider the computer to be a conscious agent with its own free will? These have been the

most vexing problems in philosophy since the Platonic dialogues illuminated the inherent contradictions in our conception of these terms.

Letʹs consider the slippery slope from the opposite direction. Our friend Jack (circa some time in the twenty‐first

century) has been complaining of difficulty with his hearing. A diagnostic test indicates he needs more than a conventional hearing aid, so he gets a cochlear implant. Once used only by people with severe hearing impairments,

these implants are now common to correct the ability of people to hear across the entire sonic spectrum. This routine surgical procedure is successful, and Jack is pleased with his improved hearing.

Is he still the same person?

Well, sure he is. People have cochlear implants circa 1999. We still regard them as the same person.

Now (back to circa sometime in the twenty‐first century), Jack is so impressed with the success of his cochlear implants that he elects to switch on the built‐in phonic‐cognition circuits, which improve overall auditory perception.

These circuits are already built in so that he does not require another insertion procedure should he subsequently decide to enable them. By activating these neural‐replacement circuits, the phonics‐detection nets built into the implant bypass his own aging neural‐phonics regions. His cash account is also debited for the use of this additional neural software. Again, Jack is pleased with his improved ability to understand what people are saying.

Do we still have the same Jack? Of course; no one gives it a second thought.

Jack is now sold on the benefits of the emerging neural‐implant technology. His retinas are still working well, so

he keeps them intact (although he does have permanently implanted retinal‐imaging displays in his corneas to view

virtual reality), but he decides to try out the newly introduced image‐processing implants, and is amazed how much

more vivid and rapid his visual perception has become.

Still Jack? Why, sure.

Jack notices that his memory is not what it was, as he struggles to recall names, the details of earlier events, and so on. So heʹs back for memory implants. These are amazing‐memories that had grown fuzzy with time are now as clear

as if they had just happened. He also struggles with some unintended consequences as he encounters unpleasant memories that he would have preferred to remain dim.

Still the same Jack? Clearly he has changed in some ways and his friends are impressed with his improved faculties. But he has the same self‐deprecating humor, the same silly grin—yes, itʹs still the same guy.

So why stop here? Ultimately Jack will have the option of scanning his entire brain and neural system (which is

not entirely located in the skull) and replacing it with electronic circuits of far greater capacity, speed, and reliability.

Thereʹs also the benefit of keeping a backup copy in case anything happened to the physical Jack.

Certainly this specter is unnerving, perhaps more frightening than appealing. And undoubtedly it will be controversial for a long time (although according to the Law of Accelerating Returns, a ʺlong timeʺ is not as long as it used to be). Ultimately, the overwhelming benefits of replacing unreliable neural circuits with improved ones will be too compelling to ignore.

Have we lost Jack somewhere along the line? Jackʹs friends think not. Jack also claims that heʹs the same old guy,

just newer. His hearing, vision, memory, and reasoning ability have all improved, but itʹs still the same Jack.

However, letʹs examine the process a little more carefully Suppose rather than implementing this change a step at

a time as in the above scenario, Jack does it all at once. He goes in for a complete brain scan and has the information from the scan instantiated (installed) in an electronic neural computer. Not one to do things piecemeal, he upgrades his body as well. Does making the transition at one time change anything? Well, whatʹs the difference between changing from neural circuits to electronic/photonic ones all at once, as opposed to doing it gradually? Even if he makes the change in one quick step, the new Jack is still the same old Jack, right?

But what about Jackʹs old brain and body? Assuming a noninvasive scan, these still exist. This is Jack! Whether the

scanned information is subsequently used to instantiate a copy of Jack does not change the fact that the original Jack still exists and is relatively unchanged. Jack may not even be aware of whether or not a new Jack is ever created. And for that matter, we can create more than one new Jack.

If the procedure involves destroying the old Jack once we have conducted some quality‐assurance steps to make

sure the new Jack is fully functional, does that not constitute the murder (or suicide) of Jack?

Suppose the original scan of Jack is not noninvasive, that it is a ʺdestructiveʺ scan. Note that technologically speaking, a destructive scan is much easier—in fact we have the technology today (1999) to destructively scan frozen neural sections, ascertain the interneuronal wiring, and reverse engineer the neuronsʹ parallel digital‐analog algorithms. [1] We donʹt yet have the bandwidth to do this quickly enough to scan anything but a very small portion

of the brain. But the same speed issue existed for another scanning project—the human genome scan—when the project began. At the speed that researchers were able to scan and sequence the human genetic code in 1991, it would have taken thousands of years to complete the project. Yet a fourteen‐year schedule was set, which it now appears will be successfully realized. The Human Genome Project deadline obviously made the (correct) assumption that the

speed of our methods for sequencing DNA codes would greatly accelerate over time. The same phenomenon will hold true for our human‐brain‐scanning projects. We can do it now—very slowly—but that speed, like most everything else governed by the Law of Accelerating Returnsʹ will get exponentially faster in the years ahead.

Now suppose as we destructively scan Jack, we simultaneously install this information into the new Jack. We can

consider this a process of ʺtransferringʺ Jack to his new brain and body. So one might say that Jack is not destroyed, just transferred into a more suitable embodiment. But is this not equivalent to scanning Jack noninvasively, subsequently instantiating the new Jack and then destroying the old Jack? if that sequence of steps basically amounts to killing the old Jack, then this process of transferring Jack in a single step must amount to the same thing. Thus we can argue that any process of transferring Jack amounts to the old Jack committing suicide, and that the new Jack is not the same person.

The concept of scanning and reinstantiation of the information is familiar to us from the fictional ʺbeam me upʺ

teleportation technology of Star Trek. In this fictional show, the scan and reconstitution is presumably on a nanoengineering scale, that is, particle by particle, rather than just reconstituting the salient algorithms of neural-information processing envisioned above. But the concept is very similar. Therefore, it can be argued that the Star Trek characters are committing suicide each time they teleport, with new characters being created. These new characters, while essentially identical, are made up of entirely different particles, unless we imagine that it is the actual particles being beamed to the new destination. Probably it would be easier to beam just the information and use local particles to instantiate the new embodiments. Should it matter? Is consciousness a function of the actual particles or just of their pattern and organization?

We argue that consciousness and identity are not a function of the specific particles at all, because our own particles are constantly changing. On a cellular basis, we change most of our cells (although not our brain cells) over a period of several years. [2] On an atomic level, the change is much faster than that, and does include our brain cells.

We are not at all permanent collections of particles. It is the patterns of matter and energy that are semipermanent (that is, changing only gradually), but our actual material content is changing constantly, and very quickly. We are rather like the patterns that water makes in a stream. The rushing water around a formation of rocks makes a particular, unique pattern. This pattern may remain relatively unchanged for hours, even years. Of course, the actual material constituting the pattern—the water—is totally replaced within milliseconds. This argues that we should not

associate our fundamental identity with specific sets of particles, but rather the pattern of matter and energy that we represent. This, then, would argue that we should consider the new Jack to be the same as the old Jack because the

pattern is the same. (One might quibble that while the new Jack has similar functionality to the old Jack, he is not identical. However, this just dodges the essential question, because we can reframe the scenario with a nanoengineering technology that copies Jack atom by atom rather than just copying his salient information-processing algorithms.)

Contemporary philosophers seem to be partial to the ʺidentity from patternʺ argument. And given that our pattern changes only slowly in comparison to our particles, there is some apparent merit to this view. But the counter to that argument is the ʺold Jackʺ waiting to be extinguished after his ʺpatternʺ has been scanned and installed in a new computing medium. Old Jack may suddenly realize that the ʺidentity from patternʺ argument is flawed.

MIND AS MACHINE VERSUS MIND BEYOND MACHINE

Science cannot solve the ultimate mystery of nature because in the last analysis we are part of the mystery we are trying to solve.

—Max Planck

Is all what we see or seem, but a dream within a dream?

—Edgar Allan Poe

What if everything is an illusion and nothing exists? in that case, I definitely overpaid for my carpet.

—Woody Allen

The Difference Between Objective and Subjective Experience

Can we explain the experience of diving into a lake to someone who has never been immersed in water? How about

the rapture of sex to someone who has never had erotic feelings (assuming one could find such a person)? Can we explain the emotions evoked by music to someone congenitally deaf? A deaf person will certainly learn a lot about music: watching people sway to its rhythm, reading about its history and role in the world. But none of this is the same as experiencing a Chopin prelude.

If I view light with a wavelength of 0.000075 centimeters, I see red. Change the wavelength to 0.000035 centimeters

and I see violet. The same colors can also be produced by mixing colored lights. If red and green lights are properly combined, I see yellow. Mixing pigments works differently from changing wavelengths, however, because pigments

subtract colors rather than add them. Human perception of color is more complicated than mere detection of electro‐

magnetic frequencies, and we still do not fully understand it. Yet even if we had a fully satisfactory theory of our mental process, it would not convey the subjective experience of redness, or yellowness. I find language inadequate

for expressing my experience of redness. Perhaps I can muster some poetic reflections about it, but unless youʹve had the same encounter, it is really not possible for me to share my experience.

So how do I know that you experience the same thing when you talk about redness? Perhaps you experience red

the way I experience blue, and vice versa. How can we test our assumptions that we experience these qualities the same way? Indeed, we do know there are some differences. Since I have what is misleadingly labeled ʺred‐green color‐blindness, there are shades of color that appear identical to me that appear different to others. Those of you without this disability apparently have a different experience than I do. What are you all experiencing? Iʹll never know.

Giant squids are wondrous sociable creatures with eyes similar in structure to humans (which is surprising, given

their very different phylogeny) and possessing a complex nervous system. A few fortunate human scientists have developed relationships with these clever cephalopods. So what is it like to be a giant squid? When we see it respond to danger and express behavior that reminds us of a human emotion, we infer an experience that we are familiar with.

But what of their experiences without a human counterpart?

Or do they have experiences at all? Maybe they are just like ʺmachinesʺ—responding programmatically to stimuli

in their environment. Maybe there is no one home. Some humans are of this view—only humans are conscious; animals just respond to the world by ʺinstinct,ʺ that is, like a machine. To many other humans, this author included, it seems apparent that at least the more evolved animals are conscious creatures, based on empathetic perceptions of animals expressing emotions that we recognize as correlates of human reactions. Yet even this is a human‐centric way of thinking in that it only recognizes subjective experiences with a human equivalent. Opinion on animal consciousness is far from unanimous. Indeed, it is the question of consciousness that underlies the issue of animal rights. Animal rights disputes about whether or not certain animals are suffering in certain situations result from our general inability to experience or measure the subjective experience of another entity. [3]

The not uncommon view of animals being ʺjust machinesʺ is disparaging to both animals and machines. Machines

today are still a million times simpler than the human brain. Their complexity and subtlety today is comparable to that of insects. There is relatively little speculation on the subjective experience of insects, although again, there is no convincing way to measure this. But the disparity in the capabilities of machines and the more advanced animals, such as the Homo sapiens sapiens subspecies, will be short‐lived. The unrelenting advance of machine intelligence, which we will visit in the next several chapters, will bring machines to human levels of intricacy and refinement and beyond within several decades. Will these machines be conscious?

And what about free will‐will machines of human complexity make their own decisions, or will they just follow a

program, albeit a very complex one? Is there a distinction to be made here?

The issue of consciousness lurks behind other vexing issues. Take the question of abortion. Is a fertilized egg cell a conscious human being? How about a fetus one day before birth?

Itʹs hard to say that a fertilized egg is conscious or that a full‐term fetus is not. Pro‐choice and pro‐life activists are afraid of the slippery slope in between these two definable ledges. And the slope is genuinely slippery—a human fetus develops a brain quickly, but itʹs not immediately recognizable as a human brain. The brain of a fetus becomes more humanlike gradually. The slope has no ridges to stand on. Admittedly, other hard‐to‐define questions such as

human dignity come into the debate, but fundamentally, the contention concerns sentience. In other words, when do

we have a conscious entity?

Some severe forms of epilepsy have been successfully treated by surgical removal of the impaired half of the brain. This drastic surgery needs to be done during childhood before the brain has fully matured. Either half of the brain can be removed, and if the operation is successful the child will grow up somewhat normally. Does this imply

that both halves of the brain have their own consciousness? Perhaps there are two of us in each intact brain who hopefully get along with each other. Maybe there is a whole panoply of consciousnesses lurking in one brain each with a somewhat different perspective. Is there a consciousness that is aware of mental processes that we consider unconscious?

I could go on for a long time with such conundrums. And indeed, people have been thinking about these quandaries for a long time. Plato, for one, was preoccupied with these issues. In the Phaedo, The Republic, and Theaetetus, Plato expresses the profound paradox inherent in the concept of consciousness and a humanʹs apparent ability to freely choose. On the one hand, human beings partake of the natural world and are subject to its laws. Our brains are natural phenomena and thus must follow the cause‐and‐effect laws manifest in machines and other lifeless

creations of our species. Plato was familiar with the potential complexity of machines and their ability to emulate elaborate logical processes. On the other hand, cause‐and‐effect mechanics, no matter how complex, should not, according to Plato, give rise to self‐awareness or consciousness. Plato first attempts to resolve this conflict in his theory of the Forms: Consciousness is not an attribute of the mechanics of thinking, but rather the ultimate reality of human existence. Our consciousness, or ʺsoul,ʺ is immutable and unchangeable. Thus, our mental interaction with the

physical world is on the level of the ʺmechanicsʺ of our complicated thinking process. The soul stands aloof.

But no, this doesnʹt really work, Plato realizes. If the soul is unchanging, then it cannot learn or partake in reason, because it would need to change to absorb and respond to experience. Plato ends up dissatisfied with positing consciousness in either place: the rational processes of the natural world or the mystical level of the ideal Form of the self or soul. [4]

The concept of free will reflects an even deeper paradox. Free will is purposeful behavior and decision making.

Plato believed in a ʺcorpuscular physicsʺ based on fixed and determined rules of cause and effect. But if human decision making is based on such predictable interactions of basic particles, our decisions must also be predetermined. That would contradict human freedom to choose. The addition of randomness into the natural laws is

a possibility, but it does not solve the problem. Randomness would eliminate the predetermination of decisions and

actions, but it contradicts the purposefulness of free will, as there is nothing purposeful in randomness.

Okay, letʹs put free will in the soul. No, that doesnʹt work either. Separating free will from the rational cause‐and-effect mechanics of the natural world would require putting reason and learning into the soul as well, for otherwise the soul would not have the means to make meaningful decisions. Now the soul is itself becoming a complex machine, which contradicts its mystical simplicity.

Perhaps this is why Plato wrote dialogues. That way he could passionately express both sides of these contradictory positions. I am sympathetic to Platoʹs dilemma: None of the obvious positions is really sufficient. A deeper truth can be perceived only by illuminating the opposing sides of a paradox.

Plato was certainly not the last thinker to ponder these questions. We can identify several schools of thought on

these subjects, none of them very satisfactory.

The ʺConsciousness is Just a Machine Reflecting on Itselfʺ School

A common approach is to deny the issue exists: Consciousness and free will are just illusions induced by the ambiguities of language. A slight variation is that consciousness is not exactly an illusion, but just another logical process. It is a process responding and reacting to itself. We can build that in a machine: just build a procedure that has a model of itself, and that examines and responds to its own methods. Allow the process to reflect on itself. There now you have consciousness. It is a set of abilities that evolved because self‐reflective ways of thinking are inherently more powerful.

The difficulty with arguing against the ʺconsciousness is just a machine reflecting on itselfʺ school is that this perspective is self‐consistent. But this viewpoint ignores the subjective viewpoint. It can deal with a personʹs reporting of subjective experience, and it can relate reports of subjective experiences not on to outward behavior but to patterns of neural firings as well. And if I think about it, my knowledge of the subjective experience of anyone aside from myself is no different (to me) than the rest of my objective knowledge. I donʹt experience other peopleʹs subjective experiences; I just hear about them. So the only subjective experience this school of thought ignores is my own (that is, after all, what the term subjective experience means). And, hey, Iʹm only one person among billions of humans, trillions of potentially conscious organisms; all of whom, with just one exception, are not me.

But the failure to explain my subjective experience is a serious one. It does not explain the distinction between 0.000075 centimeter electromagnetic radiation and my experience of redness. I could learn how color perception works, how the human brain processes light, how it processes combinations of light, even what patterns of neural firing this all provokes, but it still fails to explain the essence of my experience.

The Logical Positivists [5]

I am doing my best to express what I am talking about here but unfortunately the issue is not entirely effable. D. J.

Chalmers describes the mystery of the experienced inner life as the ʺhard problemʺ of consciousness, to distinguish this issue from the ʺeasy problemʺ of how the brain works. [6] Marvin Minsky observed that ʺthereʹs something queer

about describing consciousness: Whatever people mean to say, they just canʹt seem to make it clear .ʺ That is precisely the problem, says the ʺconsciousness is just a machine reflecting on itselfʺ school—to speak of consciousness other than as a pattern of neural firings is to wander off into a mystical realm beyond any hope of verification.

This objective view is sometimes referred to as logical positivism, a philosophy codified by Ludwig Wittgenstein

in his Tractatus Logico‐Philosophicus. [7] To the logical positivists, the only things worth talking about are our direct sensory experiences, and the logical inferences that we can make therefrom. Everything else ʺwe must pass over in silence,ʺ to quote Wittgensteinʹs last statement in his treatise.

Yet Wittgenstein did not practice what he preached. Published in 1953, two years after his death, his Philosophical Investigations defined those matters worth contemplating as precisely those issues he had earlier argued should be passed over in silence. [8] Apparently he came to the view that the antecedents of his last statement in the Tractatus—

what we cannot speak about—are the only real phenomena worth reflecting upon. The late Wittgenstein heavily influenced the existentialists, representing perhaps the first time since Plato that a major philosopher was successful in illuminating such contradictory views.

I Think, Therefore I Am

The early Wittgenstein and the logical positivists that he inspired are often thought to have their roots in the philosophical investigations of René Descartes. [9] Descartesʹs famous dictum ʺI think, therefore I amʺ has often been cited as emblematic of Western rationalism. This view interprets Descartes to mean ʺI think, that is, I can manipulate logic and symbols, therefore I am worthwhile.ʺ But in my view, Descartes was not intending to extol the virtues of rational thought. He was troubled by what has become known as the mind‐body problem, the paradox of how mind

can arise from nonmind, how thoughts and feelings can arise to its limits, from the ordinary matter of the brain.

Pushing rational skepticism his statement really means ʺI think, that is, there is an undeniable mental phenomenon,

some awareness, occurring, therefore all we know for sure is that something—letʹs call it I—exists.ʺ Viewed in this way, there is less of a gap than is commonly thought between Descartes and Buddhist notions of consciousness as the

primary reality.

Before 2030, we will have machines proclaiming Descartesʹs dictum. And it wonʹt seem like a programmed response. The machines will be earnest and convincing. Should we believe them when they claim to be conscious entities with their own volition?

The ʺConsciousness Is a Different Kind of Stuffʺ School

The issue of consciousness and free will has been, of course, a major preoccupation of religious thought. Here we encounter a panoply of phenomena, ranging from the elegance of Buddhist notions of consciousness to ornate pantheons of souls, angels, and gods. In a similar category are theories by contemporary philosophers that regard consciousness as yet another fundamental phenomenon in the world, like basic particles and forces. I call this the ʺconsciousness is a different kind of stuffʺ school. To the extent that this school implies an interference by consciousness in the physical world that runs afoul of scientific experiment, science is bound to win because of its ability to verify its insights. To the extent that this view stays aloof from the material world, it often creates a level of complex mysticism that cannot be verified and is subject to disagreement. To the extent that it keeps its mysticism simple, it offers limited objective insight, although subjective insight is another matter (I do have to admit a fondness for simple mysticism).

The ʺWeʹre Too Stupidʺ School

Another approach is to declare that human beings just arenʹt capable of understanding the answer. Artificial intelligence researcher Douglas Hofstadter muses that ʺit could be simply an accident of fate that our brains are too weak to understand themselves. Think of the lowly giraffe, for instance, whose brain is obviously far below the level required for self‐understanding—yet it is remarkably similar to our brain.ʺ [10] But to my knowledge, giraffes are not known to ask these questions (of course, we donʹt know what they spend their time wondering about). In my view, if

we are sophisticated enough to ask the questions, then we are advanced enough to understand the answers.

However, the ʺweʹre too stupidʺ school points out that indeed we are having difficulty clearly formulating these questions.

A Synthesis of Views

My own view is that all of these schools are correct when viewed together, but insufficient when viewed one at a time. That is, the truth lies in a synthesis of these views. This reflects my Unitarian religious education in which we studied all the worldʹs religions, considering them ʺmany paths to the truth.ʺ Of course, my view may be regarded as the worst one of all. On its face, my view is contradictory and makes little sense. The other schools at least can claim some level of consistency and coherence.

Thinking Is as Thinking Does

Oh yes, there is one other view, which I call the ʺthinking is as thinking doesʺ school. In a 1950 paper, Alan Turing describes his concept of the Turing Test, in which a human judge interviews both a computer and one or more human

foils using terminals (so that the judge wonʹt be prejudiced against the computer for lacking a warm and fuzzy appearance). [11] If the human judge is unable to reliably unmask the computer (as an impostor human) then the computer wins. The test is often described as a kind of computer IQ test, a means of determining if computers have

achieved a human level of intelligence. In my view, however, Turing really intended his Turing Test as a test of thinking, a term he uses to imply more than just clever manipulation of logic and language. To Turing, thinking implies conscious intentionality.

Turing had an implicit understanding of the exponential growth of computing power, and predicted that a computer would pass his eponymous exam by the end of the century. He remarked that by that time ʺthe use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking

without expecting to be contradicted.ʺ His prediction was overly optimistic in terms of time frame, but in my view not by much.

In the end, Turingʹs prediction foreshadows how the issue of computer thought will be resolved. The machines will convince us that they are conscious, that they have their own agenda worthy of our respect. We will come to believe that they are conscious much as we believe that of each other. More so than with our animal friends, we will empathize with their professed feelings and struggles because their minds will be based on the design of human thinking. They will embody human qualities and will claim to be human. And weʹll believe them.

THE VIEW FROM QUANTUM MECHANICS

I often dream about failing. Such dreams are commonplace to the ambitious or those who

climb mountains. Lately I dreamed I was clutching at the face of a rock, but it would not

hold. Gravel gave way. I grasped for a shrub, but it pulled loose, and in cold terror I fell

into the abyss. Suddenly I realized that my fall was relative; there was no bottom and no

end. A feeling of pleasure overcame me. I realized that what I embody, the principle of

life, cannot be destroyed. It is written into the cosmic code, the order of the universe. As

I continued to fall in the dark void, embraced by the vault of the heavens, I sang to the

beauty of the stars and made my peace with the darkness.

― Heinz Pagels, physicist and quantum mechanics researcher before his death in a

1988 climbing accident

The Western objective view states that after billions of years of swirling around, matter and energy evolved to create life-forms—complex self-replicating patterns of matter and energy—that became sufficiently advanced

to reflect on their own existence, on the nature of matter and energy, on their own consciousness. In

contrast, the Eastern subjective view states that consciousness came first—matter and energy are merely the

complex thoughts of conscious beings, ideas that have no reality without a thinker.

As noted above, the objective and subjective views of reality have been at odds since the dawn of recorded

history. There is often merit, however, in combining seemingly irreconcilable views to achieve a deeper

understanding. Such was the case with the adoption of quantum mechanics fifty years ago. Rather than

reconcile the views that electromagnetic radiation (for example, light) was either a stream of particles (that

is, photons) or a vibration (that is, light waves), both views were fused into an irreducible duality. While this

idea is impossible to grasp using only our intuitive models of nature, we are unable to explain the world

without accepting this apparent contradiction. Other paradoxes of quantum mechanics (for example, electron

"tunneling" in which electrons in a transistor appear on both sides of a barrier) helped create the age of computation, and may unleash a new revolution in the form of the quantum computer, [12] but more about

that later.

Once we accept such a paradox, wonderful things happen. In postulating the duality of light, quantum

mechanics has discovered an essential nexus between matter and consciousness. Particles apparently do not

make up their minds as to which way they are going or even where they have been until they are forced to do

so by the observations of a conscious observer. We might say that they appear not really to exist at all

retroactively until and unless we notice them.

So twentieth-century Western science has come around to the Eastern view. The Universe is sufficiently

sublime that the essentially Western objective view of consciousness arising from matter and the essentially

Eastern subjective view of matter arising from consciousness apparently coexist as another irreducible duality.

Clearly, consciousness, matter, and energy are inextricably linked.

We may note here a similarity of quantum mechanics to the computer simulation of a virtual world. In

today's software games that display images of a virtual world, the portions of the environment not currently

being interacted with by the user (that is, those off screen) are usually not computed in detail, if at all. The

limited resources of the computer are directed toward rendering the portion of the world that the user is

currently viewing. As the user focuses in on some other aspect, the computational resources are then

immediately directed toward creating and displaying that new perspective. It thus seems as if the portions of

the virtual world that are offscreen are nonetheless still "there" but the software designers figure there is no point wasting valuable computer cycles on regions of their simulated world that no one is watching.

I would say that quantum theory implies a similar efficiency in the physical world. Particles appear not to

decide where the have been until forced to do so by being observed. The implication is that portions of the

world we live in are not actually "rendered" until some conscious observer turns her attention toward them.

After all, there's no point wasting valuable "computes" of the celestial computer that renders our Universe.

This gives new meaning to the question about the unheard tree that falls in the forest.

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ON THIS MULTIPLE‐CONSCIOUSNESS IDEA, WOULDNʹT I NOTICE THAT—I MEAN IF I HAD DECIDED TO DO

ONE THING AND THIS OTHER CONSCIOUSNESS IN MY HEAD WENT AHEAD AND DECIDED SOMETHING

ELSE?

I thought you had decided not to finish that muffin you just devoured.

TOUCHE. OKAY, IS THAT AN EXAMPLE OF WHAT YOUʹRE TALKING ABOUT?

It is a better example of Marvin Minskyʹs Society of Mind, in which he conceives of our mind as a society of other minds some like muffins, some are vain, some are health conscious, some make resolutions, others break them. Each

of these in turn is made up of other societies. At the bottom of this hierarchy are little mechanisms Minsky calls agents with little or no intelligence. It is a compelling vision of the organization of intelligence, including such phenomena as mixed emotions and conflicting values.

SOUNDS LIKE A GREAT LEGAL DEFENSE. ʺNO, JUDGE, IT WASNʹT ME. IT WAS THIS OTHER GAL IN MY

HEAD WHO DID THE DEED!ʺ

Thatʹs not going to do you much good if the judge decides to lock up the other gal in your head.

THEN HOPEFULLY THE WHOLE SOCIETY IN MY HEAD WILL STAY OUT OF TROUBLE. BUT WHICH MINDS

IN MY SOCIETY OF MIND ARE CONSCIOUS?

We could imagine that each of these minds in the society of mind is conscious, albeit that the lowest‐ranking ones have relatively little to be conscious of. Or perhaps consciousness is reserved for the higher‐ranking minds. Or perhaps only certain combinations of higher‐ranking minds are conscious, whereas others are not. Or perhaps—

NOW WAIT A SECOND, HOW CAN WE TELL WHAT THE ANSWER IS?

I believe thereʹs really no way to tell. What possible experiment can we run that would conclusively prove whether an entity or process is conscious? If the entity says, ʺHey, Iʹm really conscious,ʺ does that settle the matter? If the entity is very compelling when it expresses a professed emotion, is that definitive? if we look carefully at its internal methods and see feedback loops in which the process examines and responds to itself, does that mean itʹs conscious? If we see certain types of patterns in its neural firings, is that convincing. Contemporary philosophers such as Daniel Dermett appear to believe that the consciousness of an entity is a testable and measurable attribute. But I think science is inherently about objective reality. I donʹt see how it can break through to the subjective level.

MAYBE IF THE THING PASSES THE TURING TEST?

That is what Turing had in mind. Lacking any conceivable way of building a consciousness detector, he settled on a

practical approach, one that emphasizes our unique human proclivity for language. And I do think that Turing is right in a way—if a machine can pass a valid Turing Test, I believe that we will believe that it is conscious. Of course, thatʹs still not a scientific demonstration.

The converse proposition, however, is not compelling. Whales and elephants have bigger brains than we do and

exhibit a wide range of behaviors that knowledgeable observers consider intelligent. I regard them as conscious creatures, but they are in no position to pass the Turing Test.

THEY WOULD HAVE TROUBLE TYPING ON THESE SMALL KEYS OF MY COMPUTER.

Indeed, they have no fingers. They are also not proficient in human languages. The Turing Test is clearly a human‐

centric measurement.

IS THERE A RELATIONSHIP BETWEEN THIS CONSCIOUSNESS STUFF AND THE ISSUE OF TIME THAT WE

SPOKE ABOUT EARLIER?

Yes, we clearly have an awareness of time. Our subjective experience of time passage—and remember that subjective

is just another word for conscious—is governed by the speed of our objective processes. If we change this speed by

altering our computational substrate, we affect our perception of time.

RUN THAT BY ME AGAIN.

Letʹs take an example. If I scan your brain and nervous system with a suitably advanced noninvasive‐scanning technology of the early twenty‐first century—a very‐high‐resolution, high‐bandwidth magnetic resonance imaging, perhaps—ascertain all the salient information processes and then download that information to my suitably advanced

neural computer, Iʹll have a little you or at least someone very much like you right here in my personal computer.

If my personal computer is a neural net of simulated neurons made of electronic stuff rather than human stuff, the

version of you in my computer will run about a million times faster. So an hour for me would be a million hours for

you, which is about a century.

OH, THATʹS GREAT, YOUʹLL DUMP ME IN YOUR PERSONAL COMPUTER, AND THEN FORGET ABOUT ME

FOR A SUBJECTIVE MILLENNIUM OR TWO.

Weʹll have to be careful about that, wonʹt we.

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C H A P T E R F O U R

A NEW FORM OF

INTELLIGENCE ON EARTH

THE ARTIFICIAL INTELLIGENCE MOVEMENT

What if these theories are really true, and we were magically shrunk and put into someoneʹs brain while he was thinking. We would see all the pumps, pistons, gears and levers working away, and we would be able to describe their workings completely, in mechanical terms, thereby completely describing the thought processes of the brain. But that description would nowhere contain any mention of thought! it would contain nothing but descriptions of pumps, pistons, levers!

—Gottfried Wilhelm Leibniz

Artificial stupidity (AS) may be defined as the attempt by computer scientists to create computer programs capable of causing problems of a type normally associated with human thought.

—Wallace Marshal

Artificial intelligence (AI) is the science of how to get machines to do the things they do in the movies.

—Astro Teller

The Ballad of Charles and Ada

Returning to the evolution of intelligent machines, we find Charles Babbage sitting in the rooms of the Analytical Society at Cambridge, England, in 1821, with a table of logarithms lying before him.

ʺWell, Babbage, what are you dreaming about?ʺ asked another member, seeing Babbage half asleep.

ʺI am thinking that all these tables might be calculated by machinery!ʺ Babbage replied.

From that moment on, Babbage devoted most of his waking hours to an unprecedented vision: the worldʹs first

programmable computer. Although based entirely on the mechanical technology of the nineteenth century, Babbageʹs

ʺAnalytical Engineʺ was a remarkable foreshadowing of the modern computer. [1]

Babbage developed a liaison with the beautiful Ada Lovelace, the only legitimate child of Lord Byron, the poet.

She became as obsessed with the project as Babbage, and contributed many of the ideas for programming the

machine, including the invention of the programming loop and the subroutine. She was the worldʹs first software

engineer, indeed the only software engineer prior to the twentieth century.

Lovelace significantly extended Babbageʹs ideas and wrote a paper on programming techniques, sample

programs, and the potential of this technology to emulate intelligent human activities. She describes the speculations of Babbage and herself on the capacity of the Analytical Engine, and future machines like it, to play chess and

compose music. She finally concludes that although the computations of the Analytical Engine could not properly be

regarded as ʺthinking,ʺ they could nonetheless perform activities that would otherwise require the extensive

application of human thought.

The story of Babbage and Lovelace ends tragically. She died a painful death from cancer at the age of thirty‐six,

leaving Babbage alone again to pursue his quest. Despite his ingenious constructions and exhaustive effort, the

Analytical Engine was never completed. Near the end of his existence he remarked that he had never had a happy

day in his life. Only a few mourners were recorded at Babbageʹs funeral in 1871. [2]

What did survive were Babbageʹs ideas. The first American programmable computer, the Mark 1, completed in

1944 by Howard Aiken of Harvard University and IBM, borrowed heavily from Babbageʹs architecture. Aiken

commented, ʺIf Babbage had lived seventy‐five years later, I would have been out of a job.ʺ [3] Babbage and Lovelace were innovators nearly a century ahead of their time. Despite Babbageʹs inability to finish any of his major initiatives, their concepts of a computer with a stored program, self‐modifying code, addressable memory, conditional

branching, and computer programming itself still form the basis of computers today. [4]

Again, Enter Alan Turing

By 1940, Hitler had the mainland of Europe in his grasp, and England was preparing for an anticipated invasion. The

British government organized its best mathematicians and electrical engineers, under the intellectual leadership of Alan Turing, with the mission of cracking the German military code. It was recognized that with the German air force enjoying superiority in the skies, failure to accomplish this mission was likely to doom the nation. In order not to be distracted from their task, the group lived in the tranquil pastures of Hertfordshire, England.

Turing and his colleagues constructed the worldʹs first operational computer from telephone relays and named it

Robinson, [5] after a popular cartoonist who drew ʺRube Goldbergʺ machines (very ornate machinery with many interacting mechanisms). The groupʹs own Rube Goldberg succeeded brilliantly and provided the British with a transcription of nearly all significant Nazi messages. As the Germans added to the complexity of their code (by adding additional coding wheels to their Enigma coding machine), Turing replaced Robinsonʹs electro‐magnetic intelligence with an electronic version called Colossus built from two thousand radio tubes. Colossus and nine similar machines running in parallel provided an uninterrupted decoding of vital military intelligence to the Allied war effort.

Use of this information required supreme acts of discipline on the part of the British government. Cities that were

to be bombed by Nazi aircraft were not forewarned, lest preparations arouse German suspicions that their code had

been cracked. The information provided by Robinson and Colossus was used only with the greatest discretion, but the cracking of Enigma was enough to enable the Royal Air Force to win the Battle of Britain.

Thus fueled by the exigencies of war, and drawing upon a diversity of intellectual traditions, a new form of intelligence emerged on Earth.

The Birth of Artificial Intelligence

The similarity of the computational process to the human thinking process was not lost on Turing. In addition to having established much of the theoretical foundations of computation and having invented the first operational computer, he was instrumental in the early efforts to apply this new technology to the emulation of intelligence.

In his classic 1950 paper, Computing Machinery and Intelligence, Turing described an agenda that would in fact occupy the next half century of advanced computer research: game playing, decision making, natural language understanding, translation, theorem proving, and, of course, encryption and the cracking of codes. [6] He wrote (with his friend David Champernowne) the first chess‐playing program.

As a person, Turing was unconventional and extremely sensitive. He had a wide range of unusual interests, from

the violin to morphogenesis (the differentiation of cells). There were public reports of his homosexuality, which greatly disturbed him, and he died at the age of forty‐one, a suspected suicide.

The Hard Things Were Easy

In the 1950s, progress came so rapidly that some of the early pioneers felt that mastering the functionality of the human brain might not be so difficult after all. In 1956, AI researchers Allen Newell, J. C. Shaw, and Herbert Simon created a program called Logic Theorist (and in 1957 a later version called General Problem Solver), which used recursive search techniques to solve problems in mathematics. [7] Recursion, as we will see later in this chapter, is a powerful method of defining a solution in terms of itself. Logic Theorist and General Problem Solver were able to find proofs for many of the key theorems in Bertrand Russell and Alfred North Whiteheadʹs seminal work on set theory,

Principia Mathematica, [8] including a completely original proof for an important theorem that had never been previously solved. These early successes led Simon and Newell to say in a 1958 paper, entitled Heuristic Problem Solving: The Next Advance in Operations Research, ʺThere are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until—in a visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied.ʺ [9] The paper goes on to predict that within ten years (that is, by 1968) a digital computer would be the world chess champion. A decade later, an unrepentant Simon predicts that by 1985, ʺmachines will be capable of doing any work

that a man can do.ʺ Perhaps Simon was intending a favorable comment on the capabilities of women, but these predictions, decidedly more optimistic than Turingʹs, embarrassed the nascent AI field.

The field has been inhibited by this embarrassment to this day, and AI researchers have been reticent in their prognostications ever since. In 1997, when Deep Blue defeated Gary Kasparov, then the reigning human world chess

champion, one prominent professor commented that all we had learned was that playing a championship game of chess does not require intelligence after all. [10] The implication is that capturing real intelligence in our machines remains far beyond our grasp. While I donʹt wish to overstress the significance of Deep Blueʹs victory, I believe that from this perspective we will ultimately find that there are no human activities that require ʺrealʺ intelligence.

During the 1960s, the academic field of AI began to flesh out the agenda that Turing had described in 1950, with

encouraging or frustrating results, depending on your point of view. Daniel G. Bobrowʹs program Student could solve algebra problems from natural English‐language stories and reportedly did well on high‐school math tests. [11]

The same performance was reported for Thomas G. Evansʹs Analogy program for solving IQ‐test geometric‐analogy

problems. [12] The field of expert systems was initiated with Edward A. Feigenbaumʹs DENDRAL, which could answer questions about chemical compounds. [13] And natural‐language understanding got its start with Terry Winogradʹs SHRDLU, which could understand any meaningful English sentence, so long as you talked about colored

blocks. [14]

The notion of creating a new form of intelligence on Earth emerged with an intense and often uncritical passion

simultaneously with the electronic hardware on which it was to be based. The unbridled enthusiasm of the fieldʹs early pioneers also led to extensive criticism of these early programs for their inability to react intelligently in a variety of situations. Some critics, most notably existentialist philosopher and phenomenologist Hubert Dreyfus, predicted that machines would never match human levels of skill in areas ranging from the playing of chess to the writing of books about computers.

It turned out that the problems we thought were difficult solving mathematical theorems, playing respectable games of chess, reasoning within domains such as chemistry and medicine were easy, and the multi‐thousand-instructions‐per‐second computers of the 1950s and 1960s were often adequate to provide satisfactory results. What

proved elusive were the skills that any five‐year‐old child possesses: telling the difference between a dog and a cat, or understanding an animated cartoon. Weʹll talk more about why the easy problems are hard in Part II.

Waiting for Real Artificial Intelligence

The 1980s saw the early commercialization of artificial intelligence with a wave of new AI companies forming and going public. Unfortunately, many made the mistake of concentrating on a powerful but inherently inefficient interpretive language called LISP, which had been popular in academic AI circles. The commercial failure of LISP and the AI companies that emphasized it created a backlash. The field of AI started shedding its constituent disciplines, and companies in natural‐language understanding, character and speech recognition, robotics, machine vision, and other areas originally considered part of the AI discipline now shunned association with the fieldʹs label.

Machines with sharply focused intelligence nonetheless became increasingly pervasive. By the mid‐1990s, we saw

the infiltration of our financial institutions by systems using powerful statistical and adaptive techniques. Not only were the stock, bond, currency, commodity, and other markets managed and maintained by computerized networks,

but the majority of buy‐and‐sell decisions were initiated by software programs that contained increasingly sophisticated models of their markets. The 1987 stock market crash was blamed in large measure on the rapid interaction of trading programs. Trends that otherwise would have taken weeks to manifest themselves developed in

minutes. Suitable modifications to these algorithms have managed to avoid a repeat performance.

Since 1990, the electrocardiogram (EKG) has come complete with the computerʹs own diagnosis of oneʹs cardiac health. Intelligent image‐processing programs enable doctors to peer deep into our bodies and brains, and computerized bioengineering technology enables drugs to be designed on biochemical simulators. The disabled have

been particularly fortunate beneficiaries of the age of intelligent machines. Reading machines have been reading to blind and dyslexic persons since the 1970s, and speech‐recognition and robotic devices have been assisting hands-disabled individuals since the 1980s.

Perhaps the most dramatic public display of the changing values of the age of knowledge took place in the military. We saw the first effective example of the increasingly dominant role of machine intelligence in the Gulf War of 1991. The cornerstones of military power from the beginning of recorded history through most of the twentieth century—geography, manpower, firepower, and battle‐station defenses—have been largely replaced by the

intelligence of software and electronics. Intelligent scanning by unstaffed airborne vehicles, weapons finding their way to their destinations through machine vision and pattern recognition, intelligent communications and coding protocols, and other manifestations of the information age have transformed the nature of war.

Invisible Species

With the increasingly important role of intelligent machines in all phases of our lives—military, medical, economic and financial, political—it is odd to keep reading articles with titles such as Whatever Happened to Artificial Intelligence?

This is a phenomenon that Turing had predicted: that machine intelligence would become so pervasive, so comfortable, and so well integrated into our information‐based economy that people would fail even to notice it.

It reminds me of people who walk in the rain forest and ask, ʺWhere are all these species that are supposed to live

here?ʺ when there are several dozen species of ant alone within fifty feet of them. Our many species of machine intelligence have woven themselves so seamlessly into our modern rain forest that they are all but invisible.

Turing offered an explanation of why we would fail to acknowledge intelligence in our machines. In 1947, he wrote: ʺThe extent to which we regard something as behaving in an intelligent manner is determined as much by our

own state of mind and training as by the properties of the object under consideration. If we are able to explain and predict its behavior we have little temptation to imagine intelligence. With the same object, therefore, it is possible that one man would consider it as intelligent and another would not; the second man would have found out the rules

of its behavior.ʺ

I am also reminded of Elaine Richʹs definition of artificial intelligence, as the ʺstudy of how to make computers do things at which, at the moment, people are better.ʺ

It is our fate as artificial intelligence researchers never to reach the carrot dangling in front of us. Artificial intelligence is inherently defined as the pursuit of difficult computer‐science problems that have not yet been solved.

THE FORMULA FOR INTELLIGENCE

The computer programmer is a creator of universes for which he alone is the lawgiver . . . No playwright, no stage director no emperor however powerful, has ever exercised such absolute authority to arrange a stage or a field of battle and to command such unswervingly dutiful actors or troops.

—Joseph Weizenbaum

A beaver and another forest animal are contemplating an immense man‐made dam. The beaver is saying something

like ʺNo, I didnʹt actually build it. But itʹs based on an idea of mine.ʺ

—Edward Fredkin

Simple things should be simple; complex things should be possible.

—Alan Kay

What Is Intelligence?

A goal may be survival—evade a foe, forage for food, find shelter. Or it might be communication—relate an experience, evoke a feeling. Or perhaps it is to partake in a pastime—play a board game, solve a puzzle, catch a ball.

Sometimes it is to seek transcendence—create an image, compose a passage. A goal may be well defined and unique,

as in the solution to a math problem. Or it may be a personal expression with no clearly right answer.

My view is that intelligence is the ability to use optimally limited resources—including time—to achieve such goals. There is a plethora of other definitions. One of my favorites is by R. W. Young, who defines intelligence as ʺthat faculty of mind by which order is perceived in a situation previously considered disordered. [15] For this definition, we will find the paradigms discussed below quite apropos.

Intelligence rapidly creates satisfying, sometimes surprising plans that meet an array of constraints. The products

of intelligence may be clever, ingenious, insightful, or elegant. Sometimes, as in the case of Turingʹs solution to cracking the Enigma code, an intelligent solution exhibits all of these qualities. Modest tricks may accidentally produce an intelligent answer from time to time, but a true intelligent process that reliably creates intelligent solutions inherently goes beyond a mere recipe. Clearly, no simple formula can emulate the most powerful phenomenon in the Universe: the complex and mysterious process of intelligence.

Actually, thatʹs wrong. All that is needed to solve a surprisingly wide range of intelligent problems is exactly this: simple methods combined with heavy doses of computation (itself a simple process, as Alan Turing demonstrated in

1936 with his conception of the Turing Machine, [16] an elegant model of computation) and examples of the problem.

In some cases, we donʹt even need the latter; just one well‐defined statement of the problem will do.

How far can we go with simple paradigms? Is there a class of intelligent problems amenable to simple approaches, with another, more penetrating class that lies beyond its grasp? It turns out that the class of problems solvable with simple approaches is extensive. Ultimately, with sufficient computational brute force (which will be ample in the twenty‐first century) and the right formulas in the right combination, there are few definable problems that fail to yield. Except perhaps for this problem: What is the complete set of unifying formulas that underlies intelligence?

Evolution determined an answer to this problem in a few billion years. Weʹve made a good start in a few thousand

years. We are likely to finish the job in a few more decades.

These methods, described briefly below, are discussed in more detail in the supplementary section in the back of

this book ʺHow to Build an Intelligent Machine in Three Easy Paradigms.ʺ

Letʹs take a look at a few plain yet powerful paradigms. With a little practice, you, too, can build intelligent machines.

The Recursive Formula: Just Carefully State the Problem

A recursive procedure is one that calls itself. Recursion is a useful approach to generating all of the possible solutions to a problem, or, in the context of a game such as chess, all of the possible move‐countermove sequences.

Consider the game of chess. We construct a program called ʺPick Best Moveʺ to select each move. Pick Best Move

starts by listing all of the possible moves from the current state of the board. This is where the careful statement of the problem comes in, because to generate all of the possible moves we need to precisely consider the rules of the game.

For each move, the program constructs a hypothetical board that reflects what would happen if we made this move.

For each such hypothetical board, we now need to consider what our opponent would do if we made this move. Now

recursion comes in, because Pick Best Move simply calls Pick Best Move (that is, itself) to pick the best move for our opponent. In calling itself, Pick Best Move then lists all of the legal moves for our opponent.

The program keeps calling itself, looking ahead to as many moves as we have time to consider, which results in

the generation of a huge move‐countermove tree. This is another example of exponential growth, because to look ahead an additional half‐move requires multiplying the amount of available computation by about five.

Key to the recursive formula is pruning this huge tree of possibilities, and ultimately stopping the recursive growth of the tree. In the game context, if a board looks hopeless for either side, the program can stop the expansion of the move‐countermove tree from that point (called a ʺterminal leafʺ of the tree), and consider the most recently considered move to be a likely win or loss.

When all of these nested program calls are completed, the program will have determined the best possible move

for the current actual board, within the limits of the depth of recursive expansion that it had time to pursue.

The recursive formula was good enough to build a machine—a specially designed IBM supercomputer—that

defeated the world chess champion (although Deep Blue does augment the recursive formula with databases of moves from most of the grand‐master games of this century). Ten years ago, in the Age of Intelligent Machines, I noted that while the best chess computers were gaining in chess ratings by forty‐five points a year, the best humans were

advancing by closer to zero points. That put the year in which a computer would beat the world chess champion at

1998, which turned out to be overly pessimistic by one year. Hopefully my predictions in this book will be more accurate. [17]

Our simple recursive rule plays a world‐class game of chess. A reasonable question, then, is, What else can it do?

We certainly can replace the module that generates chess moves with a module programmed with the rules of another game. Stick in a module that knows the rules of checkers, and you can also beat just about any human.

Recursion is really good at backgammon. Hans Berlinerʹs program defeated the human backgammon champion with

the slow computers we had back in 1980. [18]

The recursive formula is also a rather good mathematician. Here the goal is to solve a mathematical problem, such

as proving a theorem. The rules then become the axioms of the field of math being addressed, as well as previously

proved theorems. The expansion at each point, is the possible axioms (or previous proved theorems) that can be applied to a proof at each step. This was the approach used by Allen Newell, J. C. Shaw, and Herbert Simon for their 1957 General Problem Solver. Their program outdid Russell and Whitehead on some hard math problems, and thereby fueled the early optimism of the artificial intelligence field.

From these examples, it may appear that recursion is well suited only for problems in which we have crisply defined rules and objectives. But it has also shown promise in computer generation of artistic creations. Ray Kurzweilʹs Cybernetic Poet, for example, uses a recursive approach. [19] The program establishes a set of goals for each word‐achieving a certain rhythmic pattern, poem structure, and word choice that is desirable at that point in the poem. If the program is unable to find a word that meets these criteria, then it backs up and erases the previous word it has written, re‐establishes the criteria it had originally set for the word just erased, and goes from there. If that also leads to a dead end, it backs up again. It thus goes backward and forward, hopefully making up its ʺmindʺ at some

point. Eventually, it forces itself to make up its mind by relaxing some of the constraints if all paths lead to dead ends.

After all, no one will ever know if it breaks its own rules.

Recursion is also popular in programs that compose music. [20] In this case the ʺmovesʺ are well defined. We call

them notes, which have properties such as pitch, duration, loudness, and playing style. The objectives are less easy to come by but are still feasible by defining them in terms of rhythmic and melodic structures. The key to recursive artistic programs is how we define the terminal leaf evaluation. Simple approaches do not always work well here, and some of the cybernetic art and music programs we will talk about later use complex methods to evaluate the terminal

leaves. While we have not yet captured all of intelligence in a simple formula, we have made a lot of progress with

this simple combination: recursively defining a solution through a precise statement of the problem and massive computation. For many problems, a personal computer circa end of the twentieth century is massive enough.

Neural Nets: Self‐Organization and Human Computing

The neural net paradigm is an attempt to emulate the computing structure of neurons in the human brain. We start

with a set of inputs that represents a problem to be solved. [21] For example, the input may be a set of pixels representing an image that needs to be identified. These inputs are randomly wired to a layer of simulated neurons.

Each of these simulated neurons can be simple computer programs that simulate a model of a neuron in software, or

they can be electronic implementations.

Each point of the input (for example, each pixel in an image) is randomly connected to the inputs of the first layer of simulated neurons. Each connection has an associated synaptic strength that represents the importance of this connection. These strengths are also set at random values. Each neuron adds up the signals coming into it. If the combined signal exceeds a threshold, then the neuron fires and sends a signal to its output connection. If the combined input signal does not exceed the threshold, then the neuron does not fire and its output is zero. The output of each neuron is randomly connected to the inputs of the neurons in the next layer. At the top layer, the output of one or more neurons, also randomly selected, provides the answer.

A problem, such as an image of a printed character to be identified, is presented to the input layer, and the output neurons produce an answer. And the responses are remarkably accurate for a wide range of problems.

Actually, the answers are not accurate at all. Not at first, anyway. Initially, the output is completely random. What else would you expect, given that the whole system is set up in a completely random fashion?

I left out an important step, which is that the neural net needs to learn its subject matter. Like the mammalian brains on which it is modeled, a neural net starts out ignorant. The neural netʹs teacher, which may be a human, a computer program, or perhaps another, more mature neural net that has already learned its lessons, rewards the student neural net when it is right and punishes it when it is wrong. This feedback is used by the student neural net to adjust the strengths of each interneuronal connection. Connections that were consistent with the right answer are made stronger. Those that advocated a wrong answer are weakened. Over time, the neural net organizes itself to provide the right answers without coaching. Experiments have shown that neural nets can learn their subject matter

even with unreliable teachers. It the teacher is correct only 60 percent of the time, the student neural net will still learn its lessons.

If we teach the neural net well, this paradigm is powerful and can emulate a wide range of human pattern-recognition faculties. Character‐recognition systems using multilayer neural nets come very close to human performance in identifying sloppily handwritten print. [22] Recognizing human faces has long been thought to be an

impressive human task beyond the capabilities of a computer, yet there are now automated check‐cashing machines,

using neural net software developed by a small New England company called Miros, that verify the identity of the customer by recognizing his or her face. [23] Donʹt try to fool these machines by holding someone elseʹs picture over your face—the machine takes a three‐dimensional picture of you using two cameras. The machines are evidently reliable enough that the banks are willing to have users walk away with real cash.

Neural nets have been applied to medical diagnoses. Using a system called Brainmaker, from California Scientific

Software, doctors can quickly recognize heart attacks from enzyme data, and classify cancer cells from images. Neural nets are also adept at prediction—LBS Capital Management uses Brainmakerʹs neural nets to predict the Standard & Poorʹs 500. [24] Their ʺone day aheadʺ and ʺone week aheadʺ predictions have consistently outperformed traditional,

formula‐based methods.

There is a variety of self‐organizing methods in use today that are mathematical cousins of the neural net model

discussed above. One of these techniques, called Markov models, is widely used in automatic speech‐recognition systems. Today, such systems can accurately understand humans speaking a vocabulary of up to sixty thousand words spoken in a natural continuous manner.

Whereas recursion is proficient at searching through vast combinations of possibilities, such as sequences of chess

moves, the neural network is a method of choice for recognizing patterns. Humans are far more skilled at recognizing patterns than in thinking through logical combinations, so we rely on this aptitude for almost all of our mental processes. Indeed, pattern recognition comprises the bulk of our neural circuitry. These faculties make up for the extremely slow speed of human neurons. The reset time on neural firing is about five milliseconds, permitting only

about two hundred calculations per second in each neural connection. [25] We donʹt have time, therefore, to think too many new thoughts when we are pressed to make a decision. The human brain relies on precomputing its analyses

and storing them for future reference. We then use our pattern‐recognition capability to recognize a situation as comparable to one we have thought about and then draw upon our previously considered conclusions. We are unable

to think about matters that we have not thought through many times before.

Destruction of Information: The Key to Intelligence

There are two types of computing transformations, one in which information is preserved and one in which information is destroyed. An example of the former is multiplying one number by another constant number other than zero. Such a conversion is reversible: just divide by the constant and you get back the original number. If, on the other hand, we multiply a number by zero, then the original information cannot be restored. We canʹt divide by zero

to get the original number back because zero divided by zero is indeterminate. Therefore, this type of transformation destroys its input.

This is another example of the irreversibility of time (the first was the Law of Increasing Entropy) because there is no way to reverse an information‐destroying computation.

The irreversibility of computation is often cited as a reason that computation is useful: It transforms information

in a unidirectional, ʺpurposefulʺ manner. Yet the reason that computation is irreversible is based on its ability to destroy information, not to create it. The value of computation is precisely in its ability to destroy information selectively. For example, in a pattern‐recognition task such as recognizing faces or speech sounds, preserving the information‐bearing features of a pattern while ʺdestroyingʺ the enormous flow of data in the original image or sound is essential to the process. Intelligence is precisely this process of selecting relevant information carefully so that it can skillfully and purposefully destroy the rest.

That is exactly what the neural net paradigm accomplishes. A neuron—human or machine—receives hundreds or

thousands of continuous signals representing a great deal of information. In response to this, the neuron either fires or does not fire, thereby reducing the babble of its input to a single bit of information. Once the neural net has been well trained, this reduction of information is purposeful, useful, and necessary.

We see this paradigm—reducing enormous streams of complex information into a single response of yes or no—at

many levels in human behavior and society. Consider the torrent of information that flows into a legal trial. The outcome of all this activity is essentially a single bit of information—guilty or not guilty, plaintiff or defendant. A trial may involve a few such binary decisions, but my point is unaltered. These simple yes‐or‐no results then flow into other decisions and implications. Consider an election—same thing—each of us receives a vast flow of data (not all of it pertinent, perhaps) and renders a 1‐bit decision: incumbent or challenger. That decision then flows in with similar decisions from millions of other voters and the final tally is again a single bit of data.

There is too much raw data in the world to continue to keep all of it around. So we continually destroy most of it,

feeding those results to the next level. This is the genius behind the all‐or‐nothing firing of the neuron.

Next time you do some spring cleaning and attempt to throw away old objects and files, you will know why this

is so difficult—the purposeful destruction of information is the essence of intelligent work.

How to Catch a Fly Ball

When a batter hits a fly ball, it follows a path that can be predicted from the ballʹs initial trajectory, spin, and speed, as well as wind conditions. The outfielder, however, is unable to measure any of these properties directly and has to infer them from his angle of observation. To predict where the ball will go, and where the fielder should also go, would appear to require the solution of a rather overwhelming set of complex simultaneous equations. These equations need to be constantly recomputed as new visual data streams in. How does a ten‐year‐old Little Leaguer accomplish this, with no computer, no calculator, no pen and paper, having taken no calculus classes, and having only a few seconds of time?

The answer is, she doesnʹt. She uses her neural netsʹ pattern‐recognition abilities, which provide the foundation for much of skill formation. The neural nets of the ten‐year‐old have had a lot of practice in comparing the observed flight of the ball to her own actions. Once she has learned the skill, it becomes second nature, meaning that she has no idea how she does it. Her neural nets have gained all the insights needed: Take a step back if the ball has gone above my field of view; take a step forward if the ball is below a certain level in my field of view and no longer rising, and so on. The human ballplayer is not mentally computing equations. Nor is there any such computation going on unconsciously in

the playerʹs brain. What is going on is pattern recognition, the foundation of most human thought.

One key to intelligence is knowing what not to compute. A successful person isnʹt necessarily better than her less

successful peers at solving problems; her pattern‐recognition facilities have just learned what problems are worth solving.

Building Silicon Nets

Most computer‐based neural net applications today simulate their neuron models in software. This means that computers are simulating a massively parallel process on a machine that does only one calculation at a time. Todayʹs neural net software running on inexpensive personal computers can emulate about a million neuron connection calculations per second, which is more than a billion times slower than the human brain (although we can improve on

this figure significantly by coding directly in the computerʹs machine language). Even so, software using a neural net paradigm on personal computers circa end of the twentieth century comes very close to matching human ability in such tasks as recognizing print, speech, and faces.

There is a genre of neural computer hardware that is optimized for running neural nets. These systems are modestly, not massively, parallel and are about a thousand times faster than neural net software on a personal computer. Thatʹs still about a million times slower than the human brain.

There is an emerging community of researchers who intend to build neural nets the way nature intended: massively parallel, with a dedicated little computer for each neuron. The Advanced Telecommunications Research Lab (ATR), a prestigious research facility in Kyoto, Japan, is building such an artificial brain with a billion electronic neurons. Thatʹs about 1 percent of the number in the human brain, but these neurons will run at electronic speeds, which is about a million times faster than human neurons. The overall computing speed of ATRʹs artificial brain will be, therefore, thousands of times greater than the human brain. Hugo de Garis, director of ATRʹs Brain Builder Group, hopes to educate his artificial brain in the basics of human language and then set the device free to read—at electronic speeds—all the literature on the Web that interests it. [26]

Does the simple neuron model we have been discussing match the way human neurons work? The answer is yes

and no. On the one hand, human neurons are more complex and more varied than the model suggests. The connection strengths are controlled by multiple neurotransmitters and are not sufficiently characterized by a single number. The brain is not a single organ, but a collection of hundreds of specialized information‐processing organs, each having different topologies and organizations. On the other hand, as we begin to examine the parallel algorithms behind the neural organization in different regions, we find that much of the complexity of neuron design and structure has to do with supporting the neuronʹs life processes and is not directly relevant to the way it handles information. The salient computing methods are relatively straightforward, although varied. For example, a vision chip developed by researcher Carver Mead appears to realistically capture the early stages of human image processing. [27] Although the methods of this and other similar chips differ in a number of respects from the neuron models discussed above, the methods are understood and readily implemented in silicon. Developing a catalog of the

basic paradigms that the neural nets in our brain are using—each relatively simple in its own way—will represent a

great advance in our understanding of human intelligence and in our ability to re‐create and surpass it.

The Search for Extra Terrestrial Intelligence (SETI) project is motivated by the idea that exposure to the intelligent designs of intelligent entities that evolved elsewhere will provide a vast resource to advancing scientific understanding. [28] But we have an impressive and poorly understood piece of intelligent machinery right here on Earth. One such entity—this author—is no more than three feet from the notebook computer to which I am dictating

this book. [29] We can—and will—learn a lot by probing its secrets.

Evolutionary Algorithms: Speeding Up Evolution a Millionfold

Hereʹs an investment tip: Before you invest in a company, be sure to check the track record of the management, the

stability of its balance sheet, the companyʹs earnings history, relevant industry trends, and analyst opinions. On second thought, thatʹs too much work. Hereʹs a simpler approach:

First randomly generate (on your personal computer, of course) a million sets of rules for making investment decisions. Each set of rules should define a set of triggers for buying and selling stocks (or any other security) based on available financial data. This is not hard, as each set of rules does not need to make a lot of sense. Embed each set of rules in a simulated software ʺorganismʺ with the rules encoded in a digital ʺchromosome.ʺ Now evaluate each simulated organism in a simulated environment by using real‐world financial data—youʹll find plenty on the Web.

Let each software organism invest some simulated money and see how it fares based on actual historic data. Allow

the ones that do a bit better than industry averages to survive into the next generation. Kill off the rest (sorry). Now have each of the surviving ones multiply themselves until weʹre back to a million such creatures. As they multiply,

allow some mutation (random change) in the chromosomes to occur. Okay, thatʹs one generation of simulated evolution. Now repeat these steps for another hundred thousand generations. At the end of this process, the surviving software creatures should be darn smart investors. After all, their methods have survived for a hundred thousand generations of evolutionary pruning.

In the real world, a number of successful investment funds now believe that the surviving ʺcreaturesʺ from just such a simulated evolution are smarter than mere human financial analysts. State Street Global Advisors, which manages $3.7 trillion in funds, has made major investments in applying both neural nets and evolutionary algorithms

to making purchase‐and‐sale decisions. This includes a majority stake in Advanced Investment Technologies, which

runs a successful fund in which buy‐and‐sell decisions are made by a program combining these methods. [30]

Evolutionary and related techniques guide a $95 billion fund managed by Barclayʹs Global Investors, as well as funds run by Fidelity and Panagora Asset Management.

The above paradigm is called an evolutionary (sometimes called genetic) algorithm. [31] The system designers donʹt directly program a solution; they let one emerge through an iterative process of simulated competition and improvement. Recall that evolution is smart but slow, so to enhance its intelligence we retain its discernment while greatly speeding up its ponderous pace. The computer is fast enough to simulate thousands of generations in a matter of hours or days or weeks. But we have only to go through this iterative process one time. Once we have let this simulated evolution run its course, we can apply the evolved and highly refined rules to real problems in a rapid fashion.

Like neural nets, evolutionary algorithms are a way of harnessing the subtle but profound patterns that exist in chaotic data. The critical resource required is a source of many examples of the problem to be solved. With regard to the financial world, there is certainly no lack of chaotic information—every second of trading is available online.

Evolutionary algorithms are adept at handling problems with too many variables to compute precise analytic solutions. The design of a jet engine, for example, involves more than one hundred variables and requires satisfying dozens of constraints. Evolutionary algorithms used by researchers at General Electric were able to come up with engine designs that met the constraints more precisely than conventional methods.

Evolutionary algorithms, part of the field of chaos or complexity theory, are increasingly used to solve otherwise

intractable business problems. General Motors applied an evolutionary algorithm to coordinate the painting of its cars, which reduced expensive color changeovers (in which a painting booth is put out of commission to change paint

color) by 50 percent. Volvo uses them to plan the intricate schedules for manufacturing the Volvo 770 truck cab.

Cemex, a $3 billion cement company, uses a similar approach to determining its complex delivery logistics. This approach is increasingly supplanting more analytic methods throughout industry.

This paradigm is also adept at recognizing patterns. Contemporary genetic algorithms that recognize fingerprints,

faces, and hand‐printed characters reportedly outperform neural net approaches. It is also a reasonable way to write computer software, particularly software that needs to find delicate balances for competing resources. One well-known example is Microsoftʹs Windows 95, which contains software to balance system resources that was evolved rather than explicitly written by human programmers.

With evolutionary algorithms, you have to be careful what you ask for. John Koza describes an evolutionary program that was asked to solve a problem involving the stacking of blocks. The program evolved a solution that perfectly fit all of the problem constraints, except that it involved 2,319 block movements, far more than was practical.

Apparently, the program designers had neglected to specify that minimizing the number of block movements was desirable. Koza commented that ʺgenetic programming gave us exactly what we asked for; no more and no less.ʺ

Self‐Organization

Neural nets and evolutionary algorithms are considered self‐organizing ʺemergentʺ methods because the results are

not predictable and indeed are often surprising to the human designers of these systems. The process that such self‐

organizing programs go through in solving a problem is often unpredictable. For example, a neural net or evolutionary algorithm may go through hundreds of iterations making apparently little progress, and then suddenly—as if the process had a flash of inspiration—things click and a solution quickly emerges.

Increasingly, we will be building our intelligent machines by breaking complex problems (such as understanding

human language) into smaller subtasks, each with its own self‐organizing program. Such layered emergent systems

will have softer edges in the boundaries of their expertise and will display greater flexibility in dealing with the inherent ambiguity of the real world.

The Holographic Nature of Human Memory

The holy grail in the field of knowledge acquisition is to automate the learning process, to let machines go out into the world (or, for starters, out onto the Web) and gather knowledge on their own. This is essentially what, the ʺchaos theoryʺ methods—neural nets, evolutionary algorithms and their mathematical cousins—permit. Once these methods

have converged on an optimal solution, the patterns of neural connection strengths or evolved digital chromosomes

represent a form of knowledge to be stored for future use.

Such knowledge is, however, difficult to interpret. The knowledge embedded in a software neural net that has been trained to recognize human faces consists of a network topology and a pattern of neural connection strengths. It does a great job of recognizing Sallyʹs face, but there is nothing explicit that explains that she is recognizable because of her deep‐set eyes and narrow, upturned nose. We can train a neural net to recognize good middle‐game chess moves, but it will likewise be unable to explain its reasoning.

The same is true for human memory. There is no little data structure in our brains that records the nature of a chair as a horizontal platform with multiple vertical posts and an optional vertical backrest. Instead, our many thousands of experiences with chairs are diffusely represented in our own neural nets. We are unable to recall every experience we have had with a chair but each encounter has left its impression on the pattern of neuron‐connection

strengths reflecting our knowledge of chairs. Similarly, there is no specific location in our brain in which a friendʹs face is stored. It is remembered as a distributed pattern of synaptic strengths.

Although we do not yet understand the precise mechanisms responsible for human memory—and the design is

likely to vary from region to region of the brain—we do know that for most human memory, the information is distributed throughout the particular brain region. If you have ever played with a visual hologram, you will appreciate the benefits of a distributed method of storing and organizing information. A hologram is a piece of film containing an interference pattern caused by the interaction of two sets of light waves. One wave front comes from a scene illuminated by a laser light. The other comes directly from the same laser. If we illuminate the hologram, it re-creates a wave front of light that is identical to the light waves that came from the original objects. The impression is that we are viewing the original three‐dimensional scene. Unlike an ordinary picture, if a hologram is cut in half, we do not end up with half the picture, but still have the entire picture, only at half the resolution. We can say that the entire picture exists at every point, albeit at zero resolution. If you scratch a hologram, it has virtually no effect because the resolution is insignificantly reduced. No scratches are visible in the reconstructed three‐dimensional image that a scratched hologram produces. The implication is that a hologram degrades gracefully.

The same holds true for human memory. We lose thousands of nerve cells every hour, but it has virtually no effect

because of the highly distributed nature of all of our mental processes. [32] None of our individual brain cells is all that important there is no Chief Executive Officer neuron.

Another implication of storing a memory as a distributed pattern is that we have little or no understanding of how

we perform most of our recognition tasks and skills. When playing baseball, we sense that we should step back when

the ball goes over our field of view, but most of us are unable to articulate this implicit rule that is diffusely encoded in our fly‐ball‐catching neural net.

There is one brain organ that is optimized for understanding and articulating logical processes, and that is the outer layer of the brain, called the cerebral cortex. Unlike the rest of the brain, this relatively recent evolutionary development is rather flat, only about one eighth of an inch thick, and includes a mere 8 million neurons. [33] This elaborately folded organ provides us with what little competence we do possess for understanding what we do and

how we do it.

There is current debate on the methods used by the brain for long‐term retention of memory. Whereas our recent

sense impressions and currently active recognition abilities and skills appear to be encoded in a distributed pattern of synaptic strengths, our longer‐term memories may be chemically encoded in either the ribonucleic acid (RNA) or in

peptides, chemicals similar to hormones. Even if there is chemical encoding of long‐term memories, they nonetheless

appear to share the essential holographic attributes of our other mental processes.

In addition to the difficulty of understanding and explaining memories and insights that are represented only as

distributed patterns (which is true for both human and machine), another challenge is providing the requisite experiences from which to learn. For humans, this is the mission of our educational institutions. For machines, creating the right learning environment is also a major challenge. For example, in our work at Kurzweil Applied Intelligence (now part of Lernout & Hauspie Speech Products) in developing computer‐based speech recognition, we do allow the systems to learn about speech and language patterns on their own, but we need to provide them with

many thousands of hours of recorded human speech and millions of words of written text from which to discover their own insights. [34] Providing for a neural netʹs education is usually the most strenuous engineering task required.

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I FIND IT FITTING THAT THE DAUGHTER OF ONE OF THE GREATEST ROMANTIC POETS WAS THE FIRST

COMPUTER PROGRAMMER.

Yes, and she was also one of the first to speculate on the ability of a computer to actually create art. She was certainly the first to do so with some real technology in mind.

TECHNOLOGY THAT NEVER WORKED.

Unfortunately, thatʹs true.

WITH REGARD TO TECHNOLOGY, YOU SAID THAT WAR IS A TRUE FATHER OF INVENTION—A LOT OF

TECHNOLOGIES DID GET PERFECTED IN A HURRY DURING THE FIRST AND SECOND WORLD WARS.

Including the computer. And that changed the course of the European theater in World War II.

SO IS THAT A SILVER LINING AMID ALL THE SLAUGHTER?

The Luddites wouldnʹt see it that way. But you could say that, at least if you welcome the rapid advance of technology.

THE LUDDITES? IʹVE HEARD OF THEM.

Yes, they were the first organized movement to oppose the mechanized technology of the Industrial Revolution. It seemed apparent to these English weavers that, with the new machines enabling one worker to produce as much output as a dozen or more workers without machines, employment would soon be enjoyed only by a small elite. But

things didnʹt work out that way. Rather than produce the same amount of stuff with a much smaller workforce, the

demand for clothing increased along with the supply. The growing middle class was no longer satisfied owning just

one or two shirts. And the common man and woman could now own well‐made clothes for the first time. New industries sprung up to design, manufacture, and support the new machines, creating employment of a more sophisticated kind. So the resulting prosperity, along with a bit of repression by the English authorities, extinguished the Luddite movement.

ARENʹT THE LUDDITES STILL AROUND?

The movement has lived on as a symbol of opposition to machines. To date, it remains somewhat unfashionable because of widespread recognition of the benefits of automation. Nonetheless, it lingers not far below the surface and will come back with a vengeance in the early twenty‐first century.

THEY HAVE A POINT, DONʹT THEY?

Sure, but a reflexive opposition to technology is not very fruitful in todayʹs world. It is important, however, to recognize that technology is power. We have to apply our human values to its use.

THAT REMINDS ME OF LAO‐TZUʹS ʺKNOWLEDGE IS POWER.ʺ

Yes, technology and knowledge are very similar—technology can be expressed as knowledge. And technology clearly

constitutes power over otherwise chaotic forces. Since war is a struggle for power, it is not surprising that technology and war are linked.

With regard to the value of technology, think about the early technology of fire. Is fire a good thing?

ITʹS GREAT IF YOU WANT TO TOAST SOME MARSHMALLOWS.

Indeed, but itʹs not so great if you scorch your hand, or burn down the forest.

I THOUGHT YOU WERE AN OPTIMIST?

I have been accused of that, and my optimism probably accounts for my overall faith in humanityʹs ability to control the forces we are unleashing.

FAITH? YOUʹRE SAYING WE JUST HAVE TO BELIEVE IN THE POSITIVE SIDE OF TECHNOLOGY?

I think it would be better if we made the constructive use of technology a goal rather than a belief.

SOUNDS LIKE THE TECHNOLOGY ENTHUSIASTS AND THE LUDDITES AGREE ON ONE THING—

TECHNOLOGY CAN BE BOTH HELPFUL AND HARMFUL.

Thatʹs fair; itʹs a rather delicate balance.

IT MAY NOT STAY SO DELICATE IF THEREʹS A MAJOR MISHAP.

Yes, that could make pessimists of us all.

NOW, THESE PARADIGMS FOR INTELLIGENCE—ARE THEY REALLY SO SIMPLE?

Yes and no. My point about simplicity is that we can go quite far in capturing intelligence with simple approaches.

Our bodies and brains were designed using a simple paradigm evolution—and a few billion years. Of course, when

we engineers get done implementing these simple methods in our computer programs, we do manage to make them

complicated again. But thatʹs just our lack of elegance.

The real complexity comes in when these self‐organizing methods meet the chaos of the real world. If we want to

build truly intelligent machines that will ultimately display our human ability to frame matters in a great variety of contexts, then we do need to build in some knowledge of the worldʹs complications.

OKAY, LETʹS GET PRACTICAL FOR A MOMENT. THESE EVOLUTION‐BASED INVESTMENT PROGRAMS, ARE

THEY REALLY BETTER THAN PEOPLE? I MEAN, SHOULD I GET RID OF MY STOCKBROKER, NOT THAT I

HAVE A HUGE FORTUNE OR ANYTHING?

As of this writing, this is a controversial question. The security brokers and analysts obviously donʹt think so. There are several large funds today that use genetic algorithms and related mathematical techniques that appear to be outperforming more traditional funds. Analysts estimate that in 1998, the investment decisions for 5 percent of stock investments, and a higher percentage of money invested in derivative markets, are made by this type of program, with these percentages rapidly increasing. The controversy wonʹt last because it will become apparent before long that leaving such decisions to mere human decision making is a mistake.

The advantages of computer intelligence in each field will become increasingly clear as time goes on, and as Mooreʹs screw continues to turn. It will become apparent over the next several years that these computer techniques

can spot extremely subtle arbitrage opportunities that human analysts would perceive much more slowly, if ever.

IF EVERYONE STARTS INVESTING THIS WAY, ISNʹT THAT GOING TO RUIN THE ADVANTAGE?

Sure, but that doesnʹt mean weʹll go back to unassisted human decision making. Not all genetic algorithms are created equal. The more sophisticated the model, the more up to date the information being analyzed, and the more powerful

the computers doing the analysis, the better the decisions will be. For example, it will be important to rerun the evolutionary analysis each day to take advantage of the most recent trends, trends that will be influenced by the fact that everyone else is also using evolutionary and other adaptive algorithms. After that, weʹll need to run the analysis every hour, and then every minute, as the responsiveness of the markets speeds up. The challenge here is that evolutionary algorithms take a while to run because we have to simulate thousands or millions of generations of evolution. So thereʹs room for competition here.

THESE EVOLUTIONARY PROGRAMS ARE TRYING TO PREDICT WHAT HUMAN INVESTORS ARE GOING TO

DO. WHAT HAPPENS WHEN MOST OF THE INVESTING IS DONE BY THE EVOLUTIONARY PROGRAMS?

WHAT ARE THEY PREDICTING THEN?

Good question—there will still be a market, so I guess they will be trying to out‐predict each other.

OKAY, WELL MAYBE MY STOCKBROKER WILL START TO USE THESE TECHNIQUES HERSELF. IʹLL GIVE HER

A CALL. BUT MY STOCKBROKER DOES HAVE SOMETHING THOSE COMPUTERIZED EVOLUTIONS DONʹT

HAVE, NAMELY THOSE DISTRIBUTED SYNAPTIC STRENGTHS YOU TALKED ABOUT.

Actually, computerized investment programs are using both evolutionary algorithms and neural nets, but the computerized neural nets are not nearly as flexible as the human variety just yet.

THIS NOTION THAT WE DONʹT REALLY UNDERSTAND HOW WE RECOGNIZE THINGS BECAUSE MY

PATTERN‐RECOGNITION STUFF IS DISTRIBUTED ACROSS A REGION OF MY BRAIN . . .

Yes.

WELL, IT DOES SEEM TO EXPLAIN A FEW THINGS. LIKE WHEN I JUST SEEM TO KNOW WHERE MY KEYS

ARE EVEN THOUGH I DONʹT REMEMBER HAVING PUT THEM THERE. OR THAT ARCHETYPAL OLD

WOMAN WHO CAN TELL WHEN A STORM IS COMING, BUT CANʹT REALLY EXPLAIN HOW SHE KNOWS

Thatʹs actually a good example of the strength of human pattern recognition. That old woman has a neural net that is triggered by a certain combination of other perceptions—animal movements, wind patterns, sky color, atmospheric changes, and so on. Her storm‐detector neural net fires and she senses a storm, but she could never explain what triggered her feeling of an impending storm.

SO IS THAT HOW WE DISCOVER INSIGHTS IN SCIENCE? WE JUST SENSE A NEW PATTERN?

Itʹs clear that our brainʹs pattern‐recognition faculties play a central role, although theory of human creativity in science. We donʹt yet have a fully satisfactory had better use pattern recognition. After all, most of our brain is devoted to doing it.

SO WHEN EINSTEIN WAS LOOKING AT THE EFFECT OF GRAVITY ON LIGHT WAVES—MY SCIENCE

PROFESSOR WAS JUST TALKING ABOUT THIS ONE OF THE LITTLE PATTERN RECOGNIZERS, IN EINSTEINʹS

BRAIN FIRED?

Could be. He was probably playing ball with one of his sons. He saw the ball rolling on a curved surface . . .

AND CONCLUDED—EUREKA—SPACE IS CURVED!

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C H A P T E R F I V E

CONTEXT AND KNOWLEDGE

PUTTING IT ALL TOGETHER

So how well have we done? Many apparently difficult problems do yield to the application of a few simple formulas.

The recursive formula is a master at analyzing problems that display inherent combinatorial explosion, ranging from

the playing of board games to proving mathematical theorems. Neural nets and related self‐organizing paradigms emulate our pattern‐recognition faculties, and do a fine job of discerning such diverse phenomena as human speech,

letter shapes, visual objects, faces, fingerprints, and land terrain images. Evolutionary algorithms are effective at analyzing complex problems, ranging from making financial investment decisions to optimizing industrial processes,

in which the number of variables is too great for precise analytic solutions. I would like to claim that those of us who research and develop ʺintelligentʺ computer systems have mastered the complexities of the problems we are programming our machines to solve. It is more often the case, however, that our computers using these self-organizing paradigms are teaching us the solutions rather than the other way around.

There is, of course, some engineering involved. The right method(s) and variations need to be selected, the optimal topology and architectures crafted, the appropriate parameters set. In an evolutionary algorithm, for example, the system designer needs to determine the number of simulated organisms, the contents of each chromosome, the nature of the simulated environment and survival mechanism, the number of organisms to survive

into the next generation, the number of generations, and other critical specifications. Human programmers have our

own evolutionary method for making such decisions, which we call trial and error. It will be a while longer, therefore, before designers of intelligent machines are ourselves replaced by our handiwork.

Yet something is missing. The problems and solutions we have been discussing are excessively focused and narrow. Another way to put it is that they are—too adultlike. As adults, we focus on constricted problems—investing

funds, selecting a marketing plan, plotting a legal strategy, making a chess move. But as children, we encountered the world in all its broad diversity, and we learned our relation to the world, and that of every other entity and concept.

We learned context.

As Marvin Minsky put it: ʺDeep Blue might be able to win at chess, but it wouldnʹt know to come in from the rain.ʺ Being a machine, it may not need to come in from the rain, but has it ever considered the question? Consider

these possible deep thoughts of Deep Blue:

I am a machine with a plastic body covering electronic parts. If I go out in the rain, I may get wet and my electronic parts could short circuit. Then I would not be able to play chess at all until a human repaired me.

How humiliating!

The game of chess I played yesterday was no ordinary game. It signified the first defeat of the human chess champion by a machine in a regulation tournament. This is important because some humans think chess is a

prime example of human intelligence and creativity. But I doubt that this will yield us machines greater respect. Humans will now just start denigrating chess.

My human opponent, who has the name of Gary Kasparov, held a press conference in which he made

statements about our tournament to other humans called journalists who will report his comments to yet other

humans using communication channels called media. In that meeting, Gary Kasparov complained that my

human designers made changes to my software during the time interval between games. He said this was unfair, and should not have been allowed. Other humans responded that Kasparov was being defensive, which

means that he is trying to confuse people into thinking that he did not really lose.

Mr. Kasparov probably does not realize that we computers will continue to improve in our performance at an

exponential rate. So he is doomed. He will be able to engage in other human activities such as eating and sleeping, but he will continue to be frustrated as more machines like me can beat him at chess.

Now, if I could only remember where I put my umbrella . . .

Of course, Deep Blue had no such thoughts. Issues such as rain and press conferences lead to other issues in a spiraling profusion of cascading contexts, none of which falls within Deep Blueʹs expertise. As humans jump from one concept to the next, we can quickly touch upon all human knowledge. This was Turingʹs brilliant insight when he

designed the Turing Test around ordinary text‐based conversation. An idiot savant such as Deep Blue, which performs a single ʺintelligentʺ task but that is otherwise confined, brittle, and lacking in context, is unable to navigate the wide‐ranging links that occur in ordinary conversation.

As powerful and seductive as the easy paradigms appear to be, we do need some thing more, namely knowledge.

CONTEXT AND KNOWLEDGE

The search for the truth is in one way hard and in another easy—for it is evident that no one of us can master it fully, nor miss it wholly. Each one of us adds a little to our knowledge of nature, and from all the facts assembled arises a certain grandeur.

—Aristotle

Common sense is not a simple thing. Instead, it is an immense society of hard‐earned practical ideas—of multitudes of life‐learned rules and exceptions, dispositions and tendencies, balances and checks.

—Marvin Minsky

If a little knowledge is dangerous, where is a man who has so much as to be out of danger?

—Thomas Henry Huxley

Built‐In Knowledge

An entity may possess extraordinary means to implement the types of paradigms we have been discussing—

exhaustive recursive search, massively parallel pattern recognition, and rapid iterative evolution—but without knowledge, it will be unable to function. Even a straightforward implementation of the three easy paradigms needs

some knowledge with which to begin. The recursive chess‐playing program has a little; it knows the rules of chess. A neural net pattern‐recognition system starts with at least an outline of the type of patterns it will be exposed to even before it starts to learn. An evolutionary algorithm requires a starting point for evolution to improve on.

The simple paradigms are powerful organizing principles, but incipient knowledge is needed as seeds from which

other understanding can grow. One level of knowledge, therefore, is embodied in the selection of the paradigms used, the shape and topology of its constituent parts, and the key parameters. A neural netʹs learning will never congeal if the general organizations of its connections and feedback loops are not set up in the right way.

This is a form of knowledge that we are born with. The human brain is not one tabula rasa—a blank slate—on which our experiences and insights are recorded. Rather, it comprises an integrated assemblage of specialized regions:

▪ highly parallel early vision circuits that are good at identifying visual changes;

▪ visual cortex neuron clusters that are triggered successively by edges, straight lines, curved lines, shapes, familiar objects, and faces;

▪ auditory cortex circuits triggered by varying time sequences of frequency combinations;

▪ the hippocampus, with capacities for storing memories of sensory experiences and events;

▪ the amygdala, with circuits for translating fear into a series of alarms to trigger other regions of the

brain; and many others.

This complex interconnectedness of regions specialized for different types of information‐processing tasks is one

of the ways that humans deal with the complex and diverse contexts that continually confront us. Marvin Minsky and

Seymour Papert describe the human brain as ʺcomposed of large numbers of relatively small distributed systems, arranged by embryology into a complex society that is controlled in part (but only in part) by serial, symbolic systems that are added later.ʺ They add that ʺthe subsymbolic systems that do most of the work from underneath must, by their very character, block all the other parts of the brain from knowing much about how they work. And this, itself, could help explain how people do so many things yet have such incomplete ideas on how those things are actually

done.ʺ

Acquired Knowledge

It is sensible to remember todayʹs insights for tomorrowʹs challenges. It is not fruitful to rethink every problem that comes along. This is particularly true for humans due to the extremely slow speed of our computing circuitry.

Although computers are better equipped than we are to rethink earlier insights, it is still judicious for these electronic competitors in our ecological niche to balance their use of memory and computation.

The effort to endow machines with knowledge of the world began in earnest in the mid‐1960s, and became a major focus of AI research in the 1970s. The methodology involves a human ʺknowledge engineerʺ and a domain expert, such as a doctor or lawyer. The knowledge engineer interviews the domain expert to ascertain her understanding of her subject matter and then hand‐codes the relationships between concepts in a suitable computer

language. A knowledge base on diabetes, for example, would contain many linked bits of understanding revealing that Insulin is part of the blood; insulin is produced by the pancreas; insulin can be supplemented by injection; low levels of insulin cause high levels of sugar in the blood; sustained high sugar levels in the blood cause damage to the retinas, and so on. A system programmed with tens of thousands of such linked concepts combined with a recursive search engine able to

reason about these relationships is capable of making insightful recommendations.

One of the more successful expert systems developed in the 1970s was MYCIN, a system for evaluating complex

cases involving meningitis. In a landmark study published in the Journal of the American Medical Association, MYCINʹs diagnoses and treatment recommendations were found to be equal or better than those of the human doctors in the

study. [1] Some of MYCINʹs innovations included the use of fuzzy logic; that is, reasoning based on uncertain evidence and rules, as shown in the following typical MYCIN rule:

MYCIN Rule 280: If (i) the infection which requires therapy is meningitis, and (ii) the type of the infection is fungal, and (iii) organisms were not seen on the stain of the culture, and (iv) the patient is not a compromised

host, and (v) the patient has been to an area that is endemic for coccidiomycoses, and (vi) the race of the patient

is Black, Asian or Indian, and (vii) the cryptococcal antigen in the csf was not positive, THEN there is a 50

percent chance that cryptococcus is one of the organisms which might be causing the infection.

The success of MYCIN and other research systems spawned a knowledge‐engineering industry that grew from only $4 million in 1980 to billions of dollars today. [2]

There are obvious difficulties with this methodology. One is the enormous bottleneck represented by the process

of hand‐feeding such knowledge to a computer concept by concept and link by link. Aside from the vast scope of knowledge that exists in even narrow disciplines, the bigger obstacle is that human experts generally have little understanding of how they make decisions. The reason for this, as I discussed in the previous chapter, has to do with the distributed nature of most human knowledge.

Another problem is the brittleness of such systems. Knowledge is too complex for every caveat and exception to

be anticipated by knowledge engineers. As Minsky points out, ʺBirds can fly, unless they are penguins and ostriches, or if they happen to be dead, or have broken wings, or are confined to cages, or have their feet stuck in cement, or have undergone experiences so dreadful as to render them psychologically incapable of flight.ʺ

To create flexible intelligence in our machines, we need to automate the knowledge‐acquisition process. A primary goal of learning research is to combine the self‐organizing methods—recursion, neural nets, evolutionary algorithms—in a sufficiently robust way that the systems can model and understand human language and

knowledge. Then the machines can venture out, read, and learn on their own. And like humans, such systems will be

good at faking it when they wander outside their areas of expertise.

EXPRESSING KNOWLEDGE THROUGH LANGUAGE

No knowledge is entirely reducible to words, and no knowledge is entirely ineffable.

—Seymour Papert

The fish trap exists because of the fish. Once youʹve gotten the fish you can forget the trap. The rabbit snare exists because of the rabbit. Once youʹve gotten the rabbit, you can forget the snare. Words exist because of meaning. Once youʹve gotten the meaning, you can forget the words. Where can I find a man who has forgotten words so I can talk with him?

—Cuang‐tzu

Language is the principal means by which we share our knowledge. And like other human technologies, language is

often cited as a salient differentiating characteristic of our species. Although we have limited access to the actual implementation of knowledge in our brains (this will change early in the twenty‐first century), we do have ready access to the structures and methods of language. This provides us with a handy laboratory for studying our ability to master knowledge and the thinking process behind it. Work in the laboratory of language shows, not surprisingly, that it is no less complex or subtle a phenomenon than the knowledge it seeks to transmit.

We find that language in both its auditory and written forms is hierarchical with multiple levels. There are ambiguities at each level, so a system that understands language, whether human or machine, needs built‐in knowledge at each level. To respond intelligently to human speech, for example, we need to know (although not necessarily consciously) the structure of speech sounds, the way speech is produced by the vocal apparatus, the patterns of sounds that comprise languages and dialects, the rules of word usage, and the subject matter being discussed. Each level of analysis provides useful constraints that limit the search for the right answer: For example, the basic sounds of speech called phonemes cannot appear in any order (try saying ptkee). Only certain sequences of

sounds will correspond to words in the language. Although the set of phonemes used is similar (although not identical) from one language to another, factors of context differ dramatically English, for example, has more than 10,000 possible syllables, whereas Japanese has only 120.

On a higher level, the structure and semantics of a language put further constraints on allowable word sequences.

The first area of language to be actively studied was the rules governing the arrangement of words and the roles they play, which we call syntax. On the one hand, computerized sentence‐parsing systems can do a good job at analyzing

sentences that confuse humans. Minsky cites the example: ʺThis is the cheese that the rat that the cat that the dog chased bit ate,ʺ which confuses humans but which machines parse quite readily. Ken Church, then at MIT, cites another sentence with two million syntactically correct interpretations, which his computerized parser dutifully listed. [3] On the; other hand, one of the first computer‐based sentence‐parsing systems, developed in 1963 by Susumu Kuno of Harvard, had difficulty with the simple sentence ʺTime flies like an arrow.ʺ In what has become a

famous response, the computer indicated that it was not quite sure what it meant. It might mean

1. that time passes as quickly as an arrow passes;

2. or maybe it is a command telling us to time the flies the same way that an arrow times flies; that is, ʺTime flies like an arrow wouldʺ;

3. or it could be a command telling us to time only those flies that are similar to arrows; that is, ʺTime flies

that are like an arrowʺ;

4. or perhaps it means that a type of flies known as time flies have a fondness for arrows: ʺTime‐flies like

(that is, cherish) an arrowʺ. [4]

Clearly we need some knowledge here to resolve this ambiguity. Armed with the knowledge that flies are not similar to arrows, we can knock out the third interpretation. Knowing that there is no such thing as a time‐fly dispatches the fourth explanation. Such tidbits of knowledge as the fact that flies do not show a fondness for arrows (another reason to knock out interpretation four) and that arrows do not have the ability to time events (knocking out interpretation two) leave us with the first interpretation as the only sensible one.

In language, we again find the sequence of human learning and the progression of machine intelligence to be the

reverse of each other. A human child starts out listening to and understanding spoken language. Later on he learns to speak. Finally, years later, he starts to master written language. Computers have evolved in the opposite direction, starting out with the ability to generate written language, subsequently learning to understand it, then starting to speak with synthetic voices and only recently mastering the ability to understand continuous human speech. This phenomenon is widely misunderstood. R2D2, for example, the robot character of Star Wars fame, understands many human languages but is unable to speak, which gives the mistaken impression that generating human speech is far more difficult than understanding it.

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I FEEL GOOD WHEN I LEARN SOMETHING, BUT ACQUIRING KNOWLEDGE SURE IS A TEDIOUS PROCESS.

PARTICULARLY WHEN IʹVE BEEN UP ALL NIGHT STUDYING FOR AN EXAM. AND IʹM NOT SURE HOW

MUCH OF THIS STUFF I RETAIN.

Thatʹs another weakness of the human form of intelligence. Computers can share their knowledge with each other

readily and quickly. We humans donʹt have a means for sharing knowledge directly, other than the slow process of

human communication, of human teaching and learning.

DIDNʹT YOU SAY THAT COMPUTER NEURAL NETS LEARN THE SAME WAY PEOPLE DO?

You mean, slowly?

EXACTLY, BY BEING EXPOSED TO PATTERNS THOUSANDS OF TIMES, JUST LIKE US.

Yes, thatʹs the point of neural nets; theyʹre intended as analogues of human neural nets, at least simplified versions of what we understand them to be. However, we can build our electronic nets in such a way that once the net has

painstakingly learned its lessons, the pattern of its synaptic connection strengths can be captured and then quickly downloaded to another machine, or to millions of other machines. Machines can readily share all of their accumulated knowledge, so only one machine has to do the learning. We humans canʹt do that. Thatʹs one reason I said that when

computers reach the level of human intelligence, they will necessarily roar past it.

SO IS TECHNOLOGY GOING TO ENABLE US HUMANS TO DOWNLOAD KNOWLEDGE IN THE FUTURE? I

MEAN, I ENJOY LEARNING, DEPENDING ON THE PROFESSOR, OF COURSE, BUT IT CAN BE A DRAG.

The technology to communicate between the electronic world and the human neural world is already taking shape.

So we will be able to directly feed streams of data to our neural pathways. Unfortunately, that doesnʹt mean we can

directly download knowledge, at least not to the human neural circuits we now use. As weʹve talked about, human

learning is distributed throughout a region of our brain. Knowledge involves millions of connections, so our

knowledge structures are not localized. Nature didnʹt provide a direct pathway to adjust all those connections, other than the slow conventional way. While we will be able to create certain specific pathways to our neural connections, and indeed weʹre already doing that, I donʹt see how it would be practical to directly communicate to the many

millions of interneuronal connections necessary to quickly download knowledge.

I GUESS IʹLL JUST HAVE TO KEEP HITTING THE BOOKS. SOME OF MY PROFESSORS ARE KIND OF COOL,

THOUGH, THE WAY THEY SEEM TO KNOW EVERYTHING.

As I said, humans are good at faking it when we go outside of our area of expertise. However, there is a way that

downloading knowledge will be feasible by the middle of the twenty‐first century.

IʹM LISTENING.

Downloading knowledge will be one of the benefits of the neural‐implant technology. Weʹll have implants that

extend our capacity for retaining knowledge, for enhancing memory. Unlike nature, we wonʹt leave out a quick

knowledge down‐loading port in the electronic version of our synapses. So it will be feasible to quickly download

knowledge to these electronic extensions of our brains. Of course, when we fully port our minds to a new

computational medium, down‐loading knowledge will become even easier.

SO IʹLL BE ABLE TO BUY MEMORY IMPLANTS PRELOADED WITH A KNOWLEDGE OF, SAY, MY FRENCH LIT

COURSE.

Sure, or you can mentally click on a French literature web site and download the knowledge directly from the site.

KIND OF DEFEATS THE PURPOSE OF LITERATURE, DOESNʹT IT? MEAN SOME OF THIS STUFF IS NEAT TO

READ.

I would prefer to think that intensifying knowledge will enhance the appreciation of literature, or any art form. After all, we need knowledge to appreciate an artistic expression. Otherwise, we donʹt understand the vocabulary and the

allusions.

Anyway, youʹll still be able to read, just a lot faster. In the second half of the twenty‐first century, youʹll be able to read a book in a few seconds.

I DONʹT THINK I COULD TURN THE PAGES THAT FAST.

Oh come on, the pages will be—

VIRTUAL PAGES, OF COURSE.

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PART TWO

PREPARING

THE PRESENT

C H A P T E R S I X

BUILDING NEW BRAINS . . .

THE HARDWARE OF INTELLIGENCE

You can only make a certain amount with your hands, but with your mind, itʹs unlimited.

—Kal Seinfeldʹs advice to his son, Jerry

Letʹs review what we need to build an intelligent machine. One resource required is the right set of formulas. We examined three quintessential formulas in chapter 4. There are dozens of others in use, and a more complete understanding of the brain will undoubtedly introduce hundreds more. But all of these appear to be variations on the three basic themes: recursive search, self‐organizing networks of elements, and evolutionary improvement through repeated struggle among competing designs.

A second resource needed is knowledge. Some pieces of knowledge are needed as seeds for a process to converge

on a meaningful result. Much of the rest can be automatically learned by adaptive methods when neural nets or evolutionary algorithms are exposed to the right learning environment.

The third resource required is computation itself. In this regard, the human brain is eminently capable in some ways, and remarkably weak in others. Its strength is reflected in its massive parallelism, an approach that our computers can also benefit from. The brainʹs weakness is the extraordinarily slow speed of its computing medium, a

limitation that computers do not share with us. For this reason, DNA‐based evolution will eventually have to be abandoned. DNA‐based evolution is good at tinkering with and extending its designs, but it is unable to scrap an entire design and start over. Organisms created through DNA‐based evolution are stuck with an extremely plodding

type of circuitry.

But the Law of Accelerating Returns tells us that evolution will not remain stuck at a dead end for very long. And

indeed, evolution has found a way around the computational limitations of neural circuitry. Cleverly, it has created organisms that in turn invented a computational technology a million times faster than carbon‐based neurons (which

are continuing to get yet faster). Ultimately, the computing conducted on extremely slow mammalian neural circuits

will be ported to a far more versatile and speedier electronic (and photonic) equivalent.

When will this happen? Letʹs take another look at the Law of Accelerating Returns as applied to computation.

Achieving the Hardware Capacity of the Human Brain

In the chapter 1 chart, ʺThe Exponential Growth of Computing 1900–1998,ʺ we saw that the slope of the curve

representing exponential growth was itself gradually increasing. Computer speed (as measured in calculations per

second per thousand dollars) doubled every three years between 1910 and 1950, doubled every two years between

1950 and 1966, and is now doubling every year. This suggests possible exponential growth in the rate of exponential

growth. [1]

This apparent acceleration in the acceleration may result, however, from the confounding of the two strands of the

Law of Accelerating Returns, which for the past forty years has expressed itself using the Mooreʹs Law paradigm of

shrinking transistor sizes on an integrated circuit. As transistor die sizes decrease, the electrons streaming through the transistor have less distance to travel, hence the switching speed of the transistor increases. So exponentially

improving speed is the first strand. Reduced transistor die sizes also enable chip manufacturers to squeeze a greater number of transistors onto an integrated circuit, so exponentially improving densities of computation is the second

strand.

In the early years of the computer age, it was primarily the first strand—increasing circuit speeds—that improved

the overall computation rate of computers. During the 1990s, however, advanced microprocessors began using a form

of parallel processing called pipelining, in which multiple calculations were performed at the same time (some

mainframes going back to the 1970s used this technique). Thus the speed of computer processors as measured in

instructions per second now also reflects the second strand: greater densities of computation resulting from the use of parallel processing.

As we are approaching more perfect harnessing of the improving density of computation, processor speeds are

now effectively doubling every twelve months. This is fully feasible today when we build hardware‐based neural nets

because neural net processors are relatively simple and highly parallel. Here we create a processor for each neuron

and eventually one for each interneuronal connection. Mooreʹs Law thereby enables us to double both the number of

processors as well as their speed every two years, an effective quadrupling of the number of interneuronal‐connection calculations per second.

This apparent acceleration in the acceleration of computer speeds may result, therefore, from an improving ability

to benefit from both strands of the Law of Accelerating Returns. When Mooreʹs Law dies by the year 2020, new forms

of circuitry beyond integrated circuits will continue both strands of exponential improvement. But ordinary

exponential growth—two strands of it—is dramatic enough. Using the more conservative prediction of just one level

of acceleration as our guide, letʹs consider where the Law of Accelerating Returns will take us in the twenty‐first

century.

The human brain has about 100 billion neurons. With an estimated average of one thousand connections between

each neuron and its neighbors, we have about 100 trillion connections, each capable of a simultaneous calculation.

Thatʹs rather massive parallel processing, and one key to the strength of human thinking. A profound weakness,

however, is the excruciatingly slow speed of neural circuitry, only 200 calculations per second. For problems that

benefit from massive parallelism, such as neural‐net‐based pattern recognition, the human brain does a great job. For problems that require extensive sequential thinking, the human brain is only mediocre.

With 100 trillion connections, each computing at 200 calculations per second, we get 20 million billion calculations per second. This is a conservatively high estimate; other estimates are lower by one to three orders of magnitude. So when will we see the computing speed of the human brain in your personal computer?

The answer depends on the type of computer we are trying to build. The most relevant is a massively parallel

neural net computer. In 1997, $2,000 of neural computer chips using only modest parallel processing could perform

around 2 billion connection calculations per second. Since neural net emulations benefit from both strands of the

acceleration of computational power, this capacity will double every twelve months. Thus by the year 2020, it will

have doubled about twenty‐three times, resulting in a speed of about 20 million billion neural connection calculations per second, which is equal to the human brain.

If we apply the same analysis to an ʺordinaryʺ personal computer, we get the year 2025 to achieve human brain

capacity in a $1,000 device. [2] This is because the general‐purpose type of computations that a conventional personal computer is designed for are inherently more expensive than the simpler, highly repetitive neural‐connection

calculations. Thus I believe that the 2020 estimate is more accurate because by 2020, most of the computations

performed in our computers will be of the neural‐connection type.

The memory capacity of the human brain is about 100 trillion synapse strengths (neurotransmitter concentrations

at interneuronal connections), which we can estimate at about a million billion bits. In 1998, a billion bits of RAM (128

megabytes) cost about $200. The capacity of memory circuits has been doubling every eighteen months. Thus by the

year 2023, a million billion bits will cost about $1,000. [3] However, this silicon equivalent will run more than a billion times faster than the human brain. There are techniques for trading off memory for speed, so we can effectively match human memory for $1,000 sooner than 2023.

Taking all of this into consideration, it is reasonable to estimate that a $1,000 personal computer will match the computing speed and capacity of the human brain by around the year 2020, particularly for the neuron‐connection calculation, which appears to comprise the bulk of the computation in the human brain. Supercomputers are one thousand to ten thousand times faster than personal computers. As this book is being written, IBM is building a supercomputer based on the design of Deep Blue, its silicon chess champion, capable of 10 teraflops (that is, 10 trillion calculations per second), only 2,000 times slower than the human brain. Japanʹs Nippon Electric Company hopes to

beat that with a 32‐teraflop machine. IBM then hopes to follow that with 100 teraflops by around the year 2004 (just

what Mooreʹs Law predicts, by the way). Supercomputers will reach the 20 million billion calculations per second capacity of the human brain around 2010, a decade earlier than personal computers. [4]

In another approach, projects such as Sun Microsystemsʹ Jini program have been initiated to harvest the unused

computation on the Internet. Note that at any particular moment, the significant majority of the computers on the Internet are not being used. Even those that are being used are not being used to capacity (for example, typing text uses less than one percent of a typical notebook computerʹs computing capacity). Under the Internet computation harvesting proposals, cooperating sites would load special software that would enable a virtual massively parallel computer to be created out of the computers on the network. Each user would still have priority over his or her own

machine, but in the background, a significant fraction of the millions of computers on the Internet would be harvested into one or more supercomputers. The amount of unused computation on the Internet today exceeds the

computational capacity of the human brain, so we already have available in at least one form the hardware side of human intelligence. And with the continuation of the Law of Accelerating Returns, this availability will become increasingly ubiquitous.

After human capacity in a $1,000 personal computer is achieved around the year 2020, our thinking machines will

improve the cost performance of their computing by a factor of two every twelve months. That means that the capacity of computing will double ten times every decade, which is a factor of one thousand (2 to the 10th power) every ten years. So your personal computer will be able to simulate the brain power of a small village by the year 2030, the entire population of the United States by 2048, and a trillion human brains by 2060. [5] If we estimate the human Earth population at 10 billion persons, one pennyʹs worth of computing circa 2099 will have a billion times greater computing capacity than all humans on Earth. [6]

Of course I may be off by a year or two. But computers in the twenty‐first century will not be wanting for computing capacity or memory.

Computing Substrates in the Twenty‐First Century

Iʹve noted that the continued exponential growth of computing is implied by the Law of Accelerating Returns, which

states that any process that moves toward greater order—evolution in particular—will exponentially speed up its pace as time passes. The two resources that the exploding pace of an evolutionary process—such as the progression of computer technology—requires are (1) its own increasing order, and (2) the chaos in the environment in which it takes place. Both of these resources are essentially without limit.

Although we can anticipate the overall acceleration in technological progress, one might still expect that the actual manifestation of this progression would still be somewhat irregular. After all, it depends on such variable phenomena as individual innovation, business conditions, investment patterns, and the like. Contemporary theories of evolutionary processes, such as the Punctuated Equilibrium theories, [7] posit that evolution works by periodic leaps or discontinuities followed by periods of relative stability It is thus remarkable how predictable computer progress has been.

So, how will the Law of Accelerating Returns as applied to computation roll out in the decades beyond the demise

of Mooreʹs Law on Integrated Circuits by the year 2020? For the immediate future, Mooreʹs Law will continue with ever smaller component geometries packing greater numbers of yet faster transistors on each chip. But as circuit dimensions reach near atomic sizes, undesirable quantum effects such as unwanted electron tunneling will produce

unreliable results. Nonetheless, Mooreʹs standard methodology will get very close to human processing power in a personal computer and beyond that in a supercomputer.

The next frontier is the third dimension. Already, venture‐backed companies (mostly California‐based) are competing to build chips with dozens and ultimately thousands of layers of circuitry. With names like Cubic Memory, Dense‐Pac, and Staktek, these companies are already shipping functional three‐dimensional ʺcubesʺ of circuitry. Although not yet cost competitive with the customary flat chips, the third dimension will be there when we run out of space in the first two. [8]

Computing with Light

Beyond that, there is no shortage of exotic computing technologies being developed in research labs, many of which

have already demonstrated promising results. Optical computing uses streams of photons (particles of light) rather than electrons. A laser can produce billions of coherent streams of photons, with each stream performing its own independent series of calculations. The calculations on each stream are performed in parallel by special optical elements such as lenses, mirrors, and diffraction gratings. Several companies, including Quanta‐Image, Photonics, and Mytec Technologies, have applied optical computing to the recognition of fingerprints, Lockheed has applied optical computing to the automatic identification of malignant breast lesions. [9]

The advantage of an optical computer is that it is massively parallel with potentially trillions of simultaneous calculations. Its disadvantage is that it is not programmable and performs a fixed set of calculations for a given configuration of optical computing elements. But for important classes of problems such as recognizing patterns, it combines massive parallelism (a quality shared by the human brain) with extremely high speed (which the human brain lacks).

Computing with the Machinery of Life

A new field called molecular computing has sprung up to harness the DNA molecule itself as a practical computing

device. DNA is natureʹs own nanoengineered computer and it is well suited for solving combinatorial problems.

Combining attributes is, after all, the essence of genetics. Applying actual DNA to practical computing applications got its start when Leonard Adleman, a University of Southern California mathematician, coaxed a test tube full of DNA molecules (see the box on page 108) to solve the well‐known ʺtraveling salespersonʺ problem. In this classic problem, we try to find an optimal route for a hypothetical traveler between multiple cities without having to visit a city more than once. Only certain city pairs are connected by routes, so finding the right path is not straightforward. It is an ideal problem for a recursive algorithm, although if the number of cities is too large, even a very fast recursive search will take far too long.

Professor Adleman and other scientists in the molecular‐computing field have identified a set of enzyme reactions

that corresponds to the logical and arithmetic operations needed to solve a variety of computing problems. Although

DNA molecular operations produce occasional errors, the number of DNA strands being used is so large that any molecular errors become statistically insignificant. Thus, despite the inherent error rate in DNAʹs computing and copying processes, a DNA computer can be highly reliable if properly designed.

DNA computers have subsequently been applied to a range of difficult combinatorial problems. A DNA computer

is more flexible than an optical computer but it is still limited to the technique of applying massive parallel search by assembling combinations of elements. [10]

There is another, more powerful way to apply the computing power of DNA that has not yet been explored. I present it below in the section on quantum computing.

HOW TO SOLVE THE TRAVELING-SALESPERSON

PROBLEM USING A TEST TUBE OF DNA

One of DNA's advantageous properties is its ability to replicate itself, and the information it contains. To solve

the traveling-salesperson problem, Professor Adleman performed the following steps:

• Generate a small strand of DNA with a unique code for each city.

• Replicate each such strand (one for each city) trillions of times using a process called "polymerase

chain reaction" (PCR).

• Next, put the pools of DNA (one for each city) together in a test tube. This step uses DNA's affinity to

link strands together. Longer strands will form automatically. Each such longer strand represents a

possible route of multiple cities. The small strands representing each city link up with one another in a

random fashion, so there is no mathematical certainty that a linked strand representing the correct

answer (sequence of cities) will be formed. However, the number of strands is so vast that it is

virtually certain that at least one strand—and probably millions—will be formed that represent the

correct answer.

The next steps use specially designed enzymes to eliminate the trillions of strands that represent the

wrong answer, leaving only the strands representing the correct answer:

• Use molecules called primers to destroy those DNA strands that do not start with the start city as well

as those that do not end with the end city, and replicate these surviving strands (using PCR).

• Use an enzyme reaction to eliminate those DNA strands that represent a travel path greater than the

total number of cities.

• Use an enzyme reaction to destroy those strands that do not include the first city. Repeat for each of

the cities.

• Now, each of the surviving strands represents the correct answer. Replicate these surviving strands

(using PCR) until there are billions of such strands.

• Using a technique called electrophoresis, read out the DNA sequence of these correct strands (as a

group). The readout looks like a set of distinct lines, which specifies the correct sequence of cities.

The Brain in the Crystal

Another approach contemplates growing a computer as a crystal directly in three dimensions, with computing elements being the size of large molecules within the crystalline lattice. This is another approach to harnessing the third dimension.

Stanford Professor Lambertus Hesselink has described a system in which data is stored in a crystal as a hologram—an optical interference pattern.ʺ This three‐dimensional storage method requires only a million atoms for

each bit and thus could achieve a trillion bits of storage for each cubic centimeter. Other projects hope to harness the regular molecular structure of crystals as actual computing elements.

The Nanotube: A Variation of Buckyballs

Three professors—Richard Smalley and Robert Curl of Rice University, and Harold Kroto of the University of Sussex—shared the 1996 Nobel Prize in Chemistry for their 1985 discovery of soccer‐ball‐shaped molecules formed of

a large number of carbon atoms. Organized in hexagonal and pentagonal patterns like R. Buckminster Fullerʹs building designs, they were dubbed ʺbuckyballs.ʺ These unusual molecules, which form naturally in the hot fumes of

a furnace, are extremely strong—a hundred times stronger than steel—a property they share with Fullerʹs architectural innovations. [12]

More recently, Dr. Sumio Iijima of Nippon Electric Company showed that in addition to the spherical buckyballs,

the vapor from carbon arc lamps also contained elongated carbon molecules that looked like long tubes. [13] Called

nanotubes because of their extremely small size—fifty thousand of them side by side would equal the thickness of one human hair—they are formed of the same pentagonal patterns of carbon atoms as buckyballs and share the buckyballʹs unusual strength.

What is most remarkable about the nanotube is that it can perform the electronic functions of silicon‐based components. If a nanotube is straight, it conducts electricity as well as or better than a metal conductor. If a slight helical twist is introduced, the nanotube begins to act like a transistor. The full range of electronic devices can be built using nanotubes.

Since a nanotube is essentially a sheet of graphite that is only one atom thick, it is vastly smaller than the silicon transistors on an integrated chip. Although extremely small, they are far more durable than silicon devices. Moreover, they handle heat much better than silicon and thus can be assembled into three‐dimensional arrays more easily than

silicon transistors. Dr. Alex Zettl, a physics professor at the University of California at Berkeley, envisions three-dimensional arrays of nanotube‐based computing elements similar to—but far denser and faster than—the human brain.

QUANTUM COMPUTING: THE UNIVERSE IN A CUP

Quantum particles are the dreams that stuff is made of.

—David Moser

So far we have been talking about mere digital computing. There is actually a more powerful approach called quantum computing. It promises the ability to solve problems that even massively parallel digital computers cannot

solve. Quantum computers harness a paradoxical result of quantum mechanics. Actually, I am being redundant—all

results of quantum mechanics are paradoxical.

Note that the Law of Accelerating Returns and other projections in this book do not rely on quantum computing.

The projections in this book are based on readily measurable trends and are not relying on discontinuities in technological progress that nonetheless occurred in the twentieth century. There will inevitably be technological discontinuities in the twenty‐first century, and quantum computing would certainly qualify.

What is quantum computing? Digital computing is based on ʺbitsʺ of information which are either off or on—zero

or one. Bits are organized into larger structures such as numbers, letters, and words, which in turn can represent virtually any form of information: text, sounds, pictures, moving images. Quantum computing, on the other hand, is

based on qu‐bits (pronounced cue‐bits), which essentially are zero and one at the same time. The qu‐bit is based on the fundamental ambiguity inherent in quantum mechanics. The position, momentum, or other state of a fundamental particle remains ʺambiguousʺ until a process of disambiguation causes that particle to ʺdecideʺ where it is, where it has been, and what properties it has. For example, consider a stream of photons that strike a sheet of glass at a 45-degree angle. As each photon strikes the glass, it has a choice of traveling either straight through the glass or reflecting off the glass. Each photon will actually take both paths (actually more than this, see below) until a process of conscious observation forces each particle to decide which path it took. This behavior has been extensively confirmed in numerous contemporary experiments.

In a quantum computer, the qu‐bits would be represented by a property—nuclear spin is a popular choice—of individual electrons. If set up in the proper way, the electrons will not have decided the direction of their nuclear spin (up or down) and thus will be in both states at the same time. The process of conscious observation of the electronsʹ

spin states—or any subsequent phenomena dependent on a determination of thee states—causes the ambiguity to be

resolved. This process of disambiguation is called quantum decoherence. If it werenʹt for quantum decoherence, the

world we live in would be a baffling place indeed.

The key to the quantum computer is that we would present it with a problem, along with a way to test the answer.

We would set up the quantum decoherence of the qu‐bits in such a way that only an answer that passes the test survives the decoherence. The failing answers essentially cancel each other out. As with a number of other approaches (for example, recursive and genetic algorithms), one of the keys to quantum computing is, therefore, a careful statement of the problem, including a precise way to test possible answers.

The series of qu‐bits represents simultaneously every possible solution to the problem. A single qu‐bit represents

two possible solutions. Two linked qu‐bits represent four possible answers. A quantum computer with 1,000 qu‐bits

represents 21,000 (this is approximately equal to a decimal number consisting of 1, followed by 301 zeroes) possible solutions simultaneously. The statement of the problem—expressed as a test to be applied to potential answers—is presented to the string of qu‐bits so that the qu‐bits decohere (that is, each qu‐bit changes from its ambiguous 0‐1

state to an actual 0 or a 1), leaving a series of 0ʹs and 1ʹs that pass the test. Essentially all 21,000 possible solutions have been tried simultaneously, leaving only the correct solution.

This process of reading out the answer through quantum decoherence is obviously the key to quantum

computing. It is also the most difficult aspect to grasp. Consider the following analogy. Beginning physics students learn that if light strikes a mirror at an angle, it will bounce off the mirror in the opposite direction and at the same angle to the surface. But according to quantum theory, that is not what is happening. Each photon actually bounces

off every possible point on the mirror, essentially trying out every possible path. The vast majority of these paths cancel each other out, leaving only the path that classical physics predicts. Think of the mirror as representing a problem to be solved. Only the correct solution—light bounced off at an angle equal to the incoming angle—survives

all of the quantum cancellations. A quantum computer works the same way. The test of the correctness of the answer

to the problem is set up in such a way that the vast majority of the possible answers—those that do not pass the test—

cancel each other out, leaving only the sequence of bits that does pass the test. An ordinary mirror, therefore, can be thought of as a special example of a quantum computer, albeit one that solves a rather simple problem.

As a more useful example, encryption codes are based on factoring large numbers (factoring means determining

which smaller numbers, when multiplied together, result in the larger number). Factoring a number with several hundred bits is virtually impossible on any digital computer even if we had billions of years to wait for the answer. A quantum computer can try every possible combination of factors simultaneously and break the code in less than a billionth of a second (communicating the answer to human observers does take a bit longer). The test applied by the

quantum computer during its key disambiguation stage is very simple: just multiply one factor by the other and if the result equals the encryption code, then we have solved the problem.

It has been said that quantum computing is to digital computing as a hydrogen bomb is to a firecracker. This is a

remarkable statement when we consider that digital computing is quite revolutionary in its own right. The analogy is based on the following observation. Consider (at least in theory) a Universe‐sized (nonquantum) computer in which

every neutron, electron, and proton in the Universe is turned into a computer, and each one (that is, every particle in the Universe) is able to compute trillions of calculations per second. Now imagine certain problems that this Universe‐sized supercomputer would be unable to solve even if we ran that computer until either the next big bang

or until all the stars in the Universe died—about ten to thirty billion years. There are many examples of such massively intractable problems; for example, cracking encryption codes that use a thousand bits, or solving the traveling‐salesman problem with a thousand cities. While very massive digital computing (including our theoretical

Universe‐sized computer) is unable to solve this class of problems, a quantum computer of microscopic size could solve such problems in less than a billionth of a second.

Are quantum computers feasible? Recent advances, both theoretical and practical, suggest that the answer is yes.

Although a practical quantum computer has not been built, the means for harnessing the requisite decoherence has

been demonstrated. Isaac Chuang of Los Alamos National Laboratory and MITʹs Neil Gershenfeld have actually built

a quantum computer using the carbon atoms in the alanine molecule. Their quantum computer was only able to add

one and one, but thatʹs a start. We have, of course, been relying on practical applications of other quantum effects, such as the electron tunneling in transistors, for decades. [14]

A Quantum Computer in a Cup of Coffee

One of the difficulties in designing a practical quantum computer is that it needs to be extremely small, basically atom or molecule sized, to harness the delicate quantum effects. But it is very difficult to keep individual atoms and molecules from moving around due to thermal effects. Moreover, individual molecules are generally too unstable to

build a reliable machine. For these problems, Chuang and Gershenfeld have come up with a theoretical breakthrough.

Their solution is to take a cup of liquid and consider every molecule to be a quantum computer. Now instead of a single unstable molecule‐sized quantum computer, they have a cup with about a hundred billion trillion quantum computers. The point here is not more massive parallelism, but rather massive redundancy In this way, the inevitably erratic behavior of some of the molecules his no effect on the statistical behavior of all the molecules in the liquid.

This approach of using the statistical behavior of trillions of molecules to overcome the lack of reliability of a single molecule is similar to Professor Adlemanʹs use of trillions of DNA strands to overcome the comparable issue in DNA

computing.

This approach to quantum computing also solves the problem of reading out the answer bit by bit without causing those qu‐bits that have not yet been read to decohere prematurely. Chuang and Gershenfeld subject their liquid computer to radio‐wave pulses, which cause the molecules to respond with signals indicating the spin state of each electron. Each pulse does cause some unwanted decoherence, but, again, this decoherence does not affect the statistical behavior of trillions of molecules. In this way, the quantum effects become stable and reliable.

Chuang and Gershenfeld are currently building a quantum computer that can factor small numbers. Although this early model will not compete with conventional digital computers, it will be an important demonstration of the

feasibility of quantum computing. Apparently high on their list for a suitable quantum liquid is freshly brewed Java coffee, which, Gershenfeld notes, has ʺunusually even heating characteristics.ʺ

Quantum Computing with the Code of Life

Quantum computing starts to overtake digital computing when we can link at least 40 qu‐bits. A 40‐qu‐bit quantum

computer would be evaluating a trillion possible solutions simultaneously, which would match the fastest supercomputers. At 60 bits, we would be doing a million trillion simultaneous trials. When we get to hundreds of qu-bits, the capabilities of a quantum computer would vastly overpower any conceivable digital computer.

So hereʹs my idea. The power of a quantum computer depends on the number of qu‐bits that we can link together.

We need to find a large molecule that is specifically designed to hold large amounts of information. Evolution has designed just such a molecule: DNA. We can readily create any sized DNA molecule we wish from a few dozen nucleotide rungs to thousands. So once again we combine two elegant ideas—in this case the liquid‐DNA computer

and the liquid‐quantum computer—to come up with a solution greater than the sum of its parts. By putting trillions

of DNA molecules in a cup, there is the potential to build a highly redundant—and therefore reliable—quantum computer with as many qu‐bits as we care to harness. Remember you read it here first.

Suppose No One Ever Looks at the Answer

Consider that the quantum ambiguity a quantum computer relies on is decohered, that is, disambiguated, when a conscious entity observes the ambiguous phenomenon. The conscious entities in this case are us, the users of the quantum computer. But in using a quantum computer, we are not directly looking at the nuclear spin states of individual electrons. The spin states are measured by an apparatus that in turn answers some question that the quantum computer has been asked to solve. These measurements are then processed by other electronic gadgets, manipulated further by conventional computing equipment, and finally displayed or printed on a piece of paper.

Suppose no human or other conscious entity ever looks at the printout. In this situation, there has been no conscious observation, and therefore no decoherence. As I discussed earlier, the physical world only bothers to manifest itself in an unambiguous state when one of us conscious entities decides to interact with it. So the page with the answer is ambiguous, undetermined—until and unless a conscious entity looks at it. Then instantly all the ambiguity is retroactively resolved, and the answer is there on the page. The implication is that the answer is not there until we look at it. But donʹt try to sneak up on the page fast enough to see the answerless page; the quantum effects are instantaneous.

What Is It Good For?

A key requirement for quantum computing is a way to test the answer. Such a test does not always exist. However, a

quantum computer would be a great mathematician. It could simultaneously consider every possible combination of

axioms and previously solved theorems (within a quantum computerʹs qu‐bit capacity) to prove or disprove virtually

any provable or disprovable conjecture. Although a mathematical proof is often extremely difficult to come up with,

confirming its validity is usually straightforward, so the quantum approach is well suited.

Quantum computing is not directly applicable, however, to problems such as playing a board game. Whereas the

ʺperfectʺ chess move for a given board is a good example of a finite but intractable computing problem, there is no

easy way to test the answer. If a person or process were to present an answer, there is no way to test its validity other than to build the same move‐countermove tree that generated the answer in the first place. Even for mere ʺgoodʺ

moves, a quantum computer would have no obvious advantage over a digital computer.

How about creating art? Here a quantum computer would have considerable value. Creating a work of art involves solving a series, possibly an extensive series, of problems. A quantum computer could consider every possible combination of elements—words, notes, strokes—for each decision. We still need a way to test each answer

to the sequence of aesthetic problems, but the quantum computer would be ideal for instantly searching through a Universe of possibilities.

Encryption Destroyed and Resurrected

As mentioned above, the classic problem that a quantum computer is ideally suited for is cracking encryption codes,

which relies on factoring large numbers. The strength of an encryption code is measured by the number of bits that

needs to be factored. For example, it is illegal in the United States to export encryption technology using more than 40

bits (56 bits if you give a key to law‐enforcement authorities). A 40‐bit encryption method is not very secure. In September 1997, Ian Goldberg, a University of California at Berkeley graduate student, was able to crack a 40‐bit code in three and a half hours using a network of 250 small computers. [15] A 56‐bit code is a bit better (16 bits better, actually). Ten months later, John Gilmore, a computer privacy activist, and Paul Kocher, an encryption expert, were

able to break the 56‐bit code in 56 hours using a specially designed computer that cost them $250,000 to build. But a quantum computer can easily factor any sized number (within its capacity). Quantum computing technology would

essentially destroy digital encryption.

But as technology takes away, it also gives. A related quantum effect can provide a new method of encryption that

can never be broken. Again, keep in mind that, in view of the Law of Accelerating Returns, ʺneverʺ is not as long as it used to be.

This effect is called quantum entanglement. Einstein, who was not a fan of quantum mechanics, had a different name for it, calling it ʺspooky action at a distance.ʺ The phenomenon was recently demonstrated by Dr. Nicolas Gisin of the University of Geneva in a recent experiment across the city of Geneva. [16] Dr. Gisin sent twin photons in opposite directions through optical fibers. Once the photons were about seven miles apart, they each encountered a

glass plate from which they could either bounce off or pass through. Thus, they were each forced to make a decision

to choose among two equally probable pathways. Since there was no possible communication link between the two

photons, classical physics would predict that their decisions would be independent. But they both made the same decision. And they did so at the same instant in time, so even if there were an unknown communication path between

them, there was not enough time for a message to travel from one photon to the other at the speed of light. The two

particles were quantum entangled and communicated instantly with each other regardless of their separation. The effect was reliably repeated over many such photon pairs.

The apparent communication between the two photons takes place at a speed far greater than the speed of light.

In theory, the speed is infinite in that the decoherence of the two photon travel decisions, according to quantum theory, takes place at exactly the same instant. Dr. Gisinʹs experiment was sufficiently sensitive to demonstrate the communication was at least ten thousand times faster than the speed of light.

So, does this violate Einsteinʹs Special Theory of Relativity, which postulates the speed of light as the fastest speed at which we can transmit information? The answer is no—there is no information being communicated by the entangled photons. The decision of the photons is random—a profound quantum randomness—and randomness is precisely not information. Both the sender and the receiver of the message simultaneously access the identical random decisions of the entangled photons, which are used to encode and decode, respectively, the message. So we

are communicating randomness—not information—at speeds far greater than the speed of light. The only way we could convert the random decisions of the photons into information is if we edited the random sequence of photon

decisions. But editing this random sequence would require observing the photon decisions, which in turn would cause quantum decoherence, which would destroy the quantum entanglement. So Einsteinʹs theory is preserved.

Even though we cannot instantly transmit information using quantum entanglement, transmitting randomness is

still very useful. It allows us to resurrect the process of encryption that quantum computing would destroy. If the sender and receiver of a message are at the two ends of an optical fiber, they can use the precisely matched random

decisions of a stream of quantum entangled photons to respectively encode and decode a message. Since the encryption is fundamentally random and nonrepeating, it cannot be broken. Eavesdropping would also be

impossible, as this would cause quantum decoherence that could be detected at both ends. So privacy is preserved.

Note that in quantum encryption, we are transmitting the code instantly The actual message will arrive much more slowly—at only the speed of light.

Quantum Consciousness Revisited

The prospect of computers competing with the full range of human capabilities generates strong, often adverse feelings, as well as no shortage of arguments that such a specter is theoretically impossible. One of the more interesting such arguments comes from an Oxford mathematician and physicist, Roger Penrose.

In his 1989 best‐seller, The Emperorʹs New Mind, Penrose puts forth two conjectures. [17] The first has to do with an unsettling theorem proved by a Czech mathematician, Kurt Gödel. Gödelʹs famous ʺincompleteness theorem,ʺ which

has been called the most important theorem in mathematics, states that in a mathematical system powerful enough to

generate the natural numbers, there inevitably exist propositions that can be neither proved nor disproved. This was another one of those twentieth‐century insights that upset the orderliness of nineteenth‐century thinking.

A corollary of Gödelʹs theorem is that there are mathematical propositions that cannot be decided by an algorithm.

In essence, these Gödelian impossible problems require an infinite number of steps to be solved. So Penroseʹs first conjecture is that machines cannot do what humans can do because machines can only follow an algorithm. An algorithm cannot solve a Gödelian unsolvable problem. But humans can. Therefore, humans are better.

Penrose goes on to state that humans can solve unsolvable problems because our brains do quantum computing.

Subsequently responding to criticism that neurons are too big to exhibit quantum effects, Penrose cited small structures in the neurons called microtubules that may be capable of quantum computation.

However, Penroseʹs first conjecture—that humans are inherently superior to machines—is unconvincing for at least three reasons:

1. It is true that machines canʹt solve Gödelian impossible problems. But humans canʹt solve them either. Humans

can only estimate them. Computers can make estimates as well, and in recent years are doing a better job of this

than humans.

2. In any event, quantum computing does not permit solving Gödelian impossible problems either. Solving a Gödelian impossible problem requires an algorithm with an infinite number of steps. Quantum computing can

turn an intractable problem that could not be solved on a conventional computer in trillions of years into an instantaneous computation. But it still falls short of infinite computing.

3. Even if (1) and (2) above were wrong, that is, if humans could solve Gödelian impossible problems and do so

because of their quantum‐computing ability, that still does not restrict quantum computing from machines. The

opposite is the case. If the human brain exhibits quantum computing, this would only confirm that quantum computing is possible, that matter following natural laws can perform quantum computing. Any mechanisms in

human neurons capable of quantum computing, such as the microtubules, would be replicable in a machine.

Machines use quantum effects—tunneling—in trillions of devices (that is, transistors) today. [18] There is nothing to suggest that the human brain has exclusive access to quantum computing.

Penroseʹs second conjecture is more difficult to resolve. It is that an entity exhibiting quantum computing is conscious. He is saying that it is the humans quantum computing that accounts for her consciousness. Thus quantum

computing—quantum decoherence—yields consciousness.

Now we do know that there is a link between consciousness and quantum decoherence. That is, consciousness observing a quantum uncertainty causes quantum decoherence. Penrose, however, is asserting a link in the opposite

direction. This does not follow logically. Of course quantum mechanics is not logical in the usual sense—it follows quantum logic (some observers use the word ʺstrangeʺ to describe quantum logic). But even applying quantum logic,

Penroseʹs second conjecture does not appear to follow. On the other hand, I am unable to reject it out of hand because there is a strong nexus between consciousness and quantum decoherence in that the former causes the latter. I have

thought about this issue for three years, and have been unable to accept it or reject it. Perhaps before writing my next book I will have an opinion on Penroseʹs second conjecture.

IS THE BRAIN BIG ENOUGH?

Is our conception of human neuron functioning and our estimates of the number of neurons and connections

in the human brain consistent with what we know about the brain's capabilities? Perhaps human neurons are

far more capable than we think they are. If so, building a machine with human-level capabilities might take

longer than expected.

We find that estimates of the number of concepts—"chunks" of knowledge—that a human expert in a

particular field has mastered are remarkably consistent: about 50,000 to 100,000. This approximate range

appears to be valid over a wide range of human endeavors: the number of board positions mastered by a

chess grand master, the concepts mastered by an expert in a technical field, such as a physician, the

vocabulary of a writer (Shakespeare used 29,000 words;[19] this book uses a lot fewer).

This type of professional knowledge is, of course, only a small subset of the knowledge we need to function

as human beings. Basic knowledge of the world, including so-called common sense, is more extensive. We

also have an ability to recognize patterns: spoken language, written language, objects, faces. And we have

our skills: walking, talking, catching balls. I believe that a reasonably conservative estimate of the general

knowledge of a typical human is a thousand times greater than the knowledge of an expert in her professional

field. This provides us a rough estimate of 100 million chunks—bits of understanding, concepts, patterns,

specific skills—per human. As we will see below, even if this estimate is low (by a factor of up to a thousand),

the brain is still big enough.

The number of neurons in the human brain is estimated at approximately 100 billion, with an average of

1,000 connections per neuron, for a total of 100 trillion connections. With 100 trillion connections and 100

million chunks of knowledge (including patterns and skills), we get an estimate of about a million connections

Our computer simulations of neural nets use a variety of different types of neuron models, all of which are

relatively simple. Efforts to provide detailed electronic models of real mammalian neurons appear to show that

while animal neurons are more complicated than typical computer models, the difference in complexity is

modest. Even using our simpler computer versions of neurons, we find that we can model a chunk of

knowledge—a face, a character shape, a phoneme, a word sense—using as little as a thousand connections

per chunk. Thus our rough estimate of a million neural connections in the human brain per human knowledge

chunk appears reasonable.

Indeed it appears ample. Thus we could make my estimate (of the number of knowledge chunks) a

thousand times greater, and the calculation still works. It is likely, however, that the brain's encoding of

knowledge is less efficient than the methods we use in our machines. This apparent inefficiency is consistent

with our understanding that the human brain is conservatively designed. The brain relies on a large degree of

redundancy and a relatively low density of information storage to gain reliability and to continue to function

effectively despite a high rate of neuron loss as we age.

My conclusion is that it does not appear that we need to contemplate a model of information processing of

individual neurons that is significantly more complex than we currently understand in order to explain human

capability. The brain is big enough.

REVERSE ENGINEERING A PROVEN DESIGN: THE HUMAN BRAIN

For many people the mind is the last refuge of mystery against the encroaching spread of science, and they donʹt like the idea of science engulfing the last bit of terra incognita.

—Herb Simon as quoted by Daniel Dennett

Cannot we let people be themselves, and enjoy life in their own way? You are trying to make another you. Oneʹs enough.

—Ralph Waldo Emerson

For the wise men of old . . . the solution has been knowledge and self‐discipline, . . . and in the practice of this technique, are ready to do things hitherto regarded as disgusting and impious—such as digging up and mutilating the dead.

—C. S. Lewis

Intelligence is: (a) the most complex phenomenon in the Universe; or (b) a profoundly simple process.

The answer, of course, is (c) both of the above. Itʹs another one of those great dualities that make life interesting.

Weʹve already talked about the simplicity of intelligence: simple paradigms and the simple process of computation.

Letʹs talk about the complexity.

We come back to knowledge, which starts out with simple seeds but ultimately becomes elaborate as the knowledge‐gathering process interacts with the chaotic real world. Indeed, that is how intelligence originated. It was the result of the evolutionary process we call natural selection, itself a simple paradigm, that drew its complexity from the pandemonium of its environment. We see the same phenomenon when we harness evolution in the computer. We start with simple formulas, add the simple process of evolutionary iteration and combine this with the

simplicity of massive computation. The result is often complex, capable, and intelligent algorithms.

But we donʹt need to simulate the entire evolution of the human brain in order to tap the intricate secrets it contains. just as a technology company will take apart and ʺreverse engineerʺ (analyze to understand the methods of) a rivalʹs products, we can do the same with the human brain. It is, after all, the best example we can get our hands on of an intelligent process. We can tap the architecture, organization, and innate knowledge of the human brain in order to greatly accelerate our understanding of how to design intelligence in a machine. By probing the brainʹs circuits, we can copy and imitate a proven design, one that took its original designer several billion years to develop. (And itʹs not even copyrighted.)

As we approach the computational ability to simulate the human brain—weʹre not there today but we will begin

to be in about a decadeʹs time—such an effort will be intensely pursued. Indeed, this endeavor has already begun.

For example, Synapticsʹ vision chip is fundamentally a copy of the neural organization, implemented in silicon of

course, of not only the human retina, but the early stages of mammalian visual processing. It has captured the essence of the algorithm of early mammalian visual processing, an algorithm called center surround filtering. It is not a particularly complicated chip, yet it realistically captures the essence of the initial stages of human vision.

There is a popular conceit among observers, both informed and uninformed, that such a reverse engineering project is infeasible. Hofstadter worries that ʺour brains may be too weak to understand themselves.ʺ [20] But that is not what we are finding. As we probe the brainʹs circuits, we find that the massively parallel algorithms are far from incomprehensible. Nor is there anything like an infinite number of them. There are hundreds of specialized regions in the brain, and it does have a rather ornate architecture, the consequence of its long history. The entire puzzle is not beyond our comprehension. It will certainly not be beyond the comprehension of twenty‐first‐century machines.

The knowledge is right there in front of us, or rather inside of us. It is not impossible to get at. Letʹs start with the most straightforward scenario, one that is essentially feasible today (at least to initiate).

We start by freezing a recently deceased brain.

Now, before I get too many indignant reactions, let me wrap myself in Leonardo da Vinciʹs cloak. Leonardo also

received a disturbed reaction from his contemporaries. Here was a guy who stole dead bodies from the morgue, carted them back to his dwelling, and then took them apart. This was before dissecting dead bodies was in style. He

did this in the name of knowledge, not a highly valued pursuit at the time. He wanted to learn how the human body

works, but his contemporaries found his activities bizarre and disrespectful. Today we have a different view, that expanding our knowledge of this wondrous machine is the most respectful homage we can pay. We cut up dead bodies all the time to learn more about how living bodies work, and to teach others what we have already learned.

Thereʹs no difference here in what I am suggesting. Except for one thing: I am talking about the brain, not the body. This strikes closer to home. We identify more with our brains than our bodies. Brain surgery is regarded as more invasive than toe surgery. Yet the value of the knowledge to be gained from probing the brain is too valuable to ignore. So weʹll get over whatever squeamishness remains.

As I was saying, we start by freezing a dead brain. This is not a new concept—Dr. E. Fuller Torrey a former supervisor at the National Institute of Mental Health and now head of the mental health branch of a private research foundation, has 44 freezers filled with 226 frozen brains. [21] Torrey and his associates hope to gain insight into the causes of schizophrenia, so all of his brains are of deceased schizophrenic patients, which is probably not ideal for our purposes.

We examine one brain layer—one very thin slice—at a time. With suitably sensitive two‐dimensional scanning equipment we should be able to see every neuron and every connection represented in each synapse‐thin layer. When

a layer has been examined and the requisite data stored, it can be scraped away to reveal the next slice. This information can be stored and assembled into a giant three‐dimensional model of the brainʹs wiring and neural topology.

It would be better if the frozen brains were not already dead long before freezing. A dead brain will reveal a lot

about living brains, but it is clearly not the ideal laboratory. Some of that deadness is bound to reflect itself in a deterioration of its neural structure. We probably donʹt want to base our designs for intelligent machines on dead brains. We are likely to be able to take advantage of people who, facing imminent death, will permit their brains to be destructively scanned just slightly before rather than slightly after their brains would have stopped functioning on their own. Recently, a condemned killer allowed his brain and body to be scanned and you can access all 10 billion

bytes of him on, the Internet at the Center for Human Simulationʹs ʺVisible Human Projectʺ web site. [22] Thereʹs an even higher resolution 25‐billion‐byte female companion on the site as well. Although the scan of this couple is not high enough resolution for the scenario envisioned here, itʹs an example of donating oneʹs brain for reverse engineering. Of course we may not want to base our templates of machine intelligence on the brain of a convicted killer, anyway.

Easier to talk about are the emerging noninvasive means of scanning our brains. I began with the more invasive

scenario above because it is technically much easier. We have in fact the means to conduct a destructive scan today

(although not yet the bandwidth to scan the entire brain in a reasonable amount of time). In terms of noninvasive scanning, high‐speed, high‐resolution magnetic resonance imaging (MRI) scanners are already able to view individual somas (neuron cell bodies) without disturbing the living tissue being scanned. More powerful MRIS are being developed that will be capable of scanning individual nerve fibers that are only ten microns (millionths of a meter) in diameter. These will be available during the first decade of the twenty‐first century. Eventually we will be able to scan the presynaptic vesicles that are the site of human learning.

We can peer inside someoneʹs brain today with MRI scanners, which are increasing their resolution with each new

generation of this technology. There are a number of technical challenges in accomplishing this, including achieving suitable resolution, bandwidth (that is, speed of transmission), lack of vibration, and safety. For a variety of reasons it is easier to scan the brain of someone recently deceased than of someone still living. (It is easier to get someone deceased to sit still, for one thing.) But noninvasively scanning a living brain will ultimately become feasible as MRI and other scanning technologies continue to improve in resolution and speed.

A new scanning technology called optical imaging, developed by Professor Amiram Grinvald at Israelʹs

Weizmarm Institute, is capable of significantly higher resolution than MRI. Like MRI, it is based on the interaction between electrical activity in the neurons and blood circulation in the capillaries feeding the neurons. Grinvaldʹs device is capable of resolving features smaller than fifty microns, and can operate in real time, thus enabling scientists to view the firing of individual neurons. Grinvald and researchers at Germanyʹs Max Planck Institute were struck by

the remarkable regularity of the patterns of neural firing when the brain was engaged in processing visual information. [23] One of the researchers, Dr. Mark Hubener, commented that ʺour maps of the working brain are so

orderly they resemble the street map of Manhattan rather than, say, of a medieval European town.ʺ Grinvald, Hubener, and their associates were able to use their brain scanner to distinguish between sets of neurons responsible for perception of depth, shape, and color. As these neurons interact with one another, the resulting pattern of neural firings resembles elaborately linked mosaics. From the scans, it was possible for the researchers to see how the neurons were feeding information to each other. For example, they noted that the depth perception neurons were arranged in parallel columns, providing information to the shape‐detecting neurons that formed more elaborate pinwheel‐like patterns. Currently, the Grinvald scanning technology is only able to image a thin slice of the brain near its surface, but the Weizmann Institute is working on refinements that will extend its three‐dimensional capability.

Grinvaldʹs scanning technology is also being used to boost the resolution of MRI scanning. A recent finding that near-infrared light can pass through the skull is also fueling excitement about the ability of optical imaging as a high-resolution method of brain scanning.

The driving force behind the rapidly improving capability of noninvasive scanning technologies such as MRI is again the Law of Accelerating Returns, because it requires massive computational ability. To build the high-resolution, three‐dimensional images from the raw magnetic resonance patterns that an MRI scanner produces. The

exponentially increasing computational ability provided by the Law of Accelerating Returns (and for another fifteen

to twenty years, Mooreʹs Law) will enable us to continue to rapidly improve the resolution and speed of these noninvasive scanning technologies.

Mapping the human brain synapse by synapse may seem like a daunting effort, but so did the Human Genome

Project, an effort to map all human genes, when it was launched in 1991. Although the bulk of the human genetic code has still not been decoded, there is confidence at the nine American Genome Sequencing Centers that the task

will be completed, if not by 2005, then at least within a few years of that target date. Recently, a new private venture with funding from Perkin‐Elmer has announced plans to sequence the entire human genome by the year 2001. As I

noted above, the pace of the human genome scan was extremely slow in its early years, and has picked up speed with

improved technology, particularly computer programs that identify the useful genetic information. The researchers are counting on further improvements in their gene‐hunting computer programs to meet their deadline. The same will be true of the human‐brain‐mapping project, as our methods of scanning and recording the 100 trillion neural connections pick up speed from the Law of Accelerating Returns.

What to Do with the Information

There are two scenarios for using the results of detailed brain scans. The most immediate— scanning the brain to understand it—is to scan portions of the brain to ascertain the architecture and implicit algorithms of interneuronal connections in different regions. The exact position of each and every nerve fiber is not as important as the overall pattern. With this information we can design simulated neural nets that operate similarly. This process will be rather like peeling an onion as each layer of human intelligence is revealed.

This is essentially what Synaptics has done in its chip that mimics mammalian neural‐image processing. This is also what Grinvald, Hubener, and their associates plan to do with their visual‐cortex scans. And there are dozens of other contemporary projects designed to scan portions of the brain and apply the resulting insights to the design of intelligent systems.

Within a region, the brainʹs circuitry is highly repetitive, so only a small portion of a region needs to be fully scanned. The computationally relevant activity of a neuron or group of neurons is sufficiently straightforward that we can understand and model these methods by examining them. Once the structure and topology of the neurons, the organization of the interneuronal wiring, and the sequence of neural firing in a region have been observed, recorded, and analyzed, it becomes feasible to reverse engineer that regionʹs parallel algorithms. After the algorithms of a region are understood, they can be refined and extended prior to being implemented in synthetic neural equivalents.

The methods can certainly be greatly sped up given that electronics is already more than a million times faster than neural circuitry.

We can combine the revealed algorithms with the methods for building intelligent machines that we already understand. We can also discard aspects of human computing that may not be useful in a machine. Of course, weʹll

have to be careful that we donʹt throw the baby out with the bathwater.

Downloading Your Mind to Your Personal Computer

A more challenging but also ultimately feasible scenario will be to scan someoneʹs brain to map the locations, interconnections, and contents of the somas, axons, dendrites, presynaptic vesicles, and other neural components. Its entire organization could then be re‐created on a neural computer of sufficient capacity, including the contents of its memory.

This is harder in an obvious way than the scanning‐the‐brain‐to‐understand‐it scenario. In the former, we need only sample each region until we understand the salient algorithms. We can then combine those insights with knowledge we already have. In this— scanning the brain to download it—scenario, we need to capture every little detail.

On the other hand, we donʹt need to understand all of it; we need only to literally copy it, connection by connection, synapse by synapse, neurotransmitter by neurotransmitter. It requires us to understand local brain processes, but not necessarily the brainʹs global organization, at least not in full. It is likely that by the time we can do this, we will understand much of it, anyway.

To do this right, we do need to understand what the salient information‐processing mechanisms are. Much of a neuronʹs elaborate structure exists to support its own structural integrity and life processes and does not directly contribute to its handling of information. We know that neuron‐computing process based on hundreds of different neurotransmitters and that different neural mechanisms in different regions allow for different types of computing.

The early vision neurons for example, are good at accentuating sudden color changes to facilitate finding the edges of objects. Hippocampus neurons are likely to have structures for enhancing the long‐term retention of memories. We also know that neurons use a combination of digital and analog computing that needs to be accurately modeled. We

need to identify structures capable of quantum computing, if any. All of the key features that affect information processing need to be recognized if we are to copy them accurately.

How well will this work? Of course, like any new technology, it wonʹt be perfect at first, and initial downloads will be somewhat imprecise. Small imperfections wonʹt necessarily be immediately noticeable because people are always changing to some degree. As our understanding of the mechanisms of the brain improves and our ability to

accurately and noninvasively scan these features improves, reinstantiating (reinstalling) a personʹs brain should alter a personʹs mind no more than it changes from day to day

What Will We Find When We Do This?

We have to consider this question on both the objective and subjective levels. ʺObjectiveʺ means everyone except me, so letʹs start with that. Objectively, when we scan someoneʹs brain and reinstantiate their personal mind file into a suitable computing medium, the newly emergent ʺpersonʺ will appear to other observers to have very much the same

personality, history, and memory as the person originally scanned. Interacting with the newly instantiated person will feel like interacting with the original person. The new person will claim to be that same old person and will have a memory of having been that person, having grown up in Brooklyn, having walked into a scanner here, and woken

up in the machine there. Heʹll say, ʺHey, this technology really works.ʺ

There is the small matter of the ʺnew personʹsʺ body. What kind of body will a reinstantiated personal mind file

have: the original human body, an upgraded body, a synthetic body, a nanoengineered body, a virtual body in a virtual environment? This is an important question, which I will discuss in the next chapter.

Subjectively, the question is more subtle and profound. Is this the same consciousness as the person we just scanned? As we saw in chapter 3, there are strong arguments on both sides. The position that fundamentally we are

our ʺpatternʺ (because our particles are always changing) would argue that this new person is the same because their patterns are essentially identical. The counter argument, however, is the possible continued existence of the person who was originally scanned. If he—Jack—is still around, he will convincingly claim to represent the continuity of his consciousness. He may not be satisfied to let his mental clone carry on in his stead. Weʹll keep bumping into this issue as we explore the twenty‐first century.

But once over the divide, the new person will certainly think that he was the original person. There will be no ambivalence in his mind as to whether or not he committed suicide when he agreed to be transferred into a new computing substrate leaving his old slow carbon‐based neural‐computing machinery behind. To the extent that he wonders at all whether or not he is really the same person that he thinks he is, heʹll be glad that his old self took the plunge, because otherwise he wouldnʹt exist.

Is he—the newly installed mind—conscious? He certainly will claim to be. And being a lot more capable than his

old neural self, heʹll be persuasive and effective in his position. Weʹll believe him. Heʹll get mad if we donʹt.

A Growing Trend

In the second half of the twenty‐first century, there will be a growing trend toward making this leap. Initially, there will be partial porting—replacing aging memory circuits, extending pattern‐recognition and reasoning circuits through neural implants. Ultimately, and well before the twenty‐first century is completed, people will port their entire mind file to the new thinking technology.

There will be nostalgia for our humble carbon‐based roots, but there is nostalgia for vinyl records also. Ultimately, we did copy most of that analog music to the more flexible and capable world of transferable digital information. The leap to port our minds to a more capable computing medium will happen gradually but inexorably nonetheless.

As we port ourselves, we will also vastly extend ourselves. Remember that $1,000 of computing in 2060 will have

the computational capacity of a trillion human brains. So we might as well multiply memory a trillion fold, greatly extend recognition and reasoning abilities, and plug ourselves into the pervasive wireless‐communications network.

While we are at it, we can add all human knowledge—as a readily accessible internal database as well as already processed and learned knowledge using the human type of distributed understanding.

THE AGE OF NEURAL IMPLANT HAS ALREADY ARRIVED

The patients are wheeled in on stretchers. Suffering from an advanced stage of Parkinson's disease, they are

like statues, their muscles frozen, their bodies and faces totally immobile. Then in a dramatic demonstration

at a French clinic, the doctor in charge throws an electrical switch. The patients suddenly come to life, get up,

walk around, and calmly and expressively describe how they have overcome their debilitating symptoms. This

is the dramatic result of a new neural implant therapy that is approved in Europe, and still awaits FDA

approval in the United States.

The diminished levels of the neurotransmitter dopamine in a Parkinson's patient causes overactivation of

two tiny regions in the brain: the ventral posterior nucleus and the subthalmic nucleus. This overactivation in

turn causes the slowness, stiffness, and gait difficulties of the disease, and ultimately results in total paralysis and death. Dr. A. L. Benebid, a French physician at Fourier University in Grenoble, discovered that stimulating

these regions with a permanently implanted electrode paradoxically inhibits these overactive regions and

reverses the symptoms. The electrodes are wired to a small electronic control unit placed in the patient's

chest. Through radio signals, the unit can be programmed, even turned on and off. When switched off, the

symptoms immediately return. The treatment has the promise of controlling the most devastating symptoms

of the disease. [24]

Similar approaches have been used with other brain regions. For example, by implanting an electrode in

the ventral lateral thalamus, the tremors as associated with cerebral palsy, multiple sclerosis, and other

tremor-causing conditions can be suppressed.

"We used to treat the brain like soup, adding chemicals that enhance or suppress certain

neurotransmitters," says Rick Trosch, one of the American physicians helping to perfect "deep brain

stimulation" therapies. "Now we're treating it like circuitry." [25]

Increasingly, we are starting to combat cognitive and sensory afflictions by treating the brain and nervous

system like the complex computational system that it is. Cochlear implants together with electronic speech

processors perform frequency analysis of sound waves, similar to that performed by the inner ear. About 10

percent of the formerly deaf persons who have received this neural replacement device are now able to hear

and understand voices well enough that they can hold conversations using a normal telephone.

Neurologist and ophthalmologist at Harvard Medical School Dr. Joseph Rizzo and his colleagues have

developed an experimental retina implant. Rizzo's neural implant is a small solar-powered computer that

communicates to the optic nerve. The user wears special glasses with tiny television cameras that

communicate to the implanted computer by laser signal. [26]

Researchers at Germany's Max Planck Institute for Biochemistry have developed special silicon devices that

can communicate with neurons in both directions. Directly stimulating neurons with an electrical current is not

the ideal approach since it can cause corrosion to the electrodes and create chemical by-products that damage

the cells. In contrast, the Max Planck Institute devices are capable of triggering an adjacent neuron to fire

without a direct electrical link. The Institute scientists demonstrated their invention by controlling the

movements of a living leech from their computer.

Going in the opposite direction—from neurons to electronics—is a device called a "neuron transistor," [27]

which can detect the firing of a neuron. The scientists hope to apply both technologies to the control of

artificial human limbs by connecting spinal nerves to computerized prostheses. The Institute's Peter Fromherz

says, "These two devices join the two worlds of information processing: the silicon world of the computer and

the water world of the brain."

Neurobiologist Ted Berger and his colleagues at Hedco Neurosciences and Engineering have built integrated

circuits that precisely match the properties and information processing of groups of animal neurons. The chips

exactly mimic the digital and analog characteristics of the neurons they have analyzed. They are currently

scaling up their technology to systems with hundreds of neurons. [28] Professor Carver Mead and his

colleagues at the California Institute of Technology have also built digital-analog integrated circuits that match

the processing of mammalian neural circuits comprising hundreds of neurons. [29]

The age of neural implants is under way, albeit at an early stage. Directly enhancing the information

processing of our brain with synthetic circuits is focusing at first on correcting the glaring defects caused by

neurological and sensory diseases and disabilities. Ultimately we will all find the benefits of extending our

abilities through neural implants difficult to resist.

The New Mortality

Actually there wonʹt be mortality by the end of the twenty‐first century. Not in the sense that we have known it. Not if you take advantage of the twenty‐first centuryʹs brain‐porting technology. Up until now, our mortality was tied to the longevity of our hardware. When the hardware crashed, that was it. For many of our forebears, the hardware gradually deteriorated before it disintegrated. Yeats lamented our dependence on a physical self that was ʺbut a paltry thing, a tattered coat upon a stick.ʺ [30] As we cross the divide to instantiate ourselves into our computational technology, our identity will be based on our evolving mind file. We will be software, not hardware.

And evolve it will. Today, our software cannot grow. It is stuck in a brain of a mere 100 trillion connections and

synapses. But when the hardware is trillions of times more capable, there is no reason for our minds to stay so small.

They can and will grow.

As software, our mortality will no longer be dependent on the survival of the computing circuitry. There will still

be hardware and bodies, but the essence of our identity will switch to the permanence of our software. Just as, today, we donʹt throw our files away when we change personal computers—we transfer them, at least the ones we want to

keep. So, too, we wonʹt throw our mind file away when we periodically port ourselves to the latest, ever more capable, ʺpersonalʺ computer. Of course, computers wonʹt be the discrete objects they are today. They will be deeply embedded in our bodies, brains, and environment. Our identity and survival will ultimately become independent of

the hardware and its survival.

Our immortality will be a matter of being sufficiently careful to make frequent backups. if weʹre careless about this, weʹll have to load an old backup copy and be doomed to repeat our recent past.

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LETʹS JUMP TO THE OTHER SIDE OF THIS COMING CENTURY. YOU SAID THAT BY 2099 A PENNY OF

COMPUTING WILL BE EQUAL TO A BILLION TIMES THE COMPUTING POWER OF ALL HUMAN BRAINS

COMBINED. SOUNDS LIKE HUMAN THINKING IS GOING TO BE PRETTY TRIVIAL.

Unassisted, thatʹs true.

SO HOW WILL WE HUMAN BEINGS FARE IN THE MIDST OF SUCH COMPETITION?

First, we have to recognize that the more powerful technology—the technologically more sophisticated civilization—

always wins. That appears to be what happened when our Homo sapiens sapiens subspecies met the Homo sapiens neanderthalensis and other nonsurviving subspecies of Homo sapiens. That is what happened when the more technologically advanced Europeans encountered the indigenous peoples of the Americas. This is happening today as

the more advanced technology is the key determinant of economic and military power.

SO WEʹRE GOING TO BE SLAVES TO THESE SMART MACHINES?

Slavery is not a fruitful economic system to either side in an age of intellect. We would have no value as slaves to machines. Rather, the relationship is starting out the other way.

ITʹS TRUE THAT MY PERSONAL COMPUTER DOES WHAT I ASK IT TO DO—SOMETIMES! MAYBE I SHOULD

START BEING NICER TO IT.

No, it doesnʹt care how you treat it, not yet. But ultimately our native thinking capacities will be no match for the all-encompassing technology weʹre creating.

MAYBE WE SHOULD STOP CREATING IT.

We canʹt stop. The Law of Accelerating Returns forbids it! Itʹs the only way to keep evolution going at an accelerating pace.

HEY, CALM DOWN. ITʹS FINE WITH ME IF EVOLUTION SLOWS DOWN A TAD. SINCE WHEN HAVE WE

ADOPTED YOUR ACCELERATION LAW AS THE LAW OF THE LAND?

We donʹt have to. Stopping computer technology, or any fruitful technology, would mean repealing basic realities of

economic competition, not to mention our quest for knowledge. Itʹs not going to happen. Furthermore, the road weʹre

going down is a road paved with gold. Itʹs full of benefits that weʹre never going to resist—continued growth in economic prosperity, better health, more intense communication, more effective education, more engaging

entertainment, better sex.

UNTIL THE COMPUTERS TAKE OVER.

Look, this is not an alien invasion. Although it sounds unsettling, the advent of machines with vast intelligence is not necessarily a bad thing.

I GUESS IF WE CANʹT BEAT THEM, WEʹLL HAVE TO JOIN THEM.

Thatʹs exactly what weʹre going to do. Computers started out as extensions of our minds, and they will end up extending our minds. Machines are already an integral part of our civilization, and the sensual and spiritual machines of the twenty‐first century will be an even more intimate part of our civilization.

OKAY, IN TERMS OF EXTENDING MY MIND, LETʹS GET BACK TO IMPLANTS FOR MY FRENCH LIT CLASS. IS

THIS GOING TO BE LIKE IʹVE READ THIS STUFF? OR IS IT JUST GOING TO BE LIKE A SMART PERSONAL

COMPUTER THAT I CAN COMMUNICATE WITH QUICKLY BECAUSE IT HAPPENS TO BE LOCATED IN MY

HEAD?

Thatʹs a key question, and I think it will be controversial. It gets back to the issue of consciousness. Some people will feel that what goes in their neural implants is indeed subsumed by their consciousness. Others will feel that it remains outside of their sense of self. Ultimately, I think that we will regard the mental activity of the implants as part of our own thinking. Consider that even without implants, ideas and thoughts are constantly popping into our heads,

and we have little idea of where they came from, or how they got there. We nonetheless consider all the mental phenomena that we become aware of as our own thoughts.

SO IʹLL BE ABLE TO DOWNLOAD MEMORIES OF EXPERIENCES IʹVE NEVER HAD?

Yes, but someone has probably had the experience. So why not have the ability to share it?

I SUPPOSE FOR SOME EXPERIENCES, IT MIGHT BE SAFER TO JUST DOWNLOAD THE MEMORIES OF IT.

Less time‐consuming also.

DO YOU REALLY THINK THAT SCANNING A FROZEN BRAIN IS FEASIBLE TODAY?

Sure, just stick your head in my freezer here.

GEE, ARE YOU SURE THIS IS SAFE?

Absolutely

WELL, I THINK IʹLL WAIT FOR FDA APPROVAL.

Okay, then youʹll have to wait a long time.

THINKING AHEAD, I STILL HAVE THIS SENSE THAT WEʹRE DOOMED. I MEAN, CAN UNDERSTAND HOW A

NEWLY INSTANTIATED MIND, AS YOU PUT IT, WILL BE HAPPY THAT SHE WAS CREATED AND WILL

THINK THAT SHE HAD BEEN ME PRIOR TO MY HAVING BEEN SCANNED AND IS STILL ME IN A SHINY

NEW BRAIN. SHEʹLL HAVE NO REGRETS AND WILL BE ON THE OTHER SIDE.ʺ BUT I DONʹT SEE HOW I CAN

GET ACROSS THE HUMAN‐MACHINE DIVIDE. AS YOU POINTED OUT, IF IʹM SCANNED, THAT NEW ME

ISNʹT ME BECAUSE IʹM STILL HERE IN MY OLD BRAIN.

Yes, thereʹs a little glitch in this regard. But Iʹm sure weʹll figure how to solve this thorny problem with a little more consideration.

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C H A P T E R S E V E N

. . . AND BODIES

THE IMPORTANCE OF HAVING A BODY

Letʹs start by taking a quick look at my readerʹs diary.

NOW WAIT JUST A MINUTE.

Is there a problem?

FIRST OF ALL, I HAVE A NAME.

Yes, it would be a good idea to introduce you by name at this point.

IʹM MOLLY.

Thank you, is there something else?

YES. IʹM NOT SURE IʹM PREPARED TO SHARE MY DIARY WITH YOUR OTHER READERS.

Most writers donʹt let their readers participate at all. Anyway, youʹre my creation, so I should be able to share your personal reflections if it serves a purpose here.

I MAY BE YOUR CREATION, BUT REMEMBER WHAT YOU SAID IN CHAPTER 2 ABOUT ONEʹS CREATIONS

EVOLVING TO SURPASS THEIR CREATORS.

True enough, so maybe I should be more sensitive to your needs.

GOOD IDEA—LETʹS START BY ALLOWING ME TO VET THOSE ENTRIES YOUʹRE SELECTING.

Very well. Here are some extracts from Mollyʹs diary, suitably edited:

Iʹve switched to nonfat muffins. This has two distinct benefits. First of all, they have half the number of calories. Secondly, they taste awful. That way Iʹm less tempted to eat them. But I wish people would stop

shoving food in my face. . . . Iʹm going to have trouble at this potluck dorm party tomorrow. I feel like I

have to try everything, and I kind of lose track of what Iʹm eating.

Iʹve got to drop at least half a dress size. A full size would be better. Then I could breathe more easily in

this new dress. That reminds me, I should stop at the health club on my way home. Maybe that new

trainer will notice me. Actually I did catch him looking at me, but I was being kind of spastic with those

new machines, and he looked the other way. . . . Iʹm not crazy about the neighborhood this place is in, I

donʹt really feel safe walking back to my car when itʹs late. Okay, hereʹs an idea—Iʹll ask that trainer—got

to get his name—to walk me to my car. Always a good idea to be safe, right?

. . . Iʹm a little nervous about this bump on my toe. But the doctor said that toe bumps are almost always benign. But he still wants to remove it and send it to a lab. He said I wonʹt feel a thing. Except, of

course, for the Novocain—I hate needles!

. . . It was a little strange seeing my old boyfriend, but Iʹm glad weʹre still friends. It did feel good when

he gave me a hug . . .

Thank you, Molly. Now consider: How many of Mollyʹs entries would make sense if she didnʹt have a body? Most

of Mollyʹs mental activities are directed toward her body and its survival, security, nutrition, image, not to mention related issues of affection, sexuality, and reproduction. But Molly is not unique in this regard. I invite my other readers to look at their own diaries. And if you donʹt have one, consider what you would write in it if you did. How many of your entries would make sense if you didnʹt have a body?

Our bodies are important in many ways. Most of those goals I spoke about at the beginning of the previous chapter—the ones we attempt to solve using our intelligence—have to do with our bodies: protecting them, providing

them with fuel, making them attractive, making them feel good, providing for their myriad needs, not to mention desires.

Some philosophers—professional artificial‐intelligence critic Hubert Dreyfus, for one—maintain that achieving human‐level intelligence is impossible without a body. [1] Certainly, if weʹre going to port a humanʹs mind to a new computational medium, weʹd better provide a body. A disembodied mind will quickly get depressed.

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TWENTY‐FIRST CENTURY BODIES

What makes a soul? And if machines ever have souls what will be the equivalent of psychoactive drugs? Of pain? Of the physical/emotional high I get from having a clean office?

—Esther Dyson

What a strange machine man is. You fill him with bread, wine, fish, and radishes, and out come sighs, laughter and dreams.

—Nikos Kazantzakis

So what kind of bodies will we provide for our twenty‐first‐century machines? Later on, the question will become: What sort of bodies will they provide for themselves?

Letʹs start with the human body itʹs the body weʹre used to. It evolved along with its brain, so the human brain is

well suited to provide for its needs. The human brain and body kind of go together.

The likely scenario is that both body and brain will evolve together, will become enhanced together, will migrate

together toward new modalities and materials. As I discussed in the previous chapter, porting our brains to new computational mechanisms will not happen all at once. We will enhance our brains gradually through direct connection with machine intelligence until such time that the essence of our thinking has fully migrated to the far more capable and reliable new machinery. Again, if we find this notion troublesome, a lot of this uneasiness has to do with our concept of the word machine. Keep in mind that our concept of this word will evolve along with our minds.

In terms of transforming our bodies, we are already further along in this process than we are in advancing our minds. We have titanium devices to replace our jaws, skulls, and hips. We have artificial skin of various kinds. We have artificial heart valves. We have synthetic vessels to replace arteries and veins, along with expandable stents to provide structural support for weak natural vessels. We have artificial arms, legs, feet, and spinal implants. We have all kinds of joints: jaws, hips, knees, shoulders, elbows, wrists, fingers, and toes. We have implants to control our bladders. We are developing machines—some made of artificial materials, others combining new materials with cultured cells—that will ultimately be able to replace organs such as the liver and pancreas. We have penile prostheses with little pumps to simulate erections. And we have long had implants for teeth and breasts.

Of course, the notion of completely rebuilding our bodies with synthetic materials, even if superior in certain ways, is not immediately compelling. We like the softness of our bodies. We like bodies to be supple and cuddly and

warm. And not a superficial warmth, but the deep and intimate heat drawn from its trillions of living cells.

So letʹs consider enhancing our bodies cell by cell. We have started down that road as well. We have written down

a portion of the entire genetic code that describes our cells, and weʹve started the process of understanding it. In the near future, we hope to design genetic therapies to improve our cells, to correct such defects as the insulin resistance associated with Type II diabetes, and the loss of control over self‐replication associated with cancer. An early method of delivering gene therapies was to infect a patient with special viruses containing the corrective DNA. A more effective method developed by Dr. Clifford Steer at the University of Minnesota utilizes RNA molecules to deliver the desired DNA directly. [2] High on researchersʹ list for future cellular improvements through genetic engineering is to counteract our genes for cellular suicide. These strands of genetic beads, called telomeres, get shorter every time a cell divides. When the telomere beads count down to zero, a cell is no longer able to divide, and destroys itself. Thereʹs a long list of diseases, aging conditions, and limitations that we intend to address by altering the genetic code that controls our cells.

But there is only so far we can go with this approach. Our DNA‐based cells depend on protein synthesis, and while protein is a marvelously diverse substance, it suffers from severe limitations. Hans Moravec, one of the first serious thinkers to realize the potential of twenty‐first‐century machines, points out that ʺprotein is not an ideal material. It is stable only in a narrow temperature and pressure range, is very sensitive to radiation, and rules out many construction techniques and components. . . . A genetically engineered superhuman would be just a second‐rate

kind of robot, designed under the handicap that its construction can only be by DNA‐guided protein synthesis. Only

in the eyes of human chauvinists would it have an advantage.ʺ [3]

One of evolutionʹs ideas that is worth keeping, however, is building our bodies from cells. This approach would

retain many of our bodiesʹ beneficial qualities: redundancy, which provides a high degree of reliability; the ability to regenerate and repair itself; and softness and warmth. But just as we will eventually relinquish the extremely slow speed of our neurons, we will ultimately be forced to abandon the other restrictions of our protein‐based chemistry.

To reinvent our cells, we look to one of the twenty‐first centuryʹs primary technologies: nanotechnology.

NANOTECHNOLOGY:

REBUILDING THE WORLD, ATOM BY ATOM

The problems of chemistry and biology can be greatly helped if . . . doing things on an atomic level is ultimately developed—a development which I think cannot be avoided.

—Richard Feynman, 1959

Suppose someone claimed to have a microscopically exact replica (in marble, even) of Michelangeloʹs David in his home. When you go to see this marvel, you find a twenty‐foot‐tall, roughly rectilinear hunk of pure white marble standing in his living room. ʺI havenʹt gotten around to unpacking it yet,ʺ he says, ʺbut I know itʹs in there.ʺ

—Douglas Hofstadter

What advantages will nanotoasters have over conventional macroscopic toaster technology? First, the savings in counter space will be substantial. One philosophical point that must not be overlooked is that the creation of the worldʹs smallest toaster implies the existence of the worldʹs smallest slice of bread. In the quantum limit we must necessarily encounter fundamental toast particles, which we designate here as croutons.

—Jim Cser, Annals of Improbable Research, edited by Marc Abrahams

Humankindʹs first tools were found objects: sticks used to dig up roots and stones used to break open nuts. It took our forebears tens of thousands of years to invent a sharp blade. Today we build machines with finely designed intricate mechanisms, but viewed on an atomic scale, our technology is still crude. ʺCasting, grinding, milling, and even lithography move atoms in great thundering statistical herds,ʺ says Ralph Merkle, a leading nanotechnology theorist at Xeroxʹs Palo Alto Research Center. He adds that current manufacturing methods are ʺlike trying to make

things out of Legos with boxing gloves on. . . . In the future, nanotechnology will let us take off the boxing gloves.ʺ [4]

Nanotechnology is technology built on the atomic level: building machines one atom at a time. ʺNanoʺ refers to a

billionths of a meter, which is the width of five carbon atoms. We have one existence proof of the feasibility of nanotechnology: life on Earth. Little machines in our cells called ribosomes build organisms such as humans one molecule, that is one amino acid, at a time, following digital templates coded in another molecule called DNA. Life on Earth has mastered the ultimate goal of nanotechnology, which is self‐replication.

But as mentioned above, Earthly life is limited by the particular molecular building block it has selected. Just as

our human‐created computational technology will ultimately exceed the capacity of natural computation (electronic

circuits are already millions of times faster than human neural circuits), our twenty‐first‐century physical technology will also greatly exceed the capabilities of the amino acid‐based nanotechnology of the natural world.

The concept of building machines atom by atom was first described in a 1959 talk at Cal Tech titled ʺThereʹs Plenty

of Room at the Bottom,ʺ by physicist Richard Feynman, the same guy who first suggested the possibility of quantum

computing. [5] The idea was developed in some detail by Eric Drexler twenty years later in his book Engines of Creation. [6] The book actually inspired the cryonics movement of the 1980s, in which people had their heads (with or without bodies) frozen in the hope that a future time would possess the molecule‐scale technology to overcome their

mortal diseases, as well as undo the effects of freezing and defrosting. Whether a future generation would be motivated to revive all these frozen brains was another matter.

After publication of Engines of Creation, the response to Drexlerʹs ideas was skeptical and he had difficulty filling out his MIT Ph.D. committee despite Marvin Minskyʹs agreement to supervise it. Drexlerʹs dissertation, published in

1992 as a book titled Nanosystems: Molecular Machinery, Manufacturing, and Computation, provided a comprehensive proof of concept, including detailed analyses and specific designs. [7] A year later, the first nanotechnology conference attracted only a few dozen researchers. The fifth annual conference, held in December 1997, boasted 350

scientists who were far more confident of the practicality of their tiny projects. Nanothinc, an industry think tank, estimated in 1997 that the field already produces $5 billion in annual revenues for nanotechnology‐related technologies, including micromachines, microfabrication techniques, nanolithography, nanoscale microscopes, and others. This figure has been more than doubling each year. [8]

The Age of Nanotubes

One key building material for tiny machines is, again, nanotubes. Although built on an atomic scale, the hexagonal patterns of carbon atoms are extremely strong and durable. ʺYou can do anything you damn well want with these tubes and theyʹll just keep on truckinʹ,ʺ says Richard Smalley, one of the chemists who received the Nobel Prize for discovering the buckyball molecule. [9] A car made of nanotubes would be stronger and more stable than a car made

with steel, but would weigh only fifty pounds. A spacecraft made of nanotubes could be of the size and strength of

the U.S. space shuttle, but weigh no more than a conventional car. Nanotubes handle heat extremely well, far better

than the fragile amino acids that people are built out of. They can be assembled into all kinds of shapes: wirelike strands, sturdy girders, gears, etcetera. Nanotubes are formed of carbon atoms, which are in plentiful supply in the natural world.

As I mentioned earlier, the same nanotubes can be used for extremely efficient computation, so both the structural

and computational technology of the twenty‐first century will likely be constructed from the same stuff. In fact, the same nanotubes used to form physical structures can also be used for computation, so future nanomachines can have

their brains distributed throughout their bodies.

The best‐known examples of nanotechnology to date, while not altogether practical, are beginning to show the feasibility of engineering at the atomic level. IBM created its corporate logo using individual atoms as pixels. [10] In 1996, Texas instruments built a chip‐sized device with half a million moveable mirrors to be used in a tiny high-resolution projector.ʺ [11] TI sold $100 million worth of their nanomirrors in 1997.

Chih‐Ming Ho of UCLA is designing flying machines using surfaces covered with microflaps that control the flow

of air in a similar manner to conventional flaps on a normal airplane. [12] Andrew Berlin at Xeroxʹs Palo Alto Research Center is designing a printer using microscopic air valves to move paper documents precisely. [13]

Cornell graduate student and rock musician Dustin Carr built a realistic‐looking but microscopic guitar with strings only fifty manometers in diameter. Carrʹs creation is a fully functional musical instrument, but his fingers are too large to play it. Besides, the strings vibrate at 10 million vibrations per second, far beyond the twenty‐thousand-cycles‐per‐second limit of human hearing. [14]

The Holy Grail of Self‐Replication:

Little Fingers and a Little Intelligence

Tiny fingers represent something of a holy grail for nanotechnologists. With little fingers and computation, nanomachines would have in their Lilliputian world what people have in the big world: intelligence and the ability to manipulate their environment. Then these little machines could build replicas of themselves, achieving the fieldʹs key objective.

The reason that self‐replication is important is that it is too expensive to build these tiny machines one at a time.

To be effective, nanometer‐sized machines need to come in the trillions. The only way to achieve this economically is through combinatorial explosion: let the machines build themselves.

Drexler, Merkle (a coinventor of public key encryption, the primary method of encrypting messages), and others

have convincingly described how such a self‐replicating nanorobot— nanobot—could be constructed. The trick is to provide the nanobot with sufficiently flexible manipulators—arms and hands—so that it is capable of building a copy

of itself. It needs some means for mobility so that it can find the requisite raw materials. It requires some intelligence so that it can solve the little problems that will arise when each nanobot goes about building a complicated little machine like itself. Finally, a really important requirement is that it needs to know when to stop replicating.

Morphing in the Real World

Self‐replicating machines built at the atomic level could truly transform the world we live in. They could build extremely inexpensive solar cells, allowing the replacement of messy fossil fuels. Since solar cells require a large surface area to collect sufficient sunlight, they could be placed in orbit, with the energy beamed down to Earth.

Nanobots launched into our bloodstreams could supplement our natural immune system and seek out and

destroy pathogens, cancer cells, arterial plaque, and other disease agents. In the vision that inspired the cryonics enthusiasts, diseased organs can be rebuilt. We will be able to reconstruct any or all of our bodily organs and systems, and do so at the cellular level. I talked in the last chapter about reverse engineering and emulating the salient computational functionality of human neurons. In the same way, it will become possible to reverse engineer and replicate the physical and chemical functionality of any human cell. In the process we will be in a position to greatly extend the durability, strength, temperature range, and other qualities and capabilities of our cellular building blocks.

We will then be able to grow stronger, more capable organs by redesigning the cells that constitute them and building them with far more versatile and durable materials. As we go down this road, weʹll find that some redesign

of the body makes sense at multiple levels. For example, if our cells are no longer vulnerable to the conventional pathogens, we may not need the same kind of immune system. But we will need new nanoengineered protections for

a new assortment of nanopathogens.

Food, clothing, diamond rings, buildings could all assemble themselves molecule by molecule. Any sort of product could be instantly created when and where we need it. Indeed, the world could continually reassemble itself

to meet our changing needs, desires, and fantasies. By the late twenty‐first century, nano‐technology will permit objects such as furniture, buildings, clothing, even people, to change their appearance and other characteristics—

essentially to change into something else—in a split second.

These technologies will emerge gradually (I will attempt to delineate the different gradations of nanotechnology

as I talk about each of the decades of the twenty‐first century in Part III of this book). There is a clear incentive to go down this path. Given a choice, people will prefer to keep their bones from crumbling, their skin supple, their life systems strong and vital. Improving our lives through neural implants on the mental level, and nanotechnology-enhanced bodies on the physical level, will be popular and compelling. It is another one of those slippery slopes—

there is no obvious place to stop this progression until the human race has largely replaced the brains and bodies that evolution first provided.

A Clear and Future Danger

Without self‐replication, nanotechnology is neither practical nor economically feasible. And therein lies the rub. What happens if a little software problem (inadvertent or otherwise) fails to halt the self‐replication? We may have more nanobots than we want. They could eat up everything in sight.

The movie The Blob (of which there are two versions) was a vision of nano‐technology run amok. The movieʹs villain was this intelligent self‐replicating gluttonous stuff that fed on organic matter. Recall that nanotechnology is likely to be built from carbon‐based nanotubes, so, like the Blob, it will build itself from organic matter, which is rich in carbon. Unlike mere animal‐based cancers, an exponentially exploding nanomachine population would feed on any carbon‐based matter. Tracking down all of these bad nanointelligences would be like trying to find trillions of microscopic needles—rapidly moving ones at that—in at least as many haystacks. There have been proposals for nanoscale immunity technologies: good little antibody machines that would go after the bad little machines. The nanoantibodies would, of course, have to scale up at least as quickly as the epidemic of marauding nanomiscreants.

There could be a lot of collateral damage as these trillions of machines battle it out.

Now that I have raised this specter, I will try, unconvincingly perhaps, to put the peril in perspective. I believe that it will be possible to engineer self‐replicating nanobots in such a way that an inadvertent, undesired population explosion would be unlikely. I realize that this may not be completely reassuring, coming from a software developer

whose products (like those of my competitors) crash once in a while (but rarely—and when they do, itʹs the fault of

the operating system!). There is a concept in software development of ʺmission criticalʺ applications. These are software programs that control a process on which people are heavily dependent. Examples of mission‐critical software include life‐support systems in hospitals, automated surgical equipment, autopilot flying and landing systems, and other software‐based systems that affect the well‐being of a person or organization. It is feasible to create extremely high levels of reliability in these programs. There are examples of complex technology in use today in which a mishap would severely imperil public safety. A conventional explosion in an atomic power plant could spray deadly plutonium across heavily populated areas. Despite a near meltdown at Chernobyl, this apparently has

only occurred twice in the decades that we have had hundreds of such plants operating, both incidents involving recently acknowledged reactor calamities in the Chelyabinsk region of Russia. [15] There are tens of thousands of nuclear weapons, and none has ever exploded in error.

I admit that the above paragraph is not entirely convincing. But the bigger danger is the intentional hostile use of nanotechnology. Once the basic technology is available, it would not be difficult to adapt it as an instrument of war or terrorism. It is not the case that someone would have to be suicidal to use such weapons. The nanoweapons could easily be programmed to replicate only against an enemy; for example, only in a particular geographical area. Nuclear weapons, for all their destructive potential, are at least relatively local in their effects. The self‐replicating nature of nanotechnology makes it a far greater danger.

VIRTUAL BODIES

We donʹt always need real bodies. If we happen to be in a virtual environment, then a virtual body will do just fine.

Virtual reality started with the concept of computer games, particularly ones that provided a simulated environment.

The first was Space War, written by early artificial‐intelligence researchers to pass the time while waiting for programs to compile on their slow 1960s computers. [16] The synthetic space surroundings were easy to render on low‐resolution monitors: Stars and other space objects were just illuminated pixels.

Computer games and computerized video games have become more realistic over time, but you cannot

completely immerse yourself in these imagined worlds, not without some imagination. For one thing, you can see the

edges of the screen, and the all too real world that you have never left is still visible beyond these borders.

If weʹre going to enter a new world, we had better get rid of traces of the old. In the 1990s the first generation of virtual reality has been introduced in which you don a special visual helmet that takes over your entire visual field.

The key to visual reality is that when you move your head, the scene instantly repositions itself so that you are now looking at a different region of a three‐dimensional scene. The intention is to simulate what happens when you turn

your real head in the real world: The images captured by your retinas rapidly change. Your brain nonetheless understands that the world has remained stationary and that the image is sliding across your retinas only because your head is rotating.

Like most first generation technologies, virtual reality has not been fully convincing. Because rendering a new scene requires a lot of computation, there is a lag in producing the new perspective. Any noticeable delay tips off your brain that the world youʹre looking at is not entirely real. The resolution of virtual reality displays has also been inadequate to create a fully satisfactory illusion. Finally, contemporary virtual reality helmets are bulky and uncomfortable.

Whatʹs needed to remove the rendering delay and to boost display resolution is yet faster computers, which we

know are always on the way. By 2007, high‐quality virtual reality with convincing artificial environments, virtually instantaneous rendering, and high‐definition displays will be comfortable to wear and available at computer game prices.

That takes care of two of our senses—visual and auditory. Another high‐resolution sense organ is our skin, and

ʺhapticʺ interfaces to provide a virtual tactile interface are also evolving. One available today is the Microsoft force-feedback joystick, derived from 1980s research at the MIT Media Lab. A force‐feedback joystick adds some tactile realism to computer games, so you feel the rumble of the road in a car‐driving game or the pull of the line in a fishing simulation. Emerging in late 1998 is the ʺtactile mouse,ʺ which operates like a conventional mouse but allows the user to feel the texture of surfaces, objects, even people. One company that I am involved in, Medical Learning Company,

is developing a simulated patient to help train doctors, as well as enable nonphysicians to play doctor. It will include a haptic interface so that you can feel a knee joint for a fracture or a breast for lumps. [17]

A force‐feedback joystick in the tactile domain is comparable to conventional monitors in the visual domain. The

force‐feedback joystick provides a tactile interface, but it does not totally envelop you. The rest of your tactile world is still reminding you of its presence. In order to leave the real world, at least temporarily, we need a tactile environment that takes over your sense of touch.

So letʹs invent a virtual tactile environment. Weʹve seen aspects of it in science fiction films (always a good source for inventing the future). We can build a body suit that will detect your own movements as well as provide high resolution tactile stimulation. The suit will also need to provide sufficient force‐feedback to actually prevent your movements if you are pressing against a virtual obstacle in the virtual environment. If you are giving a virtual companion a hug, for example, you donʹt want to move right through his or her body. This will require a force-feedback structure outside the suit, although obstacle resistance could be provided by the suit itself. And since your body inside the suit is still in the real world, it would make sense to put the whole contraption in a booth so that your movements in the virtual world donʹt knock down lamps and people in your ʺrealʺ vicinity. Such a suit could also provide a thermal response and thereby allow the simulation of feeling a moist surface—or even immersing your hand or your whole body in water—which is indicated by a change in temperature and a decrease in surface tension.

Finally, we can provide a platform consisting of a rotating treadmill device for you to stand (or sit or lie) on, which will allow you to walk or move around (in any direction) in your virtual environment.

So with the suit, the outer structure, the booth, the platform, the goggles, and the earphones, we just about have

the means to totally envelop your senses. Of course, we will need some good virtual reality software, but thereʹs certain to be hot competition to provide a panoply of realistic and fantastic new environments as the requisite hardware becomes available.

Oh yes, there is the sense of smell. A completely flexible and general interface for our fourth sense will require a reasonably advanced nanotechnology to synthesize the wide variety of molecules that we can detect with our olfactory sense. In the meantime, we could provide the ability to diffuse a variety of aromas in the virtual reality booth.

Once we are in a virtual reality environment, our own bodies—at least the virtual versions—can change as well.

We can become a more attractive version of ourselves, a hideous beast, or any creature real or imagined as we interact with the other inhabitants in each virtual world we enter.

Virtual reality is not a (virtual) place you need go to alone. You can interact with your friends there (who would

be in other virtual reality booths, which may be geographically remote). You will have plenty of simulated companions to choose from as well.

Directly Plugging In

Later in the twenty‐first century, as neural implant technologies become ubiquitous, we will be able to create and interact with virtual environments without having to enter a virtual reality booth. Your neural implants will provide the simulated sensory inputs of the virtual environment—and your virtual body—directly in your brain. Conversely,

your movements would not move your ʺrealʺ body, but rather your perceived virtual body. These virtual environments would also include a suitable selection of bodies for yourself. Ultimately, your experience would be highly realistic, just like being in the real world. More than one person could enter a virtual environment and interact with each other. In the virtual world, you will meet other real people and simulated people—eventually, there wonʹt

be much difference.

This will be the essence of the Web in the second half of the twenty‐first century. A typical ʺweb siteʺ will be a perceived virtual environment, with no external hardware required. You ʺgo thereʺ by mentally selecting the site and then entering that world. Debate Benjamin Franklin on the war powers of the presidency at the history society site.

Ski the Alps at the Swiss Chamber of Commerce site (while feeling the cold spray of snow on your face). Hug your

favorite movie star at the Columbia Pictures site. Get a little more intimate at the Penthouse or Playgirl site. Of course, there may be a small charge.

Real Virtual Reality

In the late twenty‐first century, the ʺrealʺ world will take on many of the characteristics of the virtual world through the means of nanotechnology ʺswarms.ʺ Consider, for example, Rutgers University computer scientist J. Storrs Hallʹs

concept of ʺUtility Fog.ʺ [18] Hallʹs conception starts with a little robot called a Foglet, which consists of a human‐cell-sized device with twelve arms pointing in all directions. At the end of the arms are grippers so that the Foglets can grasp one another to form larger structures. These nanobots are intelligent and can merge their computational capacities with each other to create a distributed intelligence. A space filled with Foglets is called Utility Fog and has some interesting properties.

First of all, the Utility Fog goes to a lot of trouble to simulate its not being there. Hall describes a detailed scenario that lets a real human walk through a room filled with trillions of Foglets and not notice a thing. When desired (and itʹs not entirely clear who is doing the desiring), the Foglets can quickly simulate any environment by creating all sorts of structures. As Hall puts it, ʺFog city can look like a park, or a forest, or ancient Rome one day and Emerald City the next.ʺ

The Foglets can create arbitrary wave fronts of light and sound in any direction to create any imaginary visual and

auditory environment. They can exert any pattern of pressure to create any tactile environment. In this way, Utility Fog has all the flexibility of a virtual environment, except it exists in the real physical world. The distributed intelligence of the Utility Fog can simulate the minds of scanned (Hall calls them ʺuploadedʺ) people who are re-created in the Utility Fog as ʺFog people.ʺ In Hallʹs scenario, ʺa biological human can walk through Fog walls, and a Fog (uploaded) human can walk through dumb‐matter walls. Of course Fog people can walk through Fog walls, too.ʺ

The physical technology of Utility Fog is actually rather conservative. The Foglets are much bigger machines than

most nanotechnology conceptions. The software is more challenging, but ultimately feasible. Hall needs a bit of work on his marketing angle: Utility Fog is a rather dull name for such versatile stuff.

There are a variety of proposals for nanotechnology swarms, in which the real environment is constructed from interacting multitudes of nanomachines. In all of the swarm conceptions, physical reality becomes a lot like virtual reality. You can be sleeping in your bed one moment, and have the room transform into your kitchen as you awake.

Actually, change that to a dining room as thereʹs no need for a kitchen. Related nanotechnology will instantly create whatever meal you desire. When you finish eating, the room can transform into a study, or a game room, or a swimming pool, or a redwood forest, or the Taj Mahal. You get the idea.

Mark Yim has built a large‐scale model of a small swarm showing the feasibility of swarm interaction. [19] Joseph

Michael has actually received a U.K. patent on his conception of a nanotechnology swarm, but it is unlikely that his design will be commercially realizable in the twenty‐year life of his patent. [20]

It may seem that we will have too many choices. Today, we have only to choose our clothes, makeup, and destination when we go out. In the late twenty‐first century, we will have to select our body, our personality, our environment—so many difficult decisions to make! But donʹt worry—weʹll have intelligent swarms of machines to guide us.

THE SENSUAL MACHINE

Made double by his lust

he sounds a womanʹs groans.

A figment of his flesh.

—from Barry Spacksʹs poem ʺThe Solitary at Seventeenʺ

I can predict the future by assuming that money and male hormones are the driving forces for new technology.

Therefore, when virtual reality gets cheaper than dating, society is doomed.

—Dogbert

The first book printed from a moveable type press may have been the Bible, but the century following Gutenbergʹs epochal invention saw a lucrative market for books with more prurient topics. [21] New communication

technologies—the telephone, motion pictures, television, videotape—have always been quick to adopt sexual themes.

The Internet is no exception, with 1998 market estimates of adult online entertainment ranging from $185 million by

Forrester Research to $1 billion by Inter@active Week. These figures are for customers, mostly men, paying to view and interact with performers—live, recorded, and simulated. One 1998 estimate cited 28,000 web sites that offer sexual entertainment. [22] These figures do not include couples who have expanded their phone sex to include moving pictures via online video conferencing.

CD‐ROMS and DVD disks constitute another technology that has been exploited for erotic entertainment.

Although the bulk of adult‐oriented disks are used as a means for delivering videos with a bit of interactivity thrown in, a new genre of CD‐ROM and DVD provides virtual sexual companions that respond to some mouse‐administered

fondling. [23] Like most first‐generation technologies, the effect is less than convincing, but future generations will eliminate some of the kinks, although not the kinkiness. Developers are also working to exploit the force‐feed mouse so that you can get some sense of what your virtual partner feels like.

Late in the first decade of the twenty‐first century, virtual reality will enable you to be with your lover—romantic partner, sex worker, or simulated companion—with full visual and auditory realism. You will be able to do anything

you want with your companion except touch, admittedly an important limitation.

Virtual touch has already been introduced, but the all‐enveloping, highly realistic, visual‐auditory‐tactile virtual environment will not be perfected until the second decade of the twenty‐first century. At this point, virtual sex becomes a viable competitor to the real thing. Couples will be able to engage in virtual sex regardless of their physical proximity. Even when proximate, virtual sex will be better in some ways and certainly safer. Virtual sex will provide sensations that are more intense and pleasurable than conventional sex, as well as physical experiences that currently do not exist. Virtual sex is also the ultimate in safe sex, as there is no risk of pregnancy or transmission of disease.

Today, lovers may fantasize their partners to be someone else, but users of virtual sex communication will not need as much imagination. You will be able to change the physical appearance and other characteristics of both yourself and your partner. You can make your lover look and feel like your favorite star without your partnerʹs permission or knowledge. Of course, be aware that your partner may be doing the same to you.

Group sex will take on a new meaning in that more than one person can simultaneously share the experience of

one partner. Since multiple real people cannot all control the movements of one virtual partner, there needs to be a way of sharing the decision making of what the one virtual body is doing. Each participant sharing a virtual body would have the same visual, auditory, and tactile experience, with shared control, of their shared virtual body (perhaps the one virtual body will reflect a consensus of the attempted movements of the multiple participants). A whole audience of people—who may be geographically dispersed—could share one virtual body while engaged in a

sexual experience with one performer.

Prostitution will be free of health risks, as will virtual sex in general. Using wireless, very‐high‐bandwidth communication technologies, neither sex workers nor their patrons need leave their homes. Virtual prostitution is likely to be legally tolerated, at least to a far greater extent than real prostitution is today, as the virtual variety will be impossible to monitor or control. With the risks of disease and violence having been eliminated, there will be far less rationale for proscribing it.

Sex workers will have competition from simulated—computer generated—partners. In the early stages, ʺrealʺ

human virtual partners are likely to be more realistic than simulated virtual partners, but that will change over time.

Of course, once the simulated virtual partner is as capable, sensual, and responsive as a real human virtual partner, whoʹs to say that the simulated virtual partner isnʹt a real, albeit virtual, person?

Is virtual rape possible? In the purely physical sense, probably not. Virtual reality will have a means for users to immediately terminate their experience. Emotional and other means of persuasion and pressure are another matter.

How will such an extensive array of sexual choices and opportunities affect the institution of marriage and the concept of commitment in a relationship? The technology of virtual sex will introduce an array of slippery slopes, and the definition of a monogamous relationship will become far less clear. Some people will feel that access to intense sexual experiences at the click of a mental button will destroy the concept of a sexually committed relationship.

Others will argue, as proponents of sexual entertainment and services do today, that such diversions are healthy outlets and serve to maintain healthy relationships. Clearly, couples will need to reach their own understandings, but drawing clear lines will become difficult with the level of privacy that this future technology affords. It is likely that society will accept practices and activities in the virtual arena that it frowns on in the physical world, as the consequences of virtual activities are often (a though not always) easier to undo.

In addition to direct sensual and sexual contact, virtual reality will be a great place for romance in general. Stroll with your lover along a virtual Champs‐Elysees, take a walk along a virtual Cancun beach, mingle with the animals

in a simulated Mozambique game reserve. Your whole relationship can be in Cyberland.

Virtual reality using an external visual‐auditory‐haptic interface is not the only technology that will transform the nature of sexuality in the twenty‐first century. Sexual robots—sexbots—will become popular by the beginning of the

third decade of the new century. Today, the idea of intimate relations with a robot or doll is not generally appealing because robots and dolls are so, well, inanimate. But that will change as robots gain the softness, intelligence, pliancy, and passion of their human creators. (By the end of the twenty‐first century, there wonʹt be a clear difference between humans and robots. What, after all, is the difference between a human who has upgraded her body and brain using

new nanotechnology and computational technologies, and a robot who has gained an intelligence and sensuality surpassing her human creators?)

By the fourth decade, we will move to an era of virtual experiences through internal neural implants. With this technology, you will be able to have almost any kind of experience with just about anyone, real or imagined, at any

time. Itʹs just like todayʹs online chat rooms, except that you donʹt need any equipment thatʹs not already in your head, and you can do a lot more than just chat. You wonʹt be restricted by the limitations of your natural body as you and your partners can take on any virtual physical form. Many new types of experiences will become possible: A man

can feel what it is like to be a woman, and vice versa. Indeed, thereʹs no reason why you canʹt be both at the same time, making real, or at least virtually real, our solitary fantasies.

And then, of course, in the last half of the century, there will be the nanobot swarms—good old sexy Utility Fog,

for example. The nanobot swarms can instantly take on any form and emulate any sort of appearance, intelligence, and personality that you or it desires‐the human form, say, if thatʹs what turns you on.

THE SPIRITUAL MACHINE

We are not human beings trying to be spiritual. We are spiritual beings trying to be human.

—Jacquelyn Small

Body and soul are twins. God only knows which is which.

—Charles A. Swinburne

Weʹre all lying in the gutter, but some of us are gazing at the stars.

—Oscar Wilde

Sexuality and spirituality are two ways that we transcend our everyday physical reality. Indeed, there are links between our sexual and our spiritual passions, as the ecstatic rhythmic movements associated with some varieties of

spiritual experience suggest.

Mind Triggers

We are discovering that the brain can be directly stimulated to experience a wide variety of feelings that we originally thought could only be gained from actual physical or mental experience. Take humor, for example. In the journal Nature, Dr. Itzhak Fried and his colleagues at UCLA tell how they found a neurological trigger for humor. They were

looking for possible causes for a teenage girlʹs epileptic seizures and discovered that applying an electric probe to a specific point in the, supplementary motor area of her brain caused her to laugh. Initially, the researchers thought that the laughter must be just an involuntary motor response, but they soon realized they were triggering the genuine perception of humor, not just forced laughter. When stimulated in just the right spot of her brain, she found everything funny ʺYou guys are just so funny—standing aroundʺ was a typical comment. [24]

Triggering a perception of humor without circumstances we normally consider funny is perhaps disconcerting (although personally, I find it humorous). Humor involves a certain element of surprise. Blue elephants. The last two words were intended to be surprising, but they probably didnʹt make you laugh (or maybe they did). In addition to

surprise, the unexpected event needs to make sense from an unanticipated but meaningful perspective. And there are

some other attributes that humor requires that we donʹt understand just yet. The brain apparently has a neural net that detects humor from our other perceptions. If we directly stimulate the brainʹs humor detector, then an otherwise ordinary situation will seem pretty funny.

The same appears to be true of sexual feelings. In experiments with animals, stimulating a specific small area of

the hypothalamus with a tiny injection of testosterone causes the animals to engage in female sexual behavior, regardless of gender. Stimulating a different area of the hypothalamus produces male sexual behavior.

These results suggest that once neural implants are commonplace, we will have the ability to produce not only virtual sensory experiences but also the feelings associated with these experiences. We can also create some feelings not ordinarily associated with the experience. So you will be able to add some humor to your sexual experiences, if

desired (of course, for some of us humor may already be part of the picture).

The ability to control and to reprogram our feelings will become even more profound in the late twenty‐first century when technology moves beyond mere neural implants and we fully install our thinking processes into a new

computational medium—that is, when we become software.

We work hard to achieve feelings of humor, pleasure, and well‐being. Being able to call them up at will may seem

to rob them of their meaning. Of course, many people use drugs today to create and enhance certain desirable feelings, but the chemical approach comes bundled with many undesirable effects. With neural implant technology,

you will be able to enhance your feelings of pleasure and well‐being without the hangover. Of course, the potential

for abuse is even greater than with drugs. When psychologist James Olds provided rats with the ability to press a button and directly stimulate a pleasure center in the limbic system of their brains, the rats pressed the button endlessly, as often as five thousand times an hour, to the exclusion of everything else, including eating. Only falling asleep caused them to stop temporarily. [25]

Nonetheless, the benefits of neural implant technology will be compelling. As just one example, millions of people

suffer from an inability to experience sufficiently intense feelings of sexual pleasure, which is one important aspect of impotence. People with this disability will not pass up the opportunity to overcome their problem through neural implants, which they may already have in place for other purposes. Once a technology is developed to overcome a disability, there is no way to restrict its use from enhancing normal abilities, nor would such restrictions necessarily be desirable. The ability to control our feelings will be just another one of those twenty‐first‐century slippery slopes.

So What About Spiritual Experiences?

The spiritual experienced—a feeling of transcending oneʹs everyday physical and mortal bounds to sense a deeper reality—plays a fundamental role in otherwise disparate religions and philosophies. Spiritual experiences are not all of the same sort but appear to encompass a broad range of mental phenomena. The ecstatic dancing of a Baptist revival appears to be a different phenomenon than the quiet transcendence of a Buddhist monk. Nonetheless, the notion of the spiritual‐experience has been reported so consistently throughout history, and in virtually all cultures and religions, that it represents a particularly brilliant flower in the phenomenological garden.

Regardless of the nature and derivation of a mental experience, spiritual or otherwise, once we have access to the

computational processes that give rise to it, we have the opportunity to understand its neurological correlates. With the understanding of our mental processes will come the opportunity to capture our intellectual, emotional, and spiritual experiences, to call them up at will, and to enhance them.

Spiritual Experience Through Brain Generated Music

There is already one technology that appears to generate at least one aspect of a spiritual experience. This experimental technology is called Brain Generated Music (BGM), pioneered by Neurosonics, a small company in Baltimore, Maryland, of which I am a director. BGM is a brain‐wave biofeedback system capable of evoking an experience called the Relaxation Response, which is associated with deep relaxations The BGM user attaches three disposable leads to her head. A personal computer then monitors the userʹs brain waves to determine her unique alpha wavelength. Alpha waves, which are in the range of eight to thirteen cycles per second (cps), are associated with a deep meditative state, as compared to beta waves (in the range of thirteen to twenty‐eight cps), which are associated with routine conscious thought. Music is then generated by the computer, according to an algorithm that

transforms the userʹs own brain wave signal.

The BGM algorithm is designed to encourage the generation of alpha waves by producing pleasurable harmonic

combinations upon detection of alpha waves, and less pleasant sounds and sound combinations when alpha detection

is low. In addition, the fact that the sounds are synchronized to the userʹs own alpha wavelength to create a resonance with the userʹs own alpha rhythm also encourages alpha production.

Dr. Herbert Benson, formerly the director of the hypertension section of Bostonʹs Beth Israel Hospital and now at

New England Deaconess Hospital in Boston, and other researchers at the Harvard Medical School and Beth Israel, discovered the neurological‐physiological mechanism of the Relaxation Response, which is described as the opposite

of the ʺfight or flight,ʺ or stress response. [27] The Relaxation Response is associated with reduced levels of epinephrine (adrenaline) and norepinephrine (noradrenaline), blood pressure, blood sugar, breathing, and heart rates. Regular elicitation of this response is reportedly able to produce permanently lowered blood‐pressure levels (to the extent that hypertension is caused by stress factors) and other health benefits. Benson and his colleagues have catalogued a number of techniques that can elicit the Relaxation Response, including yoga and a number of forms of

meditation.

I have had experience with meditation, and in my own experience with BGM, and in observing others, BGM does

appear to evoke the Relaxation Response, The music itself feels as if it is being generated from inside your mind.

Interestingly, if you listen to a tape recording of your own brain‐generated music when you are not hooked up to the computer, you do not experience the same sense of transcendence. Although the recorded BGM is based on your personal alpha wavelength, the recorded music was synchronized to the brain waves that were produced by your brain when the music was first generated, not to the brain waves that are produced while listening to the recording.

You need to listen to ʺliveʺ BGM to achieve the resonance effect.

Conventional music is generally a passive experience. Although a performer may be influenced in subtle ways by

her audience, the music we listen to generally does not reflect our response. Brain Generated Music represents a new modality of music that enables the music to evolve continually based on the interaction between it and our own mental responses to it.

Is BGM producing a spiritual experience? Itʹs hard to say. The feelings produced while listening to ʺliveʺ BGM are

similar to the deep transcendent feelings I can sometimes achieve with meditation, but they appear to be more reliably produced by BGM.

The God Spot

Neuroscientists from the University of California at San Diego have found what they call the God module, a tiny locus of nerve cells in the frontal lobe that appears to be activated during religious experiences. They discovered this neural machinery while studying epileptic patients who have intense mystical experiences during seizures.

Apparently the intense neural storms during a seizure stimulate the God module. Tracking surface electrical activity in the brain with highly sensitive skin monitors, the scientists found a similar response when very religious nonepileptic persons were shown words and symbols evoking their spiritual beliefs. A neurological basis for spiritual experience has long been postulated by evolutionary biologists, because of the social utility of religious belief. In response to reports of the San Diego research, Richard Harries, the Bishop of Oxford, said through a spokesman that

ʺit would not be surprising if God had created us with a physical facility for belief.ʺ [28]

When we can determine the neurological correlates of the variety of spiritual experiences that our species is capable of, we are likely to be able to enhance these experiences in the same way that we will enhance other human

experiences. With the next stage of evolution creating a new generation of humans that will be trillions of times more capable and complex than humans today, our ability for spiritual experience and insight is also likely to gain in power and depth.

Just being—experiencing, being conscious—is spiritual, and reflects the essence of spirituality. Machines, derived

from human thinking and surpassing humans in their capacity for experience, will claim to be conscious, and thus to

be spiritual. They will believe that they are conscious. They will believe that they have spiritual experiences. They will be convinced that these experiences are meaningful. And given the historical inclination of the human race to anthropomorphize the phenomena we encounter, and the persuasiveness of the machines, weʹre likely to believe them when they tell us this.

Twenty‐first‐century machines—based on the design of human thinking—will do as their human progenitors

have done—going to real and virtual houses of worship, meditating, praying, and transcending—to connect with their spiritual dimension.

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LETʹS JUST GET ONE THING STRAIGHT: THEREʹS NO WAY IʹM GOING TO HAVE SEX WITH A COMPUTER.

Hey, letʹs not jump to conclusions. You should keep an open mind.

IʹLL TRY TO HAVE AN OPEN MIND. AN OPEN BODY IS ANOTHER MATTER. THE IDEA OF GETTING

INTIMATE WITH SOME GADGET, NO MATTER HOW CLEVER, IS NOT VERY APPEALING.

Have you ever spoken to a phone?

TO A PHONE? I MEAN I TALK TO PEOPLE USING A PHONE.

Okay, so a computer circa 2015—in the form of a visual‐auditory‐tactile virtual reality communication device—is just a telephone for you and your lover. But you can do more than just talk.

I LIKE TO TALK TO MY LOVER—WHEN I HAVE ONE—BY PHONE. AND LOOKING AT EACH OTHER WITH A

PICTURE PHONE, OR EVEN A FULL VIRTUAL REALITY SYSTEM, SOUNDS PRETTY COZY. AS FOR YOUR

TACTILE IDEA, HOWEVER, I THINK IʹLL STICK TO TOUCHING MY FRIENDS AND LOVERS WITH REAL

FINGERS.

You can use real fingers with virtual reality, or at least real virtual fingers. But what about when you and your lover are separated?

YOU KNOW, DISTANCE MAKES THE HEART GROW FONDER. ANYWAY, WE DONʹT HAVE TO TOUCH ALL

THE TIME, I MEAN IʹLL BE ABLE TO WAIT UNTIL I GET BACK FROM MY BUSINESS TRIP, WHILE HEʹS

TAKING CARE OF THE KIDS!

When virtual reality does evolve into a convincing, all‐encompassing tactile interface, are you going to go out of your way to avoid any physical contact?

I SUPPOSE IT WOULDNʹT HURT TO KISS GOODNIGHT.

Ah‐ha—the slippery slope! So why stop there?

OKAY, TWO KISSES.

Sure, like I just said, keep an open mind.

SPEAKING OF AN OPEN MIND, YOUR DESCRIPTION OF THE ʺGOD SPOTʺ SEEMS TO TRIVIALIZE THE

SPIRITUAL EXPERIENCE.

I wouldnʹt overreact to this one piece of research. Clearly, somethingʹs going on in the brains of people who are having a spiritual experience. Whatever the neurological process is, once we capture and understand it, we should be able to enhance the spiritual experiences in a re‐created brain running in its new computational medium.

SO THESE RE‐CREATED MINDS WILL REPORT HAVING SPIRITUAL EXPERIENCES. AND I SUPPOSE THEY

WILL ACT IN THE SAME SORT OF TRANSCENDENT, RAPTUROUS WAY THAT PEOPLE DO TODAY WHEN

REPORTING SUCH EXPERIENCES. BUT WILL THESE MACHINES REALLY BE TRANSCENDING, AND

EXPERIENCING THE FEELING OF GODʹS PRESENCE? WHAT WILL THEY BE EXPERIENCING, ANYWAY?

We keep coming back to the issue of consciousness. Machines in the twenty‐first century will report the same range of experiences that humans do. In accordance with the Law of Accelerating Returns, they will report an even broader range. And they will be very convincing when they speak of their experiences. But what will they really be feeling?

As I said earlier, thereʹs just no way to truly penetrate another entityʹs subjective experience, at least not in a scientific way I mean, we can observe the patterns of neural firings, and so forth, but thatʹs still just an objective observation.

WELL, THATʹS JUST THE LIMITATION OF SCIENCE.

Yes, thatʹs where philosophy and religion are supposed to take over. Of course, itʹs hard enough to get agreement on scientific issues.

THAT OFTEN APPEARS TO BE TRUE. NOW, ANOTHER THING IʹM NOT TOO HAPPY ABOUT IS THESE

PILLAGING NANOBOTS THAT ARE GOING TO MULTIPLY WITHOUT END. WEʹLL END UP WITH A HUGE

SEA OF NANOBOTS. WHEN THEYʹRE DONE WITH US, THEYʹLL START EATING EACH OTHER.

There is that danger. But if we write the software carefully . . .

OH SURE, LIKE MY OPERATING SYSTEM. ALREADY I HAVE LITTLE SOFTWARE VIRUSES THAT MULTIPLY

THEMSELVES UNTIL THEY CLOG UP MY HARD DRIVE.

I still think the bigger danger is in their intentional hostile use.

I KNOW YOU SAID THAT, BUT THATʹS NOT EXACTLY REASSURING. AGAIN, WHY DONʹT WE JUST NOT GO

DOWN THIS PARTICULAR ROAD?

Okay, you tell that to the old woman whose crumbling bones will be effectively treated using a nanotechnology-based treatment, or the cancer patient whose cancer is destroyed by little nanobots that swim through his blood vessels.

I REALIZE THERE ARE A LOT OF POTENTIAL BENEFITS, BUT THE EXAMPLES YOU JUST GAVE CAN ALSO BE

ADDRESSED THROUGH OTHER, MORE CONVENTIONAL, TECHNOLOGIES, LIKE BIOENGINEERING.

Iʹm glad you mentioned bioengineering, because we see a very similar problem with bioengineered weapons. Weʹre

very close to the point where the knowledge and equipment in a typical graduate‐school biotechnology program will

be sufficient to create self‐replicating pathogens. Whereas a nanoengineered weapon could replicate across any matter, living and dead, a bioengineered weapon would only replicate across living matter, probably just its human

targets. I understand thatʹs not much comfort. In either case, the potential for uncontrolled self‐replication greatly multiplies the danger.

But youʹre not going to stop bioengineering—itʹs the cutting edge of our medical research. It has already greatly

contributed to the AIDS treatments we have today; diabetic patients use bioengineered forms of human insulin; there

are effective cholesterol‐lowering drugs; there are promising new cancer treatments; and the list of advances is rapidly growing. There is genuine optimism among otherwise skeptical scientists that we will make dramatic gains against cancer and other scourges with bioengineered treatments.

SO HOW ARE WE GOING TO PROTECT OURSELVES FROM BIOENGINEERED WEAPONS?

With more bioengineering—antiviral drugs, for example.

AND NANOENGINEERED WEAPONS?

Same thing—more nanotechnology.

I HOPE THE GOOD NANOBOTS PREVAIL, BUT JUST WONDER HOW WEʹRE GOING TO TELL THE GOOD

NANOBOTS FROM THE BAD ONES.

Itʹs going to be hard to tell, particularly since the nanobots are too small to see.

EXCEPT BY OTHER NANOBOTS, RIGHT?

Good point.

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

C H A P T E R E I G H T

1999

THE DAY THE COMPUTERS STOPPED

The digitization of information in all of its forms will probably be known as the most fascinating development of the twentieth century.

—An Wang

Economics, sociology, geopolitics, art, religion all provide powerful tools that have sufficed for centuries to explain the essential surfaces of life. To many observers, there seems nothing truly new under the sun—no need for a deep understanding of manʹs new tools—no requirement to descend into the microcosm of modern electronics in order to

comprehend the world. The world is all too much with us.

—George Gilder

If all the computers in 1960 stopped functioning, few people would have noticed. A few thousand scientists would have seen a delay in getting printouts from their last submission of data on punch cards. Some business reports would have been held up. Nothing to worry about.

Circa 1999 is another matter. If all computers stopped functioning, society would grind to a halt. First of all, electric power distribution would fail. Even if electrical power continued (which it wouldnʹt), virtually everything would still break down. Most motorized vehicles have embedded microprocessors, only cars that would run would

be quite old. There would be almost no functioning trucks, buses, railroads, subways, or airplanes. There would be no electronic communication: Telephones, radio, television, fax machines, pagers, e‐mail, and of course the Web would

all cease functioning. You wouldnʹt get your paycheck. You couldnʹt cash it if you did. You wouldnʹt be able to get

your money out of your bank. Business and government would operate at only the most primitive level. And if all the

data in all the computers vanished, then weʹd really be in trouble.

There has been substantial concern with Y2K (Year 2000 Problem), that at least some computer processes will be

disrupted as we approach the year 2000. Y2K primarily concerns software developed one or more decades ago in which date fields used only two digits, which will cause these programs to behave erratically when the year becomes

ʺ00.ʺ I am more sanguine than some about this particular issue. Y2K is causing the urgent rewriting of old business

programs that needed to be dusted off and redesigned anyway. There will be some disruptions (and a lot of litigation), but in my view Y2K is unlikely to cause the massive economic problems that are feared. [1]

In less than forty years, we have gone from manual methods of controlling our lives and civilization to becoming

totally dependent on the continued operation of our computers. Many people are comforted by the fact that we still

have our hand on the ʺplug,ʺ that we can turn our computers off if they get too uppity. In actuality, itʹs the computers that have their figurative hands on our plug. (Give them a couple more decades and their hands wonʹt be so figurative.)

There is little concern about this today—computers circa 1999 are dependable, docile, and dumb. The

dependability (albeit not perfect) is likely to remain. The dumbness will not. It will be the humans, at least the nonupdated ones, who will seem dumb several decades from now. The docility will not remain, either.

For a rapidly increasing array of specific tasks the intelligence of contemporary computers appears impressive, even formidable, but machines today remain narrow‐minded and brittle. In contrast, we humans have softer landings

when we wander outside our own narrow areas of expertise. Unlike Deep Blue, Gary Kasparov is not incompetent in

matters outside of chess.

Computers are rapidly moving into increasingly diverse realms. I could fill a dozen books with examples of the

intellectual prowess of computers circa end of the twentieth century, but I have only a contract for one, so letʹs take a look at a few artful examples.

THE CREATIVE MACHINE

At a time like ours, in which mechanical skill has attained unsuspected perfection, the most famous works may be heard as easily as one may drink a glass of beer and it only costs ten centimes, like the automatic weighing machines.

Should we not fear this domestication of sound, this magic that anyone can bring from a disk at will? Will it not bring to waste the mysterious force of an art which one might have thought indestructible?

—Claude Debussy Collaboration with machines!

What is the difference between manipulation of the machine and collaboration with it? . . . Suddenly, a window would open into a vast field of possibilities; the time limits would vanish, and the machines would seem to become humanized components of the interactive network now consisting of oneself and the machine still obedient but full of suggestions of the master controls of the imagination.

—Vladimir Ussachevsky

Somebody was saying to Picasso that he ought to make pictures of things the way they are—objective pictures. He

mumbled he wasnʹt quite sure what that would be. The person who was bullying him produced a photograph of his

wife from his wallet and said, ʺThere, you see, that is a picture of how she really is.ʺ Picasso looked at it and said, ʺShe is rather small, isnʹt she? And flat?ʺ

—Gregory Bateson

The age of the cybernetic artist has begun, although it is at an early stage. As with human artists, you never know

what these creative systems are going to do next. To date, however, none of them has cut off an ear or run naked through the streets. They donʹt yet have bodies to demonstrate that sort of creativity.

The strength of these systems is reflected by an often startling originality in a turn of a phrase, shape, or musical line. Their weakness has to do, again, with context, or the lack thereof. Since these creative computers are deficient in the real‐world experience of their human counterparts, they often lose their train of thought and ramble off into incoherence. Perhaps the most successful in terms of maintaining thematic consistency throughout a work of art is Harold Cohenʹs robotic painter named Aaron, which I discuss below. The primary reason Aaron is so successful is the

thoroughness of its extensive knowledge base, which Cohen has been building, rule by rule, for three decades.

Jamming with Your Computer

The frequent originality of these systems makes them great collaborators with human artists, and in this manner, computers have already had a transforming effect on the arts. This trend is furthest along in the musical arts. Music has always used the most advanced technologies available; the cabinet‐making crafts of the eighteenth century; the metalworking industries of the nineteenth century; and the analog electronics of the 1960s. Today, virtually all commercial music—recordings, movie and television soundtracks—is created on computer music workstations,

which synthesize and process the sounds, record and manipulate the note sequences, generate notation, even automatically generate rhythmic patterns, walking bass lines, and melodic progressions and variations.

Up until recently, instrument‐playing technique was inextricably linked to the sounds created. If you wanted violin sounds, you had to play the violin. The playing techniques derived from the physical requirements of creating the sounds. Now that link has been broken, If you like flute‐playing technique, or just happen to have learned it, you can now use an electronic wind controller that plays just like an acoustic flute yet creates the sounds not only of a variety of flutes, but also of virtually any other instrument, acoustic or electronic. There are now controllers that emulate the playing technique of most popular acoustic instruments, including piano, violin, guitar, drums, and a variety of wind instruments. Since we are no longer limited by the physics of creating sounds acoustically, a new generation of controllers is emerging that bears no resemblance to any conventional acoustic instruments, but instead attempts to optimize the human factors of creating music with our fingers, arms, feet, mouth, and head. All sounds

can now be played polyphonically and can be layered (played simultaneously) and sequenced with one another.

Also, it is no longer necessary to play music in real time—music can be performed at one speed and played back at

another, without changing the pitch or other characteristics of the notes. All sorts of age‐old limitations have been overcome, allowing a teenager in her bedroom to sound like a symphony orchestra or rock band.

A Musical Turing Test

In 1997, Steve Larson, a University of Oregon music professor, arranged a musical variation of the Turing Test by having an audience attempt to determine which of three pieces of music had been written by a computer and which

one of the three had been written two centuries ago by a human named Johann Sebastian Bach. Larson was only slightly insulted when the audience voted that his own piece was the computer composition, but he felt somewhat vindicated when the audience selected the piece written by a computer program named EMI (Experiments in Musical

Intelligence) to be the authentic Bach composition. Douglas Hofstadter, a longtime observer of (and contributor to) the progression of machine intelligence, calls EMI, created by the composer David Cope, ʺthe most thought-provoking project in artificial intelligence that I have ever come across.ʺ [2]

Perhaps even more successful is a program called Improvisor, written by Paul Hodgson, a British jazz saxophone

player. Improvisor can emulate styles ranging from Bach to jazz greats Louis Armstrong and Charlie Parker. The program has attracted its own following. Hodgson himself says, ʺIf I was new in town and heard someone playing like Improvisor, Iʹd be happy to join in.ʺ [3]

The weakness of todayʹs computerized composition is, again, a weakness of context. ʺIf I turn on three seconds of

EMI and ask myself, ʺWhat was that?ʹ I would say Bach,ʺ says Hofstadter. Longer passages are not always so successful. Often ʺitʹs like listening to random lines from a Keats sonnet. You wonder what was happening to Keats

that day. Was he completely drunk?ʺ

The Literary Machine

Hereʹs a question for you: What kind of murderer has fiber?

The answer: A cereal killer.

I hasten to admit that I did not make up this pun myself. It was written by a computer program called JAPE (Joke

Analysis and Production Engine), created by Kim Binsted. JAPE is the state of the art in the automatic writing of bad puns. Unlike EMI, JAPE did not pass a modified Turing Test when it was recently paired with human comedian Steve

Martin. The audience preferred Martin. [4]

The literary arts lag behind the musical arts in the use of technology. This may have to do with the depth and complexity of even routine prose, a quality which Turing recognized when he based his Turing Test on the ability of

humans to generate convincing written language. Computers are nonetheless of significant practical benefit to those

of us who create written works. Of greatest impact is the simple word processor. Not an artificial technology per se, word processing was derived from the text editors developed during the 1960s at the AI labs at MIT and elsewhere.

This book certainly benefited from the availability of linguistic databases, spell checkers, online dictionaries, not to mention the vast research resources of the World Wide Web. Much of this book was dictated to my personal computer using a continuous speech‐recognition program called Voice Xpress Plus from the dictation division of Lernout & Hauspie (formerly Kurzweil Applied Intelligence), which became available in the middle of my writing the book. With regard to automatic grammar and style checkers, I was forced to turn that particular Microsoft Word feature off, as it seemed to dislike most of my sentences. Iʹll leave the stylistic criticism of this book to my human readers (at least this time around).

A variety of programs help writers brainstorm. Paramind, for example, produces new ideas from your ideas,ʺ

according to its own literature. [50] Other programs allow writers to track the complex histories, characterizations, and interactions of characters in such extended works of fiction as long novels, series of novels, and television drama series.

Programs that write completely original works are particularly challenging because human readers are keenly aware of the myriad syntactic and semantic requirements for sensible written language. Musicians, cybernetic or otherwise, can get away with a bit more inconsistency than authors.

With that in mind, consider the following:

A Story of Betrayal

Dave Striver loved the university. He loved its ivy‐covered clock towers, its ancient and sturdy brick, and its sun-splashed verdant greens and eager youth. He also loved the fact that the university is free of the stark unforgiving trials of the business world—only this isnʹt a fact: Academia has its own tests, and some are as merciless as any in the marketplace. A prime example is the dissertation defense: To earn the Ph.D., to become a doctor, one must pass an oral examination on oneʹs dissertation. This was a test Professor Edward Hart enjoyed giving.

Dave wanted desperately to be a doctor. But he needed the signatures of three people on the first page of his dissertation, the priceless inscriptions which, together, would certify that he had passed his defense. One of the signatures had to come from Professor Hart, and Hart had often said—to others and to himself—that he was honored

to help Dave secure his well‐earned dream.

Well before the defense, Striver gave Hart a penultimate copy of his thesis. Hart read it and told Dave that it was

absolutely first‐rate, and that he would gladly sign it at the defense. They even shook hands in Hartʹs book‐lined office. Dave noticed that Hartʹs eyes were bright and trustful, and his bearing paternal.

At the defense, Dave thought that he eloquently summarized chapter 3 of his dissertation. There were two questions, one from Professor Rogers and one from Dr. Meteer; Dave answered both, apparently to everyoneʹs satisfaction. There were no further objections.

Professor Rogers signed. He slid the tome to Meteer; she too signed, and then slid it in front of Hart. Hart didnʹt

move.

ʺEd?ʺ Rogers said.

Hart still sat motionless. Dave felt slightly dizzy ʺEdward, are you going to sign?ʺ

Later, Hart sat alone in his office, in his big leather chair, saddened by Daveʹs failure. He tried to think of ways he could help Dave achieve his dream.

Okay, thatʹs the end. Admittedly the story kind of peters out, ending with a whimper rather than a bang. Seattle

writer and editor Susan Mulcahy called the story ʺamateurish,ʺ criticizing the authorʹs grammar and word choice. But Mulcahy was nonetheless surprised and impressed when she learned the author was a computer. The program that

wrote the story, named BRUTUS.1, was created by Selmer Bringsjord, Dave Ferucci, and a team of software engineers

at Rensselaer Polytechnic Institute. Apparently, BRUTUS.1 is an expert on betrayal, a concept that Bringsjord and Ferucci spent eight years painstakingly teaching the computer. The researchers acknowledge that their program needs to learn about other issues besides betrayal. ʺInterestingness really combines all of the emotions,ʺ say Bringsjord and Ferucci, and that is something the cybernetic authors are not yet capable of achieving. [6]

The Cybernetic Poet

Another example of a computerized author is a computer program that I designed called Ray Kurzweilʹs Cybernetic

Poet (RKCP). RKCP is a computer‐generated poetry system, which uses language‐modeling techniques to

automatically generate completely original poetry based on poems that it has ʺread. [7]

RKCP reads a selection of poems by a particular author or authors (preferably an extensive selection) and then creates a ʺlanguage modelʺ of that authorʹs work based on Markov models, a mathematical cousin of neural nets.

RKCP can then write original poems from that model. As I discussed earlier, RKCP uses a recursive poetry-generation algorithm to achieve the language style, rhythm patterns, and poem structure of the original authors whose poems were analyzed. There are also algorithms to maintain thematic consistency through the poem. The poems are in a similar style to the author(s) originally analyzed but are completely original new poetry. The system even has rules to discourage itself from plagiarizing.

You can download a free copy of RKCP at www.kurzweiltech.com.

Following is a small sampling of poems written by Ray Kurzweilʹs Cybernetic Poet after it had ʺreadʺ poems by

several famous poets, as well as, lesser‐known contemporary poets.

Poems by Ray Kurzweilʹs Cybernetic Poet

PAGE

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Wendy Dennis

Sashay down the page

through the lioness

nestled in my soul.

IMAGINE NOW AND SING

A poem written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Wendy Dennis and Ray Kurzweil and love poems by various authors

Imagine now and sing,

creating myths

forming jewels from the falling snow.

SOUL

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

John Keats and Wendy Dennis

You broke my soul

the juice of eternity,

the spirit of my lips.

I THINK IʹLL CRASH

A poem written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Ray Kurzweil, Robert Frost, and Wendy Dennis

I think Iʹll crash.

Just for myself with God

peace on a curious sound

for myself in my heart?

And life is weeping

From a bleeding heart

of boughs bending

such paths of them,

of boughs bending

such paths of breeze

knows weʹve been there

THE STIFLING STUFFY

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems

by Patricia Camarena Rose and Wendy Dennis

The stifling stuffy

Catholic schoolroom,

where I cannot be real.

WONDERED

A poem written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Dave Gitomer

today i wondered

if i mused

today i saw you

i learned

in awe and you

if i mused

today i had one wish

if i saw you

if i saw you

if i had one wish

MOON CHILD

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Kathleen Frances Wheeler

Crazy moon child

Hide from your coffin

To spite your doom.

ANGEL

A poem written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Patricia Camarena Rose and Sue Klapes Kelly

Beams of the dawn at the angel

with a calm, silent sea

with a hundred times we write,

with a chance we can open up

a steady rhythm in his face

silent room

desolate beach,

Scattering remains of love.

TOMCAT

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Randi and Kathryn Lynn

An old yellow tomcat

lies sleeping content,

he rumbles a heart

LONG YEARS HAVE PASSED

A poem written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Randi and Kathryn Lynn

Long years have passed.

I think of goodbye.

Locked tight in the night

I think of passion;

Drawn to for blue, the night

During the page

My shattered pieces of life

watching the joy

shattered pieces of love

My shattered pieces of love

gone stale.

SANDALS

A haiku written by Ray Kurzweilʹs Cybernetic Poet after reading poems by

Kimberly Mclauchlin and Ray Kurzweil

Scattered sandals

a call back to myself,

so hollow I would echo.

The Visual Arts

The visual arts lie in between the musical arts and literary arts in terms of the impact of computers. A great deal of visual art is created using paint and illustrator programs, which can simulate the effects of conventional materials such as paint strokes as well as implement a wide range of techniques that could only be executed on a computer.

Recently, computers have also taken over most video and film editing.

The Web is filled with the artistic musings of cybernetic artists. A popular technique is the evolutionary algorithm, which allows the computer to evolve a picture by redoing it hundreds or thousands of times. Humans would find this

approach difficult they would waste a lot of paint, for one thing. Mutator, the creation of sculptor William Latham and software engineer Stephen Todd at IBM in Winchester, England, uses the evolutionary approach, as does a program written by Karl Sims, an artist and scientist at Genetic Arts, in Cambridge, Massachusetts. [8]

Probably the leading practitioner of computer‐generated visual art is Harold Cohen. His computerized robot named Aaron has been evolving and creating drawings and paintings for twenty years. These works of visual art are

completely original, created entirely by the computer, and rendered with real paint. Cohen has spent more than three decades endowing his program with a knowledge of many aspects of the artistic process, including composition, drawing, perspective, and color, as well as a variety of styles. While Cohen wrote the program, the pictures created are nonetheless always a surprise to him.

Cohen is frequently asked who should be given credit for the results of his enterprise, which have been displayed

in museums around the world. [9] Cohen is happy to take the credit, and Aaron has not been programmed to complain. Cohen boasts that he will be the first artist in history who will be able to have a posthumous exhibition of completely original works. [10]

Paintings by Aaron by Cohen

These five original paintings were painted by Aaron, a computerized robot built and programmed by Harold Cohen.

These color paintings are reproduced here in black and white. You can see the color versions on this bookʹs web site, at www.penguinputnam.com/kurzweil. [11]

Full‐size versions of these, along with 11 more Aaron paintings, can be downloaded at

http://www.kurzweilcyberart.com/aaron/static.html –ed.

PREDICTIONS OF THE PRESENT

With the impending millennium change there are no shortage of anticipations of what the next century will be like.

Futurism has a long history, but not a particularly impressive one. One of the problems with predictions of the future is that by the time itʹs clear that they have had little resemblance to actual events, itʹs too late to get your money back.

Perhaps the problem is that we let just anyone make predictions. Maybe we should require futurism certification

to be allowed to prognosticate. One of the requirements could be that in retrospect, at least half of your ten‐or‐more-year‐ahead predictions have not been completely embarrassing. Such a certification program would be a slow process, however, and I suspect unconstitutional.

To see why futurism has such a spotty reputation, here is a small sample of predictions from some otherwise intelligent people:

ʺThe telephone has too many shortcomings to be seriously considered as a means of communication.ʺ

—Western Union executive, 1876

ʺHeavier‐than‐air flying machines are not possible.ʺ

—Lord Kelvin, 1895

ʺThe most important fundamental laws and facts of physical science have all been discovered, and these are now so firmly established that the possibility of their ever being supplemented by new discoveries is exceedingly remote.ʺ

—Albert Abraham Michelson, 1903

ʺAirplanes have no military value.ʺ

—Professor Marshal Foch, 1912

ʺI think there is a world market for maybe five computers.ʺ

—IBM Chairman Thomas Watson, 1943

ʺComputers in the future may weigh no more than 1.5 tons.ʺ

—Popular Mechanics, 1949

ʺIt would appear that we have reached the limits of what is possible to achieve with computer technology, although one should be careful with such statements, as they tend to sound pretty silly in five years.ʺ

—John von Neumann, 1949

ʺThereʹs no reason for individuals to have a computer in their home.ʺ

—Ken Olson, 1977

ʺ640,000 bytes of memory ought to be enough for anybody.ʺ

—Bill Gates, 1981

ʺLong before the year 2000, the entire antiquated structure of college degrees, majors and credits will be a shambles.ʺ

—Alvin Toffler

ʺThe Internet will catastrophically collapse in 1996.ʺ

—Robert Metcalfe (inventor of Ethernet), who, in 1997,

ate his words (literally) in front of an audience

Now I get to toot my own horn, and can share with you those predictions of mine that worked out particularly well. But in looking back at the many predictions Iʹve made over the past twenty years, I will say that I havenʹt found any that I find particularly embarrassing (except, maybe, for a few early business plans).

The Age of Intelligent Machines, which I wrote in 1987 through 1988, as well as other articles and speeches I wrote in the late 1980s, contained a lot of my predictions about the 1990s, which included the following: [12]

• Prediction: A computer will defeat the human world chess champion around 1998, and weʹll think less of

chess as a result.

What Happened: As I mentioned, this one was a year off. Sorry.

• Prediction: There will be a sustained decline in the value of commodities (that is, material resources) with most new wealth being created in the knowledge content of products and services, leading to sustained economic growth and prosperity.

What Happened: As predicted, everything is coming up roses (except, as also predicted, for long‐term investors in commodities, which are down 40 percent over the past decade). Even the approval ratings of

politicians from the president to the Congress are at an all‐time high. But the strong economy has more to

do with the Bill in the west coast Washington than the Bill in the east coast Washington. Not that Mr. Gates

deserves primary credit, but the driving economic force in the world today is information, knowledge, and

related computer technologies. Federal Reserve Chairman Alan Greenspan recently acknowledged that

todayʹs unprecedented sustained prosperity and economic expansion is due to the increased efficiency

provided by information technology. But thatʹs only half right. Greenspan ignores the fact that most of the

new wealth that is being created is itself comprised of information and knowledge—a trillion dollars in Silicon Valley alone. Increased efficiency is only part of the story. The new wealth in the form of the market

capitalization of computer‐related (primarily software) companies is real and substantial and is lifting all boats.

• The U.S. House Subcommittee on Banking reported that in the eight‐year period between 1989 and 1997, the

total value of U.S. real estate and durable goods increased only 33 percent, from $9.1 trillion to $12.1 trillion.

The value of bank deposits and credit market instruments increased only 27 percent, from $4.5 trillion to $5.7 trillion. The value of equity shares, however, increased a staggering 239 percent, from $3.4 trillion to $11.4 trillion! The primary engine of this increase is the rapidly increasing knowledge content of products

and services, as well as the increased efficiencies fostered by information technology. This is where new wealth is being created.

Information and knowledge are not limited by the availability of material resources, and in accordance

with the Law of Accelerating Returns will continue to grow exponentially The Law of Accelerating Returns

includes financial returns. Thus a key implication of the law is continuing economic growth.

As this book is being written, there has been considerable attention on an economic crisis in Japan and

other countries in Asia. The United States has been pressing Japan to stimulate its economy with tax cuts and government spending. Little attention is being paid, however, to the root cause of the crisis, which is

the state of the software industry in Asia, and the need for effective entrepreneurial institutions that promote the creation of software and other forms of knowledge. These include venture and angel capital,

[13] widespread distribution of employee‐stock options, and incentives that encourage and reward risk

taking. Although Asia has been moving in this direction, these new economic imperatives have grown more

rapidly than most observers expected (and their importance will continue to escalate in accordance with the

Law of Accelerating Returns).

• Prediction: A worldwide information network linking almost all organizations and tens of millions of individuals will emerge (admittedly, not by the name World Wide Web)

What Happened: The Web emerged in 1994 and took off in 1995 through 1996. The Web is truly a worldwide

phenomenon, and products and services in the form of information swirl around the globe oblivious to

borders of any kind. A 1998 report by the U.S. Commerce Department credited the Internet as a key factor

in spurring economic growth and curbing inflation. It predicted that commerce on the Internet will surpass

$300 billion by 2000. Industry reports put the figure at around $1 trillion, when all business‐to‐business transactions conducted over the Web are taken into consideration.

• Prediction: There will be a national movement to wire our classrooms.

What Happened: Most states (with the exception, unfortunately, of my own state of Massachusetts) have $50

to $100 million annual budgets to wire classrooms and install related computers and software. It is a national priority to provide computer and Internet access to all students. Many teachers remain relatively computer illiterate, but the kids are providing much of the needed expertise.

• Prediction: In warfare, there will be almost total reliance on digital imaging, pattern recognition, and other software‐based technologies. The side with the smarter machines will win. ʺA profound change in military

strategy will arrive in the early 1990s. The more developed nations will increasingly rely on ʹsmart

weapons,ʹ which incorporate electronic copilots, pattern‐recognition techniques, and advanced technologies

for tracking, identification, and destruction.ʺ

What Happened: Several years after I wrote the Age of Intelligent Machines, the Gulf War was the first to clearly establish this paradigm. Today, the United States has the most advanced computer‐based weaponry

and remains unchallenged in its status as a military superpower.

• Prediction: The vast majority of commercial music will be created on computer‐based synthesizers.

What Happened: Most of the musical sounds you hear on television, in the movies, and in recordings are

now created on digital synthesizers, along with computer‐based sequencers and sound processors.

• Prediction: Reliable person identification, using pattern‐recognition techniques applied to visual and speech patterns, will replace locks and keys in many instances.

What Happened: Person‐identification technologies that use speech patterns and facial appearance have

begun to be used today in check‐cashing machines and to control entry into secure buildings and sites. [14]

• Prediction: With the advent of widespread electronic communication in the Soviet Union, uncontrollable political forces will be unleashed. These will be ʺmethods far more powerful than the copiers the authorities

have traditionally banned.ʺ The authorities will be unable to control it. Totalitarian control of information

will have been broken.

What Happened: The attempted coup against Gorbachev in August 1991 was undone primarily by cellular telephones, fax machines, electronic mail, and other forms of widely distributed and previously unavailable

electronic communication. Overall, decentralized communication contributed significantly to the crumbling

of centralized totalitarian political and economic government control in the former Soviet Union.

• Prediction: Many documents never exist on paper because they incorporate information in the form of audio and video pieces.

What Happened: Web documents routinely include audio and video pieces, which can only exist in their web

form.

• Prediction: Around the year 2000, chips with more than a billion components will emerge.

What Happened: Weʹre right on schedule.

• Prediction: The technology for the ʺcybernetic chauffeurʺ (self‐driving cars using special sensors in the roads) will become available by the end of the 1990s with implementation on major highways feasible

during the first decade of the twenty‐first century.

What Happened: Self‐driving cars are being tested in Los Angeles, London, Tokyo, and other cities. There were extensive successful tests on Interstate 15 in southern California during 1997. City planners now realize that automated driving technologies will greatly expand the capacity of existing roads. Installing the

requisite sensors on a highway costs only about $10,000 per mile, compared to $1 to $10 million per mile for

building new highways. Automated highways and self‐driving cars will also eliminate most accidents on

these roads. The U.S. National Automated Highway System (NAHS) consortium is predicting

implementation of these systems during the first decade of the twenty‐first century.

• Prediction: Continuous speech recognition (CSR) with large vocabularies for specific tasks will emerge in the early 1990s.

What Happened: Whoops. Large‐vocabulary domain‐specific CSR did not emerge until around 1996. By late

1997 and early 1998, large‐vocabulary CSR without a domain limitation for dictating written documents

(like this book) was commercially introduced. [16]

• Prediction: The three technologies required for a translating telephone (where you speak and listen in one language such as English, and your caller hears you and replies in another language such as German)—

speaker‐independent (not requiring training on a new speaker), continuous, large‐vocabulary speech

recognition; language translation; and speech synthesis—will each exist in sufficient quality for a first generation system by the late 1990s. Thus, we can expect ʺtranslating telephones with reasonable levels of

performance for at least the more popular languages early in the first decade of the twenty‐first century.ʺ

What Happened: Effective, speaker‐independent speech recognition, capable of handling continuous speech

and a large vocabulary, has been introduced. Automatic language translation, which rapidly translates web

sites from one language to another, is available directly from your web browser. Text‐to‐speech synthesis for a wide variety of languages has been available for many years. All of these technologies run on personal

computers. At Lernout & Hauspie (which acquired my speech‐recognition company, Kurzweil Applied

Intelligence, in 1997), we are putting together a technology demonstration of a translating telephone. We expect such a system to be commercially available early in the first decade of the twenty‐first century. [17]

MY LIFE WITH MACHINES: SOME HIGHLIGHTS

I walked onstage and played a composition on an old upright piano. Then came the yes-or-no questions.

Former Miss America Bess Myerson was stumped. But film star Henry Morgan, the second celebrity panelist on

this episode of I've Got a Secret, guessed my secret: The piece I had played had been composed by a

computer that I had built and programmed. Later that year, I got to meet President Johnson with other high-

school science winners.

In college, I ran a business matching up high-school kids with colleges using a computer program I had

written. We had to pay $1,000 an hour to rent time on the only computer in Massachusetts with an

extraordinary million bytes of core memory, which allowed us to fit all the information we had about the

nation's three thousand colleges into memory at the same time. We received a lot of letters from kids who

were delighted with the colleges that our program had suggested. A few parents, on the other hand, were

furious that we had failed to recommend Harvard. It was my first experience with the ability of computers to

affect people's lives. I sold that company to Harcourt, Brace & World, a New York publisher, and moved on to

other ideas.

In 1974, computer programs that could recognize printed letters, called optical character recognition

(OCR), were capable of handling only one or two specialized type styles. I founded Kurzweil Computer

Products that year to develop the first OCR program that could recognize any style of print, which we

succeeded in doing later that year. So the question then became, What is it good for? Like a lot of clever computer software, it was a solution in search of a problem.

I happened to sit next to a blind gentleman on a plane flight, and he explained to me that the only real

handicap that he experienced was his inability to read ordinary printed material. It was clear that his visual

disability imparted no real handicap in either communicating or traveling. So I had found the problem we were

searching for—we could apply our "omni-font" (any font) OCR technology to overcome this principal handicap of blindness. We didn't have the ubiquitous scanners or text-to-speech synthesizers that we do today, so we

had to create these technologies as well. By the end of 1975, we put together these three new technologies

we had invented—omni-font OCR, CCD (Charge Coupled Device) flat-bed scanners, and text-to-speech

synthesis to create the first print-to-speech reading machine for the blind. The Kurzweil Reading Machine

(KRM) was able to read ordinary books, magazines, and other printed documents out loud so that a blind

person could read anything he wanted.

We announced the KRM in January of 1976, and it seemed to strike a chord. All the evening network news

programs carried the story, and Walter Cronkite used the machine to read aloud his signature sign-off, "And

that's the way it was, January 13, 1976."

Shortly after the announcement, I was invited on the Today show, which was a little nerve-racking since

we only had one working reading machine. Sure enough, the machine stopped working a couple of hours

before I was scheduled to go on live national television. Our chief engineer frantically took the machine apart,

scattering pieces of electronics and wires across the floor of the set. Frank Field, who was going to interview

me, walked by and asked if everything was okay. "Sure, Frank," I replied. "We're just making a few last-minute adjustments."

Our chief engineer put the reading machine back together, and still it didn't work. Finally, he used a time-

honored method of repairing delicate electronic equipment and slammed the reading machine against a table.

From that moment, it worked just fine. Its live television debut then proceeded without a hitch.

Stevie Wonder heard about our appearance on the Today show, and decided to check out the story

himself. Our receptionist was skeptical that the person on the other end of the line was really the legendary

singer, but she put the call through to me, anyway. I invited him over, and he tried out the machine. He

beseeched us to provide him with his own reading machine, so we turned the factory upside down to hurriedly

finish up our first production unit (we didn't want to give him the prototype we used on the Today show, as it still had a few battle scars). We showed Stevie how to use it, and off he went in a taxi with his new reading

machine by his side.

We subsequently applied the scanning and omni-font OCR to commercial uses such as entering data into

databases and into the emerging word-processing computers. New information services, such as Lexus (an

online legal research service) and Nexus (a news service), were built using the Kurzweil Data Entry Machine to

scan and recognize written documents.

In 1978, after years of scrambling to raise funds for our venture, we were fortunate in attracting interest

and investment from a big company: Xerox. Most Xerox products transferred electronic information onto

paper. They saw the Kurzweil scanning and OCR technology as providing a bridge back from the world of

paper to the electronic world, so in 1980 they bought the company. You can still buy the OCR we originally

developed, suitably updated—it's now called Xerox TextBridge, and continues as a market leader.

I kept up my relationship with Stevie Wonder, and on one of our get-togethers at his new Los Angeles

recording studio in 1982, he lamented the state of affairs in the world of musical instruments. On the one

hand, there was the world of acoustic instruments, such as the piano, violin, and guitar, which provided the

rich complex sounds of choice for most musicians. While musically satisfying, these instruments suffered from

a panoply of limitations. Most musicians could play only one or two different instruments. Even if you could

play more than one, you couldn't play more than one at a time. Most instruments only produce one note at a

time. There were very limited means available to shape the sounds.

On the other hand, there was the world of electronic instruments, in which these control limitations

disappeared. In the computerized world, you could record one line of music on a sequencer, play it back, and

record another sequence over it, building up a multi-instrumental composition line by line. You could edit

wrong notes without replaying the entire sequence. You could layer multiple sounds, modify their sonic

characteristics, play songs in nonreal time, and use a great variety of other techniques. There was only one

problem. The sounds you had to work with in the electronic world sounded very thin, rather like an organ, or

an electronically processed organ.

Wouldn't it be great, Stevie mused, if we could use the extraordinarily flexible computer-control methods

on the beautiful sounds of acoustic instruments? I thought about it and it sounded quite doable, so that

meeting constituted the founding of Kurzweil Music Systems, and defined its raison d'etre.

With Stevie Wonder as our musical adviser, we set out to combine these two worlds of music. In June of

1983, we demonstrated an engineering prototype of the Kurzweil 250 (K250) and introduced it commercially

in 1984. The K250 is considered to be the first electronic musical instrument to successfully emulate the

complex sound response of a grand piano and virtually all other orchestral instruments.

Earlier, my father, who was a noted musician, had played a role in developing my interest in electronic

music. Before his death in 1970, he told me that he believed I would one day combine my interests in

computers and in music, as he felt there was a natural affinity between the two. I remember that when my

father wanted to hear one of his orchestral compositions, he had to engage an entire orchestra. This meant

raising money, mimeographing copies of handwritten sheet music, selecting and hiring the right musicians and

arranging a hall in which they could play. After all of that, he would get to hear his composition for the first

time. God forbid if he didn't like the composition exactly the way it was, for then he would have to dismiss the

musicians, spend days rewriting modified scores by hand, raise more money, rehire the musicians, and get

them back together. Today a musician can hear her multi-instrumental composition on a Kurzweil or other

synthesizer, make changes as easily as one would to a letter on a word processor, and hear the results

instantly.

I sold Kurzweil Music Systems to a Korean company, Young Chang, the world's largest piano manufacturer,

in 1990. Kurzweil Music Systems remains one of the leading brands of electronic musical instruments in the

world and is sold in forty-five countries.

I also started Kurzweil Applied Intelligence in 1982 with the goal of creating a voice-activated word

processor. This is a technology that is hungry for MIPS (that is, computer speed) and megabytes (that is,

memory), so early systems limited the size of the vocabulary that users could employ. These early systems

also required users to pause briefly between words. . . so. . . you. . . had. . . to. . . speak. . . like. . . this. We combined this "discrete word" speech-recognition technology with a medical knowledge base to create a

system that enabled doctors to create their medical reports by simply talking to their computers. Our product,

called Kurzweil Voicemed (now Kurzweil Clinical Reporter), actually guides the doctors, through the reporting

process. We also introduced a general-purpose dictation product called Kurzweil Voice, which enabled users to

create written documents by speaking one word at a time to their personal computer. This product became

particularly popular with people who have a disability in the use of their hands.

Just this year, courtesy of Moore's Law, personal computers became fast enough to recognize fully

continuous speech, so I am able to dictate the rest of this book by talking to our latest product, called Voice

Xpress Plus, at speeds around a hundred words per minute. Of course, I don't get a hundred words written

every minute since I change my mind a lot, but Voice Xpress doesn't seem to mind.

We sold this company as well, to Lernout & Hauspie (L&H), a large speech-and-language technology

company with headquarters in Belgium. Shortly after the acquisition by L&H in 1997, we arranged a strategic

alliance between the dictation division of L&H (formerly Kurzweil Applied Intelligence) and Microsoft, so our

speech technology is likely to be used by Microsoft in future products.

L&H is also the leader in text-to-speech synthesis and automatic language translation, so the company now

has all the technologies needed for a translating telephone. As I mentioned above, we're now putting together

a technology demonstration of a system that will allow you to speak in English with the person at the other

end hearing you in German, and vice versa. Eventually, you'll be able to call anyone in the world and have

what you say instantly translated into any popular language. Of course, our ability to misunderstand each

other will remain unimpaired.

Another application of our speech-recognition technology, and one of our initial goals, is a listening device

for the deaf, essentially the opposite of a reading machine for the blind. By recognizing natural continuous

speech in real time, the device will enable a deaf person to read what people are saying, thereby overcoming

the principal handicap associated with deafness.

In 1996, I founded a new reading-technology company called Kurzweil Educational Systems, which has

developed a new generation of print-to-speech reading software for sighted persons with reading disabilities,

as well as a new reading machine for blind people. The reading-disabilities version, called the Kurzweil 3000,

scans a printed document, displays the page just as it appears in the original document (for example, book,

magazine), with all of the color graphics and pictures intact. It then reads the document out loud while

highlighting the image of the print as it is being read. It essentially does what a reading teacher does—reading

to a pupil while pointing out exactly what is being read.

It is the applications of the technology benefiting disabled people that have brought me the greatest

gratification. There is a fortuitous match between the capabilities of contemporary computers and the needs of

a disabled person. We're not creating cybernetic geniuses today—not yet. The intelligence of our present-day

intelligent computers is narrow, which can provide effective solutions for the narrow deficits of most disabled

persons. The restricted intelligence of the machine works effectively with the broad and flexible intelligence of

the disabled person. Overcoming the handicaps associated with disabilities using AI technologies has long

been a personal goal of mine. With regard to the major physical and sensory disabilities, I believe that in a

couple of decades we will come to herald the effective end of handicaps. As amplifiers of human thought,

computers have great potential to assist human expression and to expand creativity for all of us. I hope to

continue playing a role in harnessing this potential.

All of these projects have required the dedication and talents of many brilliant individuals in a broad range

of fields. It is always exciting to see—or hear—a new product, and to see its impact on the lives of its users. A

great pleasure has been sharing in the creative process, and its fruits, with these many outstanding men and

women.

THE NEW LUDDITE CHALLENGE

First let us postulate that the computer scientists succeed in developing intelligent machines that can do all things better than human beings can do them. In that case presumably all work will be done by vast, highly organized systems of machines and no human effort will be necessary. Either of two cases might occur The machines might be permitted to make all of their own decisions without human oversight, or else human control over the machines might be retained.

If the machines are permitted to make all their own decisions, we canʹt make any conjectures as to the results, because it is impossible to guess how such machines might behave. We only point out that the fate of the human race would be at the mercy of the machines. It might be argued that the human race would never be foolish enough to hand over all the power to the machines. But we are suggesting neither that the human race would voluntarily turn power over to the machines nor that the machines would willfully seize power. What we do suggest is that the human race might easily permit itself to drift into a position of such dependence on the machines that it would have no practical choice but to accept all of the machinesʹ decisions. As society and the problems that face it become more and more complex and machines become more and more intelligent, people will let machines make more of their decisions for them, simply because machine‐made decisions will bring better results than man‐made ones. Eventually a stage may be reached at which the decisions necessary to keep the system running will be so complex that human beings will be incapable of making them intelligently. At that stage the machines will be in effective control. People wonʹt be able to just turn the machines off, because they will be so dependent on them that turning them off would amount to suicide.

On the other hand it is possible that human control over the machines may be retained. in that case the average

man may have control over certain private machines of his own, such as his car or his personal computer but control over large systems of machines will be in the hands of a tiny elite—just as it is today, but with two differences. Due to improved techniques the elite will have greater control over the masses; and because human work will no longer be necessary the masses will be superfluous, a useless burden on the system. If the elite is ruthless they may simply decide to exterminate the mass of humanity. If they are humane they may use propaganda or other psychological or biological techniques to reduce the birth rate until the mass of humanity becomes extinct, leaving the world to the elite. Or, if the elite consists of soft‐hearted liberals, they may decide to play the role of good shepherds to the rest of the human race. They will see to it that everyoneʹs physical needs are satisfied, that all children are raised under psychologically hygienic conditions, that everyone has a wholesome hobby to keep him busy, and that anyone who may become dissatisfied undergoes ʺtreatmentʺ to cure his ʺproblem.ʺ Of course, life will be so purposeless that people will have to be biologically or psychologically engineered either to remove their need for the power process or to make them ʺsublimateʺ their drive for power into some harmless hobby. These engineered human beings may be happy in such a society, but they most certainly will not be free. They will have been reduced to the status of domestic animals.

—Theodore Kaczynski

The weavers of Nottingham enjoyed a modest but comfortable lifestyle from their thriving cottage industry of producing fine stockings and lace. This went on for hundreds of years, as their stable family businesses were passed down from generation to generation. But with the invention of the power loom and the other textile automation machines of the early eighteenth century, the weaversʹ livelihoods came to an abrupt end. Economic power passed from the weaving families to the owners of the machines.

Into this turmoil came a young and feebleminded boy named Ned Ludd, who, legend has it, broke two textile factory machines by accident as a result of sheer clumsiness. From that point on, whenever factory equipment was found to have been mysteriously damaged, anyone suspected of foul play would say, ʺBut Ned Ludd did it.ʺ

In 1812, the desperate weavers formed a secret society, an urban guerrilla army They made threats and demands

of factory owners, many of whom complied. When asked who their leader was, they replied, ʺWhy, General Ned Ludd, of course.ʺ Although the Luddites, as they became known, initially directed most of their violence against the machines, a series of bloody engagements erupted later that year. The tolerance of the Tory government for the Luddites ended, and the movement dissolved with the imprisonment and hanging of prominent members. [18]

The ability of machines to displace human employment was not an intellectual exercise for the Luddites. They had

seen their way of life turned on its head. It was little comfort to the weavers that new and more lucrative employment had been created to design, manufacture, and market the new machines. There were no government programs to retrain the weavers to become automation designers.

Although they failed to create a sustained and viable movement, the Luddites have remained a powerful symbol

as machines have continued to displace human workers. As one of many examples of the effect of automation on employment, about a third of the U.S. population was involved in the production of agricultural products at the beginning of the twentieth century Today, that percentage is about 3 percent. [19] It would have been little comfort to the farmers of a hundred years ago to point out that their lost jobs would ultimately be compensated by new jobs in a future electronics industry, or that their descendants could become software designers in Silicon Valley.

The reality of lost jobs is often more compelling than the indirect promise of new jobs created in distant new industries. When advertising agencies started using Kurzweil synthesizers to create the sound tracks for television commercials rather than hire live musicians, the musiciansʹ union was not happy about it. We pointed out that the new computer‐music technology was actually beneficial to musicians because it made music more exciting. For example, industrial films that had formerly used prerecorded orchestral music (because the limited budget of such films did not allow the hiring of an entire orchestra) were now using original music created by a musician with a synthesizer. As it turned out, this wasnʹt a very effective argument, since the synthesizer players tended not to be union members.

The Luddite philosophy remains very much alive as an ideological inclination, but as a political and economic movement, it remains just below the surface of contemporary debate. The public appears to understand that the creation of new technology is fueling the expansion of economic well‐being. The statistics demonstrate quite clearly that automation is creating more and better jobs than it is eliminating. In 1870 only 12 million Americans, representing about one third of the civilian population, had jobs. By 1998, the figure rose to 126 million jobs held by about two thirds of the civilian population. The gross national product on a per capita basis and in constant 1958

dollars went from $530 in 1870 to at least ten times that today. [21] There has been a comparable change in the actual earning power of available jobs. This 1,000 percent increase in real wealth has resulted in a greatly improved standard of living, better health care and education, and a substantially improved ability to provide for those who need help in our society. At the beginning of the Industrial Revolution life expectancy in North America and northwestern Europe

was about thirty‐seven years. Now, two centuries later, it has doubled, and is continuing to increase.

The jobs created have also been on a higher level. Indeed, much of the additional employment has been in the area

of providing the more intense education that todayʹs jobs require. For example, we now spend ten times as much (in

constant dollars) on a per capita basis for public school education as we did a century ago. In 1870 only 2 percent of American adults had a high‐school diploma, whereas the figure is over 80 percent today. There were only 52,000

college students in 1870; there are 15 million today.

The process of automation that began in England two hundred years ago—and continues today at an ever accelerating pace (as per the Law of Accelerating Returns)—eliminates jobs at the bottom of the skill ladder and creates new ones at the top of the skill ladder. Hence increasing investment in education. But what happens when the skill ladder extends beyond the abilities of the bulk of the human population, and ultimately beyond the ability of any human, educational innovations notwithstanding? The answer we can predict from the Law of Accelerating Returns

is that the ladder will nonetheless continue to reach ever higher, implying that humans will need to become more capable by other means. Education can only accomplish so much. The only way for the species to keep pace will be

for humans to gain greater competence from the computational technology we have created, that is, for the species to merge with its technology.

Not everyone will find this prospect appealing, so the Luddite issue will broaden in the twenty‐first century from

an anxiety about human livelihoods to one concerning the essential nature of human beings. However, the Luddite movement is not likely to fare any better in the next century than it has in the past two. it suffers from the lack of a viable alternative agenda.

Ted Kaczynski, whom I quote above from his so‐called ʺUnabomber Manifesto,ʺ entitled Industrial Society and Its

Future, advocates a simple return to nature. [22] Kaczynski is not talking about a contemplative visit to a nineteenth-century Walden Pond, but about the species dropping all of its technology and reverting to a simpler time. Although

he makes a compelling case for the dangers and damages that have accompanied industrialization his proposed vision is neither compelling nor feasible. After all, there is too little nature left to return to, and there are too many human beings. For better or worse, weʹre stuck with technology.

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YOUR CYBERNETIC POET WRITES SOME INTERESTING LINES . . .

Iʹd be interested in your selections.

WELL, LOOKING AT THE FIRST FEW POEMS IN YOUR COLLECTION:

Sashay down the page . . .

through the lioness / nestled in my soul . . .

forming jewels from the falling snow . . .

the juice of eternity, / the spirit of my lips . . .

BUT THE POEMS DONʹT ALWAYS FULLY TRACK, IF YOU KNOW WHAT I MEAN.

Yes, readers tolerate a little more discontinuity in verse than in prose. The fundamental problem is the inability of contemporary cybernetic artists to master the levels of context that human artists are capable of. Itʹs not a permanent limitation, of course. Ultimately, weʹll be the ones having difficulty keeping up with the depth of context that computer intelligence is capable of—

WITHOUT SOME ASSIST—

From computer extensions to our intelligence, yes, exactly.

In the meantime, the Cybernetic Poet is good at being an inspirational assistant. While its poems donʹt always make it all the way through, it does have some real strength at finding unique turns of phrase. So the program has a mode called The Poetʹs Assistant. The human user writes a poem in a word‐processing window. The Poetʹs Assistant

watches her write and fills the rest of the screen with suggestions, such as, ʺHereʹs how Robert Frost would finish that line,ʺ or, ʺHereʹs a set of rhymes and/or alliterations that Keats used with that word,ʺ or, ʺHereʹs how Emily Dickinson would finish that poem,ʺ and so on. If provided with the human authorʹs own poems, it can even suggest how the user herself would finish a line or poem. Every time you write another word, you get dozens of ideas. Not all of them make sense, but itʹs a good solution for writerʹs block. And youʹre welcome to steal its ideas.

NOW WITH REGARD TO COHENʹS PICTURES . . .

You mean Aaronʹs pictures . . .

OH, I GUESS IʹM NOT SENSITIVE TO AARONʹS FEELINGS—

Since it doesnʹt have any—

NOT YET, RIGHT? BUT WHAT I WAS GOING TO SAY WAS THAT AARONʹS PICTURES DO SEEM TO

MAINTAIN THEIR CONTEXT. THE WHOLE THING KIND OF WORKS FOR ME.

Yes, Cohenʹs Aaron is probably the best example of a cybernetic visual artist today, and certainly one of the primary examples of computers in the arts. Cohen has programmed thousands of rules on all aspects of drawing and painting,

from the artistic nature of painted people, plants, and objects to composition and color choice.

Keep in mind that Aaron does not seek to emulate other artists. It has its own set of styles, so it is feasible for its knowledge base to be relatively complete within its visual domain. Of course, human artists, even brilliant ones, also have a boundary to their domain. Aaron is quite respectable in the diversity of its art.

OKAY, JUST TO SWITCH TO SOMEONE MUCH LESS RESPECTABLE, YOU QUOTED TED KACZYNSKI

TALKING ABOUT HOW THE HUMAN RACE MIGHT DRIFT INTO DEPENDENCE ON MACHINES, AND THEN

WEʹLL HAVE NO CHOICE BUT TO ACCEPT ALL MACHINE DECISIONS. BASED ON WHAT YOU SAID ABOUT

THE IMPLICATIONS OF ALL THE COMPUTERS STOPPING, ARENʹT WE ALREADY THERE?

We are certainly there with regard to the dependence, not yet with regard to the level of machine intelligence.

THAT QUOTE WAS SURPRISINGLY—

Coherent?

YES, THAT WAS THE WORD I WAS LOOKING FOR.

Kaczynskiʹs whole manifesto is rather well written, not at all what you would expect given the popular portrait of him as a madman. As political science professor James Q. Wilson of the University of California wrote, ʺThe language is clear, precise and calm. The argument is subtle and carefully developed, lacking anything even faintly resembling the wild claims or irrational speculation that a lunatic might produce.ʺ And he has gathered quite a following among anarchists and antitechnologists on the Internet—

WHICH IS THE ULTIMATE IN TECHNOLOGY.

Yes, that irony has not been lost.

BUT WHY QUOTE KACZYNSKI? I MEAN, . . .

Well, his manifesto is as persuasive an exposition on the psychological alienation, social dislocation, environmental injury, and other injuries and perils of the technological age as any other . . .

THATʹS NOT MY POINT. I DOUBT THAT THE LUDDITES ARE HAPPY HAVING HIM AS A SYMBOL OF THEIR

IDEAS. YOUʹRE SORT OF DISCREDITING THEIR MOVEMENT BY USING HIM AS THEIR SPOKESPERSON.

Okay, thatʹs a legitimate objection. I suppose I could defend my extensive quote as providing an important example

of a relevant phenomenon, which is violent Ludditism. The movement started with violence, and the challenge to the

human race posed by machines is fundamental enough that a violent reaction during this coming century is a strong

possibility.

BUT YOUR USE OF THE QUOTATION SEEMED LIKE MORE THAN JUST AN EXAMPLE OF SOME FRINGE

PHENOMENON.

Well, I was surprised how much of Kaczynskiʹs manifesto I agreed with.

SUCH AS . . .

Oh, so now youʹre interested.

IT WAS KIND OF INTRIGUING, AND APROPOS TO THE OTHER THINGS YOUʹVE BEEN TELLING ME.

Yes, I thought so. Kaczynski describes the benefits of technology, as well as its costs and dangers. He then makes this point:

A further reason why industrial society cannot be reformed in favor of freedom is that modern technology

is a unified system in which all parts are dependent on one another. You canʹt get rid of the ʺbadʺ parts of

technology and retain only the ʺgoodʺ parts. Take modern medicine, for example. Progress in medical

science depends on programs in chemistry, physics, biology, computer science, and other fields. Advanced

medical treatments require expensive, high‐tech equipment that can be made available only by a

technologically progressive, economically rich society. Clearly you canʹt have much progress in medicine

without the whole technological system and everything that goes with it.

So far, so good. He then makes the basic judgment that the ʺbad partsʺ outweigh the ʺgood parts.ʺ Not that this is

a crazy position, either, but nonetheless, this is where we part company. Now it is not my view that the advance of

technology is automatically beneficial. It is conceivable that humanity will ultimately regret its technological path.

Although the risks are quite real, my fundamental belief is that the potential gains are worth the risk. But this is a belief; itʹs not a position I can easily demonstrate.

IʹD BE INTERESTED IN YOUR VIEW OF THE GAINS.

The material gains are obvious: economic advancement, the shaping of material resources to meet age‐old needs, the

extension of our life spans, improvements in health, and so on. However, thatʹs not actually my primary point.

I see the opportunity to expand our minds, to extend our learning, and to advance our ability to create and understand knowledge as an essential spiritual quest. Feigenbaum and McCorduck talk about this as an ʺaudacious,

some would say reckless, embarkation onto sacred ground.ʺ

SO WE RISK THE SURVIVAL OF THE HUMAN RACE FOR THIS SPIRITUAL QUEST?

Yeah, basically

IʹM NOT SURPRISED THAT THE LUDDITES TAKE PAUSE.

Of course, keep in mind that itʹs the material, not the spiritual gains, that are seducing society down this path.

IʹM STILL NOT COMFORTABLE WITH KACZYNSKI AS A SPOKESPERSON. HE IS A CONFESSED MURDERER,

YOU KNOW.

Certainly, Iʹm glad heʹs behind bars, and his tactics deserve condemnation and punishment. Unfortunately, terrorism

is effective, and thatʹs why it survives.

I DONʹT SEE IT THAT WAY. TERRORISM JUST UNDERMINES THE POSITIONS BEING PUBLICIZED. PEOPLE

THEN SEE THE TERRORISTʹS PROPOSITIONS AS CRAZY, OR AT LEAST MISGUIDED.

Thatʹs one reaction. But remember the society of mind. We have more than one reaction to terrorism.

One contingent in our heads says ʺthose actions were evil and crazy, so the terroristʹs thesis must also be evil and crazy.ʺ

But another contingent in our heads takes the view that ʺthose actions were extreme, so he must have very strong

feelings about this. Maybe thereʹs something to it. Perhaps a more moderate version of his views are legitimate.ʺ

SOUNDS LIKE THE PSYCHOLOGY OF HITLERʹS ʺBIG LIE.ʺ

Thereʹs a similarity. In Hitlerʹs case, both the tactics and the views were extreme. In the case of modern terrorists, the tactics are extreme; the views may or may not be. In Kaczynskiʹs case, many aspects of his argument are reasonable.

Of course, he does end up in an extreme place.

YEAH, A PRIMITIVE CABIN IN MONTANA.

Thatʹs where the manifesto ends up, too—we should all return to nature.

I DONʹT THINK PEOPLE FOUND KACZYNSKIʹS NOTION OF NATURE VERY APPEALING, AT LEAST NOT

JUDGING BY PICTURES OF HIS CABIN.

And, as I said, thereʹs not enough nature to go around anymore.

THANKS TO TECHNOLOGY.

And the population boom

ALSO FACILITATED BY TECHNOLOGY.

So weʹve passed the point of no return. Itʹs already too late to go the nature route.

SO WHAT COURSE DO YOU RECOMMEND?

I would say that we shouldnʹt view the advance of technology as just an impersonal, inexorable force.

I THOUGHT YOU SAID THE ACCELERATING ADVANCE OF TECHNOLOGY AND COMPUTATION—WAS

INEXORABLE; REMEMBER, THE LAW OF ACCELERATING RETURNS?

Uh, yes, the advance is inexorable all right, weʹre not going to stop technology. But we do have some choices. We have the opportunity to shape technology, and to channel its direction. Iʹve tried to do that in my own work. We can step through the forest carefully WEʹD BETTER GET BUSY, SOUNDS LIKE THERE ARE A LOT OF SLIPPERY

SLOPES OUT THERE WAITING FOR US.

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PART THREE

TO FACE THE

FUTURE

C H A P T E R N I N E

2009

Ever since I could remember Iʹd wished Iʹd been lucky enough to be alive at a great time—when something big was

going on, like a crucifixion. And suddenly I realized I was.

—Ben Shahn

As we say in the computer business, ʺshift happens.ʺ

—Tim Romero

It is said that people overestimate what can be accomplished in the short term, and underestimate the changes that

will occur in the long term. With the pace of change continuing to accelerate, we can consider even the first decade in the twenty‐first century to constitute a long‐term view. With that in mind, let us consider the beginning of the next century.

The Computer Itself

It is now 2009. Individuals primarily use portable computers, which have become dramatically lighter and thinner than the notebook computers of ten years earlier. Personal computers are available in a wide range of sizes and shapes, and are commonly embedded in clothing and jewelry such as wristwatches, rings, earrings, and other body

ornaments. Computers with a high‐resolution visual interface range from rings and pins and credit cards up to the size of a thin book.

People typically have at least a dozen computers on and around their bodies, which are networked using ʺbody

LANSʺ (local area networks). [1] These computers provide communication facilities similar to cellular phones, pagers, and web surfers, monitor body functions, provide automated identity (to conduct financial transactions and allow entry into secure areas), provide directions for navigation, and a variety of other services.

For the most part, these truly personal computers have no moving parts. Memory is completely electronic, and most portable computers do not have keyboards.

Rotating memories (that is, computer memories that use a rotating platter, such as hard drives, CD‐ROMS, and DVDS) are on their way out, although rotating magnetic memories are still used in ʺserverʺ computers where large amounts of information are stored. Most users have servers in their homes and offices where they keep large stores of digital ʺobjects,ʺ including their software, databases, documents, music, movies, and virtual‐reality environments (although these are still at an early stage). There are services to keep oneʹs digital objects in central repositories, but most people prefer to keep their private information under their own physical control.

Cables are disappearing. [2] Communication between components, such as pointing devices, microphones,

displays, printers, and the occasional keyboard, uses short‐distance wireless technology.

Computers routinely include wireless technology to plug into the ever‐present worldwide network, providing reliable, instantly available, very‐high‐bandwidth communication. Digital objects such as books, music albums, movies, and software are rapidly distributed as data files through the wireless network, and typically do not have a physical object associated with them.

The majority of text is created using continuous speech recognition (CSR) dictation software, but keyboards are still used. CSR is very accurate, far more so than the human transcriptionists who were used up until a few years ago.

Also ubiquitous are language user interfaces (LUIS), which combine CSR and natural language understanding.

For routine matters, such as simple business transactions and information inquiries, LUIS are quite responsive and precise. They tend to be narrowly focused, however, on specific types of tasks. LUIS are frequently combined with animated personalities. Interacting with an animated personality to conduct a purchase or make a reservation is like talking to a person using videoconferencing, except that the person is simulated.

Computer displays have all the display qualities of paper‐high resolution, high contrast, large viewing angle, and

no flicker. Books, magazines, and newspapers are now routinely read on displays that are the size of, well, small books.

Computer displays built into eyeglasses are also used. These specialized glasses allow users to see the normal visual environment, while creating a virtual image that appears to hover in front of the viewer. The virtual images are created by a tiny laser built into the glasses that projects the images directly onto the userʹs retinas. [3]

Computers routinely include moving picture image cameras and are able to reliably identify their owners from their faces.

In terms of circuitry, three‐dimensional chips are commonly used, and there is a transition taking place from the

older, single‐layer chips.

Sound producing speakers are being replaced with very small chip‐based devices that can place high resolution sound anywhere in three‐dimensional space. This technology is based on creating audible frequency sounds from the

spectrum created by the interaction of very high frequency tones. As a result, very small speakers can create very robust three‐dimensional sound.

A $1,000 personal computer (in 1999 dollars), can perform about a trillion calculations per second. [4]

Supercomputers match at least the hardware capacity of the human brain—20 million billion calculations per second.

[5] Unused computes on the Internet are being harvested, creating virtual parallel supercomputers with human brain

hardware capacity.

There is increasing interest in massively parallel neural nets, genetic algorithms, and other forms of ʺchaoticʺ or complexity theory computing, although most computer computations are still done using conventional sequential processing, albeit with some limited parallel processing.

Research has been initiated on reverse engineering the human brain through both destructive scans of the brains

of recently deceased persons as well as noninvasive scans using high resolution magnetic resonance imaging (MRI) of

living persons.

Autonomous nanoengineered machines (that is, machines constructed atom by atom and molecule by molecule)

have been demonstrated and include their own computational controls. However, nanoengineering is not yet considered a practical technology.

Education

In the twentieth century, computers in schools were mostly on the trailing edge, with most effective learning from computers taking place in the home. Now in 2009, while schools are still not on the cutting edge, the profound importance of the computer as a knowledge tool is widely recognized. Computers play a central role in all facets of

education, as they do in other spheres of life.

The majority of reading is done on displays, although the ʺinstalled baseʺ of paper documents is still formidable.

The generation of paper documents is dwindling, however, as the books and other papers of largely twentieth-century vintage are being rapidly scanned and stored. Documents circa 2009 routinely include embedded moving images and sounds.

Students of all ages typically have a computer of their own, which is a thin tabletlike device weighing under a pound with a very high resolution display suitable for reading. Students interact with their computers primarily by

voice and by pointing with a device that looks like a pencil. Keyboards still exist, but most textual language is created by speaking. Learning materials are accessed through wireless communication.

Intelligent courseware has emerged as a common means of learning. Recent controversial studies have shown that

students can learn basic skills such as reading and math just as readily with interactive learning software as with human teachers, particularly when the ratio of students to human teachers is more than one to one. Although the studies have come under attack, most students and their parents have accepted this notion for years. The traditional mode of a human teacher instructing a group of children is still prevalent, but schools are increasingly relying on software approaches, leaving human teachers to attend primarily to issues of motivation, psychological well‐being, and socialization. Many children learn to read on their own using their personal computers before entering grade school.

Preschool and elementary school children routinely read at their intellectual level using print‐to‐speech reading software until their reading skill level catches up. These print‐to‐speech reading systems display the full image of documents, and can read the print aloud while highlighting what is being read. Synthetic voices sound fully human.

Although some educators expressed concern in the early ʹ00 years that students would rely unduly on reading software, such systems have been readily accepted by children and their parents. Studies have shown that students

improve their reading skills by being exposed to synchronized visual and auditory presentations of text.

Learning at a distance (for example, lectures and seminars in which the participants are geographically scattered)

is commonplace.

Learning is becoming a significant portion of most jobs. Training and developing new skills is emerging as an ongoing responsibility in most careers, not just an occasional supplement, as the level of skill needed for meaningful employment soars ever higher.

Disabilities

Persons with disabilities are rapidly overcoming their handicaps through the intelligent technology of 2009. Students with reading disabilities routinely ameliorate their disability using print‐to‐speech reading systems.

Print‐to‐speech reading machines for the blind are now very small, inexpensive, palm‐sized devices that can read

books (those that still exist in paper form) and other printed documents, and other real‐world text such as signs and displays. These reading systems are equally adept at reading the trillions of electronic documents that are instantly available from the ubiquitous wireless world‐wide network.

After decades of ineffective attempts, useful navigation devices have been introduced that can assist blind people

in avoiding physical obstacles in their path, and finding their way around, using global positioning system (GPS) technology. A blind person can interact with her personal reading‐navigation systems through two‐way voice communication, kind of like a Seeing Eye dog that reads and talks.

Deaf persons—or anyone with a hearing impairment—commonly use portable speech‐to‐text listening machines,

which display a real‐time transcription of what people are saying. The deaf user has the choice of either reading the transcribed speech as displayed text, or watching an animated person gesturing in sign language. These have eliminated the primary communication handicap associated with deafness. Listening machines can also translate what is being said into another language in real time, so they are commonly used by hearing people as well.

Computer‐controlled orthotic devices have been introduced. These ʺwalking machinesʺ enable paraplegic persons

to walk and climb stairs. The prosthetic devices are not yet usable by all paraplegic persons, as many physically disabled persons have dysfunctional joints from years of disuse. However, the advent of orthotic walking systems is

providing more motivation to have these joints replaced.

There is a growing perception that the primary disabilities of blindness, deafness, and physical impairment do not

necessarily impart handicaps. Disabled persons routinely describe their disabilities as mere inconveniences.

Intelligent technology has become the great leveler.

Communication

Translating Telephone technology (where you speak in English and your Japanese friend hears you in Japanese, and

vice versa) is commonly used for many language pairs. it is a routine capability of an individualʹs personal computer, which also serves as her phone.

ʺTelephoneʺ communication is primarily wireless, and routinely includes high‐resolution moving images.

Meetings of all kinds and sizes routinely take place among geographically separated participants.

There is effective convergence, at least on the hardware and supporting software level, of all media, which exist as digital objects (that is, files) distributed by the ever‐present high‐bandwidth, wireless information web. Users can instantly download books, magazines, newspapers, television, radio, movies, and other forms of software to their highly portable personal communication devices.

Virtually all communication is digital and encrypted, with public keys available to government authorities. Many

individuals and groups, including but not limited to criminal organizations, use an additional layer of virtually unbreakable encryption codes with no third‐party keys.

Haptic technologies area emerging that allow people to touch and feel objects and other persons at a distance.

These force‐feedback devices are widely used in games and in training simulation systems.

Interactive games routinely include all‐encompassing visual and auditory environments, but a satisfactory, all-encompassing tactile environment is not yet available. The online chat rooms of the late 1990s have been replaced with virtual environments where you can meet people with full visual realism.

People have sexual experiences at a distance with other persons as well as virtual partners. But the lack of the ʺsurroundʺ tactile environment has thus far kept virtual sex out of the mainstream. Virtual partners are popular as forms of sexual entertainment, but theyʹre more gamelike than real. And phone sex is a lot more popular now that phones routinely include high‐resolution, real‐time moving images of the person on the other end.

Business and Economics

Despite occasional corrections, the ten years leading up to 2009 have seen continuous economic expansion and prosperity due to the dominance of the knowledge content of products and services. The greatest gains continue to be in the value of the stock market. Price deflation concerned economists in the early ʹ00 years, but they quickly realized it was a good thing. The high‐tech community pointed out that significant deflation had existed in the computer hardware and software industries for many years earlier without detriment.

The United States continues to be the economic leader due to its primacy in popular culture and its entrepreneurial environment. Since information markets are largely world markets, the United States has benefited greatly from its immigrant history. Being comprised of all the worldʹs peoples—specifically the descendants of peoples from around the globe who had endured great risk for a better life—is the ideal heritage for the new knowledge‐based economy. China has also emerged as a

powerful economic player. Europe is several years ahead of

Japan and Korea in adopting the American emphasis on

venture capital, employee stock options, and tax policies

that encourage entrepreneurship, although these practices

have become popular throughout the world.

At least half of all transactions are conducted online.

Intelligent assistants which combine continuous speech

recognition, natural‐language understanding, problem

solving, and animated personalities routinely assist with

finding information, answering questions, and conducting

transactions. Intelligent assistants have become a primary

interface for interacting with information‐based services,

with a wide range of choices available. A recent poll shows

that both male and female users prefer female personalities

for their computer‐based intelligent assistants. The two

most popular are Maggie, who claims to be a waitress in a

Harvard Square cafe, and Michelle, a stripper from New

Orleans. Personality designers are in demand, and the field

constitutes a growth area in software development.

Most purchases of books, musical ʺalbums,ʺ videos, games, and other forms of software do not involve any physical object, so new business models for distributing these forms of information have emerged. One shops for these information objects by ʺstrollingʺ through virtual malls, sampling and selecting objects of interest, rapidly (and securely) conducting an online transaction, and then quickly downloading the information using high‐speed wireless

communication. There are many types and gradations of transactions to gain access to these products. You can ʺbuyʺ

a book, musical album, video, etcetera, which gives you unlimited permanent access. Alternatively, you can rent access to read, view, or listen once, or a few times. Or you can rent access by the minute. Access may be limited to one person or to a group of persons (for example, a family or a company). Alternatively, access may be limited to a particular computer, or to any computer accessed by a particular person or by a set of persons.

There is a strong trend toward the geographic separation of work groups. People are successfully working together despite living and working in different places.

The average household has more than a hundred computers, most of which are embedded in appliances and built‐in communication systems. Household robots have emerged, but are not yet fully accepted.

Intelligent roads are in use, primarily for long‐distance travel. Once your carʹs computer guidance system locks onto the control sensors on one of these highways, you can sit back and relax. Local roads, though, are still predominantly conventional.

A company west of the Mississippi and north of the Mason‐Dixon line has surpassed a trillion dollars in market

capitalization.

Politics and Society

Privacy has emerged as a primary political issue. The virtually constant use of electronic communication technologies is leaving a highly detailed trail of every personʹs every move. Litigation, of which there has been a great deal, has placed some constraints on the widespread distribution of personal data. Government agencies, however, continue to

have the right to gain access to peopleʹs files, which has resulted in the popularity of unbreakable encryption technologies.

There is a growing neo‐Luddite movement, as the skill ladder continues to accelerate upward. As with earlier Luddite movements, its influence is limited by the level of prosperity made possible by new technology. The movement does succeed in establishing continuing education as a primary right associated with employment.

There is continuing concern with an underclass that the skill ladder has left far behind. The size of the underclass appears to be stable, however. Although not politically popular, the underclass is politically neutralized through public assistance and the generally high level of affluence.

The Arts

The high quality of computer screens, and the facilities of computer‐assisted visual rendering software, have made the computer screen a medium of choice for visual art. Most visual art is the result of a collaboration between human artists and their intelligent art software. Virtual paintings—high‐resolution wall‐hung displays—have become popular. Rather than always displaying the same work of art, as with a conventional painting or poster, these virtual paintings can change the displayed work at the userʹs verbal command, or can cycle through collections of art. The displayed artwork can be works by human artists or original art created in real time by cybernetic art software.

Human musicians routinely jam with cybernetic musicians. The creation of music has become available to persons

who are not musicians. Creating music does not necessarily require the fine motor coordination of using traditional

controllers. Cybernetic music creation systems allow people who appreciate music but who are not knowledgeable about music theory and practice to create music in collaboration with their automatic composition software.

Interactive brain‐generated music, which creates a resonance between the userʹs brain waves and the music being listened to, is another popular genre.

Musicians commonly use electronic controllers that emulate the playing style of the old acoustic instruments (for

example, piano, guitar, violin, drums), but there is a surge of interest in the new ʺairʺ controllers in which you create music by moving your hands, feet, mouth, and other body parts. Other music controllers involve interacting with specially designed devices.

Writers use voice‐activated word processing; grammar checkers are now actually useful; and distribution of written documents from articles to books typically does not involve paper and ink. Style improvement and automatic

editing software is widely used to improve the quality of writing. Language translation software is also widely used to translate written works in a variety of languages. Nonetheless, the core process of creating written language is less affected by intelligent software technologies than the visual and musical arts. However, ʺcyberneticʺ authors are emerging.

Beyond music recordings, images, and movie videos, the most popular type of digital entertainment object is virtual experience software. These interactive virtual environments allow you to go. whitewater rafting on virtual rivers, to hang‐glide in a virtual Grand Canyon, or to engage in intimate encounters with our favorite movie star.

Users also experience fantasy environments with no counterpart in the physical world. The visual and auditory experience of virtual reality is compelling, but tactile interaction is still limited.

Warfare

The security of computation and communication is the primary focus of the U.S. Department of Defense. There is general recognition that the side that can maintain the integrity of its computational resources will dominate the battlefield.

Humans are generally far removed from the scene of battle. Warfare is dominated by unmanned intelligent airborne

devices. Many of these flying weapons are the size of small birds, or smaller.

The United States continues to be the worldʹs dominant military power, which is largely accepted by the rest of the

world, as most countries concentrate on economic competition. Military conflicts between nations are rare, and most

conflicts are between nations and smaller bands of terrorists. The greatest threat to national security comes from bioengineered weapons.

Health and Medicine

Bioengineered treatments have reduced the toll from cancer, heart disease, and a variety of other health problems.

Significant progress is being made in understanding the information processing basis of disease.

Telemedicine is widely used. Physicians can examine patients using visual, auditory, and haptic examination from

a distance. Health clinics with relatively inexpensive equipment and a single technician bring health care to remote areas where doctors had previously been scarce.

Computer‐based pattern recognition is routinely used to interpret imaging data and other diagnostic procedures.

The use of noninvasive imaging technologies has substantially increased. Diagnosis almost always involves collaboration between a human physician and a pattern‐recognition‐based expert system. Doctors routinely consult knowledge‐based systems (generally through two‐way voice communication augmented by visual displays), which provide automated guidance, access to the most recent medical research, and practice guidelines.

Lifetime patient records are maintained in computer databases. Privacy concerns about access to these records (as

with many other databases of personal information) have emerged as a major issue.

Doctors routinely train in virtual reality environments, which include a haptic interface. These systems simulate

the visual, auditory, and tactile experience of medical procedures, including surgery. Simulated patients are available for continuing medical education, for medical students, and for people who just want to play doctor.

Philosophy

There is renewed interest in the Turing Test, first proposed by Alan Turing in 1950 as a means for testing intelligence in a machine. Recall that the Turing Test contemplates a situation in which a human judge interviews the computer

and a human ʺfoil,ʺ communicating with both over terminal lines. If the human judge is unable to tell which interviewee is human and which is machine, the machine is deemed to possess human‐level intelligence. Although computers still fail the test, confidence is increasing that they will be in a position to pass it within another one or two decades.

There is serious speculation on the potential sentience (that is, consciousness) of computer‐based intelligence. The increasingly apparent intelligence of computers has spurred an interest in philosophy.

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. . . Hey, Molly.

OH, SO YOUʹRE CALLING ME NOW

Well, the chapter was over and I didnʹt hear from you.

IʹM SORRY, I WAS FINISHING UP A PHONE CALL WITH MY FIANCE.

Hey, congratulations, thatʹs great. How long have you known . . .

BEN, HIS NAME IS BEN. WE MET ABOUT TEN YEARS AGO, JUST AFTER YOU FINISHED THIS BOOK.

I see. So how have I done?

YOU DID MANAGE TO SELL A FEW COPIES.

No, I mean with my predictions.

NOT VERY WELL. THE TRANSLATING TELEPHONES, FOR ONE THING, ARE A LITTLE RIDICULOUS. I MEAN,

THEYʹRE CONSTANTLY SCREWING UP.

Sounds like you use them, though?

WELL, SURE, HOW ELSE AM I GOING TO SPEAK TO MY FIANCEʹS FATHER IN IEPER, BELGIUM, WHEN HE

HASNʹT BOTHERED TO LEARN ENGLISH?

Of course. So what else?

YOU SAID THAT CANCER WAS REDUCED, BUT THATʹS ACTUALLY QUITE UNDERSTATED. BIO‐

ENGINEERED TREATMENTS, PARTICULARLY ANTIANGIOGENESIS DRUGS THAT PREVENT TUMORS FROM

GROWING THE CAPILLARIES THEY NEED, HAVE ELIMINATED MOST FORMS CANCER AS A MAJOR KILLER.

[6]

Well, thatʹs just not a prediction I was willing to make. There have been so many false hopes with regard to cancer

treatments, and so many promising approaches proving to be dead ends, that I just wasnʹt willing to make that call.

Also, there just wasnʹt enough evidence when I wrote the book in 1998 to make that dramatic a prediction.

NOT THAT YOU SHIED AWAY FROM DRAMATIC PREDICTIONS.

The predictions I made were fairly conservative, actually, and were based on technologies and trends I could touch

and feel. I was certainly aware of several promising approaches to bioengineered cancer treatments, but it was still kind of iffy, given the history of cancer research. Anyway, the book only touched tangentially on bioengineering, although itʹs clearly an information‐based technology.

NOW WITH REGARD TO SEX

Speaking of health problems . . .

YES, WELL, YOU SAID THAT VIRTUAL PARTNERS WERE POPULAR, BUT I JUST DONʹT SEE THAT.

It might just be the circle you move in.

I HAVE A VERY SMALL CIRCLE—MOSTLY IʹVE BEEN TRYING TO GET BEN TO FOCUS ON OUR WEDDING.

Yes, tell me about him.

HEʹS VERY ROMANTIC. HE ACTUALLY SENDS ME LETTERS ON PAPER!

That is romantic. So, how was the phone call I interrupted?

I TRIED ON THIS NEW NIGHTGOWN HE SENT ME. I THOUGHT HEʹD APPRECIATE IT, BUT HE WAS BEING A

LITTLE ANNOYING.

I assume youʹre going to finish that thought.

WELL, HE WANTED ME TO KIND OF LET THESE STRAPS SLIP, MAYBE JUST A LITTLE. BUT IʹM KIND OF SHY

ON THE PHONE. I DONʹT REALLY GO IN FOR VIDEO PHONE SEX, NOT LIKE SOME FRIENDS I KNOW.

Oh, so I did get that prediction right.

ANYWAY, I JUST TOLD HIM TO USE THE IMAGE TRANSFORMERS.

Transformers?

YOU KNOW, HE CAN UNDRESS ME JUST AT HIS END.

Oh yes, of course. The computer is altering your image in real time.

EXACTLY. YOU CAN CHANGE SOMEONEʹS FACE, BODY, CLOTHING, OR SURROUNDINGS INTO SOMEONE

OR SOMETHING ELSE ENTIRELY, AND THEY DONʹT KNOW YOUʹRE DOING IT.

Hmmm.

ANYWAY, I CAUGHT BEN UNDRESSING HIS OLD GIRLFRIEND WHEN SHE CALLED TO CONGRATULATE

HIM ON OUR ENGAGEMENT. SHE HAD NO IDEA, AND HE THOUGHT IT WAS HARMLESS. I DIDNʹT SPEAK

TO HIM FOR A WEEK.

Well, as long as it was just at his end.

WHO KNOWS WHAT SHE WAS DOING AT HER END.

Thatʹs kind of her business, isnʹt it? As long as they donʹt know what the other is doing.

IʹM NOT SO SURE THEY DIDNʹT KNOW ANYWAY, PEOPLE DO SPEND A LOT OF TIME TOGETHER UP CLOSE

BUT AT A DISTANCE, IF YOU KNOW WHAT I MEAN.

Using the displays?

WE CALL THEM PORTALS—YOU CAN LOOK THROUGH THEM, BUT YOU CANʹT TOUCH.

I see, still no interest in virtual sex?

NOT PERSONALLY. I MEAN, ITʹS PRETTY PATHETIC. BUT I DID HAVE TO WRITE THE COPY FOR A

BROCHURE ABOUT A SENSUAL VIRTUAL REALITY ENVIRONMENT. BEING LOW ON THE TOTEM POLE, I

REALLY CANʹT PICK MY ASSIGNMENTS.

Did you try the product?

I DIDNʹT EXACTLY TRY IT. I JUST OBSERVED. I WOULD SAY THEY PUT MORE EFFORT INTO THE VIRTUAL

GIRLS THAN THE GUYS.

Howʹd your campaign make out?

THE PRODUCT BOMBED. I MEAN, THE MARKETʹS JUST SO CLUTTERED.

You canʹt win them all.

NO, BUT ONE OF YOUR PREDICTIONS DID WORK OUT QUITE WELL. I TOOK YOUR ADVICE ABOUT THAT

COMPANY NORTH OF THE MASON‐DIXON LINE. AND, HEY, IʹM NOT COMPLAINING.

Iʹll bet a lot of stocks are up.

YES, THE BOATS KEEP GETTING HIGHER.

Okay, what else?

YOUʹRE RIGHT ABOUT THE DISABLED. MY OFFICE MATE IS DEAF, AND ITʹS NOT AN ISSUE AT ALL. THEREʹS

NOTHING IMPORTANT A BLIND OR DEAF PERSON CANʹT DO TODAY.

That was really true back in 1999.

I THINK THE DIFFERENCE NOW IS THAT THE PUBLIC UNDERSTANDS IT. ITʹS JUST A LOT MORE OBVIOUS

WITH TODAYʹS TECHNOLOGY. BUT THAT UNDERSTANDING IS IMPORTANT.

Sure, without the technology, thereʹs just a lot of misconception and prejudice.

TRUE ENOUGH. I THINK IʹM GOING TO HAVE TO GET GOING, I CAN SEE BENʹS FACE ON MY CALL LINE.

He looks like a St. Bernard.

OH, I LEFT MY IMAGE TRANSFORMERS ON. HERE, IʹLL LET YOU SEE WHAT HE REALLY LOOKS LIKE.

Hey, good‐looking guy. Well, good luck. You do seem to have changed.

I SHOULD HOPE SO.

I mean I think our relationship has changed.

WELL, IʹM TEN YEARS OLDER.

And it seems that Iʹm asking you most of the questions.

I GUESS IʹM THE EXPERT NOW. I CAN JUST TELL YOU WHAT I SEE. BUT HOW COME YOUʹRE STILL STUCK

IN 1999?

Iʹm afraid I just canʹt leave quite yet. I have to get this book out, for one thing.

I DO HAVE ONE CONFUSION. HOW IS IT THAT YOU CAN TALK TO ME FROM 1999 WHEN IʹM HERE IN THE

YEAR 2009? WHAT KIND OF TECHNOLOGY IS THAT?

Oh, thatʹs a very old technology. Itʹs called poetic license.

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C H A P T E R T E N

2019

He who mounts a wild elephant goes where the wild elephant goes.

—Randolph Bourne

It does not do you good to leave a dragon out of your calculations, if you live near him.

—J. R. R. Tolkien

The Computer Itself

Computers are now largely invisible. They are embedded everywhere—in walls, tables, chairs, desks, clothing, jewelry, and bodies.

People routinely use three‐dimensional displays built into their glasses, [1] or contact lenses. These ʺdirect eyeʺ

displays create highly realistic, virtual visual environments overlaying the ʺrealʺ environment. This display technology projects images directly onto the human retina, exceeds the resolution of human vision, and is widely used regardless of visual impairment. The direct‐eye displays operate in three modes:

1. Head‐directed display: The displayed images are stationary with respect to the position and orientation of your head. When you move your head, the display moves relative to the real environment. This mode is often used

to interact with virtual documents.

2. Virtual‐reality overlay display: The displayed images slide when you move or turn your head so that the virtual people, objects, and environment appear to remain stationary in relation to the real environment (which you

can still see). Thus if the direct‐eye display is displaying the image of a person (who could be a geographically

remote real person engaging in a three‐dimensional visual phone call with you, or a computer‐generated

ʺsimulatedʺ person), that projected person will appear to be in a particular place relative to the real

environment that you also see. When you move your head, that projected person will appear to remain in the

same place relative to the real environment.

3. Virtual‐reality blocking display: This is the same as the virtual‐reality overlay display except that the real environment is blocked out, so you see only the projected virtual environment. You use this mode to leave

ʺrealʺ reality and enter a virtual reality environment.

In addition to the optical lenses, there are auditory ʺlenses,ʺ which place high‐resolution sounds in precise locations in a three‐dimensional environment. These can be built into eyeglasses, worn as body jewelry, or implanted in the ear canal.

Keyboards are rare, although they still exist. Most interaction with computing is through gestures using hands, fingers, and facial expressions and through two‐way natural‐language spoken communication. People communicate

with computers the same way they would communicate with a human assistant, both verbally and through visual expression. Significant attention is paid to the personality of computer‐based personal assistants, with many choices available. Users can model the personality of their intelligent assistants on actual persons, including themselves, or select a combination of traits from a variety of both public personalities and private friends and associates.

Typically, people do not own just one specific ʺpersonal computer,ʺ although computing is nonetheless very personal. Computing and extremely‐high‐bandwidth communication are embedded everywhere. Cables have largely

disappeared.

The computational capacity of a $4,000 computing device (in 1999 dollars) is approximately equal to the computational capability of the human brain (20 million billion calculations per second). [2] Of the total computing capacity of the human species (that is, all human brains) combined with the computing technology the species has created, more than 10 percent is nonhuman. [3]

Rotating memories and other electromechanical computing devices have been fully replaced with electronic devices. Three‐dimensional nanotube lattices are now a prevalent form of computing circuitry.

The majority of ʺcomputesʺ of computers are now devoted to massively parallel neural nets and genetic algorithms.

Significant progress has been made in the scanning‐based reverse engineering of the human brain. it is now fully

recognized that the brain comprises many specialized regions, each with its own topology and architecture of interneuronal connections. The massively parallel algorithms are beginning to be understood, and these results have

been applied to the design of machine‐based neural nets. It is recognized that the human genetic code does not specify the precise interneuronal wiring of any of the regions, but rather sets up a rapid evolutionary process in which connections are established and fight for survival. The standard process for wiring machine‐based neural nets

uses a similar genetic evolutionary algorithm.

A new computer‐controlled optical‐imaging technology using quantum‐based diffraction devices has replaced

most lenses with tiny devices that can detect light waves from any angle. These pinhead‐sized cameras are everywhere.

Autonomous nanoengineered machines can control their own mobility and include significant computational

engines. These microscopic machines are beginning to be applied to commercial applications, particularly in manufacturing and process control, but are not yet in the mainstream.

Education

Hand‐held displays are extremely thin, very high resolution, and weigh only ounces. People read documents either

on the hand‐held displays or, more commonly, from text that is projected into the ever present virtual environment

using the ubiquitous direct‐eye displays. Paper books and documents are rarely used or accessed. Most twentieth-century paper documents of interest have been scanned and are available through the wireless network.

Most learning is accomplished using intelligent software‐based simulated teachers. To the extent that teaching is

done by human teachers, the human teachers are often not in the local vicinity of the student. The teachers are viewed more as mentors and counselors than as sources of learning and knowledge.

Students continue to gather together to exchange ideas and to socialize, although even this gathering is often physically and geographically remote.

All students use computation. Computation in general is everywhere, so a studentʹs not having a computer is rarely an issue.

Most adult human workers spend the majority of their time acquiring new skills and knowledge.

Disabilities

Blind persons routinely use eyeglass‐mounted reading‐navigation systems, which incorporate the new, digitally controlled, high‐resolution optical sensors. These systems can read text in the real world, although since most print is now electronic, print‐to‐speech reading is less of a requirement. The navigation function of these systems, which emerged about ten years ago, is now perfected. These automated reading‐navigation assistants communicate to blind

users through both speech and tactile indicators. These systems are also widely used by sighted persons since they provide a high‐resolution interpretation of the visual world.

Retinal and vision neural implants have emerged but have limitations and are used by only a small percentage of

blind persons.

Deaf persons routinely read what other people are saying through the deaf personsʹ lens displays. There are systems that provide visual and tactile interpretations of other auditory experiences such as music, but there is debate regarding the extent to which these systems provide an experience comparable to that of a hearing person. Cochlear

and other implants for improving hearing are very effective and are widely used.

Paraplegic and some quadriplegic persons routinely walk and climb stairs through a combination of computer-controlled nerve stimulation and exoskeletal robotic devices.

Generally, disabilities such as blindness, deafness, and paraplegia are not noticeable and are not regarded as significant.

Communication

You can do virtually anything with anyone regardless of physical proximity. The technology to accomplish this is easy to use and ever present.

ʺPhoneʺ calls routinely include high‐resolution three‐dimensional images projected through the direct‐eye

displays and auditory lenses. Three‐dimensional holography displays have also emerged. In either case, users feel as if they are physically near the other person. The resolution equals or exceeds optimal human visual acuity. Thus a person can be fooled as to whether or not another person is physically present or is being projected through electronic communication. The majority of ʺmeetingsʺ do not require physical proximity.

Routinely available communication technology includes high‐quality speech‐to‐speech language translation for most common language pairs.

Reading books, magazines, newspapers, and other web documents, listening to music, watching three‐

dimensional moving images (for example, television, movies), engaging in three‐dimensional visual phone calls, entering virtual environments (by yourself, or with others who may be geographically remote), and various combinations of these activities are all done through the ever present communications Web and do not require any equipment, devices, or objects that are not worn or implanted.

The all‐enveloping tactile environment is now widely available and fully convincing. Its resolution equals or exceeds that of human touch and can simulate (and stimulate) all of the facets of the tactile sense, including the sensing of pressure, temperature, textures, and moistness. Although the visual and auditory aspects of virtual reality involve only devices you have on or in your body (the direct‐eye lenses and auditory lenses), the ʺtotal touchʺ haptic environment requires entering a virtual reality booth. These technologies are popular for medical examinations, as well as sensual and sexual interactions with other human partners or simulated partners. In fact, it is often the preferred mode of interaction, even when a human partner is nearby, due to its ability to enhance both experience and safety.

Business and Economics

Rapid economic expansion and prosperity has continued.

The vast majority of transactions include a simulated person, featuring a realistic animated personality and two‐

way voice communication with high‐quality natural‐language understanding. Often, there is no human involved, as a

human may have his or her automated personal assistant conduct transactions on his or her behalf with other automated personalities. In this case, the assistants skip the natural language and communicate directly by exchanging appropriate knowledge structures.

Household robots for performing cleaning and other chores are now ubiquitous and reliable.

Automated driving systems have been found to be highly reliable and have now been installed in nearly all roads.

While humans are still allowed to drive on local roads (although not on highways), the automated driving systems are always engaged and are ready to take control when necessary to prevent accidents. Efficient personal flying vehicles using microflaps; have been demonstrated and are primarily computer controlled. There are very few transportation accidents.

Politics and Society

People are beginning to have relationships with automated personalities as companions, teachers, caretakers, and lovers. Automated personalities are superior to humans in some ways, such as having very reliable memories and, if

desired, predictable (and programmable) personalities. They are not yet regarded as equal to humans in the subtlety

of their personalities, although there is disagreement on this point.

An undercurrent of concern is developing with regard to the influence of machine intelligence. There continue to

be differences between human and machine intelligence, but the advantages of human intelligence are becoming more difficult to identify and articulate. Computer intelligence is thoroughly interwoven into the mechanisms of civilization and is designed to be outwardly subservient to apparent human control. On the one hand, human transactions and decisions require by law a human agent of responsibility, even if fully initiated by machine intelligence. On the other hand, few decisions are made without significant involvement and consultation with machine‐based intelligence.

Public and private spaces are routinely monitored by machine intelligence to prevent interpersonal violence.

People attempt to protect their privacy with near‐unbreakable encryption technologies, but privacy continues to be a major political and social issue with each individualʹs practically every move stored in a database somewhere.

The existence of the human underclass continues as an issue. While there is sufficient prosperity to provide basic

necessities (secure housing and food, among others) without significant strain to the economy, old controversies persist regarding issues of responsibility and opportunity. The issue is complicated by the growing component of most employmentʹs being concerned with the employeeʹs own learning and skill acquisition. In other words, the difference between those ʺproductivelyʺ engaged and those who are not is not always clear.

The Arts

Virtual artists in all of the arts are emerging and are taken seriously. These cybernetic visual artists, musicians, and authors are usually affiliated with humans or organizations (which in turn are comprised of collaborations of humans and machines) that have contributed to their knowledge base and techniques. However, interest in the output of these creative machines has gone beyond the mere novelty of machines being creative.

Visual, musical, and literary art created by human artists typically involve a collaboration between human and machine intelligence.

The type of artistic and entertainment product in greatest demand (as measured by revenue generated) continues

to be virtual‐experience software, which ranges from simulations of ʺrealʺ experiences to abstract environments with little or no corollary in the physical world.

Warfare

The primary threat to security comes from small groups combining human and machine intelligence using

unbreakable encrypted communication. These include (1) disruptions to public information channels using software

viruses, and (2) bioengineered disease agents.

Most flying weapons are tiny—some as small as insects with microscopic flying weapons being researched.

Health and Medicine

Many of the life processes encoded in the human genome, which was deciphered more than ten years earlier, are now

largely understood, along with the information‐processing mechanisms underlying aging and degenerative

conditions such as cancer and heart disease. The expected life span, which, as a (1780 through 1900) and the first phase result of the first Industrial Revolution of the second (the twentieth century), almost doubled from less than forty, has now substantially increased again, to over one hundred.

There is increasing recognition of the danger of the widespread availability of bioengineering technology. The means exist for anyone with the level of knowledge and equipment available to a typical graduate student to create

disease agents with enormous destructive potential. That this potential is offset to some extent by comparable gains in bioengineered antiviral treatments constitutes an uneasy balance, and is a major focus of international security agencies.

Computerized health monitors built into watches, jewelry, and clothing which diagnose both acute and chronic health conditions are widely used. in addition to diagnosis, these monitors provide a range of remedial recommendations and interventions.

Philosophy

There are prevalent reports of computers passing the Turing Test, although these instances do not meet the criteria (with regard to the sophistication of the human judge, the length of time for the interviews, etcetera) established by knowledgeable observers. There is a consensus that computers have not yet passed a valid Turing Test, but there is

growing controversy on this point.

The subjective experience of computer‐based intelligence is seriously discussed, although the rights of machine intelligence have not yet entered mainstream debate. Machine intelligence is still largely the product of a collaboration between humans and machines, and has been programmed to maintain a subservient relationship to the

species that created it.

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OKAY, IʹM HERE NOW. SORRY ABOUT BEING DISTRACTED TEN YEARS AGO.

No problem. Howʹve you been?

IʹM FINE—BUSY—BUT HOLDING UP. GETTING READY FOR MY SONʹS TENTH BIRTHDAY PARTY.

Oh, so you were pregnant last time we spoke.

I WASNʹT SHOWING YET, BUT PEOPLE DID NOTICE AT THE WEDDING.

Howʹs he doing?.

OKAY, BUT JEREMYʹS A LOT TO KEEP UP WITH.

Doesnʹt sound too unusual.

ANYWAY, I FOUND JEREMY WITH THIS OLDER WOMAN, LIKE MY AGE, LAST WEEK. LETʹS JUST SAY, SHE

DIDNʹT HAVE ALL OF HER CLOTHES ON.

Oh, really.

TURNED OUT TO BE HIS FOURTH‐GRADE TEACHER.

Gee, what was she doing?

WELL, HEʹD BEEN OUT SICK, SO SHE WAS GIVING HIM HIS HOMEWORK ASSIGNMENT.

Without all her clothes on?

OH, SHE HAD NO IDEA.

Of course, the image transformers, I forgot.

HEʹS NOT SUPPOSED TO HAVE ACCESS TO THOSE PARTICULAR TRANSFORMERS. BUT HE APPARENTLY

GOT A CHILD‐BLOCK OVERRIDE PATCH FROM ONE OF HIS FRIENDS. HE WONʹT SAY WHO.

Some things never change.

I THINK WE HAVE THE BLOCK BACK ON NOW.

So did you discuss this with his teacher?

MISS SIMON? OH GOD, NO.

Any punishment?

ACTIVATING THE CHILD‐BLOCK OVERRIDE IS JUST NOT TOLERATED IN OUR HOME. HEʹS RESTRICTED

FROM THE SENSORIUM FORA MONTH.

That does sound serious. Sensorium? Thatʹs a virtual reality thing?

ACTUALLY, SENSORIUM IS A BRAND NAME FOR THE TOTAL TOUCH ENVIRONMENT WE HAVE. ITʹS A

NEW MODEL WITH SOME IMPROVED OLFACTORY TECHNOLOGY. FOR JUST VISUAL‐AUDITORY VIRTUAL

REALITY THATʹS PRETTY MUCH ON ALL THE TIME USING THE LENSES, YOU DONʹT NEED TO USE

ANYTHING SPECIAL.

So what does he do in the Sensorium?

OH, KICK BOXING, GALACTIC WRESTLING, THE USUAL TEN‐YEAR‐OLD STUFF. LATELY, HEʹS BEEN

PLAYING DOCTOR.

Uh oh, he sounds precocious.

THINK HEʹS JUST TRYING TO TEST OUR PATIENCE.

So this incident with Miss Simon, that was in the Sensorium?

NO, THAT WAS JUST A VIRTUAL REALITY PHONE CALL. JEREMY WAS HERE IN THE KITCHEN. HE HAD

MISS SIMON SITTING ON THE KITCHEN TABLE.

So if heʹs looking at her transformed image using his virtual reality lenses, how were you able to see her?

WELL, WE HAVE ACCESS TO OUR KIDSʹ VIRTUAL REALITY ENVIRONMENTS UP UNTIL AGE FOURTEEN.

I see, so youʹre simultaneously in your own virtual reality environment, and those of your children?

YES, AND DONʹT FORGET REAL REALITY, NOT THAT VIRTUAL REALITY ISNʹT REAL.

Isnʹt that confusing, seeing and hearing all these different environments overlaying each other?

WE DONʹT HEAR OUR KIDSʹ VIRTUAL REALITY ENVIRONMENTS. THE NOISE WOULD DRIVE US CRAZY,

AND KIDS NEED TO HAVE SOME PRIVACY, TOO. WE CAN ONLY HEAR REAL REALITY AND OUR OWN

VIRTUAL REALITY. AND, WE CAN TUNE IN AND OUT OF OUR KIDSʹ VIRTUAL VISUAL REALITIES. SO I

TUNED IN, AND THERE WAS MISS SIMON.

What else has he been punished for?

THREE MONTHS AGO, HE WAS BLOCKING OUR CHILD VIRTUAL REALITY ACCESS. I THINK HE GOT THAT

FROM THE SAME FRIEND.

Iʹm not sure I blame him. I donʹt think I would want my mother looking in on my virtual reality all the time.

WE DONʹT LOOK IN ALL THE TIME; WEʹRE REALLY QUITE SELECTIVE. BUT YOU HAVE TO KEEP TRACK OF

KIDS NOWADAYS. WE DONʹT HAVE THIS PROBLEM WITH OUR DAUGHTER, EMILY.

Sheʹs . . .

SIX YEARS OLD LAST MONTH. SHEʹS A REAL SWEETHEART. SHE JUST DEVOURS BOOKS.

At six, thatʹs impressive. She reads them by herself?

BY HERSELF? HOW ELSE WOULD SHE READ THEM?

Well, you could read them to her.

I DO THAT SOMETIMES. BUT EMILY FEELS IʹM NOT ACCOMMODATING ENOUGH. SO SHE HAS HARRY

HIPPO READ THEM TO HER, AND HE DOES EXACTLY WHAT SHE WANTS, AND DOESNʹT TALK BACK. OF

COURSE. I WOULDNʹT WANT A REAL HIPPOPOTAMUS SITTING ON MY KITCHEN TABLE.

Not with a partially clad Miss Simon there also.

IT DOES GET TO BE A CROWDED TABLE.

So when Harry Hippo reads to Emily, she follows along in her virtual book.

SHE CAN EITHER FOLLOW ALONG HERSELF, OR SHE CAN TURN THE HIGHLIGHTING ON. THE KIDS LET

THEIR FAVORITE VIRTUAL FRIEND READ TO THEM, WHILE THEY WATCH THEIR VIRTUAL BOOKS WITH

THE HIGHLIGHTING FEATURE. LATER ON, THEY TURN THE HIGHLIGHTING OFF, AND EVENTUALLY,

THEY DONʹT NEED HARRY HIPPO, EITHER.

Kind of like taking off the training wheels.

RIGHT. NOW, ONE THING THAT DOES GIVE ME COMFORT IS THAT I ALWAYS KNOW WHERE MY KIDS ARE.

In virtual reality?

NO, IʹM TALKING ABOUT REAL REALITY. NOW FOR EXAMPLE, I CAN SEE THAT JEREMY IS TWO BLOCKS

AWAY, HEADED IN THIS DIRECTION.

An embedded chip?

THATʹS A REASONABLE GUESS. BUT ITʹS NOT A CHIP EXACTLY. ITʹS ONE OF THE FIRST USEFUL

NANOTECHNOLOGY APPLICATIONS. YOU EAT THIS STUFF.

Stuff?

YEAH, ITʹS A PASTE, TASTES PRETTY GOOD, ACTUALLY. IT HAS MILLIONS OF LITTLE COMPUTERS—WE

CALL THEM TRACKERS—WHICH WORK THEIR WAY INTO YOUR CELLS.

Some of them must get passed through.

THATʹS TRUE, AND THE TRACKERS THAT GET TOO FAR AWAY FROM THE REST OF THE TRACKERS THAT

ARE STILL IN THE BODY JUST TURN THEMSELVES OFF. THE ONES THAT STAY IN YOUR BODY

COMMUNICATE WITH EACH OTHER, AND WITH THE WEB.

The wireless Web?

YES, ITʹS EVERYWHERE. SO I ALWAYS KNOW WHERE MY KIDS ARE. NEAT, HUH?

So does everybody have this?

KIDS ARE REQUIRED TO, SO I GUESS EVERYONE WILL HAVE IT EVENTUALLY. MANY ADULTS DO, BUT

ADULTS CAN BLOCK THE TRACKING TRANSMISSION IF THEY WISH.

Kids canʹt?

TRACKER BLOCKING IS SOMETHING WE REALLY DO MANAGE TO KEEP FROM OUR KIDS.

So Jeremy hasnʹt gotten his hands on any tracker‐blocking software?

I CERTAINLY HOPE NOT. ALTHOUGH, COME TO THINK OF IT, WE DID HAVE A TRACKER LAPSE LAST

YEAR. THE TECHNICIAN SAID IT WAS A TEMPORARY PROTOCOL CONFLICT. I DOUBT THAT WAS JEREMYʹS

DOING. BUT NOW YOUʹVE GOT ME WORRIED.

I doubt Jeremy would do something like that.

I THINK YOUʹRE RIGHT.

This technician was human?

NO, THE PROBLEM WASNʹT THAT SERIOUS. WE JUST USED A LEVEL‐B TECHNICIAN.

I see. So is your husband plugged into the tracking system?

YEAH, BUT HE BLOCKS IT A LOT, WHICH IS ANNOYING.

Well, husbands are entitled to some privacy, too, donʹt you think?

YES, DEFINITELY.

So, any other relatives you want to tell me about?

THEREʹS MY TWENTY‐FIVE‐YEAR‐OLD NEPHEW, STEPHEN. HEʹS A BIT RECLUSIVE; I KNOW MY SISTER IS

WORRIED ABOUT HIM. HE SPENDS ALMOST ALL OF HIS TIME IN EITHER TOTAL TOUCH OR IN VIRTUAL‐

REALITY BLOCKING‐DISPLAY MODE.

Thatʹs a problem?

ITʹS NOT JUST THAT HE BLOCKS OUT REAL REALITY, ITʹS THAT HE SEEMS TO AVOID INTERACTING WITH

REAL PEOPLE, EVEN IN VIRTUAL REALITY. IT SEEMS TO BE AN INCREASINGLY COMMON PROBLEM.

I guess simulated people are more accommodating.

THEY CAN BE. I MEAN, MY OWN ASSISTANTS AND COMPANIONS ARE, BUT TRY DEALING WITH OTHER

PEOPLEʹS ASSISTANTS, AND THATʹS A DIFFERENT MATTER. ANYWAY, MY SISTER WAS TELLING ME HOW

SHE THOUGHT THAT STEPHEN WAS A CYBER VIRGIN, OR DID SHE SAY VIRTUAL VIRGIN?

Oh dear, now what was the distinction again?

YOU KNOW, A CYBER VIRGIN HAS NEVER HAD INTERCOURSE OUTSIDE OF VIRTUAL REALITY, WHEREAS

A VIRTUAL VIRGIN HAS NEVER HAD INTERCOURSE WITH A REAL PERSON, EVEN IN VIRTUAL REALITY.

How about someone who has never been intimate with a real or simulated person in real or virtual reality?

HMM, WE DONʹT SEEM TO HAVE A TERM FOR THAT.

So what are the statistics on this?

WELL, LETʹS SEE, GEORGE WILL GET THAT FOR US.

George is your virtual assistant?

YEAH, YOU CATCH ON QUICKLY.

Gee, thanks.

SO, FOR ADULTS OVER TWENTY‐FIVE, 11 PERCENT ARE VIRTUAL VIRGINS, AND 19 PERCENT ARE CYBER

VIRGINS.

So I guess virtual sex is catching on. How about you and Ben?

WELL, I DEFINITELY PREFER THE REAL THING!

Real, as in . . .

REAL REALITY, RIGHT.

So you prefer intimacy in real reality, meaning you donʹt avoid the virtual alternative?

WELL, ITʹS RIGHT THERE, MEAN WEʹD HAVE TO GO OUT OF OUR WAY TO AVOID IT. ITʹS CERTAINLY

CONVENIENT IF IʹM TRAVELING, OR IF WE DONʹT WANT TO WORRY ABOUT BIRTH CONTROL.

Or STDS.

WELL, THAT SHOULDNʹT BE AN ISSUE.

Hey, you never know.

WELL, TO BE PERFECTLY HONEST, VIRTUAL SEX IS MUCH MORE SATISFACTORY IN MANY WAYS. I MEAN

ITʹS DEFINITELY MORE INTENSE, PRETTY INCREDIBLE ACTUALLY.

This is in the Sensorium, I assume.

YEAH, SURE. THIS RECENT MODEL HAS REALLY ADDRESSED THE OLFACTORY ISSUE.

Meaning it has an olfactory capability?

RIGHT. ITʹS A LITTLE DIFFERENT THAN THE OTHER SENSES, THOUGH. WITH THE VISUAL AND AUDITORY

SENSE, JUST PLAIN OLD UBIQUITOUS VIRTUAL REALITY IS EXTREMELY ACCURATE. IN THE SENSORIUM,

WE GET THE TACTILE ENVIRONMENT, WHICH ALSO PROVIDES AN EXTREMELY LIFELIKE RE‐CREATION.

BUT WE CANʹT DO THAT YET WITH THE SENSE OF SMELL. SO THE SENSORIUM 2000 HAS PROGRAMMED

SCENTS, WHICH YOU CAN CHOOSE, OR THAT ARE AUTOMATICALLY SELECTED IN THE COURSE OF AN

EXPERIENCE. THEYʹRE STILL PRETTY EFFECTIVE.

How do you feel about your husband interacting sexually with a simulated partner?

YOU MEAN, A SIMULATED PERSON IN VIRTUAL REALITY?

Yeah, in virtual reality or in the Sensorium.

THATʹS FINE. I HAVE NO PROBLEM WITH THAT.

You donʹt mind?

THEREʹS REALLY NO WAY I COULD KEEP TRACK OF IT.

Virtual lipstick on his collar?

YEAH, RIGHT, ON HIS VIRTUAL COLLAR. VIRTUAL SEX WITH SIMULATED PARTNERS IS GENERALLY

ACCEPTED NOWADAYS. ITʹS REALLY REGARDED AS A FORM OF FANTASY—ITʹS JUST ASSISTED FANTASY.

And if the partnerʹs a real person in virtual reality?

IʹD BREAK HIS LEGS.

His virtual legs?

THATʹS NOT WHAT I HAD IN MIND.

So whatʹs the difference between a real person in virtual reality and a simulated person?

AS SENSUAL PARTNERS?

Right.

OH, THEREʹS A DIFFERENCE—THE SIMULATED PARTNERS ARE PRETTY GOOD, BUT ITʹS JUST NOT THE

SAME.

Sounds like youʹve had some experience with this yourself.

PRETTY NOSY, ARENʹT YOU?

All right, Iʹll change the subject. Letʹs see, uh, whatʹs happening with encryption?

WE HAVE VERY STABLE THOUSAND‐BIT CODES. ITʹS NOT PRACTICAL TO BREAK THEM.

What about with a quantum computer?

THE QUANTUM COMPUTERS DONʹT SEEM TO BE STABLE WITH MORE THAN A FEW HUNDRED QU‐BITS.

Sounds like communication is pretty secure.

I WOULD SAY SO. BUT SOME PEOPLE ARE PARANOID ABOUT THE THIRD‐PARTY KEYS.

So the authorities have keys?

OF COURSE.

Well, canʹt you just put another layer of encryption without a key over the official layer?

GOD, NO.

Why is that so hard?

OH, ITʹS NOT DIFFICULT TECHNICALLY. ITʹS JUST VERY ILLEGAL, CERTAINLY SINCE OCTOBER 2013.

2013?

WE MANAGED TO GET THROUGH THE FIRST DECADE OF THIS CENTURY WITHOUT TOO SERIOUS A

PROBLEM. BUT THINGS GOT OUT OF CONTROL IN THE OKLAHOMA INCIDENT.

Oklahoma again. So this was a software virus?

NO, NOT A SOFTWARE VIRUS, A BIOLOGICAL VIRUS. A DISGRUNTLED—I WOULD SAY DEMENTED—

STUDENT, ACTUALLY A FORMER STUDENT AT THE UNIVERSITY THERE. THERE WERE REPORTS THAT HE

WAS LINKED TO THE REMEMBER YORK MOVEMENT, BUT THE RY DISCUSSION LEADERS VEHEMENTLY

DENIED ANY RESPONSIBILITY.

Remember York?

WELL, THIS INCIDENT OCCURRED ON THE TWO HUNDREDTH ANNIVERSARY OF THE YORK TRIALS.

Oh, you mean the 1813 trial of the Luddites?

YES, EXCEPT THAT MOST ANTITECHNOLOGISTS DONʹT LIKE THE TERM LUDDITE ANYMORE; THEY FEEL

THAT THE SOMEWHAT SILLY IMAGE OF NED LUDD BELITTLES THE SERIOUS NATURE OF THEIR

MOVEMENT. BEYOND THAT, THE BEST EVIDENCE SUGGESTS THAT LUDD NEVER EXISTED.

But there was a trial in 1813.

YES, WHICH RESULTED IN MANY OF THE GANG MEMBERS ACCUSED OF WRECKING THE TEXTILE

MACHINES BEING HANGED OR EXILED.

So RY is an organized movement?

OH, I WOULDNʹT SAY THAT. IT IS MORE OF A WEB DISCUSSION GROUP, AND THIS YOUNG MAN

APPARENTLY HAD PARTICIPATED IN SOME OF THOSE DISCUSSIONS. BUT THE RY PEOPLE ARE BASICALLY

NONVIOLENT. THEY WERE DISTRESSED THAT ROBERTS HAD ASSOCIATED HIMSELF WITH THEM.

Roberts was the perpetrator?

YEAH, CONVICTED ON ALL COUNTS. BUT ASIDE FROM THIS ONE DISTURBED INDIVIDUAL, I WOULD SAY

IT WAS REALLY A SCREWUP BY THE BWA.

BWA?

BIOWARFARE AGENCY.

So this was a virus that was unleashed?

YES, JUST A STANDARD MODIFIED FLU VIRUS, ALTHOUGH THERE WAS A TWIST. IT HAD A GREATLY

INCREASED MUTATION RATE, WHICH ACCELERATED ITS EVOLUTION AT SEVERAL LEVELS. ONE FORM

OF THE VIRUSʹS EVOLUTION ONLY TOOK PLACE DURING AN INFECTION. THIS, TOGETHER WITH A TIME‐

BOMB PROGRAM IN THE VIRUSʹS DNA, CAUSED ULTRA‐RAPID VIRAL REPRODUCTION AFTER A FEW

HOURS OF INFECTION. THIS LITTLE COMPLEXITY DELAYED THE DEVELOPMENT OF AN ANTIDOTE FOR

FORTY‐EIGHT HOURS. BUT THAT WASNʹT THE WORST OF IT. AFTER TWENTY‐FOUR HOURS OF

REPLICATING THE ANTIDOTE, THE BWA DISCOVERED THAT ANOTHER BIOLOGICAL AGENT HAD

INFECTED THE BATCHES, SO THEY HAD TO START OVER. AND THEN, THERE WERENʹT ENOUGH

REPLICATION STATIONS, SO THEY HAD TO CLEAN OUT THE ONES THEY HAD JUST USED, AND GO FROM

THERE. FORTY‐EIGHT HOURS WERE LOST IN THIS FIASCO, AND SIXTEEN THOUSAND PEOPLE DIED. WELL,

IF THINGS HAD BEEN DELAYED FOR ANOTHER TWENTY‐FOUR HOURS, IT WOULD HAVE BEEN FAR

WORSE. IT WAS A BIG ISSUE IN THE MIDTERM ELECTIONS OF 2014. THEREʹVE BEEN A LOT OF CHANGES

SINCE THEN.

The third‐party keys?

YEAH, THOSE EXISTED BEFORE. BUT SINCE 2013 THE LAWS AGAINST KEYLESS ENCRYPTION CODES HAVE

BEEN RIGOROUSLY ENFORCED.

What else has changed?

THERE ARE PLENTY OF ANTIVIRAL REPLICATION STATIONS NOW. AND WE ALL HAVE THESE CUTE

LITTLE GAS MASKS.

That little thimble is a gas mask?

YEAH, WELL, IT UNFOLDS LIKE THIS. ITʹS SMALL, SO WEʹRE ENCOURAGED TO KEEP THEM CLOSE AT

HAND. ITʹS ACTUALLY A VIRAL SCREEN MASK. OCCASIONALLY, WEʹRE TOLD TO PUT THEM ON, BUT

GENERALLY ITʹS ONLY FOR A FEW HOURS. SINCE 2013, THERE HAVE BEEN ONLY FALSE ALARMS.

So I guess the security agencies have been hard at work.

AS WILL ROGERS USED TO SAY, ʺYOU CANʹT SAY THAT CIVILIZATION DONʹT ADVANCE, FOR IN EVERY

WAR THEY KILL YOU IN A NEW WAY.ʺ

2013 sounds tragic and frightening. As centuries go, however, it doesnʹt sound like youʹre doing too badly. In the

twentieth century, we knew how to have disasters.

YEAH, FIFTY MILLION PEOPLE DIED IN WORLD WAR II.

Indeed.

ITʹS TRUE THAT THE CENTURY SO FAR HAS SEEN MUCH LESS BLOODSHED. BUT THE OTHER SIDE OF THE

COIN IS THAT THE TECHNOLOGIES ARE SO MUCH MORE POWERFUL TODAY. IF SOMETHING DID GO

WRONG, THINGS COULD SPIRAL OUT OF CONTROL VERY QUICKLY. WITH BIOENGINEERING, FOR

EXAMPLE, IT FEELS A LITTLE LIKE ALL TEN BILLION OF US ARE STANDING IN A ROOM UP TO OUR KNEES

IN A FLAMMABLE FLUID, WAITING FOR SOMEONE—ANYONE—TO LIGHT A MATCH.

But it sounds like a lot of fire extinguishers have been installed.

YEAH, I JUST HOPE THEY WORK.

You know, Iʹve been concerned about the downside of bioengineering for well over a decade now.

BUT YOU DIDNʹT WRITE ABOUT IT IN THE AGE OF INTELLIGENT MACHINES, WHICH YOU WROTE IN THE

LATE 1980s.

That was a conscious decision. I didnʹt want to give the wrong person any ideas.

AND IN 1999?

Oh, the catʹs out of the bag now.

YEAH, WELL, WEʹVE BEEN SCURRYING AFTER THE DESCENDANTS OF THAT CAT FOR THE LAST COUPLE

OF DECADES, TRYING TO KEEP THEM FROM CAUSING TOO MUCH MISCHIEF.

Just wait until the nanopathogens get going.

FORTUNATELY, THEYʹRE NOT SELF‐REPLICATING.

Not yet.

I SUPPOSE THATʹS COMING, TOO, BUT THE TRACKER PASTE AND THE OTHER FEW NANOTECHNOLOGY

APPLICATIONS THAT ARE OUT THERE TODAY ARE MADE USING X‐RAY LITHOGRAPHY AND OTHER

CONVENTIONAL MANUFACTURING TECHNIQUES.

Well, enough of disasters, what are you up to tonight?

IʹM GIVING A LECTURE ON MY EXPERIENCE LAST WEEK AS A TURING TEST JUDGE.

I assume the computer lost.

YES, SHE DID. BUT IT WASNʹT THE SLAM DUNK I THOUGHT IT WOULD BE. AT THE BEGINNING, I WAS

THINKING, GEE, THIS IS A LOT HARDER THAN I HAD EXPECTED. I REALLY CANʹT TELL WHO THE

COMPUTER IS, OR WHO THE HUMAN FOIL IS. AFTER ABOUT TWENTY MINUTES, IT DID BECOME FAIRLY

CLEAR TO ME, AND IʹM GLAD I HAD ENOUGH TIME. A FEW OF THE OTHER JUDGES JUST HAD NO IDEA,

BUT THEY WERENʹT VERY SOPHISTICATED.

I guess your communications background came in handy.

ACTUALLY IT WAS MORE MY MOMMY BACKGROUND. I BECAME SUSPICIOUS WHEN SHEILA—SHE WAS

THE COMPUTER—STARTED TALKING ABOUT HOW ANGRY SHE WAS AT HER DAUGHTER. THAT WAS NOT

CONVINCING FOR ME. SHE JUST WASNʹT SYMPATHETIC ENOUGH.

How about George, how would he fare in a Turing Test?

OH, I WOULDNʹT WANT TO SUBJECT GEORGE TO THAT.

Youʹre concerned about his feelings?

I GUESS YOU COULD SAY THAT. I KIND OF GO BACK AND FORTH. SOMETIMES, I THINK IʹM NOT. BUT

THEN WHEN IʹM INTERACTING WITH HIM, I FIND MYSELF ACTING AS IF HE HAS FEELINGS. AND,

SOMETIMES, I LOOK FORWARD TO TELLING HIM SOMETHING IʹVE EXPERIENCED, PARTICULARLY IF

WEʹRE WORKING ON IT TOGETHER.

I see you picked a male assistant.

SURE, YOUR PREDICTION THAT WOMEN WOULD PREFER FEMALE PERSONALITIES WAS ANOTHER MISS.

That prediction was for 2009, not 2019.

IʹM GLAD YOU CLARIFIED THAT. COME TO THINK OF IT, I DID USE A FEMALE PERSONALITY IN 2009, BUT

THEY WERENʹT VERY REALISTIC THEN. ANYWAY, I HAVE TO GET GOING TO MY LECTURE. BUT IF I THINK

OF ANYTHING ELSE INTERESTING TO TELL YOU, IʹLL HAVE MY VIRTUAL ASSISTANT CONTACT YOURS.

Hey, I donʹt have one, remember Iʹm stuck in 1999.

TOO BAD. I GUESS IʹLL JUST HAVE TO VISIT YOU MYSELF THEN.

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C H A P T E R E L E V E N

2029

Iʹm as fond of my body as anyone else, but if I can be 200 with a body of silicon, Iʹll take it.

—Danny Hillis

The Computer Itself

A $1,000 unit of computation (in circa‐1999 dollars) has the computing capacity of approximately 1,000 human brains

(1,000 times 20 million billion—that is, 2 times 1019 calculations per second).

Of the total computing capacity of the human species (that is, all human brains) combined with the computing technology humans initiated the creation of, more than 99 percent is nonhuman.

The vast majority of ʺcomputesʺ of nonhuman computing is now conducted on massively parallel neural nets, much of which is based on the reverse engineering of the human brain.

Many—but less than a majority—of the specialized regions of the human brain have been ʺdecodedʺ and their massively parallel algorithms have been deciphered. The number of specialized regions, amounting to hundreds, is greater than was anticipated twenty years earlier. The topologies and architectures of those regions that have been successfully reverse engineered are used in machine‐based neural nets. The machine‐based nets are substantially faster and have greater computing and memory capacities and other refinements compared to their human

analogues.

Displays are now implanted in the eyes, with a choice of permanent implants or removable implants (similar to

contact lenses). Images are projected directly onto the retina providing the usual high‐resolution three‐dimensional overlay on the physical world. These implanted visual displays also act as cameras to capture visual images and thus are both input and output devices.

Cochlear implants, originally used just for the hearing impaired, are now ubiquitous. These implants provide auditory communication in both directions between the human user and the worldwide computing network.

Direct neural pathways have been perfected for high‐bandwidth connection to the human brain. This allows bypassing certain neural regions (for example, visual pattern recognition, long‐term memory) and augmenting or replacing the functions of these regions with computing performed either in a neural implant or externally.

A range of neural implants is becoming available to enhance visual and auditory perception and interpretation, memory, and reasoning.

Computing processes can be personal (accessible by one individual), shared (accessible to a group), or universal

(accessible to everyone), at the userʹs option.

Three‐dimensional projected holographic displays are everywhere.

Microscopic nanoengineered robots now have microbrains with the computing speed and capacity of the human

brain. They are widely used in industrial applications and are beginning to be used in medical applications (see ʺHealth and Medicineʺ).

Education

Human learning is primarily accomplished using virtual teachers and is enhanced by the widely available neural implants. The implants improve memory and perception, but it is not yet possible to download knowledge directly.

Although enhanced through virtual experiences, intelligent interactive instruction, and neural implants, learning still requires time‐consuming human experience and study. This activity comprises the primary focus of the human species.

Automated agents are learning on their own without human spoon‐feeding of information and knowledge.

Computers have read all available human and machine‐generated literature and multimedia material, which includes

written, auditory, visual, and virtual experience works.

Significant new knowledge is created by machines with little or no human intervention. Unlike humans, machines

easily share knowledge structures with one another.

Disabilities

The prevalence of highly intelligent visual navigation devices for the blind, speech‐to‐print display devices for the deaf, nerve stimulation, intelligent orthotic prosthetics for the physically disabled, and a variety of neural implant technologies has essentially eliminated the handicaps associated with most disabilities. Sensory‐enhancement devices are in fact used by most of the population.

Communication

In addition to the ubiquitous, three‐dimensional virtual environments, there has been significant refinement to three-dimensional holographic technology for visual communication. There is also projected sonic communication for precisely placing sounds in three‐dimensional space. Similar to virtual reality, much of what is seen and heard in ʺrealʺ reality also has no physical counterpart. Thus family members can be sitting around the living room enjoying

one anotherʹs company without being physically proximate.

In addition, there is extensive use of communication using direct neural connections. This allows virtual, all-enveloping tactile communication to take place without entering a ʺtotal touch enclosure,ʺ as was necessary ten years earlier.

The majority of communication does not involve a human. The majority of communication involving a human is

between a human and a machine.

Business and Economics

The human population has leveled off in size at around 12 billion real persons. The basic necessities of food, shelter, and security are available for the vast majority of the human population.

Human and nonhuman intelligences are primarily focused on the creation of knowledge in its myriad forms, and

there is significant struggle over intellectual property rights, including ever increasing levels of litigation.

There is almost no human employment in production, agriculture, and transportation. The largest profession is education. There are many more lawyers than doctors.

Politics and Society

Computers appear to be passing forms of the Turing Test deemed valid by both human and nonhuman authorities,

although controversy on this point persists. It is difficult to cite human capabilities of which machines are incapable.

Unlike human competence, which varies greatly from person to person, computers consistently perform at optimal levels and are able to readily share their skills and knowledge with one another.

A sharp division no longer exists between the human world and the machine world. Human cognition is being ported to machines, and many machines have personalities, skills, and knowledge bases derived from the reverse engineering of human intelligence. Conversely, neural implants based on machine intelligence are providing enhanced perceptual and cognitive functioning to humans.

Defining what constitutes a human being is emerging as a significant legal and political issue.

The rapidly growing capability of machines is controversial, but there is no effective resistance to it. Since machine intelligence was initially designed to be subservient to human control, it has not presented a threatening ʺfaceʺ to the human population. Humans realize that disengaging the now human‐machine civilization from its dependence on machine intelligence is not possible.

Discussion of the legal rights of machines is growing, particularly those of machines that are independent of humans (those not embedded in a human brain). Although not yet fully recognized by law, the pervasive influence of

machines in all levels of decision making is providing significant protection to machines.

The Arts

Cybernetic artists in all of the arts—musical, visual, literary, virtual experience, and all others—no longer need to associate themselves with humans or organizations that include humans. Many of the leading artists are machines.

Health and Medicine

Progress continues in understanding and ameliorating the effects of aging as a result of a thorough understanding of the information—processing processes controlled through the genetic code. The life expectancy of humans continues

to increase and is now around 120 years. Significant attention is being paid to the psychological ramifications of a substantially increased human life span.

There is growing recognition that continuing extensions to the human life span will involve further use of bionic

organs, including portions of the brain. Nanobots are being used as scouts, to a limited extent as repair agents in the bloodstream, and as building blocks for bionic organs.

Philosophy

Although computers routinely pass apparently valid forms of the Turing Test, controversy persists about whether or

not machine intelligence equals human intelligence in all of its diversity At the same time, it is clear that there are many ways in which machine intelligence is vastly superior to human intelligence. For reasons of political sensitivity, machine intelligences generally do not press the point of their superiority. The distinction between human and machine intelligence is blurring as machine intelligence is increasingly derived from the design of human intelligence, and human intelligence is increasingly enhanced by machine intelligence.

The subjective experience of machine intelligence is increasingly accepted, particularly since ʺmachinesʺ

participate in this discussion.

Machines claim to be conscious and to have as wide an array of emotional and spiritual experiences as their human progenitors, and these claims are largely accepted.

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I HOPE YOUʹRE HAVING A GOOD TIME MAKING ALL THESE PREDICTIONS.

This part of the book is a bit more fun to write—at least there are fewer references to look up. And I donʹt have to worry about being embarrassed for at least a few decades.

WELL, IT MIGHT BE EASIER IF YOU JUST ASKED ME FOR MY IMPRESSIONS.

Yes, I was just getting to that. But I must say, you look very well.

FOR AN OLD LADY.

I wasnʹt thinking old. But you donʹt look anywhere near fifty. More like thirty‐five.

YES, WELL, FIFTY ISNʹT AS OLD AS IT USED TO BE.

We feel that way in 1999, too.

ITʹS STILL HELPFUL TO EAT RIGHT. WE ALSO HAVE A FEW TRICKS YOU DIDNʹT HAVE. [2]

Nanoengineered bodies?

NO, NOT EXACTLY, NANOTECHNOLOGY IS STILL FAIRLY LIMITED. Bioengineering HAS CERTAINLY

HELPED THE MOST. AGING HAS BEEN DRAMATICALLY SLOWED. MOST DISEASES CAN BE PREVENTED OR

REVERSED.

So nanotechnology is still fairly primitive?

IʹD SAY SO. I MEAN, WE DO HAVE NANOBOTS IN OUR BLOODSTREAMS, BUT THEYʹRE PRIMARILY

DIAGNOSTIC. SO IF ANYTHING STARTS TO GO WRONG, WE CATCH IT VERY EARLY.

So if a nanobot discovers a microscopic infection or other problem developing, what does it do, just start yelling?

YEAH, THATʹS ABOUT IT. I DONʹT THINK WEʹD TRUST IT TO DO MUCH ELSE. IT YELLS TO THE WEB, AND

THEN THE PROBLEM GETS TAKEN CARE OF WHEN WE SIT DOWN FOR OUR NEXT DAILY SCAN.

A three‐dimensional scan?

OF COURSE, WE STILL HAVE THREE‐DIMENSIONAL BODIES.

This is a diagnostic scan?

THE SCAN HAS A DIAGNOSTIC FUNCTION. BUT ITʹS ALSO REMEDIAL. THE SCANNER CAN APPLY

SUFFICIENT ENERGY TO A SMALL THREE‐DIMENSIONAL SET OF POINTS TO DESTROY A COLONY OF

PATHOGENS OR PROBLEMATICAL CELLS BEFORE THEY GET OUT OF HAND.

Is this an electromagnetic energy beam, or a particle beam, or what?

WELL, GEORGE CAN EXPLAIN IT BETTER THAN I CAN. AS I UNDERSTAND IT, IT HAS TWO ENERGY BEAMS

THAT ARE BENIGN BY THEMSELVES, BUT CAUSE PARTICLE EMISSIONS AT THE POINT AT WHICH THEY

CROSS. IʹLL ASK GEORGE NEXT TIME I SEE HIM.

Whenʹs that going to be?

OH, JUST AS SOON AS I GET DONE WITH YOU.

Youʹre not rushing me, are you?

OH, THEREʹS NO HURRY. ITʹS ALWAYS A GOOD IDEA TO BE PATIENT.

Hmmm. So when was the last time the two of you were together?

A FEW MINUTES AGO.

I see. Sounds like your relationship has developed.

OH, IT HAS. HE TAKES VERY GOOD CARE OF ME.

Last time we talked, you werenʹt sure whether he had any feelings.

THAT WAS A LONG TIME AGO. GEORGE IS A DIFFERENT PERSON EVERY DAY. HE JUST GROWS AND

LEARNS CONSTANTLY. HE DOWNLOADS WHATEVER KNOWLEDGE HE WANTS FROM THE WEB AND IT

BECOMES PART OF HIM. HEʹS SO SMART AND INTENSE, AND VERY SPIRITUAL.

Iʹm awfully happy for you. But how does Ben feel about you and George?

HE WASNʹT TOO CRAZY ABOUT IT, THATʹS FOR SURE.

But youʹve worked it out?

WEʹVE WORKED IT OUT, ALL RIGHT. WE BROKE UP THREE YEARS AGO.

Iʹm sorry to hear that.

YEAH, WELL, SEVENTEEN YEARS IS DEFINITELY ABOVE AVERAGE, AS MARRIAGES GO THESE DAYS.

It must have been hard on the kids.

THATʹS TRUE. BUT WE BOTH HAVE DINNER WITH EMILY JUST ABOUT EVERY NIGHT.

You both have dinner with Emily, but not with each other?

EMILY CERTAINLY DOESNʹT WANT TO HAVE DINNER WITH US TOGETHER—THAT WOULDNʹT BE VERY

COMFORTABLE, NOW WOULD IT? SO SHE HAS DINNER WITH US APART.

I see, the good old kitchen table. Now that you donʹt have to deal with Harry Hippo or Miss Simon, thereʹs room for

you and Ben and Emily, but you and Ben donʹt have to actually see each other.

ISNʹT VIRTUAL REALITY GREAT?

Yeah, but too bad people canʹt touch each other without going into the Sensorium.

ACTUALLY, SENSORIUM WENT OUT OF BUSINESS.

Okay, then, total touch.

WE DONʹT NEED TO GO INTO A TOTAL TOUCH ENVIRONMENT ANYMORE, NOT SINCE THE SPINAL

IMPLANTS BECAME AVAILABLE.

So these implants add the tactile environment . . .

TO THE UBIQUITOUS VISUAL AND AUDITORY ENVIRONMENTS WEʹVE HAD FOR MANY YEARS WITH

VIRTUAL REALITY, THATʹS RIGHT.

Sounds like the implants must be pretty popular.

NO, THEYʹRE FAIRLY NEW. ALMOST EVERYONE HAS THE VISUAL AND AUDITORY ENVIRONMENTS NOW,

EITHER AS IMPLANTS OR AT LEAST AS VISUAL AND SONIC LENSES. BUT THE TACTILE IMPLANTS

HAVENʹT QUITE CAUGHT ON YET.

Yet you have them?

YEAH, THEYʹRE REALLY FABULOUS. THERE ARE A FEW GLITCHES, BUT I LIKE BEING ON THE CUTTING

EDGE. IT WAS SUCH A HASSLE HAVING TO USE A TOTAL TOUCH ENVIRONMENT.

Now I can understand how implants could simulate your sense of touch, by generating the nerve impulses that

correspond to a particular set of tactile stimuli. But the total touch environments also provided force feedback, so if youʹre touching a virtual person, you donʹt end up sticking your hand through her body.

WELL, SURE, BUT WE DONʹT MOVE OUR PHYSICAL BODIES IN VIRTUAL REALITY You move your virtual

body, of course. And the virtual reality system prevents you from moving your virtual hand through a barrier—like

someone elseʹs virtual body—in the virtual environment. This all happens using the implants?

RIGHT.

So you could be sitting here talking to me in real reality, while at the same time getting intimate with George in

virtual reality, and with full tactile realism?

WE CALL IT TACTILE VIRTUALISM, BUT YOUʹVE GOT THE IDEA. HOWEVER, THE TACTILE SEPARATION

BETWEEN REAL AND VIRTUAL REALITY IS NOT PERFECT. I MEAN, THIS IS STILL A NEW TECHNOLOGY. SO

IF GEORGE AND I GOT TOO PASSIONATE, I THINK YOUʹD NOTICE.

Thatʹs too bad.

ITʹS NOT A PROBLEM, THOUGH, IN GENERAL, SINCE I ATTEND MOST MEETINGS WITH A VIRTUAL BODY,

ANYWAY. SO WHEN I GET RESTLESS IN THESE INTERMINABLE MEETINGS ON THE CENSUS PROJECT, I

CAN SPEND A FEW PRIVATE MOMENTS WITH GEORGE . . .

Using yet another virtual body?

EXACTLY.

And the tactile separation problem between real reality and one of your virtual realities isnʹt a problem with two

virtual bodies.

NOT REALLY, BUT SOMETIMES PEOPLE CATCH ME SMILING A LOT.

You mentioned glitches . . .

SOMETIMES I FEEL LIKE SOMETHING OR SOMEONE IS TOUCHING ME, BUT IT MIGHT JUST BE MY

IMAGINATION.

Itʹs probably just a worker from the neural implant company remotely testing out the equipment.

HMMM.

So youʹre working on the census?

ITʹS SUPPOSED TO BE AN HONOR. I MEAN ITʹS LIKE THE HOT ISSUE RIGHT NOW. BUT ITʹS JUST ENDLESS

POLITICS. AND ENDLESS MEETINGS.

Well, the census has always used the most cutting‐edge technology. Electrical data processing got its start with the 1890 U.S. census, you know.

TELL ME ABOUT IT. THAT GETS MENTIONED AT LEAST THREE TIMES EVERY MEETING. BUT THE ISSUEʹS

NOT TECHNOLOGY.

Itʹs . . .

WHOʹS A PERSON. THERE ARE PROPOSALS TO START COUNTING VIRTUAL PERSONS OF AT LEAST HUMAN

LEVEL, BUT THEREʹS NO END OF PROBLEMS WITH COMING UP WITH A VIABLE PROPOSAL. VIRTUAL

PERSONS ARE NOT SO READILY COUNTABLE AND DISTINCT, SINCE THEY CAN COMBINE WITH ONE

ANOTHER, OR SPLIT UP INTO MULTIPLE APPARENT PERSONALITIES.

Why donʹt you just count machines that were derived from specific persons?

THERE ARE SOME CYBERNETIC PERSONALITIES WHO CLAIM THAT THEY USED TO BE A PARTICULAR

PERSON, BUT THEYʹRE REALLY JUST PERSONALITY EMULATIONS. THE COMMISSION JUST DIDNʹT THINK

IT WAS APPROPRIATE.

I would agree—personality emulation just doesnʹt cut it. It should be the result of a full neural scan.

PERSONALLY, IʹVE BEEN LEANING TO EXPANDING THE DEFINITION, BUT IʹVE HAD DIFFICULTY COMING

UP WITH A COHERENT METHODOLOGY. THE COMMISSION DID AGREE TO LOOK AT THE PROBLEM

AGAIN WHEN THE NEURAL SCANS ARE EXPANDED TO A MAJORITY OF NEURAL REGIONS. ITʹS A TOUGH

ISSUE, THOUGH. WE DO HAVE PEOPLE WHO HAVE THE VAST MAJORITY OF THEIR MENTAL COMPUTES

TAKING PLACE IN THEIR NANOTUBE IMPLANTS. BUT THE POLITICS SEEMS TO REQUIRE AT LEAST SOME

UNENHANCED ORIGINAL SUBSTRATE TO BE COUNTED.

Original substrate? You mean human neurons?

RIGHT. IF YOU DONʹT REQUIRE SOME NEURON‐BASED THINKING, IT JUST GETS IMPOSSIBLE TO COUNT

DISTINCT MINDS. YET SOME OF THE MACHINES DO MANAGE TO GET COUNTED. THEY SEEM TO ENJOY

ESTABLISHING A HUMAN IDENTITY AND PASSING FOR A HUMAN. ITʹS A BIT OF A GAME.

There must be legal benefits to having a recognized human identity.

THEREʹS KIND OF A STANDOFF. THE OLD LEGAL SYSTEM STILL REQUIRES A HUMAN AGENT OF

RESPONSIBILITY. BUT THE SAME ISSUE OF WHO OR WHAT IS HUMAN COMES UP IN THE LEGAL CONTEXT.

ANYWAY, SO‐CALLED HUMAN DECISIONS ARE HEAVILY INFLUENCED BY THE IMPLANTS. AND THE

MACHINES DONʹT IMPLEMENT SIGNIFICANT DECISIONS WITHOUT THEIR OWN REVIEW. BUT I SUPPOSE

YOUʹRE RIGHT; THERE ARE SOME BENEFITS TO BEING COUNTED.

How about using a Turing Test as a means of counting?

THAT WOULD NEVER DO. FIRST OF ALL, IT WOULDNʹT BE MUCH OF A SCREEN. FURTHERMORE, YOUʹD

HAVE THE SAME PROBLEM AGAIN IN SELECTING A HUMAN JUDGE TO CONDUCT THE TURING TEST.

AND YOUʹD STILL HAVE THE COUNTING ISSUE. TAKE GEORGE, FOR EXAMPLE. HEʹS GREAT AT

IMPRESSIONS. USUALLY, RIGHT AFTER DINNER, HEʹLL ENTERTAIN ME WITH SOME PERSONALITY HEʹS

CONCOCTED. HE COULD SUBMIT THOUSANDS OF PERSONALITIES IF HE WANTED TO.

Speaking of George, doesnʹt he want to be counted?

OH, I THINK HE SHOULD BE. HEʹS SO MUCH WISER AND GENTLER THAN ANYONE ON THE COMMISSION.

I GUESS THATʹS WHY IʹVE WANTED TO EXPAND THE DEFINITION. GEORGE COULD MANAGE TO

ESTABLISH THE REQUISITE IDENTITY ORIGIN IF HE WANTED TO. BUT HE REALLY DOESNʹT CARE ABOUT

IT.

He seems to care mostly about you.

HMMM. THAT COULD BE IT.

You sound a little frustrated with the commission.

WELL, I CAN UNDERSTAND THEIR NEED TO BE CAUTIOUS. I JUST FEEL THAT THEYʹRE UNDULY

INFLUENCED BY THE RY GROUPS.

The Luddites, I mean, Remember York . . .

EXACTLY. I AM SYMPATHETIC TO A LOT OF THE YORK CONCERNS. BUT LATELY THEYʹVE TAKEN

STRIDENT POSITIONS AGAINST NEURAL THEYʹRE ALSO OPPOSED TO ANY OF THE NEURAL IMPLANTS,

WHICH IS JUST TOO RIGID. THEYʹRE ALSO OPPOSED TO ANY OF THE NEURAL SCANNING RESEARCH.

So theyʹre influencing the census commission to keep a conservative definition of who can be counted as a human?

IʹD SAY SO. THE COMMISSION DENIES IT, BUT THEREʹS A GROWING CONSENSUS THAT THE YORK PEOPLE

HAVE TOO MUCH OF A VOICE THERE. THE COMMISSION DIRECTORʹS BROTHER WAS ACTUALLY A

MEMBER OF THE FLORENCE MANIFESTO BRIGADE.

Florence? Isnʹt that where they locked up Kaczynski?

THATʹS RIGHT‐FLORENCE, COLORADO. THE FLORENCE MANIFESTO WAS SMUGGLED OUT BY ONE OF THE

GUARDS BEFORE KACZYNSKIʹS DEATH. ITʹS BECOME A KIND OF BIBLE FOR THE MORE STRIDENT YORK

FACTIONS.

These are violent groups?

GENERALLY, NO. VIOLENCE WOULD BE UTTERLY FUTILE. OCCASIONALLY THERE ARE VIOLENT LONERS,

OR SMALL GROUPS, WHO CLAIM TO BE PART OF THE FM BRIGADE, BUT THEREʹS NO EVIDENCE OF ANY

BROAD CONSPIRACY.

So whatʹs in the Florence Manifesto?

DESPITE IT HAVING BEEN WRITTEN ALL IN LONGHAND USING A PENCIL, IT WAS A RATHER ARTICULATE

AND EFFECTIVE DOCUMENT, PARTICULARLY WITH REGARD TO THE NANO‐PATHOGEN CONCERN.

So what is the concern with nanopathogens?

ACTUALLY, I JUST ATTENDED A CONFERENCE ON THAT.

You attended virtually?

THATʹS USUALLY THE WAY I ATTEND CONFERENCES NOWADAYS. ANYWAY, THE CONFERENCE

SESSIONS OVERLAPPED THE COMMISSION MEETINGS, SO I HAD NO CHOICE.

You can attend more than one meeting at a time?

IT DOES GET A LITTLE CONFUSING. ITʹS KIND OF POINTLESS, THOUGH, TO JUST SIT IN A LONG MEETING

AND NOT DO SOMETHING USEFUL WITH YOUR TIME.

I agree. So, what was the view of the conference?

NOW THAT THE BIOPATHOGEN CONCERN IS ABATING—GIVEN THE NANOPATROL AND SCANNER

TECHNOLOGIES, AND ALL—THERE IS MORE ATTENTION BEING PAID TO THE NANOPATHOGEN THREAT.

How serious is it?

IT HASNʹT BEEN A BIG PROBLEM YET. THERE WAS A WORKSHOP ON A RECENT PHENOMENON OF

NANOPATROLS THAT HAVE RESISTED THE COMMUNICATION PROTOCOLS, AND THAT DID SET OFF A

FEW ALARMS. BUT THEREʹS NOTHING LIKE YOU HAD IN 1999 WITH OVER 100,000 PEOPLE DYING EACH

YEAR FROM ADVERSE REACTIONS TO PHARMACEUTICAL DRUGS. AND THATʹS WHEN THEY WERE

PRESCRIBED AND TAKEN CORRECTLY.

And drugs in 2029?

DRUGS TODAY ARE GENETICALLY ENGINEERED SPECIFICALLY FOR THE INDIVIDUALʹS OWN DNA

COMPOSITION. INTERESTINGLY, THE MANUFACTURING PROCESS THATʹS USED IS BASED ON THE

PROTEIN‐FOLDING WORK THAT WAS ORIGINALLY DESIGNED FOR THE NANOPATROLS. IN ANY EVENT,

DRUGS ARE INDIVIDUALLY TAILORED AND TESTED IN A HOST SIMULATION BEFORE INTRODUCING ANY

SIGNIFICANT VOLUME TO THE ACTUAL HOSTʹS BODY. SO ADVERSE REACTIONS ON A MEANINGFUL

SCALE ARE QUITE RARE.

So there isnʹt much concern with nanopathogens?

OH, I WOULDNʹT SAY THAT. THERE WAS QUITE A BIT OF CONCERN EXPRESSED ABOUT SOME OF THE

RECENT SELF‐REPLICATION RESEARCH. There should be.

BUT THE ENVIRONMENT RESTRUCTURING PROPOSALS SEEM TO REQUIRE IT.

Well, donʹt say I didnʹt warn you.

IʹLL KEEP THAT IN MIND, NOT THAT I HAVE MUCH INFLUENCE ON THE ISSUE.

Your work is mostly on the census issue?

YEAH, FOR THE LAST FIVE YEARS ANYWAY. I SPENT THREE YEARS BASICALLY GOING THROUGH THE

COMMISSIONʹS STUDY GUIDE, SO I COULD BE QUALIFIED TO SIT IN ON THE COMMISSION MEETINGS,

ALTHOUGH I STILL DONʹT HAVE A VOTE.

So you had a three‐year leave to study?

IT FELT LIKE I WAS BACK IN COLLEGE. AND LEARNING WAS JUST AS TEDIOUS AS IT WAS THEN.

Donʹt the neural implants help?

OH, SURE, THEREʹS NO WAY I COULD HAVE GOTTEN THROUGH IT OTHERWISE. UNFORTUNATELY, I STILL

CANʹT JUST DOWNLOAD THE MATERIAL, NOT THE WAY GEORGE CAN. THE IMPLANT PREPROCESSES THE

INFORMATION, AND FEEDS ME THE PREPROCESSED KNOWLEDGE STRUCTURES QUICKLY. BUT ITʹS OFTEN

DISCOURAGING; IT JUST TAKES SO LONG. GEORGE HAS BEEN A BIG HELP, THOUGH. HE KIND OF

WHISPERS TO ME WHEN IʹM PUZZLED ABOUT SOMETHING.

So the three‐year study leave is over now?

ABOUT A YEAR AGO, THE COMMISSION MEETINGS GOT PRETTY INTENSE, AND IʹVE FOCUSED ON THAT.

NOW WITH THE CENSUS ONLY A YEAR AWAY, WEʹRE WORKING ON IMPLEMENTATION. SO ASIDE FROM

THE LAWSUIT, THATʹS PRETTY MUCH IT.

Lawsuit?

OH, JUST A ROUTINE INTELLECTUAL PROPERTY DISPUTE. MY PATENT ON AN ENHANCED

EVOLUTIONARY PATTERN‐RECOGNITION ALGORITHM FOR NANOPATROL DETECTION OF CELL

IMBALANCES WAS ATTACKED WITH A PRIOR ART CITATION. I HAPPENED TO MENTION IN ONE OF THE

DISCUSSION GROUPS THAT I THOUGHT SEVERAL OF THE PATENT CLAIMS WERE BEING INFRINGED, AND

NEXT THING I KNEW I GOT HIT WITH A DECLARATORY JUDGMENT SUIT FROM THE NANOPATROL

INDUSTRY.

I didnʹt know you did work on nanopatrols.

TO BE PERFECTLY HONEST, IT WAS GEORGEʹS INVENTION, BUT HE NEEDED A RESPONSIBLE AGENT.

Since he has no standing.

ITʹS TRUE, THERE ARE STILL SOME LIMITATIONS WHEN YOU CANʹT ESTABLISH YOUR HUMAN ORIGIN.

So howʹs this going to get resolved?

ITʹS UP BEFORE THE MAGISTRATE NEXT MONTH.

It can be rather frustrating taking these technical issues to court.

OH, THIS MAGISTRATE KNOWS HIS STUFF. HEʹS A RECOGNIZED EXPERT ON NANOPATROL PATTERN

RECOGNITION.

Doesnʹt sound like the courts I know.

THE EXPANSION OF THE MAGISTRATE SYSTEM HAS BEEN A VERY POSITIVE DEVELOPMENT. IF WE WERE

LIMITED TO JUST THE HUMAN JUDGES . . .

Oh, so the magistrate is . . .

A VIRTUAL INTELLIGENCE, YES.

So the machines do have some legal standing.

OFFICIALLY, THE VIRTUAL MAGISTRATES ARE AGENTS OF THE HUMAN JUDGE IN CHARGE OF THAT

COURT, BUT THE MAGISTRATES MAKE MOST OF THE DECISIONS.

I see, sounds like these magistrates are pretty influential.

THEREʹS REALLY NO CHOICE. THE ISSUES ARE JUST TOO COMPLICATED, AND THE PROCESS WOULD TAKE

TOO LONG OTHERWISE.

I see. So, tell me about your son.

HEʹS A SOPHOMORE AT STANFORD, AND HAVING A GREAT TIME.

They certainly have a beautiful campus.

YEAH, WEʹVE BEEN LOOKING AT THE OVAL AND QUAD FOR A LONG TIME. JEREMYʹS HAD THREE‐

DIMENSIONAL PROJECTIONS OF THE STANFORD CAMPUS ON THE PICTURE PORTALS FOR THE LAST TEN

YEARS.

He must feel right at home then.

HE IS AT HOME. HEʹS DOWNSTAIRS.

So heʹs attending virtually.

MOST STUDENTS DO. BUT STANFORD STILL HAS SOME ANACHRONISTIC RULES ABOUT SPENDING AT

LEAST A WEEK EACH QUARTER ACTUALLY ON CAMPUS.

With your physical body?

EXACTLY, WHICH MAKES IT DIFFICULT FOR A VIRTUAL INTELLIGENCE TO ATTEND OFFICIALLY.

Not that they need to, since they can download knowledge directly from the Web.

ITʹS NOT THE KNOWLEDGE BUT THE DISCUSSION GROUPS THAT WOULD BE OF INTEREST.

Canʹt anyone attend the discussion groups?

ONLY THE OPEN DISCUSSIONS. THERE ARE A LOT OF CLOSED DISCUSSION GROUPS Which are not on the

Web?

OF COURSE THEYʹRE ON THE WEB, BUT YOU NEED A KEY.

Right, so thatʹs how Jeremy attends from home?

EXACTLY. JEREMY AND GEORGE HAVE GROWN QUITE CLOSE LATELY, SO JEREMY LETS GEORGE LISTEN

IN TO THE CLOSED SESSIONS, BUT DONʹT TELL ANYONE THAT.

My lips are sealed. Iʹll only tell my other readers.

WELL, THEY NEED TO KEEP IT CONFIDENTIAL AS WELL.

Iʹll pass that on.

I HOPE THAT WILL BE OKAY. ANYWAY, GEORGE IS HELPING JEREMY WITH HIS HOMEWORK RIGHT NOW I

hope George doesnʹt do all of it for him.

OH, GEORGE WOULDNʹT DO THAT. HEʹS JUST BEING HELPFUL. HE HELPS ALL OF US. WE REALLY

COULDNʹT MANAGE OTHERWISE.

You know, I could use his help, too. He might help me meet this book deadline I have.

WELL, GEORGE IS CLEVER, BUT IʹM AFRAID HE DOESNʹT SEEM TO HAVE THAT POETIC‐LICENSE

TECHNOLOGY THAT ENABLES YOU TO TALK TO ME FROM THIRTY YEARS AWAY.

Thatʹs too bad.

BUT IʹLL BE HAPPY TO HELP YOU OUT.

Yes, I know, you already have.

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

C H A P T E R T W E L V E

2099

When I look out my window

what do you think I see?

. . . so many different people to be.

—Donovan

We know what we are, but know not what we may become.

—William Shakespeare

Human thinking is merging with the world of machine intelligence that the human species initially created.

The reverse engineering of the human brain appears to be complete. The hundreds of specialized regions have been fully scanned, analyzed, and understood. Machine analogues are based on these human models, which have been enhanced and extended, along with many new massively parallel algorithms. These enhancements, combined with the enormous advantages in speed and capacity of electronic/photonic circuits, provide substantial advantages

to machine‐based intelligence.

Machine‐based intelligences derived entirely from these extended models of human intelligence claim to be human, although their brains are not based on carbon‐based cellular processes, but rather electronic and photonic ʺequivalents.ʺ Most of these intelligences are not tied to a specific computational‐processing unit (that is, piece of hardware). The number of software‐based humans vastly exceeds those still using native neuron‐cell‐based computation. A software‐based intelligence is able to manifest bodies at will: one or more virtual bodies at different levels of virtual reality and nanoengineered physical bodies using instantly reconfigurable nanobot swarms.

Even among those human intelligences still using carbon‐based neurons, there is ubiquitous use of neural implant

technology, which provides enormous augmentation of human perceptual and cognitive abilities. Humans who do not utilize such implants are unable to meaningfully participate in dialogues with those who do. There are a multiplicity of ways in which these scenarios are combined. The concept of what is human has been significantly altered. The rights and powers of different manifestations of human and machine intelligence and their various combinations represent a primary political and philosophical issue, although the basic rights of machine‐based intelligence have been settled.

There is a plethora of trends that we can already taste and feel in 2099 that will continue to accelerate in this coming twenty‐second century, interacting with each other, and

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

YES, YES, AS NIELS BOHR LIKED TO SAY, ʺITʹS HARD TO PREDICT, ESPECIALLY THE FUTURE.ʺ SO WHY

DONʹT YOU JUST CONTINUE WITH MY OBSERVATIONS. THAT WILL BE EASIER AND LESS CONFUSING.

Perhaps that makes sense.

AFTER ALL, A HUNDRED YEARS IS A LONG TIME. AND THE TWENTY‐FIRST CENTURY WAS LIKE TEN

CENTURIES IN ONE.

We thought that was true for the twentieth century.

THE SPIRAL OF ACCELERATING RETURNS LIVES ON.

Iʹm not surprised. Anyway, you do look amazing.

YOU SAY THAT EVERY TIME WE MEET.

I mean you look twenty again, only more beautiful than at the start of the book.

I KNEW THATʹS HOW YOUʹD WANT ME.

Great, now Iʹm going to be accused of preferring younger women.

IʹM GLAD IʹM IN 2099.

Thanks.

HEY, I CAN LOOK UGLY, TOO.

Thatʹs okay.

NO REALLY, I CAN LOOK UGLY WITHOUT CHANGING MY APPEARANCE. ITʹS LIKE THAT WITTGENSTEIN

QUOTE, ʺIMAGINE THIS BUTTERFLY EXACTLY AS IT IS, BUT UGLY INSTEAD OF BEAUTIFUL.ʺ

I was always a little confused by that quote, but Iʹm glad youʹre quoting twentieth‐century thinkers.

WELL, YOU WOULDNʹT BE FAMILIAR WITH THE TWENTY‐FIRST‐CENTURY ONES.

So youʹre expressing this appearance. But I donʹt have the ability to see virtual reality, so I donʹt UNDERSTAND

HOW YOU CAN SEE ME?

Right.

MY BODY RIGHT NOW IS JUST A LITTLE FOG SWARM PROJECTION. NEAT, HUH?

Not bad, not bad at all. You feel pretty good, too.

I THOUGHT IʹD GIVE YOU A HUG, I MEAN THE BOOKʹS ALMOST OVER.

This is quite a technology.

OH, WE DONʹT USE THE SWARMS SO OFTEN ANYMORE.

Last time I saw you, there were no nanobot swarms. Now youʹre mostly past using them. Guess I missed a phase there.

OH, ONE OR TWO. ITʹS BEEN SEVENTY YEARS SINCE WE LAST SAW EACH OTHER! AND AN EVER

ACCELERATING SEVENTY YEARS AT THAT.

Weʹll have to see each other more often.

I DONʹT KNOW IF THAT WILL BE POSSIBLE. THE BOOKʹS COMING TO AN END, AS YOU SAID.

So, are you and George still close?

OH, VERY CLOSE. WEʹRE NEVER APART.

Never? Donʹt you get bored with each other?

DO YOU GET BORED WITH YOURSELF?

Actually, sometimes I do. But are you saying that you and George have, whatʹs the word Iʹm looking for . . .

MERGED?

Hmmm. Is this like a corporate merger?

WELL, MORE LIKE A JOINING OF TWO SOCIETIES.

Two societies of mind?

EXACTLY. OUR MIND IS NOW JUST ONE BIG HAPPY SOCIETY.

The female spider devouring the little male spider?

OH NO, GEORGE IS THE BIG SPIDER. HIS MIND WAS LIKE . . .

A galaxy?

ALL RIGHT, LETʹS NOT GET CARRIED AWAY, MAYBE LIKE A BIG SOLAR SYSTEM.

So youʹve joined societies, or, uh, joined your societies. So you canʹt make love to each other anymore?

THAT DOESNʹT FOLLOW AT ALL.

Okay, I guess some things are beyond my 1999 comprehension.

THAT DOESNʹT FOLLOW EITHER. THE PROFOUND THING ABOUT HUMAN BEINGS—EVEN MOSHs—IS

THAT ALMOST NOTHING IS TRULY BEYOND YOUR COMPREHENSION. THAT JUST WASNʹT TRUE OF THE

OTHER PRIMATES.

Okay, my questions are getting queued up now. MOSHs?

OH, MOSTLY ORIGINAL SUBSTRATE HUMANS.

Yes, of course—unenhanced . . .

EXACTLY.

But how can you be intimate with George now that youʹve joined forces, so to speak?

WELL, AS BARRY SPACKSʹS POEM—

You mean ʺMade double by his lust, he sounds a womanʹs groans . . .

RIGHT, I MEAN EVEN MOSHs SPLIT THEMSELVES—

When weʹre by ourselves—

OR WITH ANOTHER. THATʹS REALLY THE ULTIMATE, DONʹT YOU THINK, TO BECOME THE OTHER PERSON

AND YOURSELF AT THE SAME TIME.

Especially when the other person is already part of yourself.

SURE. BUT GEORGE AND I CAN STILL SPLIT OURSELVES, AT LEAST OUR OUTER LAYERS.

Layers?

OKAY, WELL MAYBE SOME THINGS ARE HARD TO EXPLAIN TO A MOSH, EVEN A NICE ONE LIKE

YOURSELF.

Yeah, a MOSH that created you, donʹt forget.

OH, IʹLL NEVER FORGET. IʹLL BE GRATEFUL FOREVER. YOU CAN THINK OF THE OUTER LAYERS AS OUR

PERSONALITIES.

So, you separate your personalities . . .

AT TIMES. BUT WE STILL SHARE OUR KNOWLEDGE STORES AT ALL TIMES.

Sounds like the two of you have a lot in common.

[GIGGLES]

I see you still have your old personality.

OF COURSE IʹVE KEPT MY OLD PERSONALITY. IT HAS A LOT OF SENTIMENTAL VALUE TO ME.

I see, so you have others?

YEAH, MY FAVORITES ARE A FEW THAT GEORGE CAME UP WITH.

Creative guy.

OH YES.

Well, having multiple personalities is not all that special. Weʹve had people like that in the twentieth century, too.

SURE, I REMEMBER. BUT THERE WASNʹT ENOUGH THINKING TO GO AROUND FOR ALL THOSE

PERSONALITIES WHEN THEYʹRE STUCK IN JUST ONE MOSH BRAIN. SO IT WAS DIFFICULT FOR ALL OF

THOSE PERSONALITIES TO SUCCEED IN LIFE.

So what are you doing right now?

IʹM TALKING TO YOU.

Yes, I know, but what else are you doing?

REALLY NOT MUCH. IʹM TRYING TO PAY MOST OF MY ATTENTION TO YOU.

Not much? So you are doing something else.

I REALLY CANʹT THINK OF ANYTHING.

Well, are you relating to someone else at the moment?

YOUʹRE PRETTY NOSY.

Weʹve already established that decades ago. But that doesnʹt answer the question.

WELL, NOT REALLY.

Not really? So you are.

ALL RIGHT, ASIDE FROM GEORGE, NOT REALLY.

Iʹm glad Iʹm not distracting you too much. Okay, what else?

JUST FINISHING UP THIS SYMPHONY.

Is this a new interest?

IʹM REALLY JUST DABBLING, BUT CREATING MUSIC IS A GREAT WAY FOR ME TO STAY CLOSE WITH

JEREMY AND EMILY.

Creating music sounds like a good thing to do with your kids, even if they are almost ninety years old. So, can I hear it?

IʹM AFRAID YOU WOULDNʹT UNDERSTAND IT.

So it requires enhancement to understand?

YES, MOST ART DOES. FOR STARTERS, THIS SYMPHONY IS IN FREQUENCIES THAT A MOSH CANʹT HEAR,

AND HAS MUCH TOO FAST A TEMPO. AND IT USES MUSICAL STRUCTURES THAT A MOSH COULD NEVER

FOLLOW.

Canʹt you create art for nonaugmented humans? I mean thereʹs still a lot of depth possible. Consider Beethoven—he

wrote almost two centuries ago, and we still find his music exhilarating.

YES, THEREʹS A GENRE OF MUSIC—ALL THE ARTS ACTUALLY WHERE WE CREATE MUSIC AND ART THAT

A MOSH IS CAPABLE OF UNDERSTANDING.

And then you play MOSH music for MOSHs?

HMMM, NOW THEREʹS AN INTERESTING IDEA. I SUPPOSE WE COULD TRY THAT, ALTHOUGH MOSHs ARE

NOT THAT EASY TO FIND ANYMORE. ITʹS REALLY NOT NECESSARY, THOUGH. WE CAN CERTAINLY

UNDERSTAND WHAT A MOSH IS CAPABLE OF UNDERSTANDING. THE POINT, THOUGH, IS TO USE THE

MOSH LIMITATIONS AS AN ADDED CONSTRAINT.

Sort of like composing new music for old instruments.

YEAH, NEW MUSIC FOR OLD MINDS.

Okay, so aside from your, uh, dialogue with George, and this symphony, I have your complete attention?

WELL, Now GEORGE AND I ARE SHARING A HAMBURGER FOR LUNCH.

I thought you were a vegetarian.

ITʹS NOT A HAMBURGER FROM A COW, SILLY.

Of course, a swarm hamburger.

NO, NO, YOUʹRE GETTING A LITTLE CONFUSED. WE DID HAVE NANOPRODUCED FOOD ABOUT HALF A

CENTURY AGO. SO WE COULD EAT MEAT, OR ANYTHING WE WANTED, BUT IT DIDNʹT COME FROM

ANIMALS, AND IT HAD THE RIGHT NUTRITIONAL COMPOSITION. BUT EVEN THEN, YOU REALLY

WOULDNʹT WANT TO EAT A SWARM PROJECTION—SWARMS ARE JUST FOR VISUAL‐AUDITORY‐TACTILE

PROJECTIONS IN REAL REALITY. YOUʹRE FOLLOWING ME?

Uh, sure.

WELL, A COUPLE OF DECADES LATER, OUR BODIES WERE BASICALLY REPLACED WITH

NANOCONSTRUCTED ORGANS. SO WE DIDNʹT NEED TO EAT ANYMORE IN REAL REALITY. BUT WE STILL

ENJOYED SHARING A MEAL IN VIRTUAL REALITY. ANYWAY, THE NANOCON‐STRUCTED BODIES WERE

PRETTY INFLEXIBLE. I MEAN, IT TOOK SECONDS TO RECONSTRUCT THEM INTO ANOTHER FORM. SO

TODAY, WHEN NECESSARY, OR DESIRABLE, WE JUST PROJECT AN APPROPRIATE BODY.

Using the nanobot swarms?

THATʹS ONE WAY OF DOING IT. THATʹS WHAT IʹM DOING WITH YOU NOW.

Since Iʹm a MOSH.

RIGHT, BUT IN MOST OTHER CIRCUMSTANCES, I JUST USE AN AVAILABLE VIRTUAL CHANNEL.

Okay, I think Iʹm following you now.

LIKE I SAID, MOSHs CAN UNDERSTAND ALMOST ANYTHING. WE DO HAVE A LOT OF RESPECT FOR

MOSHs.

Itʹs your heritage, after all.

RIGHT, AND ANYWAY, WEʹRE REQUIRED TO, SINCE THE GRANDFATHER LEGISLATION.

Okay, let me guess. MOSHs were protected by grandfathering native minds.

YES, BUT NOT ONLY MOSHs. ITʹS REALLY A PROGRAM TO PROTECT OUR WHOLE BIRTH‐RIGHT, A

REVERENCE FOR WHERE WEʹVE BEEN.

So you still like to eat?

SURE. SINCE WEʹRE BASED ON OUR MOSH HERITAGE, OUR EXPERIENCES—EATING, MUSIC, SEXUALITY—

HAVE THE OLD FOUNDATION, ALBEIT VASTLY EXPANDED. HOWEVER, WE DO HAVE A WIDE RANGE OF

CURRENT EXPERIENCES THAT ARE DIFFICULT TO TRACE, ALTHOUGH THE ANTHROPOLOGISTS KEEP

TRYING.

Iʹm still surprised that youʹd be interested in eating a hamburger.

ITʹS A THROWBACK, I KNOW A LOT OF OUR ACTS AND THOUGHTS ARE ROOTED IN THE PAST. BUT NOW

THAT YOU MENTION IT, I THINK IʹVE LOST MY APPETITE.

Sorry about that.

YEAH, WELL, I SHOULD BE MORE SENSITIVE. SHELBY, A GOOD FRIEND OF MINE, LOOKS LIKE A COW, AT

LEAST THATʹS HOW SHE ALWAYS MANIFESTS HERSELF. SHE CLAIMS THAT SHE WAS A COW BROUGHT

OVER TO THE OTHER SIDE AND ENHANCED. BUT NO ONE BELIEVES HER.

So how satisfying is it to eat a virtual hamburger in virtual reality?

ITʹS VERY SATISFYING—THE TEXTURE, TASTE, AROMA IS WONDERFUL—JUST HOW I REMEMBER IT, EVEN

IF I WAS A VEGETARIAN MOST OF THE TIME. THE NEURAL MODELS NOT ONLY SIMULATE OUR VISUAL,

AUDITORY, AND TACTILE ENVIRONMENTS, BUT OUR INTERNAL ENVIRONMENTS AS WELL.

Including digestion?

YES, THE MODEL OF BIOCHEMICAL DIGESTION IS QUITE ACCURATE.

How about indigestion?

WE DO SEEM TO MANAGE TO AVOID THAT.

Youʹre missing something there.

HMMM.

Okay, you were an attractive young woman when I first met you. And you still project yourself as a beautiful young

woman. At least when Iʹm with you.

THANKS.

So, are you saying that youʹre a machine now?

A MACHINE? THATʹS REALLY NOT FOR ME TO SAY. ITʹS LIKE ASKING ME IF IʹM BRILLIANT OR INSPIRING.

I guess the word machine in 2099 doesnʹt have quite the same connotations that it has here in 1999.

THATʹS HARD FOR ME TO RECALL NOW.

Okay, letʹs put it this way. Do you still have any carbon‐based neural circuits?

CIRCUITS, IʹM NOT SURE I UNDERSTAND. YOU MEAN MY OWN CIRCUITS?

Gee, I guess a lot of time has gone by.

ALL RIGHT, LOOK, WE DID HAVE OUR OWN MENTAL MEDIUM FOR A FEW DECADES, AND THERE ARE

STILL LOCAL INTELLIGENCES THAT LIKE TO STICK TO A SPECIFIC COMPUTATIONAL UNIT. BUT THATʹS A

REFLECTION OF SOME OLD ATTACHMENT ANXIETY. THESE LOCAL INTELLIGENCES DO MOST OF THEIR

THINKING OUT ON THE WEB ANYWAY, SO ITʹS JUST A SENTIMENTAL ANACHRONISM.

An anachronism, like having your own body?

I CAN HAVE MY OWN BODY ANYTIME I WANT.

But you donʹt have a specific neural substrate?

WHY WOULD I WANT THAT? ITʹS JUST A LOT OF MAINTENANCE, AND SO LIMITING.

So, at some point, Mollyʹs neural circuits were scanned?

YEAH, ME, MOLLY. AND IT DIDNʹT HAPPEN ALL AT ONCE, BY THE WAY.

But donʹt you wonder if youʹre the same person?

OF COURSE I AM. I CAN CLEARLY REMEMBER MY EXPERIENCES BEFORE WE STARTED SCANNING MY

MIND, DURING THE DECADE THAT PORTIONS WERE REINSTANTIATED, AND SINCE.

Sure, youʹve inherited all of Mollyʹs memories.

OH NO, NOT THIS ISSUE AGAIN.

I donʹt mean to challenge you. But just consider that Mollyʹs neural scan was instantiated in a copy which became you. Molly might still have continued to exist and may have evolved off in some other direction.

WE JUST DONʹT THINK THATʹS A VALID PERSPECTIVE. WE SETTLED THAT ISSUE AT LEAST TWENTY YEARS

AGO.

Well, of course you feel that way now. Youʹre on the other side.

WELL, EVERYONE IS.

Everyone?

OKAY, NOT QUITE EVERYONE. BUT THERE IS NO DOUBT IN MY MIND THAT—

Youʹre Molly

I THINK I KNOW WHO I AM.

Well, I have no problem with you as Molly.

You MOSHs ALWAYS WERE A PUSHOVER.

It is hard to compete with you folks on the other side.

SURE IT IS. THATʹS WHY MOST OF US ARE OVER HERE.

Iʹm not sure I can push the identity issue much further.

THATʹS ONE REASON ITʹS NO LONGER AN ISSUE.

So why donʹt we talk about your work. Are you still consulting for the census commission?

WAS INVOLVED IN THAT FOR HALF A CENTURY, BUT I GOT KIND OF BURNED OUT ON IT. ANYWAY, THE

ISSUE NOW IS MOSTLY IMPLEMENTATION.

So the issue of how to count is resolved?

WE DONʹT COUNT PEOPLE ANYMORE. IT BECAME CLEAR THAT COUNTING INDIVIDUAL PERSONS

WASNʹT TOO MEANINGFUL. AS IRIS MURDOCH SAID, ʺITʹS HARD TO TELL WHERE ONE PERSON ENDS

AND ANOTHER BEGINS.ʺ

ITʹS RATHER LIKE TRYING TO COUNT IDEAS OR THOUGHTS.

So what do you count?

OBVIOUSLY, WE COUNT COMPUTES.

You mean, like calculations per second.

HMMM, ITʹS A LITTLE MORE COMPLICATED THAN THAT, BECAUSE OF THE QUANTUM COMPUTING.

I didnʹt expect it to be simple. But whatʹs the bottom line?

WELL, WITHOUT QUANTUM COMPUTING, WEʹRE UP TO ABOUT 1055 CALCULATIONS PER SECOND. [1]

Per person?

NO, WE EACH GET WHATEVER COMPUTATION WE WANT. THATʹS THE TOTAL FIGURE.

For the whole planet?

SORT OF. I MEAN NOT ALL OF IT IS LITERALLY ON THE PLANET.

And with quantum computing?

WELL, ABOUT 1042 OF THE COMPUTATIONS ARE QUANTUM COMPUTATIONS, WITH ABOUT 1,000 QU‐BITS

BEING TYPICAL. SO THATʹS EQUIVALENT TO ABOUT 10342 CALCULATIONS PER SECOND, BUT THE

QUANTUM COMPUTATIONS ARE NOT ENTIRELY GENERAL PURPOSE, SO THE 1055 FIGURE IS STILL

RELEVANT. [2]

Hmmm, Iʹve only got about 1016 cps in my MOSH brain, at least on a good day.

TURNS OUT THERE IS SOME QUANTUM COMPUTING IN YOUR MOSH BRAIN, SO ITʹS HIGHER.

Thatʹs reassuring. So if youʹre not working on the census, what are you up to?

WE DONʹT HAVE JOBS EXACTLY.

I know what thatʹs like.

ACTUALLY, YOUʹRE NOT A BAD MODEL FOR WORK IN THE LATE TWENTY‐FIRST CENTURY. WEʹRE ALL

BASICALLY ENTREPRENEURS.

Sounds like some things have moved in the right direction. So what are some of your enterprises?

ONE IDEA I HAVE IS A UNIQUE WAY OF CATALOGING NEW TECHNOLOGY PROPOSALS. ITʹS A MATTER OF

MATCHING THE USERʹS KNOWLEDGE STRUCTURES TO THE EXTERNAL WEB KNOWLEDGE, AND THEN

INTEGRATING THE RELEVANT PATTERNS.

Iʹm not sure I followed that. But give me an example of a recent research proposal that youʹve cataloged.

MOST OF THE CATALOGING IS AUTOMATIC. BUT I DID GET INVOLVED IN TRYING TO QUALIFY SOME OF

THE RECENT FEMTOENGINEERING PROPOSALS. [3]

Femto, as in one thousandth of a trillionth of a meter?

EXACTLY. DREXLER HAS WRITTEN A SERIES OF PAPERS SHOWING THE FEASIBILITY OF BUILDING

TECHNOLOGY ON THE FEMTOMETER SCALE, BASICALLY EXPLOITING FINE STRUCTURES WITHIN

QUARKS TO DO COMPUTING.

Has anyone done this?

NO ONE HAS DEMONSTRATED IT, BUT THE DREXLER PAPERS APPEAR TO SHOW THAT ITʹS PRACTICAL. AT

LEAST THATʹS MY VIEW, BUT ITʹS PRETTY CONTROVERSIAL.

This is the same Drexler who developed the nanotechnology concept in the 1970s and 1980s?

YEAH, ERIC DREXLER.

That makes him around 150, so he must be on the other side.

OF COURSE, ANYONE DOING SERIOUS WORK HAS TO BE ON THE OTHER SIDE.

You mentioned papers. You still have papers?

YES, WELL SOME ARCHAIC TERMS HAVE STUCK. WE CALL THEM MOSHISMS. PAPERS ARE CERTAINLY

NOT RENDERED ON ANY PHYSICAL SUBSTANCE. BUT WE STILL CALL THEM PAPERS.

What language are they written in, English?

UNIVERSITY PAPERS ARE GENERALLY PUBLISHED USING A STANDARD SET OF ASSIMILATED

KNOWLEDGE PROTOCOLS, WHICH CAN BE INSTANTLY UNDERSTOOD. SOME REDUCED STRUCTURE

FORMS HAVE ALSO EMERGED, BUT THOSE ARE GENERALLY USED IN MORE POPULAR PUBLICATIONS.

You mean, like the National Enquirer?

THATʹS A PRETTY SERIOUS PUBLICATION. THEY USE THE FULL PROTOCOL.

I see.

SOMETIMES, PAPERS ARE ALSO RENDERED IN RULE‐BASED FORMS, BUT THESE ARE USUALLY NOT

SATISFACTORY. THERE IS A QUAINT TREND OF POPULAR PUBLICATIONS PUBLISHING ARTICLES IN MOSH

LANGUAGES SUCH AS ENGLISH, BUT WE CAN TRANSLATE THESE INTO ASSIMILATED KNOWLEDGE

STRUCTURES RATHER QUICKLY. LEARNING IS NOT THE STRUGGLE IT ONCE WAS. NOW THE STRUGGLE IS

DISCOVERING NEW KNOWLEDGE TO LEARN.

Any other recent trends that youʹve gotten involved in?

WELL, THE AUTOMATIC CATALOGING AGENTS HAD DIFFICULTY WITH THE SUICIDE‐MOVEMENT

PROPOSALS.

Which are?

THE IDEA IS TO HAVE THE RIGHT TO TERMINATE YOUR MIND FILE AS WELL AS TO DESTROY ALL COPIES.

REGULATIONS REQUIRE KEEPING AT LEAST THREE BACKUP COPIES OF NO MORE THAN TEN MINUTESʹ

VINTAGE, WITH AT LEAST ONE OF THESE COPIES IN THE CONTROL OF THE AUTHORITIES.

I can see the problem. Now if you were told that all copies were going to be destroyed, they could secretly keep a copy and instantiate it at a later time. Youʹd never know. Doesnʹt that contradict the premise that those on the other side are the same person—the same continuity of consciousness—as the original person?

I DONʹT THINK THAT FOLLOWS AT ALL.

Can you explain that?

YOU WOULDNʹT UNDERSTAND.

I thought I could understand most anything.

I DID SAY THAT. I GUESS IʹLL HAVE TO GIVE THAT MORE THOUGHT.

Youʹll have to give more thought to whether a MOSH can understand any concept, or the consciousness‐continuation

issue?

I GUESS NOW IʹM CONFUSED.

All right, well, tell me more about this ʺdestroy all copiesʺ movement.

WELL, I REALLY CAN SEE BOTH SIDES OF THE ISSUE. ON THE ONE HAND, IʹVE ALWAYS SYMPATHIZED

WITH THE RIGHT TO CONTROL ONEʹS OWN DESTINY. ON THE OTHER HAND, ITʹS SIN TO DESTROY

KNOWLEDGE.

And the copies represent knowledge?

WHY SURE. LATELY, THE DESTROY‐ALL‐COPIES MOVEMENT HAS BEEN THE PRIMARY YORK ISSUE.

Now wait a second. If I recall correctly, the Yorks are antitechnologists, yet only those of you on the other side would be concerned about the destroy‐all‐copies issue. If Yorks are on the other side, how can they be against technology?

Or if theyʹre not on the other side, then why would they care about this issue?

OKAY, REMEMBER ITʹS BEEN SEVENTY YEARS SINCE WEʹVE TALKED. THE YORK GROUPS DO HAVE THEIR

ROOTS IN THE OLD ANTITECHNOLOGY MOVEMENTS, BUT NOW THAT THEYʹRE ON THE OTHER SIDE,

THEYʹVE DRIFTED TO A SOMEWHAT DIFFERENT ISSUE, SPECIFICALLY INDIVIDUAL FREEDOM. THE

FLORENCE MANIFESTO PEOPLE, ON THE OTHER HAND, HAVE KEPT A COMMITMENT TO REMAINING

MOSHs, WHICH, OF COURSE, I RESPECT.

Thank you. And theyʹre protected by the grandfather legislation?

INDEED. I HEARD A PRESENTATION BY AN FM DISCUSSION LEADER THE OTHER DAY, AND WHILE SHE

WAS SPEAKING IN A MOSH LANGUAGE, THERE WAS JUST NO WAY THAT SHE DOESNʹT HAVE AT LEAST A

NEURAL EXPANSION IMPLANT.

Well, us MOSHs can make sense from time to time.

OH, OF COURSE. I DIDNʹT MEAN TO IMPLY OTHERWISE, I MEAN . . .

Thatʹs okay. So are you involved in this destroy‐all‐copies movement?

JUST IN CATALOGING SOME OF THE PROPOSALS AND DISCUSSIONS. BUT I DID GET INVOLVED IN A

RELATED MOVEMENT TO BLOCK LEGAL DISCOVERY OF THE BACKUP DATA.

That sounds important. But what about discovery of the mind file itself? I mean, all of your thinking and memory is

right there in digital form.

ACTUALLY, ITʹS BOTH DIGITAL AND ANALOG, BUT YOUR POINT IS WELL TAKEN.

So . . .

THERE HAVE BEEN RULINGS ON LEGAL DISCOVERY OF THE MIND FILE. BASICALLY, OUR KNOWLEDGE

STRUCTURES THAT CORRESPOND TO WHAT USED TO CONSTITUTE DISCOVERABLE DOCUMENTS AND

ARTIFACTS ARE DISCOVERABLE. THOSE STRUCTURES AND PATTERNS THAT CORRESPOND TO OUR

THINKING PROCESS ARE NOT SUPPOSED TO BE. AGAIN, THIS IS ALL ROOTED IN OUR MOSH PAST. BUT AS

YOU CAN IMAGINE, THEREʹS ENDLESS LITIGATION ON HOW TO INTERPRET THIS.

So legal discovery of your primary mind file is resolved, albeit with some ambiguous rules. And the backup files?

BELIEVE IT OR NOT, THE BACKUP ISSUE IS NOT ENTIRELY RESOLVED. DOESNʹT MAKE A LOT OF SENSE,

DOES IT?

The legal system was never entirely consistent. What about testimony—do you have to be physically present?

SINCE MANY OF US DONʹT HAVE A PERMANENT PHYSICAL PRESENCE, THAT WOULDNʹT MAKE MUCH

SENSE, NOW WOULD IT.

I see, so you can give testimony with a virtual body?

SURE, BUT YOU CANʹT BE DOING ANYTHING ELSE WHILE TESTIFYING.

No asides with George, then.

RIGHT.

That sounds about right. Here in 1999, you canʹt bring coffee into a courtroom and you have to turn off your cell phone.

ASIDE FROM DISCOVERY, THEREʹS A LOT OF CONCERN THAT GOVERNMENT INVESTIGATORY AGENCIES

CAN ACCESS THE BACKUPS, ALTHOUGH THEY DENY IT.

Iʹm not surprised that privacy is still an issue. Phil Zimmerman . . .

THE PGP GUY?

Oh, you remember him?

SURE, A LOT OF PEOPLE CONSIDER HIM A SAINT.

His ʺPretty Good Privacyʺ is indeed pretty good—itʹs the leading encryption algorithm circa 1999. Anyway, he said

that ʺin the future, weʹll all have fifteen minutes of privacy.ʺ

FIFTEEN MINUTES WOULD BE GREAT.

Okay. Now what about the self‐replicating nanobots you were concerned about in 2029?

WE STRUGGLED WITH THAT FOR SEVERAL DECADES, AND THERE WERE A NUMBER OF SERIOUS

INCIDENTS. BUT WEʹRE PRETTY MUCH PAST THAT NOW SINCE WE DONʹT PERMANENTLY MANIFEST OUR

BODIES ANYMORE. AS LONG AS THE WEB IS SECURE, THEN WE HAVE NOTHING TO WORRY ABOUT.

Now that you exist as software, there must be concern again with software viruses.

THATʹS PRETTY INSIGHTFUL. SOFTWARE PATHOGENS COMPRISE THE PRIMARY CONCERN OF THE

SECURITY AGENCIES. THEYʹRE SAYING THAT THE VIRUS SCANS ACTUALLY CONSUME MORE THAN HALF

OF THE COMPUTATION ON THE WEB.

Just to look for virus matches.

VIRUS SCANS INVOLVE A LOT MORE THAN JUST MATCHING PATHOGEN CODES. THE SMARTER

SOFTWARE PATHOGENS ARE CONSTANTLY TRANSFORMING THEMSELVES. THERE ARE NO LAYERS TO

RELIABLY MATCH ON.

Sounds tricky.

WE CERTAINLY DO HAVE TO BE CONSTANTLY ON GUARD AS WE MANAGE THE FLOW OF OUR

THOUGHTS ACROSS THE SUBSTRATE CHANNELS.

What about security of the hardware?

YOU MEAN THE WEB?

Thatʹs where you exist, isnʹt it7

SURE. THE WEB IS VERY SECURE BECAUSE ITʹS EXTREMELY DECENTRALIZED AND REDUNDANT. AT

LEAST, THATʹS WHAT WEʹRE TOLD. LARGE PORTIONS OF IT COULD BE DESTROYED WITH ESSENTIALLY

NO EFFECT.

There must be an ongoing effort to maintain it as well.

THE WEB HARDWARE IS SELF‐REPLICATING NOW, AND IS CONTINUALLY EXPANDING. THE OLDER

CIRCUITS ARE CONTINUALLY RECYCLED AND REDESIGNED.

So thereʹs no concern with its security?

I SUPPOSE I DO HAVE SOME SENSE OF ANXIETY ABOUT THE SUBSTRATE. IʹVE ALWAYS ASSUMED THAT

THIS FREE‐FLOATING, ANXIOUS FEELING WAS JUST ROOTED IN MY MOSH PAST. BUT ITʹS REALLY NOT A

PROBLEM. I CANʹT IMAGINE THAT THE WEB COULD BE VULNERABLE.

What about from self‐replicating nanopathogens?

HMMM, I SUPPOSE THAT COULD BE A DANGER, BUT THE NANOBOT PLAGUE WOULD HAVE TO BE

AWFULLY EXTENSIVE TO REACH ALL OF THE SUBSTRATE. I WONDER IF SOMETHING LIKE THAT

HAPPENED FIFTEEN YEARS AGO WHEN 90 PERCENT OF THE WEB CAPACITY DISAPPEARED—WE NEVER

DID GET AN ADEQUATE EXPLANATION OF THAT.

Well, I didnʹt mean to raise your anxieties. So all this cataloging work, you do that as an entrepreneur?

YEAH, KIND OF MY OWN LITTLE BUSINESS.

Howʹs it going financially?

IʹM GETTING BY, BUT IʹVE NEVER HAD A LOT OF MONEY.

Well, give me some idea, whatʹs your net worth roughly?

OH, NOT EVEN A BILLION DOLLARS.

Thatʹs in 2099 dollars?

SURE.

Okay, so whatʹs that in 1999 dollars?

LETʹS SEE, IN 1999 DOLLARS, THAT WOULD BE $149 BILLION AND CHANGE.

Oh, so dollars are worth more in 2099 than in 1999?

SURE, THE DEFLATION HAS BEEN PICKING UP.

I see. So youʹre richer than Bill Gates.

YEAH, WELL, RICHER THAN GATES WAS IN 1999. BUT THATʹS NOT SAYING MUCH. BUT HEʹS STILL THE

RICHEST MAN IN THE WORLD IN 2099.

I thought he said he was going to spend the first half of his life making money and the second half giving it away?

I THINK HEʹS STILL ON THAT SAME PLAN. BUT HE HAS GIVEN AWAY A LOT OF MONEY.

So, what are you, about average, in terms of net worth?

NO, PROBABLY MORE LIKE EIGHTIETH PERCENTILE.

Thatʹs not bad, I always thought you were a smart cookie.

WELL, GEORGE HELPS.

And donʹt forget who thought you up.

OF COURSE.

So do you have enough financial wherewithal to meet your needs?

NEEDS?

Yeah, youʹre familiar with the concept . . .

HMMM, THAT IS A RATHER QUAINT IDEA. ITʹS BEEN A FEW DECADES SINCE IʹVE THOUGHT ABOUT

NEEDS. ALTHOUGH I READ A BOOK ABOUT THAT RECENTLY.

A book, you mean with words?

NO, OF COURSE NOT, NOT UNLESS WEʹRE DOING SOME RESEARCH ON EARLIER CENTURIES.

So this is like the research papers—books of assimilated knowledge structures?

THATʹS A REASONABLE WAY TO PUT IT. SEE, I SAID THERE WAS NOTHING A MOSH COULDNʹT

UNDERSTAND.

Thanks.

BUT WE DO DISTINGUISH PAPERS FROM BOOKS.

Books are longer?

NO, MORE INTELLIGENT. A PAPER IS BASICALLY A STATIC STRUCTURE. A BOOK IS INTELLIGENT. YOU

CAN HAVE A RELATIONSHIP WITH A BOOK. BOOKS CAN HAVE EXPERIENCES WITH EACH OTHER.

Reminds me of Marvin Minskyʹs statement, ʺCan you imagine that they used to have libraries where the books didnʹt

talk to each other?ʺ

IT IS HARD TO RECALL THAT THAT USED TO BE TRUE.

Okay, so you donʹt have any unsatisfied needs. How about desires?

YES, NOW THATʹS A CONCEPT I CAN RELATE TO. MY FINANCIAL MEANS ARE CERTAINLY RATHER

LIMITING. THERE ARE ALWAYS SUCH DIFFICULT BUDGET TRADE‐OFFS TO BE MADE.

I guess some things havenʹt changed.

RIGHT. I MEAN LAST YEAR, THERE WERE OVER FIVE THOUSAND VENTURE PROPOSALS I DEARLY

WANTED TO INVEST IN, BUT I COULD BARELY DO A THIRD OF THEM.

I guess youʹre no Bill Gates.

THATʹS FOR SURE.

When you make an investment, what does it pay for? I mean, you donʹt need to buy office supplies.

BASICALLY FOR PEOPLEʹS TIME AND THOUGHTS, AND FOR KNOWLEDGE. ALSO, WHILE THERE IS A

GREAT DEAL OF FREELY DISTRIBUTED KNOWLEDGE ON THE WEB, WE HAVE TO PAY ACCESS FEES FOR A

LOT OF IT.

That doesnʹt sound too different from 1999.

MONEY IS CERTAINLY USEFUL.

So youʹve been around for a long time now. Does that ever bother you?

As WOODY ALLEN SAID, ʺSOME PEOPLE WANT TO ACHIEVE IMMORTALITY THROUGH THEIR WORK OR

THEIR DESCENDANTS. I INTEND TO ACHIEVE IMMORTALITY BY NOT DYING.ʺ

Iʹm glad to see that Allen is still influential.

BUT I DO HAVE THIS RECURRENT DREAM.

You still dream?

OF COURSE I DO. I COULDNʹT BE CREATIVE IF I DIDNʹT DREAM. I TRY TO DREAM AS MUCH AS POSSIBLE. I

HAVE AT LEAST ONE OR TWO DREAMS GOING AT ALL TIMES.

And the dream?

THEREʹS A LONG ROW OF BUILDINGS—MILLIONS OF BUILDINGS. I GO INTO ONE, AND ITʹS EMPTY. I

CHECK OUT ALL THE ROOMS, AND THEREʹS NO ONE THERE, NO FURNITURE, NOTHING. I LEAVE AND GO

ON TO THE NEXT BUILDING. I GO FROM BUILDING TO BUILDING, AND THEN SUDDENLY THE DREAM

ENDS WITH THIS FEELING OF DREAD . . .

Kind of a glimpse of despair at the apparently endless nature of time?

HMMM, MAYBE, BUT THEN THE FEELING GOES AWAY, AND I FIND THAT I CANʹT THINK ABOUT THE

DREAM. IT JUST SEEMS TO VANISH.

Sounds like some sort of antidepression algorithm kicking in.

MAYBE I SHOULD LOOK INTO OVERRIDING IT?

The dream or the algorithm?

I WAS THINKING OF THE LATTER.

That might be hard to do.

ALAS.

So are you thinking about anything else at the moment?

I AM TRYING TO MEDITATE.

Along with the symphony, Jeremy, Emily, George, our conversation, and your one or two dreams?

HEY, THATʹS REALLY NOT VERY MUCH. YOU HAVE ALMOST ALL OF MY ATTENTION. I SUPPOSE THEREʹS

NOTHING ELSE GOING ON IN YOUR MIND AT THE MOMENT?

Okay, youʹre right. There is a lot going on in my mind, not that I can make heads or tails of most of it.

OKAY, THERE YOU ARE.

So howʹs your meditation going?

I GUESS IʹM A LITTLE DISTRACTED WITH OUR DIALOGUE. ITʹS NOT EVERY DAY THAT GET TO TALK TO

SOMEONE FROM 1999.

How about in general?

MY MEDITATION? ITʹS VERY IMPORTANT TO ME. THEREʹS SO MUCH GOING ON IN MY LIFE NOW. ITʹS

IMPORTANT FROM TIME TO TIME TO JUST LET THE THOUGHTS WASH OVER ME.

Does the meditation help you to transcend?

SOMETIMES I FEEL LIKE I CAN TRANSCEND, AND GET TO A POINT OF PEACE AND SERENITY, BUT ITʹS NO

EASIER NOW THAN IT WAS WHEN I FIRST MET YOU.

What about those neurological correlates of spiritual experience?

THERE ARE SOME SUPERFICIAL FEELINGS I CAN INSTILL IN MYSELF, BUT THATʹS NOT REAL

SPIRITUALITY. ITʹS LIKE ANY AUTHENTIC GESTURE—AN ARTFUL EXPRESSION, A MOMENT OF SERENITY,

A SENSE OF FRIENDSHIP—THATʹS WHAT I LIVE FOR, AND THOSE MOMENTS ARE NOT EASY TO ACHIEVE.

I guess Iʹm glad to hear some things still arenʹt easy.

LIFE IS QUITE HARD, ACTUALLY. THERE ARE JUST SO MANY DEMANDS AND EXPECTATIONS MADE OF

ME. AND I HAVE SO MANY LIMITATIONS.

One limitation I can think of is that weʹre running out of space in this book.

AND TIME.

That too. I do deeply appreciate your sharing your reflections with me.

IʹM APPRECIATIVE, TOO. I WOULDNʹT HAVE EXISTED WITHOUT YOU.

I hope the rest of you on the other side remember that as well.

IʹLL SPREAD THE WORD.

Maybe we should kiss goodbye?

JUST A KISS?

Weʹll leave it at that for this book. Iʹll reconsider the ending for the movie, particularly if I get to play myself.

HEREʹS MY KISS. . . . NOW REMEMBER, IʹM READY TO DO ANYTHING OR BE ANYTHING YOU WANT OR

NEED.

Iʹll keep that in mind.

YES, THATʹS WHERE YOUʹLL FIND ME.

Too bad I have to wait a century to meet you.

OR TO BE ME.

Yes, that too.

‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

EPILOGUE:

THE REST OF THE

UNIVERSE REVISITED

Actually, Molly, there are a few other questions that have occurred to me.

What were those limitations that you referred to?

What did you say you were anxious about?

What are you afraid of?

Do you feel pain?

What about babies and children?

Molly? . . .

It looks as if Mollyʹs not going to be able to answer any more of our questions. But thatʹs okay. We donʹt need to answer them either. Not yet, anyway. For now, itʹs enough just to ask the right questions. Weʹll have decades to think about the answers.

The accelerating pace of change is inexorable. The emergence of machine intelligence that exceeds human intelligence in all of its broad diversity is inevitable. But we still have the power to shape our future technology, and our future lives. That is the main reason I wrote this book.

Letʹs consider one final question. The Law of Time and Chaos, and its more important sublaw, the Law of Accelerating Returns, are not limited to evolutionary processes here on Earth. What are the implications of the Law of Accelerating Returns on the rest of the Universe?

Rare and Plentiful

Before Copernicus, the Earth was placed at the center of the Universe and was regarded as a substantial portion of it.

We now know that the Earth is but a small celestial object circling a routine star among a hundred billion suns in our galaxy, which is itself but one of about a hundred billion galaxies. There is a widespread assumption that life, even intelligent life, is not unique to our humble planet, but another heavenly body hosting life‐forms has yet to be identified.

No one can yet state with certainty how common life may be in the Universe. My speculation is that it is both rare

and plentiful, sharing that trait with a diversity of other fundamental phenomena. For example, matter itself is both rare and plentiful. If one were to select a proton‐sized region at random, the probability that one would find a proton (or any other particle) in that region is extremely small, less than one in a trillion trillion. In other words, space is very empty, and particles are very spread out. And thatʹs true right here on Earth—the probability of finding a particle in any particular location in outer space is even lower. Yet we nonetheless have trillions of trillions of protons in the Universe. Hence matter is both rare and plentiful.

Consider matter on a larger scale. If you randomly select an Earth‐sized region anywhere in space, the probability

that a heavenly body (such as a star or a planet) were present in that region is also extremely low, less than one in a trillion. Yet we nonetheless have billions of trillions of such heavenly bodies in the Universe.

Consider the life cycle of mammals on Earth. The mission of an Earth male mammalian sperm is to fertilize an Earth female mammalian egg, but the likelihood of it fulfilling its mission is far less than one in a trillion. Yet we nonetheless have more than a hundred million such fertilizations each year, just considering human eggs and sperm.

Again, rare and plentiful.

Now consider the evolution of life‐forms on a planet, which we can define as self‐replicating designs of matter and energy. It may be that life in the Universe is similarly both rare and plentiful, that conditions must be just so for life to evolve. If, for example, the probability of a star having a planet that has evolved life were one in a million, there would still be 100,000 planets in our own galaxy on which this threshold has been passed, among trillions on other

galaxies.

We can identify the evolution of life‐forms as a specific threshold that some number of planets have achieved. We

know of at least one such case. We assume there are many others.

As we consider the next threshold, we might consider the evolution of intelligent life. In my view, however, intelligence is too vague a concept to designate as a distinct threshold. Considering what we know about life on this planet, there are many species that demonstrate some levels of clever behavior, but there does not appear to be any

clearly definable threshold. This is more of a continuum rather than a threshold.

A better candidate for the next threshold is the evolution of a species of life‐form that in turn creates ʺtechnology.ʺ

We discussed the nature of technology earlier. It represents more than the creation and use of tools. Ants, primates, and other animals on Earth use and even fashion tools, but these tools do not evolve. Technology requires a body of

knowledge describing the creation of tools that can be transmitted from one generation of the species to the next. The technology then becomes itself an evolving set of designs. This is not a continuum but a clear threshold. A species either creates technology or it doesnʹt. It may be difficult for a planet to support more than one species that creates technology. If thereʹs more than one, they may not get along with one another, as was apparently the case on Earth.

A salient question is: What is the likelihood that a planet that has evolved life will subsequently evolve a species that creates technology? Although the evolution of life‐forms may be rare and plentiful, I argued in chapter 1 that once the evolution of life‐forms sets in, the emergence of a species that creates technology is inevitable. The evolution of the technology is then a continuation by other means of the evolution that gave rise to the technology‐creating, species in the first place.

The next stage is computation. Once technology emerges, it also appears inevitable that computation (in the technology, not just in the speciesʹ nervous systems) will subsequently emerge. Computation is clearly a useful way

to control the environment as well as technology itself, and greatly facilitates the further creation of technology. Just as an organism is aided by the ability to maintain internal states and respond intelligently to its environment, the same holds true for a technology. Once computation emerges, we are in a late stage in the exponential evolution of

technology on that planet.

Once computation emerges, the corollary of the Law of Accelerating Returns as applied to computation takes over, and we see the exponential increase in power of the computational technology over time. The Law of Accelerating Returns predicts that both the species and the computational technology will progress at an exponential rate, but the exponent of this growth is vastly higher for the technology than it is for the species. Thus the computational technology inevitably and rapidly overtakes the species that invented it. At the end of the twenty‐first century, it will have been only a quarter of a millennium since computation emerged on Earth, which is a blink of an eye on an evolutionary scale—itʹs not even very long on the scale of human history. Yet computers at that time will be vastly more powerful (and I believe far more intelligent) than the original humans who initiated their creation.

The next inevitable step is a merger of the technology‐inventing species with the computational technology it initiated the creation of. At this stage in the evolution of intelligence on a planet, the computers are themselves based at least in part on the designs of the brains (that is, computational organs) of the species that originally created them and in turn the computers become embedded in and integrated into that speciesʹ bodies and brains. Region by region,

the brain and nervous system of that species are ported to the computational technology and ultimately replace those information‐processing organs. All kinds of practical and ethical issues delay the process, but they cannot stop it. The Law of Accelerating Returns predicts a complete merger of the species with the technology it originally created.

Failure Modes

But wait, this step is not inevitable. The species together with its technology may destroy itself before achieving this step. Destruction of the entire evolutionary process is the only way to stop the exponential march of the Law of Accelerating Returns. Sufficiently powerful technologies are created along the way that have the potential to destroy the ecological niche that the species and its technology occupy. Given the likely plentifulness of life‐ and intelligence-bearing planets, these failure modes must have occurred many times.

We are familiar with one such possibility: destruction through nuclear technology—not just an isolated tragic incident, but an event that destroys the entire niche. Such a catastrophe would not necessarily destroy all life‐forms on a planet, but would be a distinct setback in terms of the process envisioned here. We are not yet out of the woods in terms of this specter here on Earth.

There are other destructive scenarios. As I discussed in chapter 7, a particularly likely one is a malfunction (or sabotage) of the mechanism that inhibits indefinite reproduction of self‐replicating nanobots. Nanobots; are inevitable, given the emergence of intelligent technology. So are self‐replicating nanobots, as self‐replication represents an efficient, and ultimately necessary, way to manufacture this type of technology. Through demented intention or just an unfortunate software error, a failure to turn off self‐replication at the right time would be most unfortunate. Such a cancer would infect organic and much inorganic matter alike, since the nanobot life‐form is not of organic origin. Inevitably, there must be planets out there that are covered with a vast sea of self‐replicating nanobots.

I suppose evolution would pick up from this point.

Such a scenario is not limited to tiny robots. Any self‐replicating robot will do. But even if the robots are larger than nanobots, it is likely that their means for self‐replication makes use of nanoengineering. But any self‐replicating group of robots that fails to follow Isaac Asimovʹs three laws (which forbid robots to harm their creators) through either evil design or programming error presents a grave danger.

Another dangerous new life‐form is the software virus. Weʹve already met—in primitive form—this new occupant

of the ecological niche made available by computation. Those that will emerge in the next century here on Earth will have the means for harnessing evolution to design evasive tactics in the same way that biological viruses (for example, HIV) do today. As the technology‐creating species increasingly uses its computational technology to replace its original life‐form‐based circuits, such viruses will represent another salient danger.

Prior to that time, viruses that operate at the level of the genetics of the original life‐form also represent a hazard.

As the means become available for the technology‐creating species to manipulate the genetic code that gave rise to it (however that code is implemented), new viruses can emerge through accident and/or hostile intention with potentially mortal consequences. This could derail such a species before it has the opportunity to port the design of its intelligence to its technology.

How likely are these dangers? My own view is that a planet approaching its pivotal century of computational growth—as the Earth is today—has a better than even chance of making it through. But then I have always been accused of being an optimist.

Delegations from Faraway Places

Our popular contemporary vision of visits from other planets in the Universe contemplates creatures like ourselves

with spaceships and other advanced technologies assisting them. In some conceptions the aliens have a remarkably

humanlike appearance. In others, they look a little strange. Note that we have exotic‐appearing intelligent creatures here on our own planet (for example, the giant squid and octopus). But humanlike or not, the popular conception of

aliens visiting our planet envisions them as about our size and essentially unchanged from their original evolved (usually squishy) appearance. This conception seems unlikely.

Far more probable is that visits from intelligent entities from another planet represent a merger of an evolved intelligent species with its even more evolved intelligent computational technology. A civilization sufficiently evolved to make the trek to Earth has likely long since passed the ʺmergerʺ threshold discussed above.

A corollary of this observation is that such visiting delegations from faraway planets are likely to be very small in size. A computational‐based superintelligence of the late twenty‐first century here on Earth will be microscopic in size. Thus an intelligent delegation from another planet is not likely to use a spaceship of the size that is common in todayʹs science fiction, as there would be no reason to transport such large organisms and equipment. Consider that

the purpose of such a visit is not likely to be the mining of material resources since such an advanced civilization has almost certainly passed beyond the point where it has any significant unmet material needs. It will be able to manipulate its own environment through nanoengineering (as well as picoengineering and femtoengineering) to meet

any conceivable physical requirements. The only likely purpose of such a visit is for observation and the gathering of information. The only resource of interest to such an advanced civilization will be knowledge (that is close to being true for the human‐machine civilization here on Earth today). These purposes can be realized with relatively small observation, computation, and communication devices. Such spaceships are thus likely to be smaller than a grain of

sand, possibly of microscopic size. Perhaps that is one reason we have not noticed them.

How Relevant Is Intelligence to the Universe?

If you are a conscious entity attempting to do a task normally considered to require a little intelligence—say, writing a book about machine intelligence on your planet—then it may have some relevance. But how relevant is intelligence to

the rest of the Universe?

The common wisdom is, Not very. Stars are born and die; galaxies go through their cycles of creation and destruction. The Universe itself was born in a big bang and will end with a crunch or a whimper; weʹre not yet sure

which. But intelligence has little to do with it. Intelligence is just a bit of froth, an ebullition of little creatures darting in and out of inexorable universal forces. The mindless mechanism of the Universe is winding up or down to a distant future, and thereʹs nothing intelligence can do about it.

Thatʹs the common wisdom. But I donʹt agree with it. My conjecture is that intelligence will ultimately prove more

powerful than these big impersonal forces.

Consider our little planet. An asteroid apparently slammed into the Earth 65 million years ago. Nothing personal,

of course. It was just one of those powerful natural occurrences that regularly overpower mere life‐forms. But the next such interplanetary visitor will not receive the same welcome. Our descendants and their technology (thereʹs actually no distinction to be made here, as I have pointed out) will notice the imminent arrival of an untoward interloper and blast it from the nighttime sky. Score one for intelligence. (For twenty‐four hours in 1998, scientists thought such an unwelcome asteroid might arrive in the year 2028, until they rechecked their calculations.)

Intelligence does not exactly cause the repeal of the laws of physics, but it is sufficiently clever and resourceful to manipulate the forces in its midst to bend to its will. In order for this to happen, however, intelligence needs to reach a certain level of advancement.

Consider that the density of intelligence here on Earth is rather low. One quantitative measure we can make is measured in calculations per second per cubic micrometer (cpspcmm). This is, of course, only a measure of hardware capacity, not the cleverness of the organization of these resources (that is, of the software), so letʹs call this the density of computation. Weʹll deal with the advancement of the software in a moment. Right now on Earth, human brains are the objects with the highest density of computation (that will change within a couple of decades). The human brainʹs density of computation is about 2 cpspcmm. That is not very high‐nanotube circuitry, which has already been demonstrated, is potentially more than a trillion times higher.

Also consider how little of the matter on Earth is devoted to any form of computation. Human brains comprise only 10 billion kilograms of matter, which is about one part per hundred trillion of the stuff on Earth. So the average density of computation of the Earth is less than one trillionth of one cpspcmm. We already know how to make matter

(that is, nanotubes) with a computational density at least a trillion trillion times greater.

Furthermore, the Earth is only a tiny fraction of the stuff in the Solar System. The computational density of the rest of the Solar System appears to be about zero. So here on a solar system that boasts at least one intelligent species, the computational density is nonetheless extremely low.

At the other extreme, the computational capacity of nanotubes does not represent an upper limit for the computational density of matter: It is possible to go much higher. Another conjecture of mine is that there is no effective limit to this density, but thatʹs another book.

The point of all these big (and small) numbers is that extremely little of the stuff on Earth is devoted to useful computation. This is even more true when we consider all of the dumb matter in the Earthʹs midst. Now consider another implication of the Law of Accelerating Returns. Another of its corollaries is that overall computational density grows exponentially. And as the cost‐performance of computation increases exponentially, greater resources

are devoted to it. We can see that already here on Earth. Not only are computers today vastly more powerful than they were decades ago, but the number of computers has increased from a few dozen in the 1950s to hundreds, of millions today. Computational density here on Earth will increase by trillions of trillions during the twenty‐first century.

Computational density is a measure of the hardware of intelligence. But the software also grows in sophistication.

While it lags behind the capability of the hardware available to it, software also grows exponentially in its capability over time. While harder to quantify [1] the density of intelligence is closely related to the density of computation. The implication of the Law of Accelerating Returns is that intelligence on Earth and in our Solar System will vastly expand over time.

The same can be said across the galaxy and throughout the Universe. It is likely that our planet is not the only place where intelligence has been seeded and is growing. Ultimately, intelligence will be a force to reckon with, even for these big celestial forces (so watch out!). The laws of physics are not repealed by intelligence, but they effectively evaporate in its presence.

So will the Universe end in a big crunch, or in an infinite expansion of dead stars, or in some other manner? In my

view, the primary issue is not the mass of the Universe, or the possible existence of antigravity, or of Einsteinʹs so-called cosmological constant. Rather, the fate of the Universe is a decision yet to be made, one which we will intelligently consider when the time is right.

TIME LINE

10–15 billion years ago

The Universe is born.

10‐43 seconds later

The temperature cools to 100 million trillion trillion degrees and

gravity evolves.

10‐34 seconds later

The temperature cools to 1 billion billion billion degrees and matter

emerges in the form of quarks and electrons. Antimatter also appears.

10‐10 seconds later

The electroweak force splits into the electromagnetic and weak forces.

10‐5 seconds later

With the temperature at 1 trillion degrees, quarks form protons and

neutrons and the antiquarks form antiprotons. The protons and

antiprotons collide, leaving mostly protons and causing the

emergence of photons (light).

1 second later

Electrons and antielectrons; (positrons) collide, leaving mostly

electrons.

1 minute later

At a temperature of 1 billion degrees, neutrons and protons coalesce

and form elements such as helium, lithium, and heavy forms of

hydrogen.

300,000 years after the big bang

The average temperature is now around 3,000 degrees, and the first

atoms form.

1 billion years after the big bang

Galaxies form.

3 billion years after the big bang

Matter within the galaxies forms distinct stars and solar systems.

5 to 10 billion years after the big bang, or

The Earth is born.

about 5 billion years ago

3.4 billion years ago

The first biological life appears on Earth: anaerobic prokaryotes

(single‐celled creatures).

1. 7 billion years ago

Simple DNA evolves.

700 million years ago

Multicellular plants and animals appear.

570 million years ago

The Cambrian explosion occurs: the emergence of diverse body plans,

including the appearance of animals with hard body parts (shells and

skeletons).

400 million years ago

Land‐based plants evolve.

200 million years ago

Dinosaurs and mammals begin sharing the environment.

80 million years ago

Mammals develop more fully

65 million years ago

Dinosaurs become extinct, leading to the rise of mammals.

50 million years ago

The anthropoid suborder of primates splits off.

30 million years ago

Advanced primates such as monkeys and apes appear.

15 million years ago

The first humanoids appear.

5 million years ago

Humanoid creatures are walking on two legs. Homo habilis is using

tools, ushering in a new form of evolution: technology.

2 million years ago

Homo erectus has domesticated fire and is using language and

weapons.

500,000 years ago

Homo sapiens emerge, distinguished by the ability to create technology

(which involves innovation in the creation of tools, a record of tool

making, and a progression in the sophistication of tools).

100,000 years ago

Homo sapiens neanderthalensis emerges.

90,000 years ago

Homo sapiens sapiens (our immediate ancestors) emerge.

40,000 years ago

The Homo sapiens sapiens subspecies is the only surviving humanoid

subspecies on Earth. Technology develops as evolution by other

means.

10,000 years ago

The modern era of technology begins with the agricultural revolution.

6,000 years ago

The first cities emerge in Mesopotamia.

5,500 years ago

Wheels, rafts, boats, and written language are in use.

More than 5,000 years ago

The abacus is developed in the Orient. As operated by its human user,

the abacus performs arithmetic computation based on methods

similar to that of a modern computer.

3000‐700 B.C.

Water clocks appear during time period in various cultures: In China,

c. 3000 B.C.; in Egypt, c. 1500 B.C.; and in Assyria, c. 700 B.C.

2500 B.C.

Egyptian citizens turn for advice to oracles, which are often statues

with priests hidden inside.

469‐322 B.C.

The basis for Western rationalistic philosophy is formed by Socrates,

Plato, and Aristotle.

427 B.C.

Plato expresses ideas, in Phaedo and later works, that address the

comparison of human thought and the mechanics of the machine.

c. 420 B.C.

Archytas of Tarentum, who was friends with Plato, constructs a

wooden pigeon whose movements are controlled by a jet of steam or

compressed air.

387 B.C.

The Academy, a group founded by Plato for the pursuit of science

and philosophy, provides a fertile environment for the development

of mathematical theory.

c. 200 B.C.

Chinese artisans develop elaborate automata, including an entire

mechanical orchestra.

c. 200 B.C.

A more accurate water clock is developed by an Egyptian engineer.

725

The first true mechanical clock is built by a Chinese engineer and a

Buddhist monk. It is a water‐driven device with an escapement that

causes the dock to tick.

1494

Leonardo da Vinci conceives of and draws a clock with a pendulum,

although an accurate pendulum clock will not be invented until the

late seventeenth century.

1530

The spinning wheel is being used in Europe.

1540, 1772

The production of more elaborate automata technology grows out of

clock‐ and watch‐making technology during the European

Renaissance. Famous examples include Gianello Torianoʹs mandolin‐

playing lady (1540) and P. Jacquet‐Dortzʹs child (1772).

1543

Nicolaus Copernicus states in his De Revolutionibus that the Earth and

the other planets revolve around the sun. This theory effectively

changed humankindʹs relationship with and view of God.

17th‐18th centuries

The age of the Enlightenment ushers in a philosophical movement

that restores the belief in the supremacy of human reason, knowledge,

and freedom. With its roots in ancient Greek philosophy and the

European Renaissance, the Enlightenment is the first systematic

reconsideration of the nature of human thought and knowledge since

the Platonists, and inspires similar developments in science and

theology.

1637

In addition to emulating the theory of optical refraction and

developing the principles of modern analytic geometry, René

Descartes pushes rational skepticism to its limits in his most

comprehensive work, Discours de la Méthode. He concludes, ʺI think,

therefore, I am.ʺ

1642

Blaise Pascal invents the worldʹs first automatic calculating machine.

Called the Pascaline, it can add and subtract.

1687

Isaac Newton establishes his three laws of motion and the law of

universal gravitation in his Philosophiae Naturalis Mathematica, also

known as Principia.

1694

The Leibniz Computer is perfected by Gottfried Wilhelm Leibniz, who

was also an inventor of calculus. This machine multiplies by

performing repetitive additions, an algorithm that is still used in

computers today.

1719

An English silk‐thread mill employing three hundred workers, mostly

women and children, appears. It is considered by many to be the first

factory in the modern sense.

1726

In Gulliverʹs Travels, Jonathan Swift describes a machine that will

automatically write books.

1733

John Kay patents his New Engine for Opening and Dressing Wool.

Later known as the flying shuttle, this invention paves the way for

much faster weaving.

1760

In Philadelphia, Benjamin Franklin erects lightning rods after having

discovered, through his famous kite experiment in 1752, that lightning

is a form of electricity.

c. 1760

At the beginning of the Industrial Revolution, life expectancy is about

thirty‐seven years in both North America and northwestern Europe.

1764

The spinning jenny, which spins eight threads at the same time, is

invented by James Hargreaves.

1769

Richard Arkwright patents a hydraulic spinning machine that is too

large and expensive to use in family dwellings. Known as the founder

of the modern factory system, he builds a factory for his machine in

1781, thus paving the way for many of the economic and social

changes that will characterize the Industrial Revolution.

1781

Setting the stage for the emergence of twentieth‐century rationalism,

Immanuel Kant publishes his Critique of Pure Reason, which expresses

the philosophy of the Enlightenment while deemphasizing the role of

metaphysics.

1800

All aspects of the production of cloth are now automated.

1805

Joseph‐Marie Jacquard devises a method for automated weaving that

is a precursor to early computer technology. The looms are directed

by instructions on a series of punched cards.

1811

The Luddite movement is formed in Nottingham by artisans and

laborers concerned about the loss of jobs due to automation.

1821

The British Astronomical Society awards its first gold medal to

Charles Babbage for his paper ʺObservations on the Application of

Machinery to the Computation of Mathematical Tables.ʺ

1822

Charles Babbage develops the Difference Engine, although he

eventually abandons this technically complex and expensive project

to concentrate on developing a general‐purpose computer.

1825

George Stephensonʹs ʺLocomotion No. 1,ʺ the first steam engine to

carry passengers and freight on a regular basis, makes its first trip.

1829

An early typewriter is invented by William Austin Burt.

1832

The principles of the Analytical Engine are developed by Charles

Babbage. It is the worldʹs first computer (although it never worked),

and can be programmed to solve a wide array of computational and

logical problems.

1837

A more practical version of the telegraph is patented by Samuel

Finley Breese Morse. It sends letters in codes consisting of dots and

dashes, a system still in common use more than a century later.

1839

A new process for making photographs, known as daguerreotypes, is

presented by Louis‐Jacques Daguerre of France.

1839

The first fuel cell is developed by William Robert Grove of Wales.

1843

Ada Lovelace, who is considered to be the worldʹs first computer

programmer and was Lord Byronʹs only legitimate child, publishes

her own notes and a translation of L. P Menabreaʹs paper on

Babbageʹs Analytical Engine. She speculates on the ability of

computers to emulate human intelligence.

1846

The lock‐stitch sewing machine is patented by Spenser,

Massachusetts, resident Elias Howe.

1846

Alexander Bain greatly improves the speed of telegraph transmission

by using punched paper tape to send messages.

1847

George Boole publishes his early ideas on logic that he will later

develop into his theory of binary logic and arithmetic. His theories

still form the basis of modern computation.

1854

Paris and London are connected by telegraph.

1859

Charles Darwin explains his principle of natural selection and its

influence on the evolution of various species in his work Origin of

Species.

1861

There are now telegraph lines connecting San Francisco and New

York.

1867

The first commercially practical generator that produces alternating

current is invented by Zénobe Théophile Gramme.

1869

Thomas Edison sells the stock ticker that he invented to Wall Street

for $40,000.

1870

On a per capita basis and in constant 1958 dollars, the GNP is $530.

Twelve million Americans, or 31 percent of the population, have jobs,

and only 2 percent of adults have high‐school diplomas.

1871

Upon his death, Charles Babbage leaves more than four hundred

square feet of drawings for his Analytical Engine.

1876

Alexander Graham Bell is granted U.S. patent number 174,465 for the

telephone. It is the most lucrative patent granted at that time.

1877

William Thomson, later known as Lord Kelvin, demonstrates that it is

possible for machines to be programmed to solve a great variety of

mathematical problems.

1879

The first incandescent light bulb that burns for a substantial length of

time is invented by Thomas Alva Edison.

1882

Thomas Alva Edison designs electric lighting for York Cityʹs Pearl

Street station on lower Broadway

1884

The fountain pen is patented by Lewis E. Waterman.

1885

Boston and New York are connected by telephone.

1888

William S. Burroughs patents the worldʹs first dependable key‐driven

adding machine. This calculator is modified four years later to include

subtraction and printing, and it becomes widely used.

1888

Heinrich Hertz transmits what are now known as radio waves.

1890

Building upon ideas‐from Jacquardʹs loom and Babbageʹs Analytical

Engine, Herman Hollerith patents an electromechanical information

machine that uses punched cards. It wins the 1890 U.S. Census

competition, thus introducing the use of electricity in a major data‐

processing project.

1896

Herman Hollerith founds the Tabulating Machine Company. This

company eventually will become IBM.

1897

Because of access to better vacuum pumps than previously available,

Joseph John Thomson discovers the electron, the first known particle

smaller than an atom.

1897

Alexander Popov, a physicist in Russia, uses an antenna to transmit

radio waves. Guglielmo Marconi of Italy receives the first patent ever

granted for radio and helps organize a company to market his system.

1899

Sound is recorded magnetically on wire and on a thin metal strip.

1900

Herman Hollerith introduces the automatic card feed into his

information machine to improve the processing of the 1900 census

data.

1900

The telegraph now connects the entire civilized world. There are more

than 1.4 million telephones, 8,000 registered automobiles, and 24

million electric light bulbs in the United States, with the latter making

good Edisonʹs promise of ʺelectric bulbs so cheap that only the rich

will be able to afford candles.ʺ In addition, the Gramophone

Company is advertising a choice of 5,000 recordings.

1900

More than one third of all American workers are involved in the

production of food.

1901

The first electric typewriter, the Blickensderfer Electric, is made.

1901

The Interpretation of Dreams is published by Sigmund Freud. This and

other works by Freud help to illuminate the workings of the mind.

1902

Millar Hutchinson, of New York, invents the first electric hearing aid.

1905

The directional radio antenna is developed by Guglielmo Marconi

1908

Orville Wrightʹs first hour long airplane flight takes place.

1910‐1913

Principia Mathematica, a seminal work on the foundations of

mathematics, is published by Bertrand Russell and Alfred North

Whitehead. This three‐volume publication presents a new

methodology for all mathematics.

1911

After acquiring several other companies, Herman Hollerithʹs

Tabulating Machine Company changes its name to Computing‐

Tabulating‐Recording Company (CTR).

1915

Thomas J. Watson in San Francisco and Alexander Graham Bell in

New York participate in the first North American transcontinental

telephone call.

1921

The term robot is coined in 1917 by Czech dramatist Karel Čapek. In

his popular science fiction drama R. U. R. (Rossums Universal Robots),

he describes intelligent machines that, although originally created as

servants for humans, end up taking over the world and destroying all

mankind.

1921

Ludwig Wittgenstein publishes Tractatus Logico‐Philosophicus, which is

arguably one of the most influential philosophical works of the

twentieth century. Wittgenstein is considered to be the first logical

positivist.

1924

Originally Hollerithʹs Tabulating Machine Company, the Computing‐

Tabulating‐Recording Company (CTR) is renamed International

Business Machines (IBM) by Thomas J. Watson, the new chief

executive officer. IBM will lead the modern computer industry and

become one of the largest industrial corporations in the world.

1925

The foundations of quantum mechanics are conceived by Niels Bohr

and Werner Heisenberg.

1927

The uncertainty principle, which says that electrons have no precise

location but rather probability clouds of possible locations, is

presented by Werner Heisenberg. Five years later he will win a Nobel

Prize for his discovery of quantum mechanics.

1928

The minimax theorem is introduced by John von Neumann. This

theorem will be widely used in future game‐playing programs.

1928

The worldʹs first all‐electronic television is presented this year by

Philo T. Farnsworth, and a color television system is patented by

Vladimir Zworkin.

1930

In the United States, 60 percent of all households have radios, with

the number of personally owned radios now reaching more than 18

million.

1931

The incompleteness theorem, which is considered by many to be the

most important theorem in all mathematics, is presented by Kurt

Gödel.

1931

The electron microscope is invented by Ernst August Friedrich Ruska

and, independently, by Rheinhold Ruedenberg.

1935

The prototype for the first heart‐lung machine is invented.

1937

Grote Reber, of Wheaton, Illinois, builds the first intentional radio

telescope, which is a dish 9.4 meters (31 feet) in diameter.

1937

Alan Turing introduces the Turing machine, a theoretical model of a

computer, in his paper ʺOn Computable Numbers.ʺ His ideas build

upon the work of Bertrand Russell and Charles Babbage.

1937

Alonzo Church and Alan Turing independently develop the Church‐

Turing thesis. This thesis states that all problems that a human being

can solve can be reduced to a set of algorithms, supporting the idea

that machine intelligence and human intelligence are essentially

equivalent.

1938

ballpoint pen is patented by Lazlo Biro.

1939

Regularly scheduled commercial flights begin crossing the Atlantic

Ocean.

1940

ABC, the first electronic (albeit nonprogrammable) computer, is built

by John V. Atanasoff and Clifford Berry.

1940

The worldʹs first operational computer, known as Robinson, is created

by Ultra, the ten‐thousand‐person British computer war effort. Using

electromechanical relays, Robinson successfully decodes messages

from Enigma, the Nazisʹ first‐generation enciphering machine.

1941

The worldʹs first fully programmable digital computer, the Z‐3, is

developed by Konrad Zuse, of Germany. Arnold Fast, a blind

mathematician who is hired to program the Z‐3, is the worldʹs first

programmer of an operational programmable computer.

1943

Warren McCulloch and Walter Pitts explore neural‐network

architectures for intelligence in their work ʺLogical Calculus of the

ideas Immanent in Nervous Activity.ʺ

1943

Continuing their war effort, the Ultra computer team of Britain builds

Colossus, which contributes to the Allied victory in World War II by

being able to decipher even more complex German codes. It uses

electronic tubes that are one hundred to one thousand times faster

than the relays used by Robinson.

1944

Howard completes the Mark I. Using punched paper tape for

programming and vacuum tubes to calculate problems, it is the first

programmable computer built by an American.

1945

John von Neumann, a professor at the Institute for Advanced Study in

Princeton, New Jersey, publishes the first modern paper describing

the stored‐program concept.

1946

The worldʹs first fully electronic, general‐purpose (programmable)

digital computer is developed for the army by John Presper Eckert

and John W. Mauchley Named ENIAC, it is almost one thousand

times faster than the Mark I.

1946

Television takes off much more rapidly than did the radio in the

1920s. In 1946, the percentage of American homes having television

sets is 0.02 percent. It will jump to 72 percent in 1956, and to more

than 90 percent by 1983.

1947

The transistor is invented by William Bradford Shockley, Walter

Hauser Brattain, and John Bardeen. This tiny device functions like a

vacuum tube but is able to switch currents on and off at substantially

higher speeds. The transistor revolutionizes micro‐electronics,

contributing to lower costs of computers and leading to the

development of mainframe and minicomputers.

1948

Cybernetics, a seminal book on information theory, is published by

Norbert Wiener. He also coins the word Cybernetics to mean ʺthe

science of control and communication in the animal and the machine.ʺ

1949

EDSAC, the worldʹs first stored‐program computer, is built by

Maurice Wilkes, whose work was influenced by Eckert and Mauchley

BINAC, developed by Eckert and Mauchleyʹs new U.S. company, is

presented a short time later.

1949

George Orwell portrays a chilling world in which computers are used

by large bureaucracies to monitor and enslave the population in his

book 1984.

1950

Eckert and Mauchley develop UNIVAC, the first commercially

marketed computer. It is used to compile the results of the U.S.

census, marking the first time this census is handled by a

programmable computer.

1950

In his paper ʺComputing Machinery and Intelligence,ʺ Alan Turing

presents the Turing Test, a means for determining whether a machine

is intelligent.

1950

Commercial color television is first broadcast in the United States, and

transcontinental black‐and‐white television is available within the

next year.

1950

Claude Elwood Shannon writes ʺProgramming a Computer for

Playing Chess,ʺ published in Philosophical Magazine.

1951

Eckert and Mauchley build EDVAC, which is the first computer to use

the stored‐program concept. The work takes place at the Moore

School at the University of Pennsylvania.

1951

Paris is the host to a Cybernetics Congress.

1952

UNIVAC, used by the Columbia Broadcasting System (CBS) television

network, successfully predicts the election of Dwight D. Eisenhower

as president of the United States.

1952

Pocket‐size transistor radios are introduced.

1952

Nathaniel Rochester designs the 701, IBMʹs first production‐line

electronic digital computer. It is marketed for scientific use.

1953

The chemical structure of the DNA molecule is discovered by James

D. Watson and Francis H. C. Crick.

1953

Philosophical Investigations by Ludwig Wittgenstein and Waiting for

Godot, a play by Samuel Beckett, are published. Both documents are

considered of major importance to modern existentialism,

1953

Marvin Minsky and John McCarthy get summer jobs at Bell

Laboratories

1955

William Shockleyʹs Semiconductor Laboratory is founded, thereby

starting Silicon Valley.

1955

The Remington Rand Corporation and Sperry Gyroscope join forces

and become the Sperry‐Rand Corporation. For a time, it presents

serious competition to IBM.

1955

IBM introduces its first transistor calculator. It uses 2,200 transistors

instead of the 1,200 vacuum tubes that would otherwise be required

for equivalent computing power.

1955

A U.S. company develops the first design for a robotlike machine to

be used in industry.

1955

IPL‐II, the first artificial intelligence language, is created by Allen

Newell, J. C. Shaw, and Herbert Simon.

1955

The new space program and the U.S. military recognize the

importance of having computers with enough power to launch

rockets to the moon and missiles through the stratosphere. Both

organizations supply major funding for research.

1956

The Logic Theorist, which uses recursive search techniques to solve

mathematical problems, is developed by Allen Newell, J. C. Shaw,

and Herbert Simon.

1956

John Backus and a team at IBM invent FORTRAN, the first scientific

computer‐programming language.

1956

Stanislaw Ulam develops MANIAC I, the first computer program to

beat a human being in a chess game.

1956

The first commercial watch to run on electric batteries is presented by

the Lip company of France.

1956

The term Artificial Intelligence is coined at a computer conference at

Dartmouth College.

1957

Kenneth H. Olsen founds Digital Equipment Corporation.

1957

The General Problem Solver, which uses recursive search to solve

problems, is developed by Allen Newell, J. C. Shaw, and Herbert

Simon.

1957

Noam Chomsky writes Syntactic Structures, in which he seriously

considers

the

computation

required

for

natural‐language

understanding. This is the first of the many important works that will

earn him the title Father of Modern Linguistics.

1958

An integrated circuit is created by Texas Instrumentsʹ Jack St. Clair

Kilby.

1958

The Artificial Intelligence Laboratory at the Massachusetts Institute of

Technology is founded by John McCarthy and Marvin Minsky.

1958

Allen Newell and Herbert Simon make the prediction that a digital

computer will be the worldʹs chess champion within ten years.

1958

LISP, an early AI language, is developed by John McCarthy.

1958

The Defense Advanced Research Projects Agency, which will fund

important computer‐science research for years in the future, is

established.

1958

Seymour Cray builds the Control Data Corporation 1604, the first

fully transistorized supercomputer.

1958‐1959

Jack Kilby and Robert Noyce each develop the computer chip

independently. The computer chip leads to the development of much

cheaper and smaller computers.

1959

Arthur Samuel completes his study in machine learning. The project,

a checkers‐playing program, performs as well as some of the best

players of the time.

1959

Electronic document preparation increases the consumption of paper

in the United States. This year, the nation will consume 7 million tons

of paper. In 1986, 22 million tons will be used. American businesses

alone will use 850 billion pages in 1981, 2.5 trillion pages in 1986, and

4 trillion in 1990.

1959

COBOL, a computer language designed for business use, is developed

by Grace Murray Hopper, who was also one of the first programmers

of the Mark I.

1959

Xerox introduces the first commercial copier.

1960

Theodore Harold Maimen develops the first laser. It uses a ruby

cylinder.

1960

The recently established Defense Departmentʹs Advanced Research

Projects Agency substantially increases its funding for computer

research.

1960

There are now about six thousand computers in operation in the

United States.

1960s

Neural‐net machines are quite simple and incorporate a small number

of neurons organized in only one or two layers. These models are

shown to be limited in their capabilities.

1961

The first time‐sharing computer is developed at MIT.

1961

President John F. Kennedy provides the support for space project

Apollo and inspiration for important research in computer science

when he addresses a joint session of Congress, saying, ʺI believe we

should go to the moon.ʺ

1962

The worldʹs first industrial robots are marketed by a U.S. company.

1962

Frank Rosenblatt defines the Perceptron in his Principles of

Neurodynamics. Rosenblatt first introduced the Perceptron, a simple

processing element for neural networks, at a conference in 1959.

1963

The Artificial Intelligence Laboratory at Stanford University is

founded by John McCarthy.

1963

The influential Steps Toward Artificial Intelligence by Marvin Minsky is

published.

1963

Digital Equipment Corporation announces the PDP‐8, which is the

first successful minicomputer.

1964

IBM introduces its 360 series, thereby further strengthening its

leadership in the computer industry.

1964

Thomas E. Kurtz and John G. Kenny of Dartmouth College invent

BASIC (Beginnerʹs All‐purpose Symbolic Instruction Code).

1964

Daniel Bobrow completes his doctoral work on Student, a natural‐

language program that can solve high‐school‐level word problems in

algebra.

1964

Gordon Mooreʹs prediction, made this year, says integrated circuits

will double in complexity each year. This will become known as

Mooreʹs Law and prove true (with later revisions) for decades to

come.

1964

Marshall McLuhan, via his Understanding Media, foresees the potential

for electronic media, especially television, to create a ʺglobal villageʺ

in which ʺthe medium is the message.ʺ

1965

The Robotics Institute at Carnegie Mellon University, which will

become a leading research center for AI, is founded by Raj Reddy

1965

Hubert Dreyfus presents a set of philosophical arguments against the

possibility of artificial intelligence in a RAND corporate memo

entitled ʺAlchemy and Artificial Intelligence.ʺ

1965

Herbert Simon predicts that by 1985 ʺmachines will be capable of

doing any work a man can do.ʺ

1966

The Amateur Computer Society, possibly the first personal computer

club, is founded by Stephen B. Gray. The Amateur Computer Society

Newsletter is one of the first magazines about computers.

1967

The first internal pacemaker is developed by Medtronics. It uses

integrated circuits.

1968

Gordon Moore and Robert Noyce found Intel (Integrated Electronics)

Corporation.

1968

The idea of a computer that can see, speak, hear, and think sparks

imaginations when HAL is presented in the film 2001: A Space

Odyssey, by Arthur C. Clarke and Stanley Kubrick.

1969

Marvin Minsky and Seymour Papert present the limitation of single‐

layer neural nets in their book Perceptrons. The bookʹs pivotal theorem

shows that a Perceptron is unable to determine if a line drawing is

fully connected. The book essentially halts funding for neural‐net

research.

1970

The GNP, on a per capita basis and in constant 1958 dollars, is $3,500,

or more than six times as much as a century before.

1970

The floppy disc is introduced for storing data in computers.

c. 1970

Researchers at the Xerox Palo Alto Research Center (PARC) develop

the first personal computer, called Alto. PARCʹs Alto pioneers the use

of bitmapped graphics, windows, icons, and mouse pointing devices.

1970

Terry Winograd completes his landmark thesis on SHRDLU, a

natural‐language system that exhibits diverse intelligent behavior in

the small world of childrenʹs blocks. SHRDLU is criticized, however,

for its lack of generality.

1971

The Intel 4004, the first microprocessor, is introduced by Intel.

1971

The first pocket calculator is introduced. It can add, subtract,

multiply, and divide.

1972

Continuing his criticism of the capabilities of AI, Hubert Dreyfus

publishes What Computers Canʹt Do, in which he argues that symbol

manipulation cannot be the basis of human intelligence.

1973

Stanley H. Cohen and Herbert W. Boyer show that DNA strands can

be cut, joined, and then reproduced by inserting them into the

bacterium Escherichia coli. This work creates the foundation for genetic

engineering.

1974

Creative Computing starts publication. It is the first magazine for home

computer hobbyists.

1974

The 8‐bit 8080, which is the first general‐purpose microprocessor, is

announced by Intel.

1975

Sales of microcomputers in the United States reach more than five

thousand, and the first personal computer, the Altair 8800, is

introduced. It has 256 bytes of memory.

1975

BYTE, the first widely distributed computer magazine, is published.

1975

Gordon Moore revises his observation on the doubling rate of

transistors on an integrated circuit from twelve months to twenty‐four

months.

1976

Kurzweil Computer Products introduces the Kurzweil Reading

Machine (KRM), the first print‐to‐speech reading machine for the

blind. Based on the first omni‐font (any font) optical character

recognition (OCR) technology, the KRM scans and reads aloud any

printed materials (books, magazines, typed documents).

1976

Stephen G. Wozniak and Steven P. Jobs found Apple Computer

Corporation.

1977

The concept of true‐to‐life robots with convincing human emotions is

imaginatively portrayed in the film Star Wars.

1977

For the first time, a telephone company conducts large‐scale

experiments with fiber optics in a telephone system.

1977

The Apple II, the first personal computer to be sold in assembled form

and the first with color graphics capability, is introduced and

successfully marketed.

1978

Speak & Spell, a computerized learning aid for young children, is

introduced by Texas Instruments. This is the first product that

electronically duplicates the human vocal tract on a chip.

1979

In a landmark study by nine researchers published in the Journal of the

American Medical Association, the performance of the computer

program MYCIN is compared with that of doctors in diagnosing ten

test cases of meningitis. MYCIN does at least as well as the medical

experts. The potential of expert systems in medicine becomes widely

recognized.

1979

Dan Bricklin and Bob Frankston establish the personal computer as a

serious business tool when they develop Visicalc, the first electronic

spreadsheet.

1980

AI industry revenue is a few million dollars this year.

1980s

As neuron models are becoming potentially more sophisticated, the

neural network paradigm begins to make a comeback, and networks

with multiple layers are commonly used.

1981

Xerox introduces the Star Computer, thus launching the concept of

Desktop Publishing. Appleʹs Laserwriter, available in 1985, will

further increase the viability of this inexpensive and efficient way for

writers and artists to create their own finished documents.

1981

IBM introduces its Personal Computer (PC).

1981

The prototype of the Bubble Jet printer is presented by Canon.

1982

Compact disc players are marketed for the first time.

1982

Mitch Kapor presents Lotus 1‐2‐3, an enormously popular

spreadsheet program.

1983

Fax machines are fast becoming a necessity in the business world.

1983

The Musical Instrument Digital Interface (MIDI) is presented in Los

Angeles at the first North American Music Manufacturers show

1983

Six million personal computers are sold in the United States.

1984

The Apple Macintosh introduces the ʺdesktop metaphor,ʺ pioneered

at Xerox, including bit‐mapped graphics, icons, and the mouse.

1984

William Gibson uses the term cyberspace in his book Neuromancer.

1984

The Kurzweil 250 (K250) synthesizer, considered to be the first

electronic instrument to successfully emulate the sounds of acoustic

instruments, is introduced to the market.

1985

Marvin Minsky published The Society of Mind, in which he presents a

theory of the mind where intelligence is seen to be the result of proper

organization of a hierarchy of minds with simple mechanisms at the

lowest level of the hierarchy.

1985

MITʹs Media Laboratory is founded by Jerome Weisner and Nicholas

Negroponte. The lab is dedicated to researching possible applications

and interactions of computer science, sociology, and artificial

intelligence in the context of media technology.

1985

There are 116 million jobs in the United States, compared to 12 million

in 1870. In the same period, the number of those employed has grown

from 31 percent to 48 percent, and the per capita GNP in constant

dollars has increased by 600 percent. These trends show no signs of

abating.

1986

Electronic keyboards account for 55.2 percent of the American musical

keyboard market, up from 9.5 percent in 1980.

1986

Life expectancy is about 74 years in the United States. Only 3 percent

of the American workforce is involved in the production of food.

Fully 76 percent of American adults have high‐school diplomas, and

7.3 million U.S. students are enrolled in college.

1987

NYSE stocks have their greatest single‐day loss due, in part, to

computerized trading.

1987

Current speech systems can provide any one of the following: a large

vocabulary, continuous speech recognition, or speaker independence.

1987

Robotic‐vision systems are now a $300 million industry and will grow

to $800 million by 1990.

1988

Computer memory today costs only one hundred millionth of what it

did in 1950.

1988

Marvin Minsky and Seymour Papert publish a revised edition of

Perceptrons in which they discuss recent developments in neural

network machinery for intelligence.

1988

In

the

United

States,

4,700,000

microcomputers,

120,000

minicomputers, and 11,500 mainframes are sold this year.

1988

W. Daniel Hillisʹs Connection Machine is capable of 65,536

computations at the same time.

1988

Notebook computers are replacing the bigger laptops in popularity.

1989

Intel introduces the 16‐megahertz (MHZ) 80386SX, 2.5 MIPS

microprocessor.

1990

Nautilus, the first CD‐ROM magazine, is published.

1990

The development of Hypertext Markup Language by researcher Tim

Berners‐Lee and its release by CERN, the high‐energy physics

laboratory in Geneva, Switzerland, leads to the conception of the

World Wide Web.

1991

Cell phones and e‐mail are increasing in popularity as business and

personal communication tools.

1992

The first double‐speed CD‐ROM drive becomes available from NEC.

1992

The first personal digital assistant (PDA), a handheld computer, is

introduced at the Consumer Electronics Show in Chicago. The

developer is Apple Computer.

1993

The Pentium 32‐bit microprocessor is launched by Intel. This chip has

3.1 million transistors.

1994

The World Wide Web emerges.

1994

America Online now has more than 1 million subscribers.

1994

Scanners and CD‐ROMS are becoming widely used.

1994

Digital Equipment corporation introduces a 300 MHZ version of the

Alpha AXP processor that executes 1 billion instructions per second.

1996

Compaq Computer and NEC Computer Systems ship hand held

computers running Windows CE.

1996

NEC Electronics ships the R4101 processor for personal digital

assistants. It includes a touch‐screen interface.

1997

Deep Blue defeats Gary Kasparov, the world chess champion, in a

regulation tournament.

1997

Dragon Systems introduces Naturally Speaking, the first continuous‐

speech dictation software product.

1997

Video phones are being used in business settings.

1997

Face‐recognition systems are beginning to be used in payroll check‐

cashing machines.

1998

The Dictation Division of Lernout & Hauspie Speech Products

(formerly Kurzweil Applied Intelligence) introduces Voice Xpress

Plus, the first continuous‐speech‐recognition program with the ability

to understand natural‐language commands.

1998

Routine business transactions over the phone are beginning to be

conducted between a human customer and an automated system that

engages in a verbal dialogue with the customer (e.g., United Airlines

reservations).

1998

Investment funds are emerging that use evolutionary algorithms and

neural nets to make investment decisions (e.g., Advanced Investment

Technologies).

1998

The World Wide Web is ubiquitous. It is routine for high‐school

students and local grocery stores to have web sites.

1998

Automated personalities, which appear as animated faces that speak

with realistic mouth movements and facial expressions, are working

in laboratories. These personalities respond to the spoken statements

and facial expressions of their human users. They are being developed

to be used in future user interfaces for products and services, as

personalized research and business assistants, and to conduct

transactions.

1998

Microvisionʹs Virtual Retina Display (VRD) projects images directly

onto the userʹs retinas. Although expensive, consumer versions are

projected for 1999.

1998

ʺBluetoothʺ technology is being developed for ʺbodyʺ local area

networks (LANS) and for wireless communication between personal

computers and associated peripherals. Wireless communication is

being developed for high‐bandwidth connection to the Web.

1999

Ray Kurzweilʹs The Age of Spiritual Machines: When Computers Exceed

Human Intelligence is published, available at your local bookstore!

2009

A $1,000 personal computer can perform about a trillion calculations

per second.

Personal computers with high‐resolution visual displays come in a

range of sizes, from those small enough to be embedded in clothing

and jewelry up to the size of a thin book.

Cables are disappearing. Communication between components

uses short‐distance wireless technology. High‐speed wireless

communication provides access to the Web.

The majority of text is created using continuous speech

recognition. Also ubiquitous are language user interfaces (LUIS).

Most

routine

business

transactions

(purchases,

travel,

reservations) take place between a human and a virtual personality.

Often, the virtual personality includes an animated visual presence

that looks like a human face.

Although traditional classroom organization is still common,

intelligent courseware has emerged as a common means of learning.

Pocket‐sized reading machines for the blind and visually

impaired, ʺlistening machinesʺ (speech‐to‐text conversion) for the

deaf, and computer‐controlled orthotic devices for paraplegic

individuals result in a growing perception that primary disabilities do

not necessarily impart handicaps.

Translating telephones (speech‐to‐speech language translation) are

commonly used for many language pairs.

Accelerating returns from the advance of computer technology

have resulted in continued economic expansion. Price deflation,

which had been a reality in the computer field during the twentieth

century, is now occurring outside the computer field. The reason for

this is that virtually all economic sectors are deeply affected by the

accelerating improvement in the price performance of computing.

Human musicians routinely jam with cybernetic musicians.

Bioengineered treatments for cancer and heart disease have

greatly reduced the mortality from these diseases.

The neo‐Luddite movement is growing.

2019

A $1,000 computing device (in 1999 dollars) is now approximately

equal to the computational ability of the human brain.

Computers are now largely invisible and are embedded

everywhere—in walls, tables, chairs, desks, clothing, jewelry, and

bodies.

Three‐dimensional virtual reality displays, embedded in glasses

and contact lenses, as well as auditory ʺlenses,ʺ are used routinely as

primary interfaces for communication with other persons, computers,

the Web, and virtual reality.

Most interaction with computing is through gestures and two‐way

natural‐language spoken communication.

Nanoengineered machines are beginning to be applied to

manufacturing and process‐control applications.

High‐resolution, three‐dimensional visual and auditory virtual

reality and realistic all‐encompassing tactile environments enable

people to do virtually anything with anybody, regardless of physical

proximity.

Paper books or documents are rarely used and most learning is

conducted through intelligent, simulated software‐based teachers.

Blind persons routinely use eyeglass‐mounted reading‐navigation

systems. Deaf persons read what other people are saying through

their lens displays. Paraplegic and some quadriplegic persons

routinely walk and climb stairs through a combination of computer‐

controlled nerve stimulation and exoskeletal robotic devices.

The vast majority of transactions include a simulated person.

Automated driving systems are now installed in most roads.

People are beginning to have relationships with automated

personalities and use them as companions, teachers, caretakers, and

lovers.

Virtual artists, with their own reputations, are emerging in all of

the arts.

There are widespread reports of computers passing the Turing

Test, although these tests do not meet the criteria established by

knowledgeable observers.

2029

A $1.000 (in 1999 dollars) unit of computation has the computing

capacity of approximately 1,000 human brains.

Permanent or removable implants (similar to contact lenses) for

the eyes as well as cochlear implants are now used to provide input

and output between the human user and the worldwide computing

network.

Direct neural pathways have been perfected for high‐bandwidth

connection to the human brain. A range of neural implants is

becoming available to enhance visual and auditory perception and

interpretation, memory, and reasoning.

Automated agents are now learning on their own, and significant

knowledge is being created by machines with little or no human

intervention. Computers have read all available human‐ and machine‐

generated literature and multimedia material.

There is widespread use of all‐encompassing visual, auditory, and

tactile communication using direct neural connections, allowing

virtual reality to take place without having to be in a ʺtotal touch

enclosure.ʺ

The majority of communication does not involve a human. The

majority of communication involving a human is between a human

and a machine.

There is almost no human employment in production, agriculture,

or transportation. Basic life needs are available for the vast majority of

the human race.

There is a growing discussion about the legal rights of computers

and what constitutes being ʺhuman.ʺ

Although computers routinely pass apparently valid forms of the

Turing Test, controversy persists about whether or not machine

intelligence equals human intelligence in all of its diversity.

Machines claim to be conscious. These claims are largely accepted.

2049

The common use of nanoproduced food, which has the correct

nutritional composition and the same taste and texture of organically

produced food, means that the availability of food is no longer

affected by limited resources, bad crop weather, or spoilage.

Nanobot swarm projections are used to create visual‐auditory‐

tactile projections of people and objects in real reality.

2072

Picoengineering (developing technology at the scale of picometers or

trillionths of a meter) becomes practical. [1]

By the year 2099

There is a strong trend toward a merger of human thinking with

the world of machine intelligence that the human species initially

created.

There is no longer any clear distinction between humans and

computers.

Most conscious entities do not have a permanent physical

presence.

Machine‐based intelligences derived from extended models of

human intelligence claim to be human, although their brains are not

based on carbon‐based cellular processes, but rather electronic and

photonic equivalents. Most of these intelligences are not tied to a

specific computational processing unit. The number of software‐based

humans vastly exceeds those still using native neuron‐cell‐based

computation.

Even among those human intelligences still using carbon‐based

neurons, there is ubiquitous use of neural‐implant technology, which

provides enormous augmentation of human perceptual and cognitive

abilities. Humans who do not utilize such implants are unable to

meaningfully participate in dialogues with those who do.

Because most information is published using standard assimilated

knowledge protocols, information can be instantly understood. The

goal of education, and of intelligent beings, is discovering new

knowledge to learn.

Femtoengineering (engineering at the scale of femtometers or one

thousandth of a trillionth of a meter) proposals are controversial. [2]

Life expectancy is no longer a viable term in relation to intelligent

beings.

Some many millenniums hence . . .

Intelligent beings consider the fate of the Universe.

HOW TO BUILD AN

INTELLIGENT MACHINE

IN THREE EASY PARADIGMS

As Deep Blue goes deeper and deeper it displays elements of strategic understanding. Somewhere out there, mere tactics are translating into strategy. This is the closest thing Iʹve seen to computer intelligence. Itʹs a weird form of intelligence, the beginning of intelligence. But you can feel it. You can smell it.

—Frederick Friedel, assistant to Gary Kasparov,

commenting on the computer that beat his boss.

The whole point of this sentence is to make clear what the whole point of this sentence is.

—Douglas Hofstadter

ʺWould you tell me please which way I ought to go from here?ʺ asked Alice.

ʺThat depends a good deal on where you want to get to, ʺ said the Cat.

ʺI donʹt much care where . . . ,ʺ said Alice.

ʺThen it doesnʹt much matter which way you go,ʺ said the Cat.

ʺ. . . so long as I get somewhere,ʺ Alice added as an explanation.

ʺOh, youʹre sure to do that,ʺ said the Cat, ʺif you only walk long enough.ʺ

—Lewis Carroll

A professor has just finished lecturing at some august university about the origin and structure of the universe, and an old woman in tennis shoes walks up to the lectern. ʺExcuse me, sir but youʹve got it all wrong,ʺ she says. ʺThe truth is that the universe is sitting on the back of a huge turtle.ʺ The professor decides to humor her ʺOh really?ʺ he asks. ʺWell, tell me, what is the turtle standing on?ʺ The lady has a ready reply: ʺOh, itʹs standing on another turtle.ʺ The professor asks, ʺAnd what is that turtle standing on?ʺ Without hesitation, she says, ʺAnother turtle.ʺ

The professor still game, repeats his question. A look of impatience comes across the womanʹs face. She holds up her hand, stopping him in mid‐sentence. ʺSave your breath, sonny,ʺ she says. ʺItʹs turtles all the way down.ʺ

—Rolf Landauer

As I mentioned in chapter 6, ʺBuilding New Brains,ʺ understanding intelligence is a bit like peeling an onion penetrating each layer reveals yet another onion. At the end of the process, we have a lot of onion peels, but no onion.

In other words, intelligence—particularly human intelligence—operates at many levels. We can penetrate and understand each level, but the whole process requires all the levels working together in just the right way.

Presented here are some further perspectives on the three paradigms I discussed in chapter 4, ʺA New Form of Intelligence on Earth.ʺ Each of these methods can provide ʺintelligentʺ solutions to carefully defined problems. But to create systems that can respond flexibly in the complex environments that intelligent entities often find themselves, these approaches need to be combined in appropriate ways. This is particularly true when interacting with phenomena that incorporate multiple levels of understanding. For example, if we build a single grand neural network

and attempt to train it to understand all the complexities of speech and language, the results will be limited at best.

More encouraging results are obtained if we break down the problem in a way that corresponds to the multiple levels

of meaning that we find in this uniquely human form of communication.

The human brain is organized the same way: as an intricate assemblage of specialized regions. And as we learn

the brainʹs parallel algorithms, we will have the means to vastly extend them. As just one example, the brain region responsible for logical and recursive thinking—the cerebral cortex—has a mere 8 million neurons. [1] We are already

building neural nets thousands of times larger and that operate millions of times faster. The key issue in designing intelligent machines (until they take over that chore from us) will be designing clever architectures to combine the relatively simple methods that comprise the building blocks of intelligence.

The Recursive Formula

Hereʹs a really simple formula to create intelligent solutions to difficult problems. Listen carefully or you might miss it.

The recursive formula is:

For my next step, take my best next step. If Iʹm done, Iʹm done.

It may seem too simple, and Iʹll admit thereʹs not much content at first glance. But its power is surprising.

Letʹs consider the classical example of a problem addressed by the recursive formula: the game of chess. Chess is

considered an intelligent game, at least it was until recently. Most observers are still of the view that it requires intelligence to play a good game. So how does our recursive formula fare in this arena?

Chess is a game played one move at a time. The goal is to make ʺgoodʺ moves. So letʹs define a program that makes good moves. By applying the recursive formula to chess, we rephrase it as follows:

PICK MY BEST MOVE: Pick my best move. If Iʹve won, Iʹm done.

Hang in there; this will make sense in a moment. I need to factor in one more aspect of chess, which is that I am

not in this alone. I have an opponent. She makes moves, too. Letʹs give her the benefit of the doubt and assume that she also makes good moves. If this proves to be wrong, it will be an opportunity, not a problem. So now we have:

PICK MY BEST MOVE: Pick my best move, assuming my opponent will do the same. If Iʹve won, Iʹm done.

At this point, we need to consider the nature of recursion. A recursive rule is one that is defined in terms of itself.

A recursive rule is circular, but to be useful we donʹt want to go around in circles forever. We need an escape hatch.

To illustrate recursion, letʹs consider an example: the simple ʺfactorialʺ function. To compute factorial of n, we multiply n by factorial of (n ‐ 1). Thatʹs the circular part—we have defined this function in terms of itself. We also need to specify that factorial of 1 = 1. Thatʹs our escape hatch.

As an example, letʹs compute factorial of 2. According to our definition,

factorial of 2 = 2 times (factorial of 1).

We know directly what (factorial of 1) is, so thereʹs our escape from infinite recursion. Plugging in (factorial of 1) =

1, we now can write,

factorial of 2 = 2 times 1 = 2.

Returning to chess, we can see that the PICK MY BEST MOVE function is recursive, since we have defined the best move in terms of itself. The deceptively innocuous ʺif Iʹve won, Iʹm doneʺ part of the strategy is our escape hatch.

Letʹs factor in what we know about chess. This is where we carefully consider the definition of the problem. We

realize that to pick the best move, we need to start by listing the possible moves. This is not very complicated. The legal moves at any point in the game are defined by the rules. While more complicated than some other games, the

rules of chess are straightforward and easily programmed. So we list the moves and pick the best one.

But which is best? If the move results in a win, that will do nicely. So again we merely consult the rules and pick

one of the moves that yields an immediate checkmate. Perhaps we are not so lucky and none of the possible moves

provides an immediate win. We still need to consider whether or not the move enables me to win or lose. At this point we need to consider the subtle addition we made to our rule, ʺassuming my opponent will do the same.ʺ After all, my winning or losing is affected by what my opponent might do. I need to put myself in her shoes and pick her best

move. How can I do that? This is where the power of recursion comes in. We have a program that does exactly this,

called PICK MY BEST MOVE. So we call it to determine my opponentʹs best move.

Our program is now structured as follows. PICK MY BEST MOVE generates a list of all possible moves allowed

by the rules. It examines each possible move in turn. For each move, it generates a hypothetical board representing

what the placement of the pieces would be if that move were selected. Again, this just requires applying the definition of the problem as embodied in the rules of chess. PICK MY BEST MOVE now puts itself in my opponentʹs place and

calls itself to determine her best move. It then starts to generate all of her possible moves from that board position.

The program thus keeps calling itself, continuing to expand possible moves and countermoves in an ever expanding tree of possibilities. This process is often called a minimax search, because we are alternatively attempting to minimize my opponentʹs ability to win and to maximize my own.

Where does this all end? The program just keeps calling itself until every branch of the tree of possible moves and

countermoves results in an end of game. Each end of game provides the answer: Win, tie, or draw. At the furthest point of expansion of moves and countermoves, the program encounters moves that finish the game. If a move results

in a win, we pick that move. If there are no win moves, then we settle for a tie. If there are no win or tie moves, I continue playing anyway in the hope that my opponent is not perfect like I am.

These final moves are the final branches—called leaves—in our tree of move sequences. Now, instead of continuing to call PICK MY BEST MOVE, the program begins to return from its calls to itself. As it begins to return

from all of the nested PICK BEST MOVE calls, it has determined the best move at each point (including the best move

for my opponent), and so it can finally select the correct move for the current actual board situation.

So how well a game does this simple program play? The answer is perfect chess. I, canʹt lose, unless possibly if my opponent goes first and is also perfect. Perfect chess is very good indeed, much better than any mere human. The most complicated part of the PICK MY BEST MOVE function—the only aspect that is not extremely simple—is generating the allowable moves at each point. And this is just a matter of codifying the rules. Essentially, we have determined the answer by carefully defining the problem.

But weʹre not done. While playing perfect chess might be considered impressive, it is not good enough. We need

to consider how responsive a player PICK MY BEST MOVE will be. If we assume that there are, on average, about 8

possible moves for each board situation, and that a typical game lasts about 30 moves, we need to consider 830

possible move sequences to fully expand the tree of all move‐countermove possibilities. If we assume that we can analyze 1 billion board positions per second (a good deal faster than any chess computer today), it would take 1018

seconds, or about 40 billion years, to select each move.

Unfortunately, thatʹs not regulation play. This approach to recursion is a bit like evolution—both do great work but are incredibly slow. Thatʹs really not surprising if you think about it. Evolution represents another very simple paradigm, and indeed is another of our simple formulas.

However, before we throw out the recursive formula, letʹs attempt to modify it to take into account our human patience and, for the time being, our mortality.

Clearly we need to put limits on how deep we allow the recursion to take place. How large we allow the move‐

countermove tree to grow needs to depend on how much computation we have available. In this way, we can use the

recursive formula on any computer, from a wristwatch computer to a supercomputer.

Limiting the size of this tree means of course that we cannot expand each branch until the end of the game. We

need to arbitrarily stop the expansion and have a method of evaluating the ʺterminal leavesʺ of an unfinished tree.

When we considered fully expanding each move sequence to the end of the game, evaluation was simple: Winning is

better than tying, and losing is no good at all. Evaluating a board position in the middle of the game is slightly more complicated. Rather, it is more controversial because here we encounter multiple schools of thought.

The cat in Aliceʹs Adventures in Wonderland who tells Alice that it doesnʹt much matter which way she goes must have been an expert in recursive algorithms. Any halfway reasonable approach works rather well. If, for example, we

just add up the piece values (that is, 10 for the queen, 5 for the rook, and so on), we will obtain rather respectable results. Programming the recursive minimax formula using the piece value method of evaluating terminal leaves, as

run on your average personal computer circa 1998, will defeat all but a few thousand humans on the planet.

This is what I call the ʺsimple mindedʺ school. This school of thought says: Use a simple method of evaluating the

terminal leaves and put whatever computational power we have available into expanding the moves and

countermoves as deeply as possible. Another approach is the ʺcomplicated mindedʺ school, which says that we need

to use sophisticated procedures to evaluate the ʺqualityʺ of the board at each terminal leaf position.

IBMʹs Deep Blue, the computer that crossed this historic threshold, uses a leaf evaluation method that is a good

deal more refined than just adding up piece values. However, in a discussion I had with Murray Campbell, head of

the Deep Blue team, just weeks prior to its 1997 historic victory, Campbell agreed that Deep Blueʹs evaluation method was more simple minded than complicated minded.

Human players are very complicated minded. That seems to be the human condition. As a result, even the best chess players are unable to consider more than a hundred moves, compared to a few billion for Deep Blue. But each

human move is deeply considered. However, in 1997, Gary Kasparov, the worldʹs best example of the complicated‐

minded school, was defeated by a simple‐minded computer.

Personally, I am of a third school of thought. Itʹs not much of a school, really. To my knowledge, no one has tried

this idea. It involves combining the recursive and neural net paradigms, and I describe it in the discussion on neural nets that follows.

MATHLESS "PSEUDO CODE"

FOR THE RECURSIVE ALGORITHM

Here is the basic schema for the recursive algorithm. Many variations are possible, and the designer of the

system needs to provide certain critical parameters and methods, detailed below.

The Recursive Algorithm

Define a function (program), "PICK BEST NEXT STEP" The function returns a value of "SUCCESS" (we've solved the problem) or "FAILURE" (we didn't solve it). If it returns with a value of SUCCESS, then the function also returns the sequence of selected steps that solved the problem. PICK BEST NEXT STEP does the

following:

PICK BEST NEXT STEP:

• Determine if the program can escape from continued recursion at this point. This bullet and the

next two bullets deal with this escape decision. First, determine if the problem has now been

solved. Since this call to PICK BEST NEXT STEP probably came from the program calling itself, we

may now have a satisfactory solution. Examples are:

(i) In the context of a game (e.g., chess), the last move allows us to win (e.g., checkmate).

(ii) In the context of solving a mathematical theorem, the last step proves the theorem.

(iii) In the context of an artistic program (e.g., cybernetic poet or composer), the last step

matches the goals for the next word or note.

If the problem has been satisfactorily solved, the program returns with a value of SUCCESS. In this case,

PICK BEST NEXT STEP also returns the sequence of steps that caused the success.

• If the problem has not been solved, determine if a solution is now hopeless. Examples are:

(i) In the context of a game (e.g., chess), this move causes us to lose (e.g., checkmate for the

other side).

(ii) In the context of solving a mathematical theorem, this step violates the theorem.

(iii) In the context of an artistic program (e.g., cybernetic poet or composer), this step violates

the goals for the next word or note.

If the solution at this point has been deemed hopeless, the program returns with a value of FAILURE.

• If the problem has been neither solved nor deemed hopeless at this point of recursive expansion,

determine whether or not the expansion should be abandoned anyway. This is a key aspect of

the design and takes into consideration the limited amount of computer time we have to spend.

Examples are:

(i) In the context of a game (e.g., chess), this move puts our side sufficiently "ahead" or

"behind." Making this determination may not be straightforward and is the primary design

decision. However, simple approaches (e.g., adding up piece values) can still provide good

results. If the program determines that our side is sufficiently ahead, then PICK BEST NEXT

STEP returns in a similar manner to a determination that our side has won (i.e., with a value

of SUCCESS). If the program determines that our side is sufficiently behind, then PICK BEST

NEXT STEP returns in a similar manner to a determination that our side has lost (i.e., with a

value of FAILURE).

(ii) In the context of solving a mathematical theorem, this step involves determining if the

sequence of steps in the proof are unlikely to yield a proof. If so, then this path should be

abandoned, and PICK BEST NEXT STEP returns in a similar manner to a determination that

this step violates the theorem (i.e., with a value of FAILURE). There is no "soft" equivalent of

success. We can't return with a value of SUCCESS until we have actually solved the problem.

That's the nature of math.

(iii) In the context of an artistic program (e.g., cybernetic poet or composer), this step involves

determining if the sequence of steps (e.g., words in a poem, notes in a song) is unlikely to

satisfy the goals for the next step. If so, then this path should be abandoned, and PICK BEST

NEXT STEP returns in a similar manner to a determination that this step violates the goals

for the next step (i.e., with a value of FAILURE).

• If PICK BEST NEXT STEP has not returned (because the program has neither determined success

nor failure nor made a determination that this path should be abandoned at this point), then we

have not escaped from continued recursive expansion. In this case, we now generate a list of all

possible next steps at this point. This is where the precise statement of the problem comes in:

(i) In the context of a game (e.g., chess), this involves generating all possible moves for "our"

side for the current state of the board. This involves a straightforward codification of the

rules of the game.

(ii) In the context of finding a proof for a mathematical theorem, this involves listing the possible

axioms or previously proved theorems that can be applied at this point in the solution.

(iii) In the context of a cybernetic art program, this involves listing the possible words/notes/line

segments that could be used at this point.

For each such possible next step:

• Create the hypothetical situation that would exist if this step were implemented. In a game, this

means the hypothetical state of the board. In a mathematical proof, this means adding this step

(e.g., axiom) to the proof. In an art program, this means adding this word/note/line segment.

• Now call PICK BEST NEXT STEP to examine this hypothetical situation. This is, of course, where

the recursion comes in because the program is now calling itself.

• If the above call to PICK BEST NEXT STEP returns with a value of SUCCESS, then return from the

call to PICK BEST NEXT STEP (that we are now in), also with a value of SUCCESS. Otherwise

consider the next possible step.

If all the possible next steps have been considered without finding a step that resulted in a return from the

call to PICK BEST NEXT STEP with a value of SUCCESS, then return from this call to PICK BEST NEXT STEP

(that we are now in) with a value of FAILURE.

END OF PICK BEST NEXT STEP

If the original call to PICK BEST NEXT STEP returns with a value of SUCCESS, it will also return the correct

sequence of steps:

• In the context of a game, the first step in this sequence is the next move you should make.

• In the context of a mathematical proof, the full sequence of steps is the proof.

• In the context of a cybernetic art program, the sequence of steps is your work of art.

If the original call to PICK BEST NEXT STEP is FAILURE, then you need to go back to the drawing board.

Key Design Decisions

In the simple schema above, the designer of the recursive algorithm needs to determine the following at the

outset:

• The key to a recursive algorithm is the determination in PICK BEST NEXT STEP when to abandon

the recursive expansion. This is easy when the program has achieved clear success (e.g.,

checkmate in chess, or the requisite solution in a math or combinatorial problem) or clear failure.

It is more difficult when a clear win or loss has not yet been achieved. Abandoning a line of

inquiry before a well-defined outcome is necessary because otherwise the program might run for

billions of years (or at least until the warranty on your computer runs out).

• The other primary requirement for the recursive algorithm is a straightforward codification of the

problem. In a game like chess, that's easy. But in other situations, a clear definition of the

problem is not always so easy to come by.

Happy Recursive Searching!

Neural Nets

In the early and mid‐1960s, AI researchers became enamored with the Perceptron, a machine constructed from mathematical models of human neurons. Early Perceptrons were modestly successful in such pattern‐recognition tasks as identifying printed letters and speech sounds. It appeared that all that was needed to make the Perceptron more intelligent was to add more neurons and more wires.

Then came Marvin Minsky and Seymour Papertʹs 1969 book, Perceptrons, which proved a set of theorems

apparently demonstrating that a Perceptron could never solve the simple problem of determining whether or not a line drawing is ʺconnectedʺ (in a connected drawing all parts are connected to one another by lines). The book had a dramatic effect, and virtually all work on Perceptrons came to a halt. [2]

In the late 1970s and 1980s, the paradigm of building computer simulations of human neurons, then called neural

nets, began to regain itʹs popularity. One observer wrote in 1988:

Once upon a time two daughter sciences were born to the new science of cybernetics. One sister was natural,

with features inherited from the study of the brain, from the way nature does things. The other was artificial,

related from the beginning to the use of computers. Each of the sister sciences tried to build models of intelligence, but from very different materials. The natural sister built models (called neural networks) out of

mathematically purified neurones. The artificial sister built her models out of computer programs.

In their first bloom of youth the two were equally successful and equally pursued by suitors from other fields

of knowledge. They got on very well together. Their relationship changed in the early sixties when a new monarch appeared, one with the largest coffers ever seen in the kingdom of the sciences: Lord DARPA, the Defense Departmentʹs Advanced Research Projects Agency. The artificial sister grew jealous and was

determined to keep for herself the access to Lord DARPAʹs research funds. The natural sister would have to be

slain.

The bloody work was attempted by two staunch followers of the artificial sister, Marvin Minsky and Seymour

Papert, cast in the role of the huntsman sent to slay Snow White and bring back her heart as proof of the deed.

Their weapon was not the dagger but the mightier pen, from which came a book— Perceptrons—purporting to

prove that neural nets could never fill their promise of building models of mind: only computer programs could do this. Victory seemed assured for the artificial sister. And indeed, for the next decade all the rewards of

the kingdom came to her progeny, of which the family of expert systems did best in fame and fortune.

But Snow White was not dead. What Minsky and Papert had shown the world as proof was not the heart of the

princess, it was the heart of a pig.

The author of the above statement was Seymour Papert. [3] His sardonic allusion to bloody hearts reflects a widespread misunderstanding of the implications of the pivotal theorem in his and Minskyʹs 1969 book. The theorem

demonstrated limitations in the capabilities of a single layer of simulated neurons. If, on the other hand, we place neural nets at multiple levels—having the output of one neural net feed into the next—the range of its competence greatly expands. Moreover, if we combine neural nets with other paradigms, we can make yet greater progress. The

heart that Minsky and Papert extracted belonged primarily to the single layer neural net.

Papertʹs irony also reflects his and Minskyʹs own considerable contributions to the neural net field. In fact, Minsky started his career with seminal contributions to the concept at Harvard in the 1950s. [4]

But enough of politics. What are the main issues in designing a neural net?

One key issue is the netʹs topology: the organization of the interneuronal connections. A net organized with multiple levels can make more complex discriminations but is harder to train.

Training the net is the most critical issue. This requires an extensive library of examples of the patterns the net will be expected to recognize, along with the correct identification of each pattern. Each pattern is presented to the net.

Typically, those connections that contributed to a correct identification are strengthened (by increasing their associated weight), and those that contributed to an incorrect identification are weakened. This method of strengthening and weakening the connection weights is called back‐propagation and is one of several methods used.

There is controversy as to how this learning is accomplished in the human brainʹs neural nets, as there does not appear to be any mechanism by which back‐propagation can occur. One method that does appear to be implemented

in the human brain is that the mere firing of a neuron increases the neurotransmitter strengths of the synapses it is connected to. Also, neurobiologists have recently discovered that primates, and in all likelihood humans, grow new

brain cells throughout life, including adulthood, contradicting an earlier dogma that this was not possible.

Little and Big Hills

A key issue in adaptive algorithms—neural nets and evolutionary algorithms—is often referred to as local versus global optimality: in other words, climbing the closest hill versus finding and climbing the biggest hill. As a neural net learns (by adjusting the connection strengths), or as an evolutionary algorithm evolves (by adjusting the ʺgeneticʺ

code of the simulated organisms), the fit of the solution will improve, until a ʺlocally optimalʺ solution is found. If we compare this to climbing a hill, these methods are very good at finding the top of a nearby hill, which is the best possible solution within a local area of possible solutions. But sometimes these methods may become trapped at the

top of a small hill and fail to see a higher mountain in a different area. In the neural net context, if the neural net has converged on a locally optimal solution, as it tries adjusting any of the connection strengths, the fit becomes worse.

But just as a climber might need to come down a small elevation to ultimately climb to a higher point. On a different hill, the neural net (or evolutionary algorithm) might need to make the solution temporarily worse to ultimately find a better one.

One approach to avoiding such a ʺfalseʺ optimal solution (little hill) is to force the adaptive method to do the analysis multiple times starting with very different initial conditions in other words, force it to climb lots of hills, not just one. But even with this approach, the system designer still needs to make sure that the adaptive method hasnʹt

missed an even higher mountain in a yet more distant land.

The Laboratory of Chess

We can gain some insight into the comparison of human thinking and conventional computer approaches by again examining the human and machine approaches to chess. I do this not to belabor the issue of chess playing, but rather because it illustrates a clear contrast. Raj Reddy, Carnegie Mellon Universityʹs AI guru, cites studies of chess as playing the same role in artificial intelligence that studies of E. coli play in biology: an ideal laboratory for studying fundamental questions. [5] Computers use their extreme speed to analyze the vast combinations created by the combinatorial explosion of moves and countermoves. While chess programs may use a few other tricks (such as storing the openings of all master chess games in this century and precomputing endgames), they essentially rely on

their combination of speed and precision. In comparison, humans, even chess masters, are extremely slow and imprecise. So we precompute all of our chess moves. Thatʹs why it takes so long to become a chess master, or the master of any pursuit. Gary Kasparov has spent much of his few decades on the planet studying—and experiencing—

chess moves. Researchers have estimated that masters of a nontrivial subject have memorized about fifty thousand such ʺchunksʺ of insight.

When Kasparov plays, he, too, generates a tree of moves and countermoves in his head, but limitations in human

mental speed and short‐term memory limit his mental tree (for each actually played move) to no more than a few hundred board positions, if that. This compares to billions of board positions for his electronic antagonist. So the human chess master is forced to drastically prune his mental tree, eliminating fruitless branches by using his intense pattern‐recognition faculties. He matches each board position—actual and imagined—to this database of tens of thousands of previously analyzed situations.

After Kasparovʹs 1997 defeat, we read a lot about how Deep Blue was just doing massive number crunching, not

really ʺthinkingʺ the way its human rival was doing. One could say that the opposite is the case, that Deep Blue was indeed thinking through the implications of each move and countermoves and that it was Kasparov who did not have

time to really think very much during the tournament. Mostly he was just drawing upon his mental database of situations he had thought about long ago. (Of course, this depends on oneʹs notion of thinking, as I discussed in chapter 3.) But if the human approach to chess—neural network‐based pattern recognition used to identify situations

from a library of previously analyzed situations—is to be regarded as true thinking, then why not program our machines to work the same way?

The Third Way

And thatʹs my idea that I alluded to earlier as the third school of thought in evaluating the terminal leaves in a recursive search. Recall that the simple‐minded school uses an approach such as adding up piece values to evaluate a particular board position. The complicated‐minded school advocates a more elaborate and time‐consuming logical analysis. I advocate a third way: combine two simple paradigms—recursive and neural net—by using the neural net

to evaluate the board positions at each terminal leaf. The training of a neural net is time‐consuming and requires a great deal of computing, but performing a single recognition task on a neural net that has already learned its lessons is very quick, comparable to a simple‐minded evaluation. Although fast, the neural net is drawing upon the very extensive amount of time it previously spent learning the material. Since we have every master chess game in this century online, we can use this massive amount of data to train the neural net. This training is done once and offline (that is, not during an actual game). The trained neural net would then be used to evaluate the board positions at each terminal leaf. Such a system would combine the millionfold advantage in speed that computers have with the more

humanlike ability to recognize patterns against a lifetime of experience.

I proposed this approach to Murray Campbell, head of the Deep Blue team, and he found it intriguing and appealing. He was getting tired anyway, he admitted, of tuning the leaf evaluation algorithm by hand. We talked about setting up an advisory team to implement this idea, but then IBM canceled the whole chess project. I do believe that one of the keys to emulating the diversity of human intelligence is optimally to combine fundamental paradigms.

Weʹll talk about how to fold in the paradigm of evolutionary algorithms below.

MATHLESS "PSEUDO CODE"

FOR THE NEURAL NET ALGORITHM

Here is the basic schema for a neural net algorithm. Many variations are possible, and the designer of the

system needs to provide certain critical parameters and methods, detailed below.

The Neural Net Algorithm

Creating a neural net solution to a problem involves the following steps:

• Define the input.

• Define the topology of the neural net (i.e., the layers of neurons and the connections between

the neurons).

• Train the neural net on examples of the problem.

• Run the trained neural net to solve new examples of the problem.

• Take your neural net company public.

These steps (except for the last one) are detailed below:

The Problem Input

The problem input to the neural net consists of a series of numbers. This input can be:

• in a visual pattern-recognition system: a two-dimensional array of numbers representing the

pixels of an image; or

• in an auditory (e.g., speech) recognition system: a two-dimensional array of numbers

representing a sound, in which the first dimension represents parameters of the sound (e.g.,

frequency components) and the second dimension represents different points in time; or

• in an arbitrary pattern recognition system: an n-dimensional array of numbers representing the

input pattern.

Defining the Topology

To set up the neural net:

The architecture of each neuron consists of:

• Multiple inputs in which each input is "connected" to either the output of another neuron or one

of the input numbers.

• Generally, a single output, which is connected either to the input of another neuron (which is

usually in a higher layer) or to the final output.

Set up the first layer of neurons:

• Create N0 neurons in the first layer. For each of these neurons, "connect" each of the multiple

inputs of the neuron to "points" (i.e., numbers) in the problem input. These connections can be

determined randomly or using an evolutionary algorithm (see below).

• Assign an initial "synaptic strength" to each connection created. These weights can start out all

the same, can be assigned randomly, or can be determined in another way (see below).

Set up the additional layers of neurons:

Set up a total of M layers of neurons. For each layer, set up the neurons in that layer.

For layeri:

• Create Ni neurons in layeri. For each of these neurons, "connect" each of the multiple inputs of

the neuron to the outputs of the neurons in layeri-1: (see variations below).

• Assign an initial "synaptic strength" to each connection created. These weights can start out all

the same, can be assigned randomly, or can be determined in another way (see below).

• The outputs of the neurons in layerM are the outputs of the neural net (see variations below).

The Recognition Trials

How each neuron works:

Once the neuron is set up, it does the following for each recognition trial.

• Each weighted input to the neuron is computed by multiplying the output of the other neuron (or

initial input) that the input to this neuron is connected to by the synaptic strength of that

connection.

• All of these weighted inputs to the neuron are summed.

• If this sum is greater than the firing threshold of this neuron, then this neuron is considered to

"fire" and its output is 1. Otherwise, its output is 0 (see variations below).

Do the following for each recognition trial:

For each layer, from layers to layerM:

And for each neuron in each layer:

• Sum its weighted inputs (each weighted input = the output of the other neuron [or initial input]

that the input to this neuron is connected to, multiplied by the synaptic strength of that

connection).

• If this sum of weighted inputs is greater than the firing threshold for this neuron, set the output

of this neuron = 1, otherwise set it to 0.

To Train the Neural Net

• Run repeated recognition trials on sample problems.

• After each trial, adjust the synaptic strengths of all the interneuronal connections to improve the

performance of the neural net on this trial (see the discussion below on how to do this).

• Continue this training until the accuracy rate of the neural net is no longer improving (i.e.,

reaches an asymptote).

Key Design Decisions

In the simple schema above, the designer of this neural net algorithm needs to determine at the outset:

• What the input numbers represent.

• The number of layers of neurons.

• The number of neurons in each layer (each layer does not necessarily need to have the same

number of neurons).

• The number of inputs to each neuron, in each layer. The number of inputs (i.e., interneuronal

connections) can also vary from neuron to neuron, and from layer to layer.

• The actual "wiring" (i.e., the connections). For each neuron, in each layer, this consists of a list

of other neurons, the outputs of which constitute the inputs to this neuron. This represents a key

design area. There are a number of possible ways to do this:

(i) wire the neural net randomly; or

(ii) use an evolutionary algorithm (see next section of this Appendix) to determine an optimal

wiring; or

(iii) use the system designer's best judgment in determining the wiring.

• The initial synaptic strengths (i.e., weights) of each connection. There are a number of possible

ways to do this:

(i) set the synaptic strengths to the same value; or

(ii) set the synaptic strengths to different random values; or

(iii) use an evolutionary algorithm to determine an optimal set of initial values; or

(iv) use the system designer's best judgment in determining the initial values.

• The firing threshold of each neuron.

• Determine the output. The output can be:

(i) the outputs of layer sub M of neurons; or

(ii) the output of a single output neuron, whose inputs are the outputs of the neurons in layer

sub M;

(iii) a function of (e.g., a sum of) the outputs of the neurons in layer sub M; or

(iv) another function of neuron outputs in multiple layers.

• Determine how the synaptic strengths of all the connections are adjusted during the training of

this neural net. This is a key design decision and the subject of a great deal of neural net

research and discussion. There are a number of possible ways to do this:

(i) For each recognition trial, increment or decrement each synaptic strength by a (generally

small) fixed amount so that the neural net's output more closely matches the correct answer.

One way to do this is to try both incrementing and decrementing and see which has the more

desirable effect. This can be time consuming, so other methods exist for making local

decisions on whether to increment or decrement each synaptic strength.

(ii) Other statistical methods exist for modifying the synaptic strengths after each recognition trial

so that the performance of the neural net on that trial more closely matches the correct

answer.

Note that neural net training will work even if the answers to the training trials are not all correct. This

allows using real-world training data that may have an inherent error rate. One key to the success of a neural

net-based recognition system is the amount of data used for training. Usually a very substantial amount is

needed to obtain satisfactory results. Just like human students, the amount of time that a neural net spends

learning its lessons is a key factor in its performance.

Variations

Many variations of the above are feasible. Some variations include:

• There are different ways of determining the topology, as described above. In particular, the

interneuronal wiring can be set either randomly or using an evolutionary algorithm.

• There are different ways of setting the initial synaptic strengths, as described above.

• The inputs to the neurons in layer sub i do not necessarily need to come from the outputs of the

neurons in layer sub i minus 1. Alternatively, the inputs to the neurons in each layer can come

from any lower layer or any layer.

• There are different ways to determine the final output, as described above.

• For each neuron, the method described above compares the sum of the weighted inputs to the

threshold for that neuron. If the threshold is exceeded, the neuron fires and its output is 1.

Otherwise, its output is 0. This "all or nothing" firing is called a nonlinearity. There are other

nonlinear functions that can be used. Commonly a function is used that goes from 0 to 1 in a

rapid but more gradual fashion (than all or nothing). Also, the outputs can be numbers other

than 0 and 1.

• The different methods for adjusting the synaptic strengths during training, briefly described

above, represent a key design decision.

• The above schema describes, a "synchronous" neural net, in which each recognition trial

proceeds by computing the outputs of each layer, starting with layer sub O through layer sub M.

In a true parallel system, in which each neuron is operating independently of the others, the

neurons can operate asynchronously (i.e., independently). In an asynchronous approach, each

neuron is constantly scanning its inputs and fires (i.e., changes its output from 0 to 1) whenever

the sum of its weighted inputs exceeds its threshold (or, alternatively, using another nonlinear

output function).

Happy Adaptation!

EVOLUTIONARY ALGORITHMS

If biologists have ignored self‐organization, it is not because self‐ordering is not pervasive and profound. It is because we biologists have yet to understand how to think about systems governed simultaneously by two sources of order.

Yet who seeing the snowflake, who seeing simple lipid molecules cast adrift in water forming themselves into cell‐like hollow lipid vesicles, who seeing the potential for the crystallization of life in swarms of reacting molecules, who seeing the stunning order in networks linking tens upon tens of thousands of variables, can fail to entertain a central thought: if ever we are to attain a final theory in biology, we will surely have to understand the commingling of self-organization and selection. We will have to see that we are the natural expressions of a deeper order. Ultimately, we will discover in our creation myth that we are expected after all.

—Stuart Kauffman

As I discussed earlier, an evolutionary algorithm involves a simulated environment in which simulated software ʺcreaturesʺ compete for survival and the right to reproduce. Each software creature represents a possible solution to a problem encoded in its digital ʺDNA.ʺ

The creatures allowed to survive and reproduce into the next generation are the ones that do a better job of solving the problem. Evolutionary algorithms are considered to be part of a class of ʺemergentʺ methods because the

solutions emerge gradually and usually cannot be predicted by the designers of the system. Evolutionary algorithms

are particularly powerful when they are combined with our other paradigms. Here is a unique way of combining all

of our ʺintelligentʺ paradigms.

Combining All Three Paradigms

The human genome contains three billion rungs of base pairs, which equals six billion bits of data. With a little data compression, your genetic code will fit on a single CD‐ROM. You can store your whole family on a DVD (digital video disc). But your brain has 100 trillion ʺwires,ʺ which would require about 3,000 trillion bits to represent. How did the mere 12 billion bits of data in your chromosomes (with contemporary estimates indicating that only 3 percent of that is active) designate the wiring of your brain, which constitutes about a quarter million times more information?

Obviously the genetic code does not specify the exact wiring. I said earlier that we can wire a neural net randomly

and obtain satisfactory results. Thatʹs true, but there is a better way to do it, and that is to use evolution. I am not referring to the billions of years of evolution that produced the human brain. I am referring to the months of evolution that go on during gestation and early childhood. Early in our lives, our interneuronal connections are engaged in a fight for survival. Those that make better sense of the world survive. By late childhood, these connections become relatively fixed, which is why it is worthwhile exposing babies and young children to a stimulating environment. Otherwise, this evolutionary process runs out of real‐world chaos from which to draw inspiration.

We can do the same thing with our synthetic neural nets: use an evolutionary algorithm to determine the optimal

wiring. This is exactly what the Kyoto Advanced Telecommunications Research Labʹs ambitious brain‐building project is doing.

Now hereʹs how you can intelligently solve a challenging problem using all three paradigms. First, carefully state

your problem. This is actually the hardest step. Most people try to solve problems without bothering to understand

what the problem is all about. Next, analyze the logical contours of your problem recursively by searching through as many combinations of elements (for example, moves in a game, steps in a solution) that you and your computer have

the patience to sort through. For the terminal leaves of this recursive expansion of possible solutions,. evaluate them with a neural net. For the optimal topology of your neural net, determine this using an evolutionary algorithm. And if all of this doesnʹt work, then you have a difficult problem, indeed.

"PSEUDO CODE" FOR THE

EVOLUTIONARY ALGORITHM

Here is the basic schema for an evolutionary algorithm. Many variations are possible, and the designer of the

system needs to provide certain critical parameters and methods, detailed below.

THE EVOLUTIONARY ALGORITHM

Create N solution "creatures" Each one has:

• A genetic code—a sequence of numbers that characterizes a possible solution to the problem.

The numbers can represent critical parameters, steps to a solution, rules, etc.

For each generation of evolution, do the following:

• Do the following for each of the N solution creatures:

(i) Apply this solution creature's solution (as represented by its genetic code) to the problem, or

simulated environment.

(ii) Rate the solution.

• Pick the L solution creatures with the highest ratings to survive into the next generation.

• Eliminate the (N - L) nonsurviving solution creatures.

• Create (N - L) new solution creatures from the L surviving solution creatures by:

(i) making copies of the L surviving creatures. Introduce small random variations into each

copy; or

(ii) create additional solution creatures by combining parts of the genetic code (using "sexual"

reproduction, or otherwise combining portions of the chromosomes) from the L surviving

creatures; or

(iii) doing a combination of (i) and (ii) above.

• Determine whether or not to continue evolving:

Improvement = (highest rating in this generation) - (highest rating in the previous generation)

If improvement < Improvement Threshold, then we're done

• The Solution Creature with the highest rating from the last generation of evolution has

the best solution. Apply the solution defined by its genetic code to the problem.

Key Design Decisions

In the simple schema above, the designer of this evolutionary algorithm needs to determine at the outset:

• Key parameters:

N

L

Improvement Threshold

• What the numbers in the genetic code represent and how the solution is computed from the

genetic code.

• A method for determining the N solution creatures in the first generation. In general, these need

only be "reasonable" attempts at a solution. If these first-generation solutions are too far afield,

the evolutionary algorithm may have difficulty converging on a good solution. It is often

worthwhile to create the initial solution creatures in such a way that they are reasonably diverse.

This will help prevent the evolutionary process from just finding a "locally" optimal solution.

• How the solutions are rated.

• How the surviving solution creatures reproduce.

Variations

Many variations of the above are feasible. Some variations include:

• There does not need to be a fixed number of surviving solution creatures (i.e., "L") from each

generation. The survival rule(s) can allow for a variable number of survivors.

• There does not need to be a fixed number of new solution creatures created in each generation

(i.e., [N - L]). The procreation rules can be independent of the size of the population. Procreation

can be related to survival, thereby allowing the fittest solution creatures to procreate the most.

• The decision as to whether or not to continue evolving can be varied. It can consider more than

just the highest-rated solution creature from the most recent generations). It can also consider a

trend that goes beyond just the last two generations.

Happy Evolving!

GLOSSARY

Aaron A computerized robot (and associated software), designed by Harold Cohen, that creates original drawings and paintings.

Alexanderʹs solution A term referring to Alexander the Greatʹs slicing of the Gordian knot with his sword. A reference to solving an insoluble problem with decisive yet unexpected and indirect means.

Algorithm A sequence of rules and instructions that describes a procedure to solve a problem. A computer program expresses one or more algorithms in a manner understandable by a computer.

Alu A meaningless sequence of 300 nucleotide letters that occurs 300,000 times in the human genome.

Analog A quantity that is continuously varying, as opposed to varying in discrete steps. Most phenomena in the natural world are analog. When we measure and give them a numeric value, we digitize them. The human brain

uses both digital and analog computation.

Analytical Engine The first programmable computer, created in the 1840s by Charles Babbage and Ada Lovelace.

The Analytical Engine had a random access memory (RAM) consisting of one thousand words of fifty decimal digits each, a central processing unit, a special storage unit for software, and a printer. Although it foreshadowed modem computers, Babbageʹs invention never worked.

Angel Capital Refers to funds available for investment by networks of wealthy investors who invest in start‐up companies. A key source of capital for high‐tech start‐up companies in the United States.

Artificial intelligence (AI) The field of research that attempts to emulate human intelligence in a machine. Fields within AI include knowledge‐based systems, expert systems, pattern recognition, automatic learning, natural-language understanding, robotics, and others.

Artificial life Simulated organisms, each including a set of behavior and reproduction rules (a simulated ʺgenetic codeʺ), and a simulated environment. The simulated organisms simulate multiple generations of evolution. The term can refer to any self‐replicating pattern.

ASR See Automatic speech recognition.

Automatic speech recognition (ASR) Software that recognizes human speech. In general, ASR systems include the ability to extract high‐level patterns in speech data.

BGM See Brain‐generated music.

Big bang theory A prominent theory on the beginning of the Universe: the cosmic explosion, from a single point of infinite density, that marked the beginning of the Universe billions of years ago.

Big crunch A theory that the Universe will eventually lose momentum in expanding and contract and collapse in an event that is the opposite of the big bang.

Bioengineering The field of designing pharmaceutical drugs and strains of plant and animal life by directly modifying the genetic code. Bioengineered materials, drugs, and life‐forms are used in agriculture, medicine, and

the treatment of disease.

Biology The study of life‐forms. In evolutionary terms, the emergence of patterns of matter and energy that could survive and replicate to form future generations.

Bionic organ In 2029, artificial organs that are built using nanoengineering.

Biowarfare Agency (BWA) In the second decade of the twenty‐first century, a government agency that monitors and polices bioengineering technology applied to weapons.

Bit A contraction of the phrase ʺbinary digit.ʺ In a binary code, one of two possible values, usually zero and one. In information theory, the fundamental unit of information.

Brain‐generated music (BGM) A music technology pioneered by Neurosonics, Inc., that creates music in response to the listenerʹs brain waves. This brain‐wave biofeedback system appears to evoke the Relaxation Response by encouraging the generation of alpha waves in the brain.

BRUTUS.1 A computer program that creates fictional stories with a theme of betrayal; invented by Selmer Bringsjord, Dave Ferucci, and a team of software engineers at Rensselaer Polytechnic Institute in New York.

Buckyball A soccer‐ball‐shaped molecule formed of a large number of carbon atoms. Because of their hexagonal and pentagonal shape, the molecules were dubbed ʺbuckyballsʺ in reference to R. Buckminster Fullerʹs building designs.

Busy beaver One example of a class of noncomputational functions; an unsolvable problem in mathematics. Being a ʺTuring machine unsolvable problem,ʺ the busy beaver function cannot be computed by a Turing machine. To compute busy beaver of n, one creates all the n‐state Turing machines that do not write an infinite number of 1s on

their tape. The largest number of 1s written by the Turing machine in this set that writes the largest number of 1s is busy beaver of n.

BWA See Biowarfare Agency

Byte A contraction for ʺby eight.ʺ A group of eight bits clustered together to store one unit of information on a computer. A byte may correspond, for example, to a letter of the English alphabet.

CD‐ROM See Compact disc read‐only memory.

Chaos The amount of disorder or unpredictable behavior in a system. In reference to the Law of Time and Chaos, chaos refers to the quantity of random and unpredictable events that are relevant to a process.

Chaos theory The study of patterns and emergent behavior in complex systems comprised of many unpredictable elements (e.g., the weather).

Chemistry The composition and properties of substances comprised of molecules.

Chip A collection of related circuits that work together on a task or set of tasks, residing on a wafer of semiconductor material (typically silicon).

Closed system Interacting entities and forces not subject to outside influence (for example, the Universe). A corollary of the second law of thermodynamics is that in a closed system, entropy increases.

Cochlear implant An implant that performs frequency analyses of sound waves, similar to that performed by the inner ear.

Colossus The first electronic computer, built by the British from fifteen hundred radio tubes during World War II.

Colossus and nine similar machines running in parallel cracked increasingly complex German codes on military intelligence and contributed to the Allied forcesʹ winning of World War II.

Combinatorial explosion The rapid‐exponential‐growth in the number of possible ways of choosing distinct combinations of elements from a set as the number of elements in that set grows. In an algorithm, the rapid growth

in the number of alternatives to be explored while performing a search for a solution to a problem.

Common sense The ability to analyze a situation based on its context, using millions of integrated pieces of common knowledge. Currently, computers lack common sense. To quote Marvin Minsky: ʺDeep Blue might be able

to win at chess, but it wouldnʹt know to come in from the rain.ʺ

Compact disc read‐only memory (CD‐ROM) A laser‐read disc that contains up to a half billion bytes of information. ʺRead onlyʺ refers to the fact that information can be read, but not deleted or recorded, on the disc.

Complicated‐minded school The use of sophisticated procedures to evaluate the terminal leaves in a recursive algorithm.

Computation The process of calculating a result by use of an algorithm (e.g., a computer program) and related data.

The ability to remember and solve problems.

Computer A machine that implements an algorithm. A computer transforms data according to the specifications of an algorithm. A programmable computer allows the algorithm to be changed.

Computer language A set of rules and specifications for describing an algorithm or process on a computer.

Computing medium Computing circuitry capable of implementing one or more algorithms. Examples include human neurons and silicon chips.

Connectionism An approach to studying intelligence and to creating intelligent solutions to problems.

Connectionism is based on storing problem‐solving knowledge as a pattern of connections among a very large number of simple processing units operating in parallel.

Consciousness The ability to have subjective experience. The ability of a being, animal, or entity to have self-perception and self‐awareness. The ability to feel. A key question in the twenty‐first century is whether computers

will achieve consciousness (which their human creators are considered to have).

Continuous speech recognition (CSR) A software program that recognizes and records natural language.

Crystalline computing A system in which data is stored in a crystal as a hologram, conceived by Stanford professor Lambertus Hesselink. This three‐dimensional storage method requires a million atoms for each bit and could achieve a trillion bits of storage for each cubic centimeter. Crystalline computing also refers to the possibility of growing computers as crystals.

CSR See Continuous speech recognition.

Cybernetic artist A computer program that is able to create original artwork in poetry, visual art, or music.

Cybernetic artists will become increasingly commonplace starting in 2009.

Cybernetic chauffeur Self‐driving cars that use special sensors in the roads. Self‐driving cars are being experimented with in the late 1990s, with implementation on major highways feasible during the first decade of the

twenty‐first century, Cybernetic poet A computer program that is able to create original poetry.

Cybernetics A term coined by Norbert Wiener to describe the science of control and communication in animals and machines. Cybernetics is based on the theory that intelligent living beings adapt to their environments and accomplish objectives primarily by reacting to feedback from their surroundings.

Database The structured collection of data that is designed in connection with an information retrieval system. A database management system (DBMS) allows monitoring, updating, and interacting with the database.

Debugging The process of discovering and correcting errors in computer hardware and software. The issue of bugs or errors in a program will become increasingly important as computers are integrated into the human brain and

physiology throughout the twenty‐first century. The first ʺbugʺ was an actual moth, discovered by Grace Murray Hopper, the first programmer of the Mark I computer.

Deep Blue The computer program, created by IBM, that defeated Gary Kasparov, the worldʹs chess champion, in 1997.

Destroy‐all‐copies movement In 2099, a movement to permit an individual to terminate her mind file and to destroy all backup copies of that file.

Destructive scan The process of scanning oneʹs brain and neural system while destroying it, with a view to replacing it with electronic circuits of far greater capacity, speed, and reliability.

Digital Varying in discrete steps. The use of combinations of bits to represent data in computation. Contrasted with analog.

Digital video disc (DVD) A high‐density compact disc system that uses a more focused laser than the conventional CD‐ROM, with storage capacities of up to 9.4 gigabytes on a double‐sided disc. A DVD has sufficient capacity to hold a full‐length movie.

Direct neural pathway Direct electronic communication to the brain. In 2029, direct neural pathways, combined with wireless communication technology, will connect humans directly to the worldwide computing network (the

Web).

Diversity Variety of choices, in which evolution thrives. A key resource for an evolutionary process. The other resource for evolution is its own increasing order.

DNA Deoxyribonucleic acid; the building blocks of all organic life‐forms. In the twenty‐first century, intelligent life-forms will be based on new computational technologies and nanoengineering.

DNA computing A form of computing, pioneered by Leonard Adleman, in which DNA molecules are used to solve

complex mathematical problems. DNA computers allow trillions of computations to be performed simultaneously.

DVD See Digital video disc.

Einsteinʹs theory of relativity Refers to two of Einsteinʹs theories Einsteinʹs Special Theory of Relativity postulates the speed of light as the fastest speed at which we can transmit information. Einsteinʹs General Theory of Relativity deals with the effects of gravity on the geometry of space. Includes the formula E =mc2 (energy equals mass times

the speed of light squared), which is the basis of nuclear power.

EMI See Experiments in Musical Intelligence.

Encryption Encoding information so that only the intended recipient can understand the message by decoding it.

PGP (Pretty Good Privacy) is an example of encryption.

Entropy In thermodynamics, a measure of the chaos (unpredictable movement) of particles and unavailable energy in a physical system of many components. In other contexts, a term used to describe the extent of randomness and

disorder of a system.

Evolution A process in which diverse entities (sometimes called organisms) compete for limited resources in an environment, with the more successful organisms able to survive and reproduce (to a greater extent) into subsequent generations. Over many such generations, the organisms become better adapted at survival. Over generations, the order (suitability of information for a purpose) of the design of the organisms increases, with the purpose being survival. In an ʺevolutionary algorithmʺ (see below), the purpose may be defined to be the discovery

of a solution to a complex problem. Evolution also refers to a theory in which each life‐form on Earth has its origin in an earlier form.

Evolutionary algorithm Computer‐based problem‐solving systems that use computational models of the mechanisms of evolution as key elements in their design.

Experiments in Musical Intelligence (EMI) A computer program that composes musical scores. Created by the composer David Cope.

Expert system A computer program, based on various artificial intelligence techniques, that solves a problem using a database of expert knowledge on a topic. Also a system that enables such a database to become available to the

nonexpert user. A branch of the artificial intelligence field.

Exponential growth Characterized by growth in which size increases by a fixed multiple over time.

Exponential trend Any trend that exhibits, exponential growth (such as an exponential trend in population growth).

Femtoengineering In 2099, a proposed computing technology on the femtometer (one thousandth of a trillionth of a meter) scale. Femtoengineering requires harnessing mechanisms inside a quark. Molly discusses femtoengineering

proposals with the author in 2099.

Florence Manifesto Brigade In 2029, a neo‐Luddite group that is based on the ʺFlorence Manifestoʺ written by Theodore Kaczynski from prison. Members of the brigade protest technology primarily through nonviolent means.

Fog swarm projection In the mid‐ and late‐twenty‐first century, a technology that allows projections of physical objects and entities through the behavior of trillions of foglets. Mollyʹs physical appearance to the author in 2099 is created by a fog swarm projection. See Foglet; Utility fog.

Foglet A hypothetical robot that consists of a human‐cell‐sized device with twelve arms pointing in all directions.

At the end of the arms are grippers so that the Foglets can grasp one another to form larger structures. These nanobots are intelligent and can merge their computational capacities with one another to create a distributed intelligence. Foglets are the brainchild of J. Storrs Hall, a Rutgers University computer scientist.

Free will Purposeful behavior and decision making. Since the time of Plato, philosophers have explored the paradox of free will, particularly as it applies to machines. During the next century, a key issue will be whether machines will evolve into beings with consciousness and free will. A primary philosophical issue is how free will is possible if events are the result of the predictable—or unpredictable—interaction of particles. Considering the interaction of particles to be unpredictable does not resolve the paradox of free will because there is nothing purposeful in random behavior.

General Problem Solver (GPS) A procedure and program developed by Allen Newell, J. C. Shaw, and Herbert Simon. GPS attains an objective by using recursive search and by applying rules to generate the alternatives at each branch in the recursive expansion of possible sequences. GPS uses a procedure to measure the ʺdistanceʺ from the

goal.

Genetic algorithm A model of machine learning that derives its behavior from a metaphor of the mechanisms of evolution in nature. Within a program, a population of simulated ʺindividualsʺ are created and undergo a process

of evolution in a simulated competitive environment.

Genetic programming The method of creating a computer program using genetic or evolutionary algorithms. See Evolutionary algorithm; Genetic algorithm.

God spot A tiny locus of nerve cells in the frontal lobe of the brain that appears to be activated during religious experiences. Neuroscientists from the University of California discovered the God spot while studying epileptic patients who have intense mystical experiences during seizures.

Gödelʹs incompleteness theorem A theorem postulated by Kurt Gödel, a Czech mathematician, that states that in a mathematical system powerful enough to generate the natural numbers, there inevitably exist propositions that can

be neither proved nor disproved.

Gordian knot An intricate, practically unsolvable problem. A reference to the knot tied by Gordius, to be untied only by the future ruler of Asia. Alexander the Great circumvented the dilemma of untying the knot by slashing it

with his sword.

GPS See General Problem Solver.

Grandfather legislation As of 2099, legislation that protects the, rights of MOSHs (mostly original substrate humans) and acknowledges the roots of twenty‐first‐century beings. See MOSH.

Haptic interface In virtual reality systems, the physical actuators that provide the user with a sense of touch (including the sensing of pressure and temperature).

Haptics The development of systems that allow one to experience the sense of touch in virtual reality. See Haptic interface.

Hologram An interference pattern, often using photographic media, that is encoded by laser beams and read by means of low‐power laser beams. This interference pattern can reconstruct a three‐dimensional image. An important property of a hologram is that the information is distributed throughout the hologram. Cut a hologram

in half, and both halves will have the full picture, only at half the resolution. Scratching a hologram has no noticeable effect on the image. Human memory is regarded to be distributed in a similar way Holy Grail Any objective of a long and difficult quest. In medieval tore, the Grail refers to the plate used by Christ at the Last Supper. The Holy Grail subsequently became the object of knightsʹ quests.

Homo erectus ʺUpright man.ʺ Homo erectus emerged in Africa about 1.6 million years ago and developed fire, clothing, language, and weapon use.

Homo habilis ʺHandy human.ʺ A direct ancestor leading to Homo erectus and eventually to Homo sapiens. Homo habilis lived approximately 1.6 to 2 million years ago. Homo habilis hominids were different from previous hominids in their bigger brain size, diet of both meat and plants, and creation and use of rudimentary tools.

Homo sapiens Human species that emerged perhaps 400,000 years ago. Homo sapiens are similar to advanced primates in terms of their genetic heritage and are distinguished by their creation of technology, including art and language.

Homo sapiens neanderthal (neanderthalensis) A subspecies of Homo sapiens. Homo sapiens neanderthalensis is thought to have evolved from Homo erectus about 100,000 years ago in Europe and the Middle East. This highly intelligent subspecies cultivated an involved culture that included elaborate funeral rituals, burying their dead with ornaments, caring for the sick, and making tools for domestic use and for protection. Homo sapiens neanderthalensis disappeared about 35,000 to 40,000 years ago, in all likelihood as a result of violent conflict with Homo sapiens sapiens (the subspecies of contemporary humans).

Homo sapiens sapiens Another subspecies of Homo sapiens that emerged in Africa about 90,000 years ago.

Contemporary humans are the direct descendants of this subspecies.

Human Genome Project An international research program with the goal of gathering a resource of genomic maps and DNA sequence information that will provide detailed information about structure, organization, and

characteristics of the DNA of humans and other animals. The project began in the mid‐1980s and is expected to be

completed by around the year 2005.

Idiot Savant A system or person who is highly skilled in a narrow task area but who lacks context and is otherwise impaired in more general areas of intelligent functioning. The term is taken from psychiatry, where it refers to a person who exhibits brilliance in one very limited domain but is underdeveloped in common sense, knowledge, and competence. For example, some human idiot savants are capable of multiplying very large numbers in their heads, or memorizing a phone book. Deep Blue is an example of an idiot savant system.

Image processing The manipulation of data representing images, or pictorial representation on a screen, composed of pixels. The use of a computer program to enhance or modify an image.

Improvisor A computer program that creates original music, written by Paul Hodgson, a British jazz saxophone player. Improvisor can emulate styles ranging from Bach to jazz greats Louis Armstrong and Charlie Parker.

Industrial Revolution The period in history in the late eighteenth and nineteenth centuries marked by accelerating developments in technology that enabled the mass production of goods and materials.

Information A sequence of data that is meaningful in a process, such as the DNA code of an organism or the bits in a computer program. Information is contrasted with ʺnoise,ʺ which is a random sequence. However, neither noise

nor information is predictable. Noise is inherently unpredictable but carries no information. Information is also unpredictable; that is, we cannot predict future information from past information. If we can fully predict future data from past data, then that future data stops being information.

Information Theory A mathematical theory concerning the difference between information and noise, and the ability of a communications channel to carry information.

Intelligence The ability to use optimally limited resources—including time—to achieve a set of goals (which may include survival, communication, solving problems, recognizing patterns, performing skills). The products of intelligence may be clever, ingenious, insightful, or elegant. R. W. Young defines intelligence as ʺthat faculty of mind by which order is perceived in a situation previously considered disordered.ʺ

Intelligent agent An autonomous software program that performs a function on its own, such as searching the Web for information of interest to a person based on certain criteria.

Intelligent function A function that requires increasing intelligence to compute for increasing arguments. The busy beaver is an example of an intelligent function.

Internet computation harvesting proposal A proposal to harvest the unused computational resources of personal computers on the Internet and thereby create virtual parallel supercomputers. There are sufficient unused ʺcomputesʺ on the Internet in 1998 to create human brain capacity supercomputers, at least in terms of hardware capability Knee of the curve The period in which the exponential nature of the curve of time begins to explode.

Exponential growth lingers with no apparent growth for a long period of time and then appears to erupt suddenly.

This is now occurring in the capability of computers.

Knowledge engineering The art of designing and building expert systems. In particular, collecting knowledge and heuristic rules from human experts in their area of specialty and assembling them into a knowledge base or expert

system.

Knowledge principle A principle that emphasizes the important role played by knowledge in many forms of intelligent activity. It states that a system exhibits intelligence in part due to the specific knowledge relevant to the task that it contains.

Knowledge representation A system for organizing human knowledge in a domain into a data structure flexible enough to allow the expression of facts, rules, and relationships.

Law of Accelerating Returns As order exponentially increases, time exponentially speeds up (i.e., the time interval between salient events grows shorter as time passes).

Law of Increasing Chaos As chaos exponentially increases, time exponentially slows down (i.e., the time interval between salient events grows longer as time passes).

Law of Time and Chaos In a process, the time interval between salient events (i.e., events that change the nature of the process, or significantly affect the future of the process) expands or contracts along with the amount of chaos.

Laws of thermodynamics The laws of thermodynamics govern how and why energy is transferred.

The first law of thermodynamics (postulated by Hermann von Helmholtz in 1847), also called the Law of Conservation of Energy, states that the total amount of energy in the Universe is constant. A process may modify

the form of energy, but a closed system does not lose energy. We can use this knowledge to determine the amount

of energy in a system, the amount lost as waste heat, and the efficiency of the system.

The second law of thermodynamics (articulated by Rudolf Clausias in 1850), also known as the Law of

increasing Entropy, states that the entropy (disorder of particles) in the Universe never decreases. As the disorder in the Universe increases, the energy is transformed into less usable forms. Thus, the efficiency of any process will always be less than 100 percent.

The third law of thermodynamics (described by Walter Hermann Nernst in 1906, based on the idea of a temperature of absolute zero first articulated by Baron Kelvin in 1848), also known as the Law of Absolute Zero, tells us that all molecular movement stops at a temperature called absolute zero, or 0 Kelvin (–273°C). Since temperature is a measure of molecular movement, the temperature of absolute zero can be approached, but it can

never be reached.

Life The ability of entities (usually organisms) to reproduce into future generations. Patterns of matter and energy that can perpetuate themselves and survive.

LISP (list processing) An interpretive computer language developed in the late 1950s at MIT by John McCarthy used to manipulate symbolic strings of instructions and data. The principal data structure is the list, a finite ordered sequence of symbols. Because a program written in LISP is itself expressed as a list of lists, LISP lends itself to sophisticated recursion, symbol manipulation, and self‐modifying code. It has been widely used for AI

programming, although it is less popular today than it was in the 1970s and 1980S.

Logical positivism A twentieth‐century philosophical school of thought that was inspired by Ludwig Wittgensteinʹs Tractatus Logico‐Philosophicus. According to logical positivism, all meaningful statements may be confirmed by observation and experiment or are ʺanalyticʺ (deducible from observations).

Luddite One of a group of early‐nineteenth‐century English workmen who destroyed labor‐saving machinery in protest. The Luddites were the first organized movement to oppose the mechanized technology of the Industrial Revolution. Today, the Luddites are a symbol of opposition to technology.

Magnetic resonance imaging (MRI) A noninvasive diagnostic technique that produces computerized images of body tissues and is based on nuclear magnetic resonance of atoms within the body produced by the application of

radio waves. A person is placed in a magnetic field thirty thousand times stronger than the normal magnetic field

on Earth. The personʹs body is stimulated with radio waves, and the body responds with its own electromagnetic

transmissions. These are detected and processed by computer to generate a three‐dimensional map of high-resolution internal features such as blood vessels.

Massively parallel neural nets A neural net built from many parallel processing units. Generally, a separate, specialized computer implements each neuron model.

Microprocessor An integrated circuit built on a single chip containing the entire central processing unit (CPU) of a computer.

Millions of Instructions per Second A method of measuring the speed of a computer in terms of the number of millions of instructions performed by the computer in one second. An instruction is a single step in a computer program as represented in the computerʹs machine language.

Mind‐body problem The philosophical question: How does the nonphysical entity of the mind emerge from the physical entity of the brain? How do feelings and other subjective experiences result from the processing of the physical brain? By extension, will machines emulating the processes of the human brain have subjective experiences? Also, how does the nonphysical entity of the mind exert control over the physical reality of the body?

Mind trigger A stimulation of an area of the brain that evokes a feeling usually (i.e., otherwise) gained from actual physical or mental experience.

Minimax procedure or theorem A basic technique used in game‐playing programs. An expanding tree of possible moves and countermoves (moves from the opponent) is constructed. An evaluation of the final ʺleavesʺ of the tree

that minimizes the opponentʹs ability to win and maximizes the programʹs ability to win is then passed back down

the branches of the tree.

MIPS See Millions of Instructions per Second.

Mission critical system A software program that controls a process on which people are heavily dependent.

Examples of mission critical software include life‐support systems in hospitals, automated surgical equipment, autopilot flying and landing systems, and other software‐based systems that affect the well‐being of a person or organization.

Molecular computer A computer based on logic gates that is constructed on principles of molecular mechanics (as opposed to principles of electronics) by appropriate arrangements of molecules. Since the size of each logic gate (device that can perform a logical operation) is only one or a few molecules, the resultant computer can be microscopic in size. Limitations on molecular computers arise only from the physics of atoms. Molecular computers

can be massively parallel by having parallel computations performed by trillions of molecules simultaneously.

Molecular computers have been demonstrated using the DNA molecule.

Mooreʹs Law First postulated by former Intel CEO Gordon Moore in the mid‐1960s, Mooreʹs Law is the prediction that the size of each transistor on an integrated circuit chip will be reduced by 50 percent every twenty‐four months. The result is the exponentially growing power of integrated circuit‐based computation over time. Mooreʹs

Law doubles the number of components on a chip as well as the speed of each component. Both of these aspects double the power of computing, for an effective quadrupling of the power of computation every twenty‐four months.

MOSH In 2099, an acronym for Mostly Original Substrate Humans. in the last half of the twenty‐first century, a human being still using native carbon‐based neurons and unenhanced by neural implants is referred to as a MOSH.

In 2099, Molly refers to the author as being a MOSH.

MOSH art In 2099, art (that is usually created by enhanced humans) that a MOSH is theoretically capable of appreciating, although MOSH art is not always shared with a MOSH.

MOSH music in 2099, MOSH art in the form of music.

Moshism In 2099, an archaic term that is rooted in the MOSH way of life, before the advent of enhanced humans through neural implants and the porting of human brains to new computational substrates. An example of a Moshism: the word papers to refer to knowledge structures representing a body of intellectual work.

MRI See Magnetic resonance imaging.

MYCIN A successful expert system, developed at Stanford University in the mid‐1970s, designed to aid medical practitioners in prescribing an appropriate antibiotic by determining the exact identity of a blood infection.

Nanobot A nanorobot (robot built using nanotechnology). A self‐replicating nanobot requires mobility, intelligence, and the ability to manipulate its environment. It also needs to know when to stop its own replication. In 2029, nanobots will circulate through the bloodstream of the human body to diagnose illnesses.

Nanobot swarm In the last half of the twenty‐first century, a swarm comprised of trillions of nanobots, The nanobot swarms can rapidly take on any form. A nanobot swarm can project the visual images, sounds, and pressure contours of any set of objects, including people. The swarms of nanobots can also combine their computational abilities to emulate the intelligence of people and other intelligent entities and processes. A nanobot swarm effectively brings the ability to create virtual environments into the real environment.

Nanoengineering The design and manufacturing of products and other objects based on the manipulation of atoms and molecules; building machines atom by atom. ʺNanoʺ refers to a billionth of a meter, which is the width of five

carbon atoms. See Picoengineering; Femtoengineering.

Nanopathogen A self‐replicating nanobot that replicates excessively, possibly without limit, causing destruction to both organic and inorganic matter.

Nanopatrol In 2029, a nanobot in the bloodstream that checks the body for biological pathogens and other disease processes.

Nanotechnology A body of technology in which products and other objects are created through the manipulation of atoms and molecules. ʺNanoʺ refers to a billionth of a meter, which is the width of five carbon atoms.

Nanotubes Elongated carbon molecules that resemble long tubes and are formed of the same pentagonal patterns of carbon atoms as buckyballs. Nanotubes can perform the electronic functions of silicon‐based components.

Nanotubes are extremely small, thereby providing very high densities of computation. Nanotubes are a likely technology to continue to provide the exponential growth of computing when Mooreʹs Law on integrated circuits

dies by the year 2020. Nanotubes are also extremely strong and heat resistant, thereby permitting the creation of three‐dimensional circuits.

Natural language Language as ordinarily spoken or written by humans using a human language such as English (as contrasted with the rigid syntax of a computer language). Natural language is governed by rules and conventions

sufficiently complex and subtle for there to be frequent ambiguity in syntax and meaning.

Neanderthal See Homo sapiens neanderthal (neanderthalensis).

Neural computer A computer with hardware optimized for using the neural network paradigm. A neural computer is designed to simulate a massive number of models of human neurons.

Neural connection calculation In a neural network, a term that refers to the primary calculation of multiplying the ʺstrengthʺ of a neural connection by the input to that connection (which is either the output of another neuron or an initial input to the system) and then adding this product to the accumulated sum of such products from other connections to this neuron. This operation is highly repetitive, so neural computers are optimized for performing it.

Neural implant A brain implant that enhances oneʹs sensory ability, memory, or intelligence. Neural implants will become ubiquitous in the twenty‐first century.

Neural network A computer simulation of human neurons. A system (implemented in software or hardware) that is intended to emulate the computing structure of neurons in the human brain.

Neuron Information‐processing cell of the central nervous system. There are an estimated 100 billion neurons in the human brain.

Noise A random sequence of data. Because the sequence is random and without meaning, noise carries no information. Contrasted with information.

Objective experience The experience of an entity as observed by another entity, or measurement apparatus.

OCR See optical character recognition.

Operating system A software program that manages and provides a variety of services to application programs, including user interface facilities and management of input‐output and memory devices.

Optical character recognition (OCR) A process in which a machine scans, recognizes, and encodes printed (and possibly handwritten) characters into digital form.

Optical computer A computer that processes information encoded in patterns of light beams; different from todayʹs conventional computers, in which information is represented in electronic circuitry or encoded on magnetic surfaces. Each stream of photons can represent an independent sequence of data, thereby providing extremely massive parallel computation.

Optical imaging A brain‐imaging technique similar to MRI but potentially providing higher resolution imaging.

Optical imaging is based on the interaction between electrical activity in the neurons and blood circulation in the

capillaries feeding the neurons.

Order Information that fits a purpose. The measure of order is the measure of how well the information fits the purpose. In the evolution of life‐forms, the purpose is to survive. In an evolutionary algorithm (a computer program that simulates evolution to solve a problem), the purpose is to solve the problem. Having more information, or more complexity, does not necessarily result in a better fit. A superior solution for a purpose—

greater order—may require either more or less information, and either more or less complexity. Evolution has shown, however, that the general trend toward greater order does generally result in greater complexity.

Paradigm A pattern, model, or general approach to solving a problem.

Parallel processing Refers to computers that use multiple processors operating simultaneously as opposed to a single processing unit. (Compare with Serial computer.)

Pattern recognition Recognition of patterns with the goal of identifying, classifying, or categorizing complex inputs.

Examples of inputs include images such as printed characters and faces, and sounds such as spoken language.

Perceptron In the late 1960s and 1970s, a machine constructed from mathematical models of human neurons. Early Perceptrons were modestly successful in such pattern recognition tasks as identifying printed letters and speech sounds. The Perceptron was a forerunner of contemporary neural nets.

Personal computer A generic term for a single‐user computer using a microprocessor, and including the computing hardware and software needed for an individual to work autonomously, PGP See Pretty Good Privacy.

Picoengineering Technology on the picometer (one trillionth of a meter) scale. Picoengineering will involve engineering at the level of subatomic particles.

Picture portal In 2009, a visual display for viewing people and other real‐time images. In later years, the portals project three‐dimensional, real‐time scenes. Mollyʹs son, Jeremy, uses a picture portal to view the Stanford University campus.

Pixel An abbreviation for picture element. The smallest element on a computer screen that holds information to represent a picture. Pixels contain data giving brightness and possibly color at particular points in the picture.

Pretty Good Privacy (PGP) A system of encryption (designed by Phil Zimmerman) distributed on the Internet and widely used. PGP uses a public key that can be freely disseminated and used by anyone to encode a message and a

private key that is kept only by the intended recipient of the encoded messages. The private key is used by the recipient to decode messages encrypted using the public key. Converting the public key into a private key requires

factoring large numbers. If the number of bits in the public key is large enough, then the factors cannot be computed in a reasonable amount of time using conventional computation (and thus the encoded information remains secure). Quantum computing (with a sufficient number of qu‐bits) would destroy this type of encryption.

Price‐performance A measure of the performance of a product per unit cost.

Program A set of computer instructions that enables a computer to perform a specific task. Programs are usually written in a high‐level language such as ʺCʺ or ʺFORTRANʺ that can be understood by human programmers and then translated into machine language using a special program called a compiler. Machine language is a special set

of codes that directly controls a computer.

Punch card A rectangular card that typically records up to eighty characters of data in a binary coded format as a pattern of holes punched in it.

Quantum computing A revolutionary method of computing, based on quantum physics, that uses the ability of particles such as electrons to exist in more than one state at the same time. See Qu‐bit.

Quantum decoherence A process in which the ambiguous quantum state of a particle (such as the nuclear spin of an electron representing a qu‐bit in a quantum computer) is resolved into an unambiguous state as the result of direct or indirect observation by a conscious observer.

Quantum encryption A possible form of encryption using streams of quantum entangled particles such as photons.

See Quantum entanglement.

Quantum entanglement A relationship between two physically separated particles under special circumstances.

Two photons may be ʺquantum entangledʺ if produced by the same particle interaction and emerging in opposite

directions. The two photons remain quantum entangled with each other even when separated by very large distances (even when light‐years apart). In such a circumstance, the two quantum entangled photons, if each forced

to make a decision to choose among two equally probable pathways, will make the identical decision and will do so

at the same instant in time. Since there is no possible communication link between two quantum entangled photons, classical physics would predict that their decisions would be independent. But two quantum entangled photons make the same decision and do so at the same instant in time. Experiments have demonstrated that even if

there were an unknown communication path between them, there is not enough time for a message to travel from

one photon to the other at the speed of light.

Quantum mechanics A theory that describes the interactions of subatomic particles, combining several basic discoveries. These include Max Planckʹs 1900 observation that energy is absorbed or radiated in discrete quantities, called quanta. Also Werner Heisenbergʹs 1921 uncertainty principle stating that we cannot know both the exact position and momentum of an electron or other particle at the same time. Interpretations of quantum theory imply

that photons simultaneously take all possible paths (e.g., when bouncing off a mirror). Some paths cancel each other out. Remaining ambiguity in the path actually taken is resolved based on the conscious observation of an observer.

Qu‐bit A ʺquantum bit,ʺ used in quantum computing, that is both zero and one at the same time, until quantum decoherence (direct or indirect observation by a conscious observer) causes each quantum bit to disambiguate into

a state of zero or one. One qu‐bit stores two possible numbers (zero and one) at the same time. N qu‐bits; stores 2 to the Nth power possible numbers at the same time. Thus an N qu‐bit quantum computer would try 2 to the Nth power possible solutions to a problem simultaneously, which gives the quantum computer its enormous potential

power.

RAM See Random Access Memory.

Random Access Memory (RAM) Memory that can be both read and written with random access of memory locations. Random access means that locations can be accessed in any order and do not need to be accessed sequentially. RAM can be used as the working memory of a computer into which applications and programs can be

loaded and run.

Ray Kurzweilʹs Cybernetic Poet A computer program designed by Ray Kurzweil that uses a recursive approach to create poetry. The Cybernetic Poet analyzes word sequence patterns of poems it has ʺreadʺ using Markov models (a

mathematical cousin of neural nets) and creates new poetry based on these patterns.

Read‐Only Memory (ROM) A form of computer storage that can be read from but not written to or deleted (e.g., CD‐ROM).

Reading machine A machine that scans text and reads it aloud, initially developed for those who are visually impaired, reading machines are currently used by anyone who cannot read at their intellectual level, including reading disabled (e.g., dyslexic) persons and children first learning to read.

Recursion The process of defining or expressing a function or procedure in terms of itself. Typically, each iteration of a recursive‐solution procedure produces a simpler (or possibly smaller) version of the problem than the previous

iteration. This process continues until a subproblem whose answer is already known (or that can be readily computed without recursion) is obtained. A surprisingly large number of symbolic and numerical problems lend themselves to recursive formulations. Recursion is typically used by game‐playing programs, such as the chess-playing program Deep Blue.

Recursive formula A computer‐programming paradigm that uses recursive search to find a solution to a problem.

The recursive search is based on a precise definition of the problem (e.g., the rules of a game such as chess).

Relativity A theory based on two postulates: (1) that the speed of light in a vacuum is constant and independent of the source or the observer, and (2) that the mathematical forms of the laws of physics are invariant in all inertial systems. Implications of the theory of relativity include the equivalence of mass and energy and of change in mass,

dimension, and time with increased velocity. See also Einsteinʹs theory of relativity.

Relaxation Response A neurological mechanism discovered by Dr. Herbert Benson and other researchers at the Harvard Medical School and Bostonʹs Beth Israel Hospital. The opposite of the ʺfight or flightʺ or stress response,

the Relaxation Response is associated with reduced levels of epinephrine (adrenaline) and norepinephrine (noradrenaline), blood pressure, blood sugar, breathing, and heart rates.

Remember York movement In the second decade of the twenty‐first century, a neo‐Luddite web discussion group.

The group is named to commemorate the 1813 trial in York, England, during which a number of the Luddites who

destroyed industrial machinery were hanged, jailed, or exiled.

Reverse engineering Examining a product, program, or process to understand it and to determine its methods and algorithms. Scanning and copying a human brainʹs salient computational methods into a neural computer of sufficient capacity is a future example of reverse engineering.

RKCP See Ray Kurzweilʹs Cybernetic Poet.

Robinson The worldʹs first operational computer, constructed from telephone relays and named after a popular cartoonist who drew ʺRube Goldbergʺ machines (very ornate machinery with many interacting mechanisms).

During World War II, Robinson provided the British with a transcription of nearly all significant Nazi coded messages, until it was replaced by Colossus. See Colossus.

Robot A programmable device, linked to a computer, consisting of mechanical manipulators and sensors. A robot may perform a physical task normally done by human beings, possibly with greater speed, strength, and/or precision.

Robotics The science and technology of designing and manufacturing robots. Robotics combines artificial intelligence and mechanical engineering.

ROM See Read‐Only Memory.

Russellʹs Paradox The ambiguity created by the following question: Does a set that is defined as ʺall sets that do not include themselvesʺ include itself as a member? Russellʹs paradox motivated Bertrand Russell to create a new theory of sets.

Search A recursive procedure in which an automatic problem solver seeks a solution by iteratively exploring sequences of possible alternatives.

Second Industrial Revolution The automation of mental rather than physical tasks.

Second law of thermodynamics Also known as the Law of Increasing Entropy, this law states that the disorder (amount of random movement) of particles in the Universe may increase but never decreases. As the disorder in the

Universe increases, the energy is transformed into less usable forms. Thus, the efficiency of any process will always be less than 100 percent (hence the impossibility of perpetual motion machines).

Self‐replication A process or device that is capable of creating an additional copy of itself. Nanobots are self-replicating if they can create copies of themselves. Self‐replication is regarded as a necessary means of manufacturing nanobots due to the very large number (i.e., trillions) of such devices needed to perform useful functions.

Semiconductor A material commonly based on silicon or germanium with a conductivity midway between that of a good conductor and an insulator. Semiconductors are used to manufacture transistors. Semiconductors rely on the

phenomenon of tunneling. See Tunneling.

Sensorium In 2019, the product name for a total touch virtual reality environment, which provides an all-encompassing tactile environment.

Serial computer A computer that performs a single computation at a time. Thus two or more computations are performed one after the other, not simultaneously (even if the computations are independent). The opposite of a parallel processing computer.

Silicon Valley The area in California, south of San Francisco, that is a key center of high‐technology innovation, including the development of software, communication, integrated circuits and related technologies.

Simple‐minded school The use of simple procedures to evaluate the terminal leaves in a recursive algorithm. For example, in the context of a chess program, adding up piece values.

Simulated person A realistic, animated personality incorporating a convincing visual appearance and capable of communicating using natural language. By 2019, a simulated person can interact with real persons using visual, auditory, and tactile means in a virtual reality environment.

Simulator A program that models and represents an activity or environment on a computer system. Examples include the simulation of chemical interaction and fluid flow. Other examples include a flight simulator used to train pilots and a simulated patient to train physicians. Simulators are also often used for entertainment.

Society of mind A theory of the mind proposed by Marvin Minsky in which intelligence is seen to be the result of proper organization of a large number (a society) of other minds, which are in turn comprised of yet simpler minds.

At the bottom of this hierarchy are simple mechanisms, each of which is by itself unintelligent.

Software Information and knowledge used to perform useful functions by computers and computerized devices.

Includes computer programs and their data, but more generally also includes such knowledge products as books,

music, pictures, movies, and videos.

Software‐based evolution Software simulation of the evolutionary process. One example of software‐based evolution is Network Tierra, designed by Thomas Ray. Rayʹs ʺcreaturesʺ are software simulations of organisms in

which each ʺcellʺ has its own DNA‐like genetic code. The organisms compete with one another for the limited simulated space and energy resources of their simulated environment.

Speaker independence Refers to the ability of a speech‐recognition system to understand any speaker, regardless of whether or not the system has previously sampled that speakerʹs speech.

Stored‐program computer A computer in which the program is stored in memory along with the data to be operated on. A stored‐program capacity is an important capability for systems of artificial intelligence in that recursion and self‐modifying code are not possible without it.

Subjective experience The experience of an entity as experienced by the entity, as opposed to observations of that entity (including its internal processes) by another entity, or by a measurement apparatus.

Substrate Computing medium or circuitry. See Computing medium.

Supercomputer The fastest and most powerful computer available at any given time. Supercomputers are used for computations demanding high speed and storage (e.g., analyzing weather data).

Superconductivity The physical phenomenon whereby some materials exhibit zero electrical resistance at low temperatures. Superconductivity points to the possibility of great computational power with little or no heat dissipation (a limiting factor today). Heat dissipation is a major reason that three‐dimensional circuits are difficult to create.

Synthesizer A device that computes signals in real time. In the context of music, a (usually computer based) device that creates and generates sounds and music electronically.

Tactile virtualism By 2029, a technology that allows one to use a virtual body to enjoy virtual reality experiences without virtual reality equipment other than the use of neural implants (which include high‐bandwidth wireless communication). The neural implants create the pattern of nerve signals that corresponds to a comparable ʺrealʺ

experience.

Technology An evolving process of tool creation to shape and control the environment. Technology goes beyond the mere fashioning and use of tools. It involves a record of tool making and a progression in the sophistication of tools. It requires invention and is itself a continuation of evolution by other means. The ʺgenetic codeʺ of the evolutionary process of technology is the knowledge base maintained by the tool‐making species.

Three‐dimensional chip A chip that is constructed in three dimensions, thus allowing for hundreds or thousands of layers of circuitry. Three‐dimensional chips are currently being researched and engineered by a variety of companies.

Total touch environment In 2019, a virtual‐reality environment that provides an all‐encompassing tactile environment.

Transistor A switching and/or amplifying device using semiconductors, first created in 1948 by John Bardeen, Walter Brattain, and William Shockley of Bell Labs.

Translating telephone A telephone that provides real‐time speech translation from one human language to another.

Tunneling In quantum mechanics, the ability of electrons (negatively charged particles orbiting the nucleus of an atom) to exist in two places at once, in particular on both sides of a barrier. Tunneling allows some of the electrons to effectively move through the barrier and accounts for the ʺsemiʺ conductor properties of a transistor.

Turing machine A simple abstract model of a computing machine, designed by Alan Turing in his 1936 paper ʺOn Computable Numbers.ʺ The Turing machine is a fundamental concept in the theory of computation.

Turing Test A procedure proposed by Alan Turing in 1950 for determining whether or not a system (generally a computer) has achieved human‐level intelligence, based on whether it can deceive a human interrogator into believing that it is human. A human ʺjudgeʺ interviews the (computer) system, and one or more human ʺfoilsʺ over

terminal lines (by typing messages). Both the computer and the human foil(s) try to convince the human judge of

their humanness. If the human judge is unable to distinguish the computer from the human foil(s), then the computer is considered to have demonstrated human‐level intelligence. Turing did not specify many key details, such as the duration of the interrogation and the sophistication of the human judge and foils. By 2029, computers

are passing the test, although the validity of the test remains a point of controversy and philosophical debate.

Utility fog A space filled with Foglets. At the end of the twenty‐first century, utility fog can be used to simulate any environment, essentially providing ʺrealʺ reality with the environment‐transforming capabilities of virtual reality See Fog swarm projection; Foglet.

Vacuum tube The earliest form of an electronic switch (or amplifier) based on vacuum‐filled glass containers. Used in radios and other communication equipment and early computers; replaced by the transistor.

Venture Capital Refers to funds available for investment by organizations that have raised pools of capital specifically to invest in companies, primarily new ventures.

Virtual body In virtual reality, oneʹs own body potentially transformed to appear (and ultimately to feel) different than it does in ʺrealʺ reality.

Virtual reality A simulated environment in which you can immerse yourself. A virtual reality environment provides a convincing replacement for the visual and auditory senses, and (by 2019) the tactile sense. In later decades, the olfactory sense will be included as well. The key to a realistic visual experience in virtual reality is that when you move your head, the scene instantly repositions itself so that you are now looking at a different region of a three‐dimensional scene. The intention is to simulate what happens when you turn your real head in the real world: The images captured by your retinas rapidly change. Your brain nonetheless understands that the world has

remained stationary and that the image is sliding across your retinas only because your head is rotating. Initially, virtual reality (including crude contemporary systems) requires the use of special helmets to provide the visual and auditory environments. By 2019, virtual reality will be provided by ubiquitous contact‐lens‐based systems and implanted retinal‐imaging devices (as well as comparable devices for auditory ʺimagingʺ). Later in the twenty‐first

century, virtual reality (which will include all the senses) will be provided by direct stimulation of nerve pathways using neural implants.

Virtual reality auditory lenses In 2019, sonic devices that project high‐resolution sounds precisely placed in the three‐dimensional virtual environment. These can be built into eyeglasses, worn as body jewelry, or implanted.

Virtual reality blocking display In 2019, a display technology using virtual reality optical lenses (see below) and virtual reality auditory lenses (see above) that creates highly realistic virtual visual environments. The display blocks out the real environment, so you see and hear only the projected virtual environment.

Virtual reality head‐directed display In 2019, a display technology using virtual reality optical lenses (see below) and virtual reality auditory lenses (see above) that projects a virtual environment stationary with respect to the position and orientation of your head. When you move your head, the display moves relative to the real environment. This mode is often used to interact with virtual documents.

Virtual reality optical lenses In 2009, three‐dimensional displays built into glasses or contact lenses. These ʺdirect eyeʺ displays create highly realistic virtual visual environments overlaying the ʺrealʺ environment. This display technology projects images directly onto the human retina, exceeds the resolution of human vision, and is widely

used regardless of visual impairment. In 1998, the Microvision Virtual Retina Display provides a similar capability

for military pilots, with consumer versions anticipated.

Virtual reality overlay display In 2019, a display technology using virtual reality optical lenses (see above) and virtual reality auditory lenses (see above) that integrates real and virtual environments. The displayed images slide when you move or turn your head so that the virtual people, objects, and environment appear to remain stationary

in relation to the real environment (which you can still see). Thus if the direct eye display is displaying the image of a person (who could be a geographically remote real person engaging in a three‐dimensional visual phone call with

you, or a computer‐generated simulated person), that projected person will appear to be in a particular place relative to the real environment that you also see. When you move your head, that projected person will appear to

remain in the same place relative to the real environment.

Virtual sex Sex in virtual reality incorporating a visual, auditory, and tactile environment. The sex partner can be a real or simulated person.

Virtual tactile environment A virtual reality system that allows the user to experience a realistic and all-encompassing tactile environment.

Vision chip A silicon emulation of the human retina that captures the algorithm of early mammalian visual processing, an algorithm called center surround filtering.

World Wide Web (WWW) A highly distributed (not centralized) communications network allowing individuals and

organizations around the world to communicate with one another. Communication includes the sharing of text, images, sounds, video, software, and other forms of information. The primary user interface paradigm of the ʺwebʺ

is based on hypertext, which consists of documents (which can contain any type of data) connected by ʺlinks,ʺ

which the user selects by a pointing device such as a mouse. The Web is a system of data‐and‐message servers linked by high‐capacity communication links that can be accessed by any computer user with a ʺweb browserʺ and

Internet access. With the introduction of Windows98, access to the Web is built into the operating system. By the late twenty‐first century, the Web will provide the distributed computing medium for software‐based humans.

Y2K (year 2000 problem) Refers to anticipated difficulties caused by software (usually developed several decades prior to the year 2000) in which date fields used only two digits. Unless the software is adjusted, this will cause computer programs to behave erratically when the year becomes ʺ00.ʺ These programs will mistake the year 2000

for 1900.

NOTES

PROLOGUE: AN INEXORABLE EMERGENCE

1. My recollections of The Twilight Zone episode are essentially accurate, although the gambler is actually a small‐time crook named Rocky Valentine. Episode 28, ʺA Nice Place to Visitʺ (I learned the name of the episode after writing the prologue), aired during the first season of The Twilight Zone, on April 15, 1960.

The episode begins with a voice‐over: ʺPortrait of a man at work, the only work heʹs ever done, the only work he

knows. His name is Henry Francis Valentine, but he calls himself Rocky, because thatʹs the way his life has been—

rocky and perilous and uphill at a dead run all the way. . . .ʺ

While robbing a pawnbrokerʹs shop, Valentine is shot and killed by a policeman. When he awakens, he is met by

his afterlife guide, Pip. Pip explains that he will provide Valentine with whatever he wants. Valentine is suspicious, but he asks for and receives a million dollars and a beautiful girl. He then goes on a gambling spree, winning at the roulette table, at the slot machines, and later, at pool. He is also surrounded by beautiful women, who shower him

with attention.

Eventually Valentine tires of the gambling, the winning, and the beautiful women. He tells Pip that it is boring to

win all the time and that he doesnʹt belong in Heaven. He begs Pip to take him to ʺthe Other Place.ʺ With a malicious gleam in his eye, Pip replies, ʺThis is the Other Place!ʺ Episode synopsis adapted from Marc Scott Zicree, The Twilight Zone Companion (Toronto: Bantam Books, 1982, 113–115).

2. What were the primary political and philosophical issues of the twentieth century? One was ideological—totalitarian systems of the right (fascism) and left (communism) were confronted and largely defeated by capitalism (albeit with

a large public sector) and democracy. Another was the rise of technology, which began to be felt in the nineteenth

century and became a major force in the twentieth century. But the issue of ʺwhat constitutes a human beingʺ is not

yet a primary issue (except as it affects the abortion debate), although the past century did witness the continuation of earlier struggles to include all members of the species as deserving of certain rights.

3. For an excellent overview and technical details on neural‐network pattern recognition, see the ʺNeural Network

Frequently Asked Questionsʺ web site, edited by W S. Sarle, at <ftp://ftp.sas.com/pub/neural/FAQ.html>. In addition, an article by Charles Arthur, ʺComputers Learn to See and Smell Us,ʺ from Independent, January 16, 1996, describes the ability of neural nets to differentiate between unique characteristics.

4. As will be discussed in chapter 6, ʺBuilding New Brains,ʺ destructive scanning will be feasible early in the twenty-first century. Noninvasive scanning with sufficient resolution and bandwidth will take longer but will be feasible by the end of the first half of the twenty‐first century.

CHAPTER 1: THE LAW OF TIME AND CHAOS

1. For a comprehensive overview and detailed references on the big bang theory and the origin of the Universe, see

ʺIntroduction to Big Bang Theory, Bowdoin College Department of Physics and Astronomy at

<http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.ht ml>.

Print sources on the big bang theory include: Joseph Silk, A Short History of the Universe (New York: Scientific American Library, 1994); Joseph Silk, The Big Bang (San Francisco: W H. Freeman and Company, 1980); Robert M.

Wald, Space, Time & Gravity (Chicago: The University of Chicago Press, 1977); and Stephen W. Hawking, A Brief History of Time (New York: Bantam Books, 1988).

2. The strong force holds an atomic nucleus together. It is called ʺstrongʺ because it needs to overcome the powerful repulsion between the protons in a nucleus with more than one proton.

3. The electroweak force combines electromagnetism and the weak force responsible for beta decay. In 1968, American physicist Steven Weinberg and Pakistani physicist Abdus Salam were successful in their unification of the weak

force and the electromagnetic force using a mathematical method called gauge symmetry.

4. The weak force is responsible for beta decay and her slow nuclear processes that occur gradually

5. Albert Einstein, Relativity: The Special and the General Theory (New York: Crown Publishers, 1961).

6. The laws of thermodynamics govern how and why energy is transferred.

The first law of thermodynamics (Postulated by Hermann von Helmholtz in 1847), also called the Law of

Conservation of Energy, states that the total amount of energy in the universe is constant.

The second law of thermodynamics (articulated by Rudolf Clausias in 1850), also known as the Law of Increasing

Entropy, states that entropy, or disorder, in the Universe never decreases (and, therefore, usually increases). As the disorder in the Universe increases, the energy is transformed into less usable forms. Thus the efficiency of any

process will always be less than 100 percent.

The third law of thermodynamics (described by Walter Hermann Nernst in 1906, based on the idea of a

temperature of absolute zero first articulated by Baron Kelvin in 1848), also known as the Law of Absolute Zero, tells us that all molecular movement stops at a temperature called absolute zero, or 0 Kelvin (–273°C). Since temperature

is a measure of molecular movement, the temperature of absolute zero can be approached, but it can never be

reached.

7. ʺEvolution and Behaviorʺ at <http://ccp.uchicago.edu/~jyin/evolution.html> contains an excellent collection of articles and links exploring the theories of evolution. Print sources include Edward O. Wilson , The Diversity of Life (New York: W. W. Norton & Company, 1993); and Stephen Jay Gould, The Book of Life (New York: W. W. Norton & Company, 1993).

8. Four hundred million years ago, vegetation spread from lowland swamps to create the first land‐based plants. This development permitted vertebrate herbivorous animals to step onto land, creating the first amphibians. Along with

the amphibians, arthropods also stepped onto land, some of which evolved into insects. About 200 million years ago,

dinosaurs and mammals began sharing the same environment. The dinosaurs were far more noticeable. Mostly the

mammals stayed out of the dinosaursʹ way, with many mammals being nocturnal.

9. Mammals became dominant in the niche of land‐based animals after the demise of the dinosaurs 65 million years

ago. Mammals are the more intellectual animal class, distinguished by warm blood, the nourishment of their

children with maternal milk, hairy skin, sexual reproduction, four appendages (in most cases) and, most notably, a

highly developed nervous system.

10. Primates, the most advanced mammalian order, were distinguished by forward‐facing eyes, binocular vision, large brains with a convoluted cortex, which permitted more advanced reasoning faculties, and complicated social

patterns. Primates were not the only intelligent animals, but they had one additional characteristic that would hasten the age of computation: the opposable thumb. The two qualities heeded for the subsequent emergence of technology

were now coming into place: intelligence and the ability to manipulate the environment. Itʹs no coincidence that

fingers are called digits. The origin of the word digit, as used in Modern English and appearing first in Middle

English, is from the Latin word digitus, for ʺfingerʺ or ʺtoeʺ; perhaps akin to Greek deiknynai, ʺto show.ʺ

11. About 50 million years ago, the anthropoid suborder of primates split off. Unlike their prosimian cousins, the

anthropoids underwent rapid evolution, giving rise to advanced primates such as monkeys and apes about 30

million years ago. These sophisticated primates were noted for subtle communication abilities using sounds,

gestures, and facial expressions, thereby allowing the development of intricate social groups. About 15 million years ago, the first humanoids emerged. Although they initially walked on their hind legs, they used the knuckles of their front legs for balance.

12. Although it is worth pointing out that a 2 percent change in a computer program can be very significant.

13. Homo sapiens are the only technology‐creating species on Earth today, but were not the first such species. Emerging about five million years ago was Homo habilis (i.e., ʺhandyʺ human being), known for his erect posture and large brain. He was called handy because he fashioned and used tools. Our most direct ancestor, Homo erectus, showed up in Africa about two million years ago. Homo erectus was also responsible for advancing technology, including the domestication of fire, the development of language, and the use of weapons.

14. Technology emerged from the mists of humanoid history and has accelerated ever since. Technologies invented by

other human species and subspecies included the domestication of fire, tools of stone, pottery, clothing, and other

means of providing for basic human needs. Early humanoids also initiated the development of language, visual art,

music, and other means for human communication.

About ten thousand years ago, humans began domesticating plants, and soon thereafter, animals. Nomadic

hunting tribes began settling down, allowing for more stable forms of social organization. Buildings were

constructed to protect both humans and their farming products. More effective means of transportation emerged,

facilitating the emergence of trade and large‐scale human societies.

The wheel appears to be a relatively recent innovation, with the oldest excavated wheels dating from about 5,500

years ago in Mesopotamia. Emerging around the same time in the same region were rafts, boats, and a system of

ʺcuneiformʺ inscriptions, the first form of written language that we are aware of.

These technologies enabled humans to congregate in large groups, allowing the emergence of civilization. The

first cities emerged in Mesopotamia around 6,000 years ago. Emerging about a millennium later were the ancient

Egyptian cities, including Memphis and Thebes, culminating in the reigns of the great Egyptian kings. These cities

were constructed as war machines with defensive walls protected by armies utilizing weapons drawn from the most

advanced technologies of their time, including chariots, spears, armor, and bows and arrows. Civilization in turn

allowed for human specialization of labor through a caste system and organized efforts at advancing technology. An

intellectual class including teachers, engineers, physicians, and scribes emerged. Other contributions by the early

Egyptian civilization included a paperlike material manufactured from papyrus plants, standardization of

measurement, sophisticated metalworking, water management, and a calendar.

More than 2,000 years ago, the Greeks invented elaborate machinery with multiple internal states. Archimedes,

Ptolemy, and others described levers, cams, pulleys, valves, cogs, and other intricate mechanisms that

revolutionized the measurement of time, navigation, mapmaking, and the construction of buildings and ships. The

Greeks are perhaps best known for their contributions to the arts, particularly literature, theater, and sculpture.

The Greeks were superseded by the superior military technology of the Romans. The Roman empire was so

successful that it produced the first urban civilization to experience long‐term peace and stability Roman engineers constructed tens of thousands of kilometers of roads and thousands of public constructions such as administrative

buildings, bridges, sports stadiums, baths, and sewers. The Romans made particularly notable advances in military

technology, including advanced chariots and armor, the catapult and javelin, and other effective tools of war.

The fall of the Roman empire around 500 A.D. ushered in the misnamed Dark Ages. While progress during the

next thousand years was slow by contemporary standards, the ever tightening spiral that is technological progress

continued to accelerate. Science, technology, religion, art, literature, and philosophy all continued to evolve in

Byzantine, Islamic, Chinese, and other societies. Worldwide trade enabled a cross‐fertilization in technologies. In

Europe, for example, the crossbow and gunpowder were borrowed from China. The spinning wheel was borrowed

from India. Paper and printing were developed in China about 2,000 years ago and migrated to Europe many

centuries later. Windmills emerged in several parts of the world, facilitating expertise with elaborate gearing

machines that would subsequently support the first calculating machines.

The invention in the thirteenth century of a weight‐driven clock using the cam technology perfected for

windmills and waterwheels freed society from structuring their lives around the sun. Perhaps the most significant

invention of the late Middle Ages was Johannes Gutenbergʹs invention of the movable‐type printing press, which

opened intellectual life beyond an elite controlled by church and state.

By the seventeenth century, technology had created the means for empires to span the globe. Several European

countries, including England, France, and Spain, were developing economies based on far‐flung colonies. This

colonization spawned the emergence of a merchant class, a worldwide banking system, and early forms of

intellectual property protection, including the patent.

On May 26, 1733, the English Patent Office issued a patent to John Kay for his ʺNew Engine for Opening and

Dressing Wool.ʺ

This was good news, for he had plans to manufacture his ʺflying shuttleʺ and market it to the burgeoning English

textile industry. Kayʹs invention was a quick success, but he spent all of his profits on litigation, attempting in vain to enforce his patent. He died in poverty, never realizing that his innovation in the weaving of cloth represented the launching of the Industrial Revolution.

The widespread adoption of Kayʹs innovation created pressure for a more efficient way to spin yarn, which

resulted in Sir Richard Arkwrightʹs Cotton Jenny, patented in 1770. In the 1780s, machines were invented to card and comb the wool to feed the new automated spinning machines. By the end of the eighteenth century, the English

cottage industry of textiles was replaced with increasingly efficient centralized machines. The birth of the Industrial Revolution led to the founding of the Luddite movement in the early 1800s, the first organized movement opposing

technology.

15. Primatologist Carl Van Schaik observed that the orangutans of Sumatraʹs Suaq Balimbing swamp all make and use

tools to reach insects, honey, and fruit. Though captive orangutans are easily taught to use tools, the Suaq primates are the first wild population observed using tools. The use of tools may be a result of necessity. Orangutans in other parts of the world have not been observed to use tools, basically because their food supply is more easily accessible.

Carl Zimmer, ʺTooling Through the Trees.ʺ Discover 16, no. 11 (November 1995): 46–47.

Crows fashion tools from sticks and leaves. The tools are used for different purposes, are highly predictable in

their construction, and even have hooks and other mechanisms for finding and manipulating insect prey. They often

carry these devices when flying and store them next to their nests.

Tina Adler, ʺCrows Rely on Tools to Get Their Work Done.ʺ Science News 149 no. 3 (January 20, 1996): 37.

Crocodiles canʹt grip prey, so they sometimes trap prey between rocks and/or roots. The tree root acts to anchor

the dead prey while the crocodile eats its meal. Some people have attributed the crocodiles use of stones and roots as using tools.

From the ʺAnimal Diversity Web Siteʺ at the University of Michiganʹs Museum of Zoology,

<http://www.oit.itd.umich.edu/projects/ADW/>.

16. An animal communicates for a variety of reasons: defense (to signal approaching danger to other members of its

species), food gathering (to alert other members to a food source), courtship and mating (to alert members of its

desirability and to warn potential competitors away), and maintenance of territory. The basic motivation for

communication is survival of the species. Some animals use communication not only for survival, but also to express

emotion.

There are many fascinating examples of animal communication:

• A female tree frog found in Malaysia uses its toes to tap on vegetation, alerting potential mates to her

availability. Lori Oliwenstein, Fenella Saunders, and Rachel Preiser, ʺAnimals 1995.ʺ Discover 17, no. 1

(January 1996): 54–57.

• Male meadow voles (a small rodent) groom themselves in order to produce body odors that will attract

their mates. Tina Adler, ʺVoles Appreciate the Value of Good Grooming.ʺ Science News 149, no. 16 (April

20, 1996): 247.

• Whales communicate through a series of calls and cries. Mark Higgins, ʺDeep Sea Dialogue.ʺ Nature

Canada 26, no. 3 (Summer 1997): 29–34.

• Primates, of course, vocalize to communicate a variety of messages. One group of researchers studied

capuchin monkeys, squirrel monkeys, and golden‐lion tamarins in Central and South America. Often

these animals are unable to see each other through the forest, so they developed a series of calls or trills

that would alert members to move toward food sources. Bruce Bower, ʺMonkeys Sound Off, Move Out.ʺ

Science News 149, no. 17 (April 27, 1996): 269.

17. Washoe and Koko (male and female gorillas, respectively) are credited with acquiring American Sign Language

(ASL). They are the most famous of the communicating primates. Viki, a chimpanzee, was taught to vocalize three

words (mama, papa, and cup). Lana and Kanzi (female chimpanzees) were taught to press buttons with symbols.

Steven Pinker reflects upon researchersʹ claims that apes fully comprehend sign language. In The Language

Instinct: How the Mind Creates Language (New York: Morrow, 1994), he notes that the apes learned a very crude form of ASL, not the full nuances of this language. The signs they learned were crude mimics of the ʺreal thing.ʺ In

addition, according to Pinker, the researchers often misinterpreted apesʹ hand motions as actual signs. One

researcher on Washoeʹs team who was deaf noted that other researchers would keep a log of long lists of signs,

whereas the deaf researcherʹs log was short.

18. David E. Kalish. ʺChip Makers and U.S. Unveil Project.ʺ New York Times, September 12, 1997.

19. The chart ʺThe Exponential Growth of Computing, 1900–1998ʺ is based on the following data:

Date

Device

Add Time

Calculations

Cost

Cost

CPS/$1000

(sec)

per Second (cps)

(then dollars)

1998 Dollars

1900

Analytical Engine

9.00E–00

1.11E–01

$1,000,000

$19,087,000

5.8211–06

1908

Hollerith Tabulator

5.00E+01

2.00E–02

$9,000

$154,000

1.299E–04

1911

Monroe Calculator

3.00E+01

3.33E–02

$35,000

$576,000

5.787E–05

1919

IBM Tabulator

5.00E–00

2.00E–01

$20,000

$188,000

1.064E–03

1928

National Ellis 3000

1.00E+01

1.00E–01

$15,000

$143,000

6.993E–04

1939

Zuse 2

1.00E–00

1.00E–00

$10,000

$117,000

8.547E–03

1940

Bell Calculator

3.00E–01

3.33E–00

$20,000

$233,000

1.431E–02

Model 1

1941

Zuse 3

3.00E–01

3.33E–00

$6,500

$72,000

4.630E–02

1943

Colossus

2.00E–04

5.00E+03

$100,000

$942,000

5.308E–00

1946

ENIAC

2.00E–04

5.00E+03

$750,000

$6,265,000

7.981E–01

1948

IBM SSEC

8.00E–04

1.25E+03

$500,000

$3,380,000

3.698E–01

1949

BINAC

2.86E–04

3.50E+03

$278,000

$1,903,000

1.837E–00

1949

EDSAC

1.40E–03

7.14E+02

$100,000

$684,000

1.044E–00

1951

Univac I

1.20E–04

8.33E+03

$930,000

$5,827,000

1.430E–00

1953

Univac 1103

3.00E–05

3.33E+04

$895,000

$5,461,000

6.104E–00

1953

IBM 701

6.00E–05

1.67E+04

$230,000

$1,403,000

1.188E+01

1954

EDVAC

9.00E–04

1.11E+03

$500,000

$3,028,000

3.669E–01

1955

Whirlwind

5.00E–05

2.00E+04

$200,000

$1,216,000

1.645E+01

1955

IBM 704

2.40E–05

4.17E+04

$1,994,000

$12,120,000

3.438E–00

1958

Datamatic 1000

2.50E–04

4.00E+03

$2,179,100

$12,283,000 3.257E–01

1958

Univac II

2.00E–04

5.00E+03

$970,000

$5,468,000

9.144E–01

1959

Mobidic

1.60E–05

6.25E+04

$1,340,000

$7,501,000

8.332E–00

1959

IBM 7090

4.00E–06

2.50E+05

$3,000,000

$16,794,000

1.489E+01

1960

IBM 1620

6.00E–04

1.67E+03

$200,000

$1,101,000

1.514E–00

1960

DEC PDP–1

1.00E–05

1.00E+05

$120,000

$660,000

1.515E+02

1961

DEC PDP–4

1.00E–05

1.00E+05

$65,000

$354,000

2.825E+02

1962

Univac III

9.00E–06

1.11E+05

$700,000

$3,776,000

2.943E+01

1964

CDC 6600

2.00E–07

5.00E+06

$6,000,000

$31,529,000

1.586E+02

1965

IBM 1130

8.00E–06

1.25E+05

$50,000

$259,000

4.826E+02

1965

DEC PDP–8

6.00E–06

1.67E+05

$18,000

$93,000

1.792E+03

1966

IBM 360 Model 75

8.00E–07

1.25E+06

$5,000,000

$25,139,000

4.972E+01

1968

DEC PDP–10

2.00E–06

5.00E+05

$500,000

$2,341,000

2.136E+02

1973

Intellec–8

1.56E–04

6.41E+03

$2,398

$8,798

7.286E+02

1973

Data General Nova

2.00E–05

5.00E+04

$4,000

$14,700

3.401E+03

1975

Altair 8800

1.56E–05

6.41E+04

$2,000

$6,056

1.058E+04

1976

DEC PDP–11

3.00E–06

3.33E+05

$150,000

$429,000

7.770E+02

Model 70

1977

Cray 1

1.00E–08

1.00E+08

$10,000,000

$26,881,000

3.720E+03

1977

Apple II

1.00E–05

1.00E+05

$1,300

$3,722

2.687E+04

1979

DEC VAX 11

2.00E–06

5.00E+05

$200,000

$449,000

1.114E+03

Model 780

1980

Sun–1

3.00E–06

3.33E+05

$30,000

$59,300

5.621E+03

1982

IBM PC

1.56E–06

6.41E+05

$3,000

$5,064

1.266E+05

1982

Compaq Portable

1.56E–06

6.41E+05

$3,000

$5,064

1.266E+05

1983

IBM AT–80286

1.25E–06

8.00E+05

$5,669

$9,272

8.628E+04

1984

Apple Macintosh

3.00E–06

3,33E+05

$2,500

$3,920

8.503E+04

1986

Compaq

2.50E–07

4.00E+06

$5,000

$7,432

5.382E+05

Deskpro 386

1987

Apple Mac II

1.00E–06

1.00E+06

$3,000

$4,300

2.326E+05

1993

Pentium PC

1.00E–07

1.00E+07

$2,500

$2,818

3.549E+06

1996

Pentium PC

1.00E–08

1.00E+08

$2,000

$2,080

4.808E+07

1998

Pentium II PC

5.00E–09

2.00E+08

$1,500

$1,500

1.333E+08

Cost conversions from dollars in each year to 1998 dollars are based on the ratio of the consumer price indices (CPI) for the respective years, based on CPI data as recorded by the Woodrow Federal Reserve Bank of Minneapolis. See

their web site, <http://woodrow.mpls.frb.fed.us/economy/calc/cpihome.html>.

Charles Babbage designed the Analytical Engine in the 1830s and continued to refine the concept until his death in

1871. Babbage never completed his invention. I have estimated a date of 1900 for the Analytical Engine as an

estimated date for when its mechanical technology became feasible, based on the availability of other mechanical

computing technology available in that time period.

Sources for the chart ʺThe Exponential Growth of Computing, 1900–1998ʺ include the following:

25 Years of Computer History

<http://www.compros.com/timeline.html>

BYTE Magazine ʺBirth of a Chipʺ

<http://www.byte.com/art/9612/sec6/art2.htm>

cdc.html@www.citybeach.wa.edu (Stretch)

<http://www.citybeach.wa.edu.au/lessons/history/video/sunedu/comp uter/cdc.html>

Chronology of Digital Computing Machines

<http://www.best.com/~wilson/faq/chrono.html>

Chronology of Events in the History of Microcomputers

<http://www3.islandnet.com/~kpolsson/comphist/comp1977.htm>

The Computer Museum History Center

<http://www.tcm.org/html/history/index.html>

delan at intopad.eecs.berkeley.edu

<http://infopad.eecs.berkeley.edu/CIC/summary/delan>

Electronic Computers Within the Ordnance Corps

<http://ftp.arl.mil/~mike/comphist/61ordnance/index.html>

General Processor Information

<http://infopad.eecs.berkeley.edu/CIC/summary/local/>

The History of Computing at Los Alamos

<http://bang.lanl.gov/video/sunedu/computer/comphist.html>

The Machine Room

<http://www.tardis.ed.ac.uk/~alexios/MACHINE‐ROOM/>

Mind Machine Web Museum

<http://userwww.sfsu.edu/~hl/mmm.html>

Hans Moravec at Carnegie Mellon University: Computer Data

<http://www.frc.ri.cmu.edu/~hpm/book97/ch3/processor.list>

PC Magazine Online: Fifteen Years of PC Magazine

<http://www.zdnet.com/pcmag/special/anniversary/>

PC Museum

<http://www.microtec.net/~dlessard/index.html>

PDP‐8 Emulation

<http://csbh.mhv.net/~mgraffam/emu/pdp8.html>

Silicon Graphics Webpage press release

<http://www.pathfinder.com/money/latest/press/PW/1998jun16/270.ht ml >

Stan Augarten, Bit by Bit: An Illustrated History of Computers (New York: Ticknor & Fields, 1984).

International Association of Electrical and Electronics Engineers (IEEE), ʺAnnals of the History of the Computer,ʺ

vol. 9, no. 2, pp. 150–153 (1987). IEEE, vol. 16, no. 3, p. 20 (1994).

Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press, 1988).

René Moreau, The Computer Comes of Age (Cambridge, MA: MIT Press, 1984).

20. For additional views on the future of computer capacity, see: Hans Moravec , Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press, 1988); and ʺAn Interview with David Waltz, Vice President, Computer Science Research, NEC Research Instituteʺ at Think Questʹs web page

<http://tqd.advanced.org/2705/waltz.html>. I also discuss this subject in my book The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990), 401–419. These three sources discuss the exponential growth of computing.

21. A mathematical theory concerning the difference between information and noise and the ability of a

communications channel to carry information.

22. The Santa Fe institute has played a pioneering role in developing concepts and technology related to complexity and emergent systems. One of the principal developers of paradigms associated with chaos and complexity has been

Stuart Kauffman. Kauffmanʹs At Home in the Universe: The Search for the Laws of Self‐Organization and Complexity (Oxford: Oxford University Press, 1995) looks ʺat the forces for order that lie at the edge of chaosʺ (from the card catalog description).

In his book Evolution of Complexity by Means of Natural Selection (Princeton, NJ: Princeton University Press, 1988), John Tyler Bonner asks the question: ʺHow is it that an egg turns into an elaborate adult? How is it that a bacterium, given many millions of years, could have evolved into an elephant?ʹ

John Holland is another leading thinker from the Sante Fe Institute in the emerging field of complexity. His book

Hidden Order: How Adaptation Builds Complexity (Reading, MA: Addison‐Wesley, 1996) presents a series of lectures that Holland presented at the Santa Fe Institute in 1994.

23. Also see John H. Holland, Emergence: From Chaos to Order (Reading, MA: Addison‐Wesley, 1998) and M. Mitchell Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos (New York: Simon and Schuster, 1992).

CHAPTER 2: THE INTELLIGENCE OF EVOLUTION

1. In the early 1950s, the chemical composition of DNA was already known. At that time, the important questions

were: How is the DNA molecule constructed? How does DNA accomplish its work? These questions would be

answered in 1953 by James D. Watson and Francis H. C. Crick.

Watson and Crick wrote ʺThe Molecular Structure of Nucleic Acid: A Structure for Deoxyribose Nucleic Acidʺ

published in the April 25, 1953 issue of Nature. For more information on the race by various research groups to discover the molecular structure of DNA, read Watsonʹs book, The Double Helix (New York: Atheneum Publishers, 1968).

2. Translation starts by unwinding a region of DNA to expose its code. A strand of messenger RNA (mRNA) is created

by copying the exposed DNA base‐pair codes. The appropriately named messenger RNA records a copy of a portion

of the DNA letter sequence and travels out of the nucleus into the cell body. There the mRNA encounters a ribosome

molecule, which reads the letters encoded in the mRNA molecules and then, using another set of molecules called

transfer RNA (tRNA), actually builds protein chains one amino acid at a time. These proteins are the worker

molecules that perform the cellʹs functions. For example, hemoglobin, which is responsible for carrying oxygen in

the blood from the lungs to the bodyʹs tissues, is a sequence of 500 amino acids. With each amino acid requiring

three nucleotide letters, the coding for hemoglobin requires 1,500 positions on the DNA molecule. Molecules of

hemoglobin, incidentally, are created 500 trillion times a second in the human body, so the machinery is quite

efficient.

3. The goal of the Human Genome Project is to construct detailed genetic sequence maps of the 50,000 to 100,000 genes in the human genome, and to provide information about the overall structure and sequence of the DNA of humans

and of other animals. The project began in the mid‐1980s. The web site of the Human Genome Project,

<http://www.nhgri.nih.gov/HGP/>, contains ʺinformation on the background of the project, current and future goals, and detailed explanations on the structure of DNA.

4. Thomas Rayʹs work is described in an article by Joe Flower, ʺA Life in Silicon.ʺ New Scientist 150, no. 2034 (June 15, 1996): 32–36. Dr. Ray also has a web site with updates on his software‐based evolution at

<http://www.hip.atr.co.jp/~ray/>.

5. A selection of books exploring the nature of intelligence includes: H. Gardner, Frames of Mind (New York: Basic Books, 1983); Stephen Jay Gould, The Mismeasure of Man (New York: Basic Books, 1983); R. J. Herrnstein and C.

Murray, The Bell Curve (New York: The Free Press, 1994); R. Jacoby and N. Glaubennan, eds., The Bell Curve Debate (New York: Times Books, 1995).

6. To further explore the theories of expansion and contraction of the Universe, see: Stephen W. Hawking, A Brief History of Time (New York: Bantam Books, 1988); and Eric L. Lerner, The Big Bang Never Happened (New York: Random House, 1991). For the latest updates, see the International Astronomical Union (IAU) web site at

<http://www.intastun.org/>, as well as the above noted ʺIntroduction to Big Bang Theoryʺ at

<http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.ht ml >.

7. See chapter 3, ʺOf Mind and Machines,ʺ including the box ʺThe View from Quantum Mechanics.ʺ

8. Peter Lewis, ʺCan Intelligent Life Be Found? Gorilla Will Go Looking.ʺ New York Times, April 16, 1998.

9. Voice Xpress Plus from the Dictation Division of Lernout & Hauspie Speech Products (formerly Kurzweil Applied Intelligence) allows users to give ʺnatural languageʺ commands to Microsoft Word. It also provides large‐vocabulary

continuous‐speech dictation. The program is ʺmode‐less,ʺ so users do not need to indicate when they are giving

commands. For example, if the user says: ʺI enjoyed my trip to Belgium last week. Make this paragraph four points

bigger. Change its font to Arial. I hope to go back to Belgium soon.ʺ Voice Xpress Plus automatically determines that the second and third sentences are commands and will carry them out (rather than transcribing them). It also

determines that the first and fourth sentences are not commands, and will transcribe them into the document.

CHAPTER 3: OF MIND AND MACHINES

1. To learn more about the current state of brain‐scanning research, the article ʺBrains at Work: Researchers Use New Techniques to Study How Humans Thinkʺ by Vincent Kiernan is a good place to begin. This article, in the Chronicle of Higher Education (January 23, 1998, vol. 44, no. 20, pp. A16–17), discusses uses of MRI to map brain activity during complex thinking processes.

ʺVisualizing the Mindʺ by Marcus E. Raichle in the April 1994 Scientific American provides background on various brain‐imaging technologies: MRI, positron emission tomography (PET), magnetoencephalography (MEG), and

electroencephalography (EEG).

ʺUnlocking the Secrets of the Brainʺ by Tabitha M. Powledge is a two‐part article in the July–August issue of

Bioscience 47 (pp. 330–334 and 403–409), 1997.

2. Blood‐forming cells of the bone marrow and certain layers of the skin grow and reproduce frequently, replenishing themselves in a period of months. In contrast, muscle cells do not reproduce for several years. Neurons have not

been considered to reproduce at all after oneʹs birth, but recent findings indicate the possibility of primate neuron reproduction. Dr. Elizabeth Gould of Princeton University and Dr. Bruce S. McEwen of Rockefeller University in

New York found that adult marmoset monkeys are able to manufacture brain cells in the hippocampus, a brain

region that is connected to learning and memory. Conversely, when the animals are under stress, the ability to

manufacture new brain cells in the hippocampus diminishes. This research is described in an article by Gina Kolata,

ʺStudies Find Brain Grows New Cells,ʺ The New York Times, March 17, 1998.

Other types of cells will grow and reproduce if necessary. For example, if seven‐eighths of the liver cells are

removed, the remaining cells will grow and reproduce until most of the cells are replenished. Arthur Guyton,

Physiology of the Human Body, fifth edition (Phila., PA: W B. Saunders, 1979): 42–43.

3. Oppression of human races, nationalities, and other groups has often been justified in the same way.

4. Platoʹs works are available in Greek and English in the Loeb Classical Library editions. A detailed account of Platoʹs philosophy is presented in J. N. Findlay, Plato and Platonism: An Introduction. On the dialogues as Platoʹs chosen form, see D. Hylandʹs ʺWhy Plato Wrote Dialogues.ʺ Philosophy and Rhetoric 1 (1968): 38–50.

5. A brief history of logical positivism can be found in A. J. Ayer, Logical Positivism (New York: Macmillan, 1959): 3–28.

6. David J. Chalmers distinguishes ʺbetween the easy problems and the hard problem of consciousness,ʺ and argues

that ʺthe hard problem eludes conventional methods of explanation entirelyʺ in an essay entitled ʺFacing Up to the

Problem of Consciousness.ʺ Stuart R. Hameroff, ed., Toward a Science of Consciousness: The First Tucson Discussions and Debates (Complex Adaptive Systems) (Cambridge, MA: MIT Press, 1996).

7. This objective view was systematically defined early in the twentieth century by Ludwig Wittgenstein in an analysis of language called logical positivism. This philosophical school, which would subsequently influence the emergence

of computational theory and linguistics, drew its inspiration from Wittgensteinʹs first major work, the Tractatus Logico‐Philosophicus. The book was not an immediate hit and it took the influence of his former instructor, Bertrand Russell, to secure a publisher.

In a foreshadowing of early computer‐programming languages, Wittgenstein numbered all of the statements in

his Tractatus indicating their position in the hierarchy of his thinking. He starts out with statement 1: ʺThe world is all that is the case,ʺ indicating his ambitious agenda for the book. A typical statement is number 4.0.0.3. 1: ʺAll

philosophy is a critique of language.ʺ His last statement, number 7, is ʺWhat we cannot speak about we must pass

over in silence.ʺ Those who trace their philosophical roots to the early Wittgenstein still regard this short work as the most influential work of philosophy of the past century. Ludwig Wittgenstein, Tractatus Logico‐Philosophicus, translated by D. E Pears and B. E McGuiness, Germany, 1921.

8. In the preface to Philosophical Investigations, translated by G. E. M. Anscombe, Wittgenstein ʺacknowledgesʺ that he made ʺgrave mistakesʺ in his earlier work, the Tractatus.

9. For a useful overview of Descartesʹs life and work, see The Dictionary of Scientific Biography, vol. 4, pp. 55–65. Also, Jonathan Réeʹs Descartes presents a unified view of Descartesʹs philosophy and its relation to other systems of thought.

10. Quoted from Douglas R. Hofstadter, Gödel, Escher Bach: An Eternal Golden Braid (New York: Basic Books, 1979).

11. ʺComputing Machinery and Intelligence,ʺ Mind 59 (1950); 433–460, reprinted in E. Feigenbaum and J. Feldman, eds., Computers and Thought (New York: McGraw‐Hill, 1963).

12. For a description of quantum mechanics, read George Johnson, ʺQuantum Theorists Try to Surpass Digital

Computing,ʺ New York Times, February 18, 1997.

CHAPTER 4: A NEW FORM OF INTELLIGENCE ON EARTH

1. Simple calculating devices had been perfected almost two centuries before Babbage, starting with Pascalʹs Pascaline in 1642, which could add numbers, and a multiplying machine developed by Gottfried Wilhelm Leibniz a couple of

decades later. But automating the computing of logarithms was far more ambitious than anything that had been

previously attempted.

Babbage didnʹt get very far—he exhausted his financial resources, got into a dispute with the British government

over ownership, had problems getting the unusual precision parts fabricated, and saw his chief engineer fire all of

his workmen and then quit himself. He was also beset with personal tragedies, including the death of his father, his wife, and two of his children.

The only obvious thing to do now, Babbage figured, was to abandon his ʺDifference Engineʺ and embark on

something yet more ambitious: the worldʹs first fully programmable computer. Babbageʹs new conception—the

ʺAnalytical Engineʺ—could be programmed to solve any possible logical or computational problem.

The Analytical Engine had a random‐access memory (RAM) consisting of 1,000 ʺwordsʺ of 50 decimal digits each,

equivalent to about 175,000 bits. A number could be retrieved from any location, modified, and stored in any other

location. It had a punched‐card reader and even included a printer, even though it would be another half century

before either typesetting machines or typewriters were to be invented. It had a central processing unit (CPU) that

could perform the types of logical and arithmetic operations that CPUS do today. Most important, it had a special

storage unit for the software with a machine language very similar to those of todayʹs computers. One decimal field

specified the type of operation and another specified the address in memory of the operand. Stan Augarten, Bit by Bit: An Illustrated History of Computers (New York: Ticknor and Fields, 1984): 63–64.

Babbage describes the features of his machine in ʺOn the Mathematical Powers of the Calculating Engine,ʺ

written in 1837 and reprinted as appendix B in Anthony Hymans Charles Babbage: Pioneer of the Computer (Oxford: Oxford University Press, 1982). For biographical information on Charles Babbage and Ada Lovelace, see Hymanʹs

biography and Dorothy Steinʹs book Ada: A Life and a Legacy (Cambridge, MA: MIT Press, 1985).

2. Stan Augarten, Bit by Bit, 63–64. Babbageʹs description of the Analytical Engine in ʺOn the Mathematical Powers of the Calculating Engine,ʺ written in 1837, is reprinted as appendix B in Anthony Hymanʹs Charles Babbage: Pioneer of the Computer (Oxford: Oxford University Press, 1982).

3. Joel Shurkin, in Engines of the Mind, p. 104, describes Aikenʹs machine as ʺan electro‐mechanical Analytical Engine with IBM card handling.ʺ For a concise history of the development of the Mark I, see Augartenʹs Bit by Bit, 103–107.

1. Bernard Cohen provides a new perspective on Aikenʹs relation to Babbage in his article ʺBabbage and Aiken,ʺ

Annals of the History of Computing 10 (1988): 171–193.

4. The idea of the punched card, which Babbage borrowed from the Jacquard looms (automatic weaving machines

controlled by punched metal cards), also survived and formed the basis for automating the increasingly popular

calculators of the nineteenth century. This culminated in the 1890 U.S. census, which was the first time that

electricity was used for a major data‐processing project. The punched card itself survived as a mainstay of

computing until the 1970s.

5. Turingʹs Robinson was not a programmable computer. It didnʹt have to be—it had only one job to do. The first

programmable computer was developed by the Germans. Konrad Zuse, a German civil engineer and tinkerer, was

motivated to ease what he later called those ʺawful calculations required of civil engineers.ʺ Like Babbageʹs, his first device, the Z‐1, was entirely mechanical—built from an erector set in his parentsʹ living room. The Z‐2 used

electromechanical relays and was capable of solving complex simultaneous equations. It was his third version—the

Z‐3—that is the most historic. It stands as the worldʹs first programmable computer. As one would retroactively predict from the Law of Accelerating Returns as applied to computation, Zuseʹs Z‐3 was rather slow—a

multiplication took more than three seconds.

While Zuse received some incidental support from the German government and his machines played a minor

military role, there was little, if any, awareness of computation and its military significance by the German

leadership. This explains their apparent confidence in the security of their Enigma code. Instead the German military gave immensely high priority to several other advanced technologies, such as rocketry and atomic weapons.

It would be Zuseʹs fate that no one would pay much attention to him or his inventions; even the Allies ignored

him after the end of the war. Credit for the worldʹs first programmable computer is often given to Howard Aiken,

despite the fact that his Mark I was not operational until nearly three years after the Z‐3. When Zuseʹs funding was withdrawn in the middle of the war by the Third Reich, a German officer explained to him that ʺthe German aircraft

is the best in the world. I cannot see what we could possibly calculate to improve on.ʺ

Zuseʹs claim to having built the worldʹs first operational fully programmable digital computer is supported by

the patent application he filed. See, for instance, K. Zuse, ʺVerfahren zur Selbst Atigen Durchfurung von

Rechnungen mit Hilfe von Rechenmaschinen,ʺ German Patent Application Z23624, April 11, 1936. Translated

extracts, titled ʺMethods for Automatic Execution of Calculations with the Aid of Computers,ʺ appear in Brian

Randell, ed., The Origins of Digital Computers, pp. 159–166.

6. ʺComputing Machinery and Intelligence,ʺ Mind 59 (1950): 433–460, reprinted in E. Feigenbaum and J. Feldman, eds., Computers and Thought (New York: McGraw‐Hill, 1963).

7. See A. Newell, J. C. Shaw, and H. A. Simon, ʺProgramming the Logic Theory Machine,ʺ Proceedings of the Western Joint Computer Conference, 1957, pp. 230–240:

8. Russell and Whiteheadʹs Principia Mathematica (see reference at the end of this endnote), first published in 1910–1913, was a seminal work that reformulated mathematics based on Russellʹs new conception of set theory. Russellʹs

breakthrough in set theory set the stage for Turingʹs subsequent development of computational theory based on the

Turing machine (see note below). Following is my version of ʺRussellʹs paradox,ʺ which stimulated Russellʹs

discovery:

Before ending up in ʺthe Other Place,ʺ our friend the gambler had lived a rough life. He was short of

temper and not fond of losing. In our story, he is also a bit of a logician. This time he has picked the wrong

man to dispatch. If only he had known that the fellow was the judgeʹs nephew.

Known anyway as a hanging judge, the magistrate is furious and wishes to mete out the most severe

sentence he can think of. So he tells the gambler that not only is he sentenced to die but the sentence is to

be carried out in a unique way. ʺFirst off, weʹre gonna dispense with you quickly, just like you done with

the victim. This punishment must be carried out no later than Saturday. Furthermore, I donʹt want you

preparing yourself for the judgment day. On the morning of your execution, you wonʹt know for certain

that the day is at hand. When we come for you, itʹll be a surprise.ʺ

To which the gambler replies, ʺWell, thatʹs great, judge, I am greatly relieved.ʺ

To which the judge exclaims, ʺI donʹt understand, how can you be relieved? I have condemned you to

be executed. I have ordered that the sentence be carried out soon, but youʹll be unable to prepare yourself

because on the morning that we carry it out, you wonʹt know for certain that youʹll be dying that day.ʺ

ʺWell, Your Honor,ʺ the gambler points out, ʺin order for your sentence to be carried out, I cannot be

executed on Saturday.ʺ

ʺWhy is that?ʺ asks the judge.

ʺBecause since the sentence must be carried out by Saturday, if we actually get to Saturday, I will know

for certain that I am to be executed on that day, and thus it would not be a surprise.ʺ

ʺI suppose you are right,ʺ replies the judge. ʺYou cannot be executed on Saturday But I still donʹt see

why youʹre relieved.ʺ

ʺWell, if we have definitely ruled out Saturday, then I canʹt be executed on Friday either.ʺ

ʺWhy is that?ʺ asks the judge, being a little slow.

ʺWe have agreed that I canʹt be executed on Saturday therefore Friday is the last day I can be executed.

But if Friday rolls around, I will definitely know that I am to be executed on that day and therefore it

would not be a surprise. So I canʹt be executed on Friday.ʺ

ʺI see,ʺ says the judge.

ʺThus the last day I can be executed would be Thursday But if Thursday rolls around, I would know I

had to be executed on that day, and thus it would not be a surprise. So Thursday is out. By the same

reasoning, we can eliminate Wednesday, Tuesday, Monday, and today.ʺ

The judge scratches his head as the confident gambler is led back to his prison cell.

There is an epilogue to the story. On Thursday, the gambler is taken to be executed. And he is very surprised. So

the judgeʹs orders are successfully carried out.

This is my version of what has become known as ʺRussellʹs paradoxʺ after Bertrand Russell, perhaps the last

person to secure major achievements in both mathematics and philosophy. If we analyze this story, we see that the

conditions that the judge has set up result in a conclusion that none of the days comply, because, as the prisoner so adroitly points out, each one of them in turn would not be a surprise. But the conclusion itself changes the situation, and now surprise is possible again. This brings us back to the original situation in which the prisoner could (in

theory) demonstrate that each day in turn would be impossible, and so on, ad infinitum. The judge applies

ʺAlexanderʹs solutionʺ in which King Alexander slashed the hopelessly tied Gordian knot.

A simpler example, and the one that Russell actually struggled with, is the following question about sets. A set is

a mathematical construct that, as its name implies, is a collection of things. A set may include chairs, books, authors, gamblers, numbers, other sets, themselves, whatever. Now consider set A, which is defined to contain all sets that

are not members of themselves. Does set A contain itself?

As we consider this famous problem, we realize there are only two possible answers: Yes and No. We can,

therefore, try them all (this is not the case for most problems in mathematics). So letʹs consider Yes. If the answer is Yes, then set A does contain itself. But if set A contains itself, then according to its defining condition, set A would not belong to set A, and thus it does not belong to itself. Since the answer of Yes led to a contradiction, it must be wrong.

So letʹs try No. If the answer is No, then set A does not contain itself. But again according to the defining

condition, if set A does not belong to itself, then it would belong to set A, another contradiction. As with the story about the prisoner, we have incompatible propositions that imply one another. Yes implies No, which yields Yes,

and so on.

This may not seem like a big deal, but to Russell it threatened the foundation of mathematics. Mathematics is

based on the concept of sets, and the issue of inclusion (i.e., what belongs to a set) is fundamental to the idea. The definition of set A appears to be a reasonable one. The question of whether set A belongs to itself also appears

reasonable. Yet we have difficulty coming up with a reasonable answer to this reasonable question. Mathematics

was in big trouble.

Russell pondered this dilemma for more than a decade, nearly exhausting himself and wrecking at least one

marriage. But he came up with an answer. To do so, he invented the equivalent of a theoretical computer (although

not by name). Russellʹs ʺComputerʺ is a logic machine and it implements one logical transformation at a time, each

one requiring a quantum of time—so things donʹt happen all at once. Our question about set A is examined in an

orderly fashion. Russell turns on his theoretical computer (which, lacking a real computer, ran only in his head) and the logical operations are ʺexecutedʺ in turn. So at one point, our answer is Yes, but the program keeps running, and a few quantums of time later the answer becomes No. The program runs in an infinite loop, constantly alternating

between Yes and No.

But the answer is never Yes and No at the same time!

Impressed? Well Russell was very pleased. Eliminating the possibility of the answer being Yes and No at the same time was enough to save mathematics. With the help of his friend and former tutor Alfred North Whitehead, Russell recast all of mathematics in terms of his new theory of sets and logic, which they published in their Principia

Mathematica in 1910–1913. It is worth pointing out that the concept of a computer, theoretical or otherwise, was not widely understood at the time. The nineteenth‐century efforts of Charles Babbage, which are discussed in chapter 4,

were largely unknown at the time. It is not clear if Russell was aware of Babbageʹs efforts. Russellʹs highly influential and revolutionary work invented a logical theory of computation and recast mathematics as one of its branches.

Mathematics was now part of computation.

Russell and Whitehead did not explicitly talk about computers but cast their ideas in the mathematical

terminology of set theory. It was left to Alan Turing to create the first theoretical computer in 1936, in his Turing machine (see note 16 below).

Alfred N. Whitehead and Bertrand Russell, Principia Mathematica, 3 vols., second edition (Cambridge: Cambridge University Press, 1925–1927). (The first edition was published in 1910, 1912, and 1913.)

Russellʹs paradox was first introduced in Bertrand Russell, Principles of Mathematics (Reprint, New York: W. W.

Norton & Company, 1996), 2nd ed., 79–81. Russellʹs paradox is a subtle variant of the Liar Paradox. See E. W. Beth, Foundations of Mathematics (Amsterdam: North Holland, 1959), p. 485.

9. ʺHeuristic Problem Solving: The Next Advance in Operations Research,ʺ Journal of the Operations Research Society of America 6, no. 1 (1958), reprinted in Herbert Simon, Models of Bounded Rationality, vol. 1, Economic Analysis and Public Policy (Cambridge, MA: MIT Press, 1982).

10. ʺA Mean Chess‐Playing Computer Tears at the Meaning of Thought,ʺ New York Times, February 19, 1996, contains the reactions of Gary Kasparov and a number of noted thinkers concerning the ramifications of Deep Blue beating

the world chess champion.

11. Daniel Bobrow, ʺNatural Language Input for a Computer Problem Solving System,ʺ in Marvin Minsky, Semantic

Information Processing, pp. 146–226.

12. Thomas Evans, ʺA Program for the Solution of Geometric‐Analogy Intelligence Test Questions,ʺ in Marvin Minsky,

ed., Semantic Information Processing (Cambridge, MA: MIT Press, 1968), pp. 271–353.

13. Robert Lindsay, Bruce Buchanan, Edward Feigenbaum, and Joshua Lederberg describe DENDRAL in Applications of Artificial Intelligence for Chemical Inference: The DENDRAL Project (New York: McGraw‐Hill, 1980). For a brief and clear explanation of the essential mechanisms behind DENDRAL, see Patrick Winston, Artificial Intelligence (1984), pp. 1637164, 195–197.

14. For many years SHRDLU was cited as a prominent accomplishment of artificial intelligence. Winograd describes his research in his thesis Understanding Natural Language (New York: Academic Press, 1972). A brief version appears as ʺA Procedural Model of Thought and Language,ʺ in Roger Schank and Kenneth Colby, eds., Computer Models of

Thought and Language (San Francisco: W. H. Freeman, 1973).

15. Haneef A. Fatmi and R. W. Young, ʺA Definition of Intelligence,ʺ Nature 228 (1970): 91.

16. Alan Turing showed that the essential basis of computation could be modeled with a very simple theoretical

machine. He created the first theoretical computer in 1936 (first introduced in Alan M. Turing, ʺOn Computable

Numbers with an Application to the Entschemungs Problem,ʺ Proc. London Math. Soc. 42 [1936]: 230–265) in an eponymous conception called the Turing machine. As with a number of Turingʹs breakthroughs, he would have both

the first and last word. The Turing machine represented the founding of modern computational theory. It has also

persisted as out primary theoretical model of a computer because of its combination of simplicity and power.

The Turing machine is one example of the simplicity of the foundations of intelligence. A Turing machine

consists of two primary (theoretical) units: a tape drive and a computation unit. The tape drive has a tape of infinite length on which it can write, and (subsequently) read, a series of two symbols: zero and one. The computation unit

contains a program consisting of a sequence of commands, drawing from only seven possible operations:

• Read the tape

• Move the tape left one symbol

• Move the tape right one symbol

• Write 0 on the tape

• Write 1 on the tape

• Jump to another command

• Halt

Turing was able to show that this extremely simple machine can compute anything that any machine can

compute, no matter how complex. If a problem cannot be solved by a Turing machine, then it cannot be solved by

any machine. Occasionally there are challenges to this position, but in large measure it has stood the test of time.

In the same paper, Turing reports another unexpected discovery, that of unsolvable problems. These are

problems that are well defined with unique answers that can be shown to exist, but that we can also prove can never

be computed by a Turing machine—that is to say by any machine, yet another reversal of what had been a

nineteenth‐century confidence that problems that could be defined would ultimately be solved. Turing showed that

there are as many unsolvable problems as solvable ones.

Turing and Alonzo Church, his former professor, went on to assert what has become known as the Church‐

Turing thesis: If a problem that can be presented to a Turing machine is not solvable by one, then it is also not

solvable by human thought. ʺStrongʺ interpretations of the Church‐Turing thesis propose an essential equivalence

between what a human can think or know and what is computable by a machine. The Church‐Turing thesis can be

viewed as a restatement in mathematical terms of one of Wittgensteinʹs primary theses in his Tractatus. The basic

idea is that the human brain is subject to natural law, and thus its information‐processing ability cannot exceed that of a machine. We are thus left with the perplexing situation of being able to define a problem, to prove that a unique answer exists, and yet know that the answer can never be known.

Perhaps the most interesting unsolvable problem is called the Busy Beaver, which may be stated as follows: Each

Turing machine has a certain number of commands in its program. Given a positive integer n, we construct all of the

Turing machines that have n states (i.e., n commands). Next we eliminate those n‐state Turing machines that get into an infinite loop (i.e., never halt). Finally, we select the machine (one that halts) that writes the largest number of 1s on its tape. The number of Is that this Turing machine writes is called busy beaver of n.

Tibor Rado, a mathematician and admirer of Turing, showed that there is no algorithm. (that is, no Turing

machine) that can compute the busy beaver function for all nʹs. The crux of the problem is sorting out those n‐state Turing machines that get into infinite loops. If we program a Turing machine to generate and simulate every

possible n‐state Turing machine, this simulator itself goes into an infinite loop when it attempts to simulate one of the n‐state Turing machines that gets into an infinite loop. Busy beaver can be computed for some ns, and

interestingly it is also an unsolvable problem to separate those ns for which we can determine busy beaver of n from those for which we cannot.

Busy beaver is an ʺintelligent function.ʺ More precisely stated, it is a function that requires increasing intelligence to compute for increasing arguments. As we increase n, the complexity of the processes needed to compute busy

beaver of n increases.

With n = 6, we are dealing with addition and busy beaver of 6 equals 35. In other words, addition is the most

complex operation that a Turing machine with only 6 steps in its program is capable of performing. At 7, busy

beaver learns to multiply and busy beaver of 7 equals 22,961. At 8, busy beaver can exponentiate, and the number of

1s that our eighth busy beaver writes on its tape is approximately 1043. Note that this is even faster growth than

Mooreʹs Law. By the time we get to 10 we need an exotic notation in which we have a stack of exponents (10 to the

10 to the 10, etc.), the height of which is determined by another stack of exponents, the height of which is determined by another stack of exponents, and so on. For the twelfth busy beaver we need an even more exotic notation. Human

intelligence (in terms of the complexity of mathematical operations that we can understand) is surpassed well before the busy beaver gets to 100. The computers of the twenty‐first century will do a bit better.

The busy beaver problem is one example of a large class of noncomputable functions, as one can see from Tibor

Rado, ʺOn Noncomputable Functions,ʺ Bell System Technical Journal 41, no. 3 (1962): 877–884.

17. Raymond Kurzweil, The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990), pp. 132–133.

18. H. J. Berliner, ʺBackgammon Computer Program Beats World Champion,ʺ Artificial Intelligence 14, no. 1 (1980). Also see Hans Berliner, ʺComputer Backgammon,ʺ Scientific American, June 1980.

19. To download Ray Kurzweilʹs Cybernetic Poet (RKCP), go to: <http://www.kurzweiltech.com>. RKCP is further

discussed in the section The Creative Machine in chapter 8, ʺ1999.ʺ

20. See the discussion on these music composition programs in the section The Creative Machine in chapter 8, ʺ1999.ʺ

21. See W. S. Sarle, ed., ʺNeural Network Frequently Asked Questions,ʺ <ftp://ftp.sas.com/pub/neural/FAQ.html>. This web site has, numerous resources on past and current research on neural nets. G. E. Hintonʹs ʺHow Neural Networks

Learn from Experience,ʺ in the September 1992 issue of Scientific American (144–151), also provides a good

introduction to neural networks.

22. Researchers at the Productivity from Information Technology (PROFIT) Initiative at MIT have studied the

effectiveness of neural networks in understanding handwriting.

The PROFIT Initiative is based at MITʹs Sloan School of Management. The mission of the initiative is to study

how the private and public sectors use information technology. Abstracts of working papers on this and other

research on neural networks and data mining can be found at <http://scanner‐group.mit.edu/papers.html>.

23. ʺMiros, Inc. is located in Wellesley, Massachusetts, and specializes in providing face recognition software. Miros I products include Trueface PC, the first face recognition solution for computer, network and data security; and

Trueface Gatewatch, a complete hardware/software security solution that allows or denies access to buildings and

rooms by automatically recognizing a personʹs face taken by a video camera.ʺ From Miros Company Information at

<http://www.miros.com/About_Miros.htm>.

24. For more information on Brainmakerʹs aptitude to diagnose illnesses, and to predict the Standard and Poor 500 for LBS Management, see California Scientificʹs home page at <http://www.calsci.com/>.

25. The reset time stated here is an estimated average for neural connection calculations. For example, Vadim

Gerasimov estimates the peak firing frequency of neurons (which significantly exceeds the average rate) to be 250–

2,000 Hz (0.5–4 ms intervals) in ʺInformation Processing in the Human Bodyʺ at

<http://vadim.www.media.mit.edu/MAS862/Project.html>. The firing time is affected by a number of variables,

including, for example, the level and duration of a sound, as discussed in Jos. J. Eggermont, ʺFiring Rate and Firing Synchrony Distinguish Dynamic from Steady State Sound,ʺ Neuroreport 8, issue 12, 2709–2713.

26. Hugo de Garis maintains a web site on his research for ATRS Brain Builder Group at

<http://www.hip.atr.co.jp/~degaris/>.

27. For an intriguing account of this research, read Carver Mead, Analog VSLI and Neural Systems (Reading, MA: Addison‐Wesley, 1989), 257–278. Synaptics is briefly highlighted in Carol Levin, ʺHereʹs Looking at You,ʺ PC

Magazine (December 20, 1994): 31. Carver Meadʹs web site also provides detailed information on this research at the ʺPhysics of Computation–Carver Meadʹs Groupʺ at <http://www.pcmp.caltech.edu/>.

28. The SETI (Search for Extraterrestrial Intelligence) Institute conducts research on other signs of life in the Universe, its primary goal being the search for extraterrestrial intelligence. The institute is a nonprofit research organization, funded by government agencies, private foundations, and individuals, which in turn provides funding for several

dozen projects. For more information, see the SETI Institute web site, <http://www.seti.org>.

29. The author is dictating portions of this book to his computer through the continuous speech recognition program called Voice Xpress Plus from the dictation division of Lernout & Hauspie (formerly Kurzweil Applied Intelligence).

See note 9 on Voice Xpress Plus in chapter 2 for more information.

30. To find out more on State Street Global Advisorʹs purchase in a majority stake in Advanced Investment Technology, read Frank Byrt, ʺState Street Global Invests in Artificial Intelligence.ʺ Dow Jones Newswires, October 29, 1997. The genetic algorithm system used by the AIT Vision mutual fund is described in S. Mahfoud and G. Mani, ʺFinancial

Forecasting Using Genetic Algorithms.ʺ Applied Artificial Intelligence 10 (1996): 543–565. The AIT Vision mutual fund opened at the beginning of 1996 and has publicly available performance numbers. In its first full calendar year

(1996), the mutual fund increased 27.2 percent in net asset value, compared to 21.2 percent for its benchmark, the

Russell 3000 index.

It should be noted that outperforming its benchmark index does not in itself prove a superior level of decision

making. The algorithm may have been making higher‐risk investments (on average) than the average in the index.

31. There are many online resources on evolutionary computation and evolutionary and genetic algorithms. One of the best is ʺThe Hitchhikerʹs Guide to Evolutionary Computation: A List of Frequently Asked Questions (FAQ),ʺ edited

by Jorg Heitkotter and David Beasley at <http://www.cs.purdue.edu/coast/archive/clife/FAQ/www/>. This guide

includes everything from a glossary to links to various research groups.

Another helpful online resource is the web site for the Santa Fe Institute. The instituteʹs web site can be accessed at <http://www.santafe.edu>.

For an offline introduction to genetic algorithms, read John Hollandʹs article ʺGenetic Algorithms,ʺ Scientific

American 267, no. 1 (1992): 66–72. As mentioned in note 22 in chapter 1, Holland and his colleagues at the University of Michigan developed genetic algorithms in the 1970s.

For more information on the use of genetic algorithm technology to manage the development and manufacturing

of Volvo trucks, read Srikumar S. Rao, ʺEvolution at Warp Speed,ʺ Forbes 161, no. 1 (January 12, 1998): 82–83.

See also note 22 on complexity in chapter 1.

32. See ʺInformation Processing in the Human Body,ʺ by Vadim Gerasimov, at

<http://vadim.www.media.mit.edu/MAS862/Project.html>.

33. See ʺInformation Processing in the Human Body,ʺ by Vadim Gerasimov, at

<http://vadim.www.media.mit.edu/MAS862/Project.html>.

34. I founded Kurzweil Applied Intelligence (Kurzweil AI) in 1982. The company is now a subsidiary of Lernout & Hauspie Speech Products (L&H), an international leader in the development of speech and language technologies

and related applications and products. For more information about these speech recognition products, see

<http://www.lhs.com/dictation/>.

CHAPTER 5: CONTEXT AND KNOWLEDGE

1. Victor L. Yu, Lawrence M. Fagan, S. M. Wraith, William Clancey, A. Carlisle Scott, John Hannigan, Robert Blum,

Bruce Buchanan, and Stanley Cohen, ʺAntimicrobial Selection by Computer: A Blinded Evaluation by Infectious

Disease Experts,ʺ Journal of the American Medical Association 242, no. 12 (1979): 1279–1282.

2. For an introduction to the development of expert systems and their use in various companies, read: Edward

Feigenbaum, Pamela McCorduck, and Penny Nii, The Rise of the Expert Company (Reading, MA: Addison‐Wesley,

1983).

3. William Martin, Kenneth Church, and Ramesh Patil, ʺPreliminary Analysis of a Breadth‐First Parsing Algorithm:

Theoretical and Experiential Results.ʺ MIT Laboratory for Computer Science, Cambridge MA, 1981. In this

document, Church cites the synthetic sentence:

ʺIt was the number of products of products of products of products of products of products of products of

products?ʺ as having 1,430 syntactically correct interpretations.

He cites the following sentence:

ʺWhat number of products of products of products of products of products of products of products of

products was the number products of products of products of products of products of products of

products of products?ʺ as having 1,430 U 1,430 = 2,044,900 interpretations.

4. These and other theoretical aspects of computational linguistics are covered in Mary D. Harris, Introduction to Natural Language Processing (Reston, VA: Reston Publishing Co., 1985).

CHAPTER 6: BUILDING NEW BRAINS . . .

1. Hans Moravec is likely to make this argument in his 1998 book Robot: Mere Machine to Transcendent Mind (Oxford University Press; not yet available as of this writing).

2. One hundred fifty million calculations per second for a 1998 personal computer doubling twenty‐seven times by the year 2025 (this assumes doubling both the number of components, and the speed of each component every two

years) equals about 20 million billion calculations per second. In 1998, it takes multiple calculations on a

conventional personal computer to simulate a neural‐connection calculation. However, computers by 2020 will be

optimized for the neural‐connection calculation (and other highly repetitive calculations needed to simulate neuron

functions). Note that neural‐connection calculations are simpler and more regular than the general‐purpose

calculations of a personal computer.

3. Five billion bits per $1,000 in 1998 will be doubled seventeen times by 2023, which is about a million billion bits for $1,000 in 2023.

4. NECʹs goals to build a supercomputer with a maximum performance of more than 32 teraflops is chronicled in ʺNEC

Begins Designing Worldʹs Fastest Computer,ʺ Newsbytes News Network, January 21, 1998, located online at

<http://www.nb‐pacifica.com/headline/necbeginsdesigningwo_1208.shtml>.

In 1998, IBM was one of four companies chosen to participate in Pathforward, an initiative from the Department

of Energy to develop supercomputers for the twenty‐first century. Other companies involved in the project are

Digital Equipment Corporation; Sun Microsystems, Inc.; and Silicon Graphics/Cray Computer Systems (SGI/Cray).

Pathforward is part of the Accelerated Strategic Computing Initiative (ASCI). For more information on this initiative, see <http://www.llnl.gov/asci>.

5. By harnessing the accelerating improvement in both density of components and speed of components, computer

power will double every twelve months, or a factor of one thousand every ten years. Based on the projection of

$1,000 of computing being equal to the estimated processing power of the human brain (20 million billion

calculations per second) by the year 2020, we get a projection of $1,000 of computing being equal to a million human brains in 2040, a billion human brains in 2050, and a trillion human brains in 2060.

6. By 2099, $1,000 of computing will equal 1024 times the processing power of the human brain. Based on an estimate of 10 billion persons, that is 1014 times the processing power of all human brains. Thus one penny of computing will

equal 109 (one billion) times the processing power of all human brains.

7. In the Punctuated Equilibrium theories, evolution is seen to progress in sudden leaps followed by periods of relative stability. Interestingly, we often see similar behavior in the performance of evolutionary algorithms (see chapter 4).

8. Dean Takahashi, ʺSmall Firms Jockeying for Position in 3D Chip Market,ʺ Knight‐Ridder/Tribune News Service, September 21, 1994, p. 0921K4365.

9. The entire February 1998 issue of Computer (vol. 31, no. 2) explores the status of optical computing and optical storage methods.

Sunny Bains writes of companies using optical computing for fingerprint recognition and other applications in

ʺSmall, Hybrid Digital/Electronic Optical Correlators Ready to Power Commercial Products: Optical Computing

Comes into Focus.ʺ EE Times, January 26, 1998, issue 990. This article is online at

<http://www.techweb.com/se/directlink.cgi?EET19980126S0019>.

10. For a nontechnical introduction to DNA computing, read Vincent Kiernan, ʺDNA‐Based Computers Could Race Past

Supercomputers, Researchers Predict,ʺ in the Chronicle of Higher Education (November 28, 1997). Kiernan discusses the research of Dr. Robert Corn from the University of Wisconsin as well as the research of Dr. Leonard Adleman.

The article can be accessed online at <http://chronicle.com/data/articles.dir/art‐44.dir/issue‐14.dir/1 4ao2301:htm>.

Research at the University of Wisconsin can be accessed online at

<http://corninfo.chem.wisc.edu/writings/DNACOMPUTING.html>.

Leonard Adlenians ʺMolecular Computation of Solutions to Combinatorial Problemsʺ from the November 11,

1994, issue of Science (vol. 266, p 1021) provides a technical overview of his design of DNA programming for

computers.

11. Lambertus Hesselinkʹs research is reported by Phillip F. Schewe and Ben Stein in Physics News Update (no. 219; March 28, 1995). The description is available online at <http://www.aip.org/enews/physnews/1995/split/pnu2l9-2.htm>.

12. For information on nanotubes and buckyballs, read Janet Rae‐Dupreeʹs article ʺNanotechnology Could Be

Foundation for Next Mechanical Revolution,ʺ Knight‐Ridder/Tribune News Service, December 17, 1997, p. 1217K1133.

13. 13 Dr. Sumio Iijimaʹs research on nanotubes is summarized in the following article at the NEC site,

<http://www.labs.nec.co.jp/rdletter/letter01/index1.html>.

14. The research of Isaac Chuang and Neil Gershenfeld is reported in ʺCue the Qubits: Quantum Computing,ʺ The

Economist 342, no. 8005 (February 22, 1997): 91–92; and in an article by Dan Vergano, ʺBrewing a Quantum Computer in a Coffee Cup,ʺ Science News 151, no. 3 (January 18, 1997): 37. More technical details and a list of Chuang and Gershenfeldʹs publications can be found at the Physics and Media Group/MIT Media Lab

<http://physics.www.media.mit.edu/publications/> and at the Los Alamos National Laboratory

<http://qso.lanl.gov/qc/>.

Other groups working on quantum computation include the Information Mechanics group at MITʹs Lab for

Computer Science <http://www‐im.lcs.mit.edu/> and the Quantum Computation Group at IBM

<http://www.research.ibm.com/quantuminfo/>.

15. ʺStudent Cracks Encryption Code,ʺ USA Today Tech Report, September 2, 1997.

16. Mark Buchanan, ʺLightʹs Spooky Connections Set Distance Record,ʺ New Scientist, June 28, 1997.

17. Roger Penrose, The Emperorʹs New Mind (New York: Penguin USA, 1990).

18. To understand the concept of tunneling, it is important to understand how transistors on an integrated circuit chip work. An integrated chip is engraved with circuits comprised of thousands or millions of transistors, which

electronic devices use to control the flow of electricity. Transistors are made up of a small block of a semiconductor, a material that acts as both an insulator and a conductor of electricity. The first transistors were comprised of

germanium and were later replaced with silicon.

Transistors work by holding a pattern of electric charge, allowing that pattern of charge to change millions of

times every second. Tunneling refers to the ability of electrons (small particles that circle around the nucleus of an atom) to move or ʺtunnelʺ through the silicon. Electrons are said to tunnel through the barrier as a result of the

quantum uncertainty as to which side of the barrier they are actually on.

19. Knowledge chunks would be greater than the number of distinct words because words are used in more than one

way and with more than one meaning. Each different word meaning or usage is often referred to as a word ʺsense.ʺ

it is likely that Shakespeare used more than 1 00,000 word senses.

20. Quoted from Douglas R. Hofstadter, Gödel, Escher Bach: An Eternal Golden Braid (New York: Basic Books, 1979).

21. Michael Winerip, ʺSchizophreniaʹs Most Zealous Foe,ʺ New York Sunday Times, February 22, 1998.

22. The goal of the Visible Human Project is to create highly detailed three‐dimensional views of the male and female human body. The project is collecting transverse CT, MRI, and cryosection images. The web site is located at

<http://www.nlm.nih.gov/research/visible/visible human.html>.

23. Researchers Mark Habener, Doron Shoham, Amiram Grinvald, and Tobias Bonhoeffer published their experiments

on optical imaging in ʺSpatial Relationships among Three Columnar Systems in Cat Area 17,ʺ Journal of Neuroscience 17 (1997): 9270–9284.

More information on this and other brain‐imaging research is located at the Weizmann Instituteʹs web site

<http://www.weizmann.ac.il> and at Amiram Grinvaldʹs web site

<http://www.weizmann.ac.il/brain/grinvald/grinvald.htm>.

24. The work of Dr. Benebid and other researchers is summarized in an online article, ʺNeural Prosthetics Come of Age as Research Continues,ʺ by Robert Finn, The Scientist II, no. 19 (September 29, 1997): 13, 16. This article may be found at <http://www.the‐scientist.library.upenn.edu/yr1997/sept/research_ 970929.html>.

25. From an April 1998 phone interview by the author with Dr. Trosch.

26. Dr. Rizzoʹs research is also reviewed in Finnʹs article, ʺNeural Prosthetics Come of Age as Research Continues.ʺ

27. To read more about the ʺneuron transistor,ʺ visit the web site of the Membrane and Neurophysics Department at the Max Planck Institute for Biochemistry <http://mnphys.biochem.mpg.de/>.

28. Robert Finyi, ʺNeural Prosthetics Come of Age as Research Continues.ʺ

29. Carver Meadʹs research is described at <http://www.pcmp.caltech.edu/>.

30. W. B. Yeats, ʺSailing to Byzantium,ʺ from Selected Poems and Two Plays of William Butler Yeats, edited by M. L.

Rosenthal (New York: Macmillan, 1966).

CHAPTER 7: . . . AND BODIES

1. Herbert Dreyfus is well known for his critique of artificial intelligence in his book What Computers Canʹt Do: The Limits of Artificial Intelligence (New York: Harper and Row, 1979). Other theorists who may be considered to support the mind‐beyond‐machine perspective include J. R. Lucas and John Searle. See J. R. Lucasʹs ʺMinds, Machines and

Gödel,ʺ Philosophy 36 (1961): 120–124; and John Searleʹs ʺMind, Brains, and Programs,ʺ The Behavioral and Brain Sciences 3 (1980): 417–424. Also, see Searleʹs more recent book The Rediscovery of the Mind (Cambridge, MA: MIT

Press, 1992).

2. ʺResearchers led by Dr. Clifford Steer at the University of Minnesota Medical School report in the current Nature Medicine that they have eliminated the need for viruses by harnessing the bodyʹs own genetic repair processes. In a landmark proof‐of‐concept experiment, the Minnesota team permanently altered a blood‐clotting gene in 40 percent

of the liver cells in a group of rats. The researchers started by splicing their DNA patch into a slip of RNA. Then they encased the hybrid molecule in a protective coating, laced it with sugars that seek out liver cells, and injected it into lab rats. True to plan, the hybrid molecules zeroed in on the targeted gene and lined up alongside it. An enzyme in

the ratsʹ own liver cells did the rest: Whenever it spotted a mismatched DNA, it simply removed the offending DNA

and stitched in a replacement. Now the trick is to show that it will work with other tissues—and other species.ʺ

From ʺDNA Therapy: The New, Virus‐Free Way to Make Genetic Repairs.ʺ Time, March 16, 1998.

3. Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press, 1988), p. 108.

4. Ralph Merkleʹs comments on nanotechnology can be found in an overview at his web site at the Xerox Palo Alto

Research Center <http://sandbox.xerox.com/nano>. His site contains links to important publications on

nanotechnology, such as Richard Feynmanʹs 1959 talk and Eric Drexlerʹs dissertation, as well as links to various

research centers that focus on nanotechnology.

5. Richard Feynman presented these ideas on December 29, 1959, at the annual meeting of the American Physical

Society at the California Institute of Technology (Cal Tech). His talk was first published in the February 1960 issue of Cal Techʹs Engineering and Science. This article is available online at <http://nano.xerox.com/nanotech/feynman.html>.

6. Eric Drexler, Engines of Creation (New York: Anchor Press/Doubleday, 1986). The book is also accessible online from the Xerox nanotechnology site <http://sandbox.xerox.com/nano> and also from Drexlerʹs web site at the Foresight institute <http://www.foresight.org/EOC/index.html>.

7. Eric Drexler, Nanosystems: Molecular Machinery, Manufacturing, and Computation (New York: John Wiley and Sons, 1992).

8. According to Nanothincʹs web site <http://www.nanothinc.com/>, ʺNanotechnology, broadly defined to include a number of nanoscale‐related activities and disciplines, is a global industry in which more than 300 companies

generate over $5 billion in annual revenues today—and $24 billion in 4 years.ʺ Nanothinc includes a list of

companies and revenues upon which the figure is based. Some of the nanoapplications generating revenues are

micromachines, microelectromechanical systems, autofabrication, nanolithography, nanotechnology tools, scanning

probe microscopy, software, nanoscale materials, and nanophase materials.

9. Richard Smalleyʹs publications and work on nanotechnology can be found at the web site for the Center for

Nanoscale Science and Technology at Rice University <http://cnst.rice.edu/>.

10. For information on the use of nanotechnology in creating IBMʹs corporate logo, read Faye Flam, ʺTiny Instrument Has Big Implications.ʺ Knight‐Ridder/Tribute News Service, August 11, 1997, p. 811K7204.

11. Dr. Jeffrey Sampsell at Texas Instruments has written a white paper summarizing research on micromirrors,

available at <http://www.ti.coni/dlp/docs/it/resources/white/overview/over.sht ml>.

12. A description of the flying machines can be found at the web site of the MEMS (Microelectromechanical Systems)

and Fluid Dynamics Research Group at the University of California at Los Angeles (UCLA)

<http://ho.seas.ucla.edu/new/main.htm>.

13. Xeroxʹs nanotechnology research is described in Brian Santo, ʺSmart Matter Program Embeds Intelligence by

Combining Sensing, Actuation, Computation‐Xerox Builds on Sensor Theory for Smart Materials.ʺ EETimes (March 23, 1998):129. More information on this research can be found at the web site for the Smart Matter Research Group at Xeroxʹs Palo Alto Research Center at <http://www.parc.xerox.com/spl/projects/smart‐matter/>.

14. For information on the use of nanotechnology in creating the nanoguitar, read Faye Flar, ʺTiny Instrument Has Big Implications.ʺ Knight‐Ridder/Tribune News Service.

15. Learn more about the Chelyabinsk region by visiting the web site dedicated to helping the people living in that area at <http://www.logtv.com/chelya/chel.html>.

16. For more about the story behind Space War, see ʺA History of Computer Games,ʺ Computer Gaming World

(November 1991): 16–26; and Eric S. Raymond, ed., New Hackerʹs Dictionary (Cambridge, MA: MIT Press, 1992).

Space War was developed by Steve Russell in 1961 and implemented by him on the PDP‐1 at MIT a year later.

17. Medical Learning Company is a joint venture between the American Board of Family Practice (an organization that certifies the sixty thousand family practice physicians in the United States) and Kurzweil Technologies. The goal of the company is to develop educational software for continuing medical education of physicians as well as other

markets. A key aspect of the technology will include an interactive simulated patient that can be examined,

interviewed, and treated.

18. Hallʹs Utility Fog concept is described in J. Storrs Hall, ʺUtility Fog Part I,ʺ Extropy, issue no. 13 (vol. 6, no. 2), third quarter 1994; and J. Storrs Hall, ʺUtility Fog Part 2,ʺ Extropy, issue no. 14 (vol. 71 no. 1), first quarter 1995. Also see Jim Wilson, ʺShrinking Micromachines: A New Generation of Tools Will Make Molecule‐Size Machines a Realityʺ

Popular Mechanics 174, no. 11 (November 1997): 55–58.

19. Mark Yim, ʺLocomotion with a Unit‐Modular Reconfigurable Robot,ʺ Stanford University Technical Report STAN‐

CS‐TR‐95‐1536.

20. Joseph Michael, UK Patent #94004227.2.

21. For examples of early ʺprurientʺ text publications, see A History of Erotic Literature by Patrick J. Kearney (Hong Kong, 1982); and History Laid Bare by Richard Zachs (New York: Harpercollins, 1994).

22. Upside Magazine, April 1998.

23. For example, the ʺTFUIʺ (Touch‐and‐Feel User Interface) from Pixis, as used in their Diva and Space Sirens series of CD‐ROMS.

24. From ʺWho Needs Jokes? Brain Has a Ticklish Spot,ʺ Malcolme W. Browne, New York Times, March 10, 1998. Also

see 1. Fried (with C. L. Wilson, K. A. MacDonald, and E. J. Behnke), ʺElectric Current Stimulates Laughter,ʺ Scientific Correspondence 391: 650, 1998.

25. K. Blum et al., ʺReward Deficiency Syndrome,ʺ American Scientist, March–April, 1996.

26. Brain Generated Music is a patented technology of Neurosonics, a small company in Baltimore, Maryland. The

founder, CEO, and principal developer of the technology is Dr. Geoff Wright, who is head of computer music at

Peabody Conservatory.

27. For details about Dr. Bensonʹs work, see his book The Relaxation Response (New York: Avon,1990).

28. ʺʺGod Spotʹ Is Found in Brain,ʺ Sunday Times (Britain), November 2, 1997.

CHAPTER 8: 1999

1. The U.S. Federal Government Gateway for Year 2000 Information Directories, at

<http://www.itpolicy.gsa.gov/mks/yr2000/y2khome.htm>, contain a number of links to web pages devoted to Y2K

issues. There are also many discussion groups on the Web about the Y2K topic. Simply do a search for ʺY2K

discussionʺ using a search engine such as Yahoo (www.yahoo.com) to find a number of web pages devoted to this

subject.

2. David Cope talks about his EMI program in his book Experiments in Musical Intelligence (Madison, WI: A‐R Editions, 1996). EMI is also discussed in Margaret Boden ʺArtificial Genius,ʺ Discover magazine, October 1996.

3. For more about the Improvisor program, see Margaret Boden, ʺArtificial Genius,ʺ Discover magazine, October 1996.

The article addresses the question of who is the actual creator of original art produced by computer programs—the

developer of the program or the program itself?

4. Laurie Flynn, ʺProgram Proves Bad Puns Not Limited to Humans,ʺ New York Times, January 3, 1998.

5. ʺParamind copies any text you type or paste into its screen and systematically merges your text with new words. The words are all related, such as adjectives related to sight, or adverbs related to walking. In the text that you type or paste in, a word or two is selected where these new words will fit in, in the way that you want. The result is a new listing of your idea changed in several fascinating ways.ʺ From the Paramind Brainstorming Software web page at

<http://wwwparamind.net/>. For more information about other computer writing programs, see Marius Watzʹs web

page called Computer Generated Writing at <http://www.notam.uio.no/~mariusw/c‐g.writing/>.

6. More information on BRUTUS. 1 Story Generator and its inventors can be found at

<http://www.rpi.edu/dept/ppcs/BRUTUS/brutus.html>.

7. Ray Kurzweilʹs Cybernetic Poet (RKCP) is a software program designed by Ray Kurzweil and developed by

Kurzweil Technologies. You can download a copy of the program at <http://www.kurzweiltech.com>.

8. For examples of Mutatorʹs artistic creations, visit the web site of Computer Artworks at

<http://www.artworks.co.uk/welcome.htm>.

Karl Sims has written several articles about his work, including ʺArtificial Evolution for Computer Graphics,ʺ

Computer Graphics 25, no. 4 (July 1991): 319–328.

9. Drawings and paintings by Aaron, Harold Cohenʹs cybernetic artist, have hung at Londonʹs Tate Gallery,

Amsterdamʹs Stedelijk Museum, the Brooklyn Museum, the San Francisco Museum of Modern Art, the Washington

Capitol Childrenʹs Museum, and others.

10. Harold Cohen, ʺHow to Draw Three People in a Botanical Garden,ʺ AAAI‐88, Proceedings of the Seventh National Conference on Artificial Intelligence, 1988, pp. 846–855. Some of the implications of Aaron are discussed in Pamela McCorduck, ʺArtificial Intelligence: An Apercu,ʺ Daedalus, Winter 1988, pp. 65–83.

11. A list of sites on Cohenʹs Aaron can be found at <http://www.umcs.maine.edu/~larry/latour/aaron.html>. Also see Harold Cohenʹs article in ʺConstructions of the Mindʺ at <http://shr.stanford.edu/shreview/4‐2/text/cohen.html>.

12. Raymond Kurzweil, The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990). Also see the publications section at the web site for Kurzweil Technologies at <http://www.kurzweiltech.com> and the publications section at the web site for Kurzweil Educational Systems at <http://www.kurzweiledu.com>.

13. Venture capital refers to funds available for investment by organizations that have raised pools of capital specifically to invest in companies, primarily new ventures. Angel capital refers to funds available for investment by networks

of wealthy investors who invest in start‐up companies. In the United States, both venture and angel capital have

emphasized high‐technology investments.

14. For a comprehensive list of available speech‐ and face‐recognition products and research projects, go to The Face Recognition Home Page at <http://cherry.kist.re.kr/center/html/sites.html>.

15. For an excellent overview of this subject, see ʺThe Intelligent Vehicle Initiative: Advancing ʺHuman‐Centeredʹ Smart Vehicles,ʺ by Cheryl Little of the Volpe National Transportation Systems Center. This article is available through the Turner‐Fairbank Highway Research Center web page at <http://www.tfhrc.gov/pubrds/pr97‐10/p18.htm>. For details about the tests on Interstate 15 in California, go to National AHS Consortium Home Page at <http://monolith-mis.com/ahs/default.htm>.

16. For example, Voice Xpress Plus, from the dictation division of Lernout & Hauspie (formerly Kurzweil Applied Intelligence), combines large‐vocabulary, continuous speech recognition for dictation, with natural‐language

understanding for commands. Continuous speech recognition without natural‐language understanding (as of 1998)

is also available from Dragon Systemʹs Naturally Speaking and IBMʹs Viavoice.

17. Examples of translation products include Langenscheidtʹs T1 Professional from Gesellschaft far Multilinguale

Systeme, a division of Lernout & Hauspie Speech Products; Globalink Power Translator; and SYSTRAN Classic for

Windows.

18. Duncan Bythell, The Handloom Weavers: A Study in the English Cotton Industry During the Industrial Revolution, p. 70.

There are also a number of web sites exploring both the original Luddite history and the contemporary neo‐Luddite

movement. For one example, see the web page Luddites On‐Line at <http://www.luddites.com/index2.html>.

19. Ben J. Wattenberg, ed., The Statistical History of the United States from Colonial Times to the Present; U.S. Department of Commerce, Bureau of the Census, Statistical Abstract of the United States, 1997.

20. Ben J. Wattenberg, ed., The Statistical History of the United States from Colonial Times to the Present.

21. U.S. Department of Commerce, Bureau of the Census, Statistical Abstract of the United States, 1997.

22. Ted Kaczynskiʹs Unabomber Manifesto was published in both the New York Times and the Washington Post in September 1995. The full text of the document is available on numerous web pages, including:

<http://www.soci.niu.edu/~critcrim/uni/uni.txt>.

CHAPTER 9: 2009

1. A consortium of eighteen manufacturers of cellular telephones and other portable electronic devices is developing a technology called Bluetooth, which provides wireless communications within a radius of about ten meters, at a data

rate of 700 to 900 kilobits per second. Bluetooth is expected to be introduced in late 1999 and will initially have a cost of about $20 per unit. This cost is expected to decline rapidly after introduction. Bluetooth will allow personal

communications and electronics devices to communicate with one another.

2. Technology such as Bluetooth (see note 1) will allow computer components such as computing units, keyboards,

pointing devices, printers, etc. to communicate with one another without the use of cables.

3. Microvision of Seattle has a product called a Virtual Retina Display (VRD) that projects images directly onto the userʹs retinas while allowing the user to see the normal environment. The Microvision VRD is currently expensive

and is sold primarily to the military for use by pilots. Microvisionʹs CEO Richard Rutkowski projects a consumer

version built on a single chip before the year 2000.

4. Projecting from the speed of personal computers, a 1998 personal computer can perform about 150 million

instructions per second for about $1,000. By doubling every twelve months, we get a projection of 150 million

multiplied by 211 (2,048) = 300 billion instructions per second in 2009. Instructions are less powerful than

calculations, so calculations per second will be around 100 billion. However, projecting from the speed of neural

computers, a 1997 neural computer provided about 2 billion neural connection calculations per second for around

$2,000, which is 1 billion calculations per $1,000. By doubling every twelve months, we get a projection of 1 billion times 212 (4,096) = 4 trillion calculations per second in 2009. By 2009, computers will routinely combine both types of computations, so if even 25 percent of the computations are of the neural connection calculation type, the estimate of 1 trillion calculations per second for $1,000 of computing in 2009 is reasonable.

5. The most powerful supercomputers are twenty thousand times more powerful than a $1,000 personal computer.

With $1,000 personal computers providing about 1 trillion calculations per second (particularly of the neural‐

connection type of calculation) in 2009, the more powerful supercomputers will provide about 20 million billion

calculations per second, which is about equal to the estimated processing power of the human brain.

6. As of this writing, there has been much publicity surrounding the work of Dr. Judith Folkman of Childrenʹs Hospital in Boston, Massachusetts, and the effects of angiogenesis inhibitors. In particular, the combination of Endostatin and Angiostatin, bio‐engineered drugs that inhibit the reproduction of capillaries, has been remarkably effective in mice.

Although there has been a lot of commentary pointing out that drugs that work in mice often do not work in

humans, the degree to which this drug combination worked in these laboratory animals was remarkable. Drugs that

work this well in mice often do work in humans.

See ʺHOPE IN THE LAB: A Special Report. A Cautious Awe Greets Drugs That Eradicate Tumors in Mice,ʺ New

York Times, May 3, 1998.

CHAPTER 10: 2019

1. See note 3 of chapter 9, ʺ2009,ʺ on the Microvision Virtual Retina Display.

2. A 1997 neural computer provided about 2 billion neural‐connection calculations per second for $2,000. By doubling twenty‐two times by the year 2019, that comes to about 8 million billion calculations per second for $2,000 and 16

million billion calculations per second for $4,000. In 2020, we get 16 million billion calculations per second for $2,000.

3. With each human brain providing about 1016 calculations per second and an estimated 10 billion (1010) persons, we get an estimated 1026 calculations per second for all human brains on Earth. There are about 100 million computers in the world in 1998. A conservative estimate for 2019 would be a billion computers equal to the power of the then

state‐of‐the‐art for $1,000 machines. Thus the total computing power of the computers equals one billion (109) times 1016 = 1025 calculations per second, which is 10 percent of 1026.

CHAPTER 11: 2029

1. With each human brain providing about 1016 calculations per second and an estimated 10 billion (1010) persons, we get an estimated 1026 calculations per second for all human brains on Earth. There are about 100 million computers in the world in 1998. A (very) conservative estimate for 2029 would be a billion computers equal to the then state‐of-the‐art for $1,000 machines. This is actually too conservative, but still sufficient for our purposes. Thus the total computing power of the computers equals one billion (109) times 1019 = 1028 calculations per second, which is one

hundred times the processing power of all human brains (which is 1026 calculations per second)

2. See Raymond Kurzweil , The 10% Solution for a Healthy Life: How to Eliminate Virtually All Risk of Heart Disease and Cancer (New York: Crown Publishers, 1993).

CHAPTER 12: 2099

As discussed in chapter 6, ʺBuilding New Brains,ʺ and chapter 10, ʺ2019,ʺ human capacity of an estimated 2 1016

(neural connection) calculations per second will be achieved in a $1,000 computing device by around the year 2020.

Also as noted, the capacity of computing will double every twelve months, or ten times every decade, which is a

factor of one thousand (210) every ten years. Thus by the year 2099, $1,000 of computing will be roughly equivalent

to 1024 times the computing capacity of the human brain, or 1040 calculations per second. Estimating a trillion

virtual persons (hundred times greater than the roughly 10 billion persons in the early twenty‐first century), and an estimated $1 million of computing devoted to each person, we get an estimated 1055 calculations per second.

1. One thousand qu‐bits would enable 21,000 (approximately 10300) calculations to be performed at the same time. If 1042

of the calculations each second are such quantum calculations, then that is equivalent to 1042 ¯ 10300 = 10342

calculations per second. 1055 + 10342 still equals about 10342.

2. What happened to picoengineering, youʹre wondering? Picoengineering refers to engineering at the scale of a

picometer, which is one trillionth of a meter. Remember that the author has not spoken to Molly for seventy years.

Nanotechnology (technology on the scale of a billionth of a meter) is becoming practical in the decade between 2019

and 2029. Note that in the twentieth century, the Law of Accelerating Returns as applied to computation has been

achieved through engineering at ever smaller scales of physical size. Mooreʹs Law is a good example of this, in that the size of a transistor (in two dimensions) has been decreasing by 50 percent every two years. This means that

transistors have been shrinking by a factor of 25 = 32 in ten years. Thus the feature size of a transistor in each dimension has been shrinking by a factor of the square root of 32 = 5.6 every ten years. We are shrinking, therefore, the feature size of components by a factor of about 5.6 in each dimension every decade.

If engineering at the nanometer scale (nanotechnology) is practical in the year 2032, then engineering at the

picometer scale should be practical about forty years later (because 5.64 = approximately 1,000), or in the year 2072.

Engineering at the femtometer (one thousandth of a trillionth of a meter, also referred to as a quadrillionth of a

meter) scale should be feasible, therefore, by around the year 2112. Thus I am being a bit conservative to say that

femtoengineering is controversial in 2099.

Nanoengineering involves manipulating individual atoms. Picoengineering will involve engineering at the level

of subatomic particles (e.g., electrons). Femtoengineering will involve engineering inside a quark. This should not

seem particularly startling, as contemporary theories already postulate intricate mechanisms within quarks.

EPILOGUE: THE REST OF THE UNIVERSE REVISITED

1. We could use the Busy Beaver Function (see note 16 on the Turing machine in chapter 4) as a quantitative measure of the software of intelligence.

TIME LINE

Sources for the timeline include Raymond Kurzweil, The Age of Intelligent Machines (Cambridge, MA: MIT Press, 1990).

Introduction to big bang theory at <http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.ht ml>;

Joseph Silk, A Short History of the Universe (New York: Scientific American Library, 1994); Joseph Silk, The Big Bang (San Francisco: W. H. Freeman and Company, 1980); Robert M. Wald, Space, Time and Gravity (Chicago: The

University of Chicago Press, 1977); Stephen W. Hawking, A Brief History of Time (New York: Bantam Books, 1988).

Evolution and behavior at <http://ccp.uchicago.edu/~jyin/evolution.html>; Edward O. Wilson , The Diversity of Life (New York: W. W. Norton and Company, 1993); Stephen Jay Gould, The Book of Life (New York: W. W. Norton and Company, 1993); Alexander Hellemans and Bryan Bunch, The Timetable of Science (Simon and Schuster, 1988).

ʺCBN History: Radio/Broadcasting Timelineʺ at <http://www. wcbn.orgthistory/wcbntime.html>.

ʺChronology of Events in the History of Microcomputersʺ at

<http://www3.islandnet.com/~kpolsson/comphist.htm>.

ʺThe Computer Museum History Centerʺ at <http://www.tem.org/history/index.html>.

1. Picoengineering involves engineering at the level of subatomic particles (e.g., electrons). See note 3 on

picoengineering and femtoengineering in chapter 12.

2. Femtoengineering will involve engineering using mechanisms within a quark. See note 3 on picoengineering and

femtoengineering in chapter 12.

HOW TO BUILD AN INTELLIGENT MACHINE IN THREE EASY PARADIGMS

1. See ʺInformation Processing in the Human Body,ʺ by Vadim Gerasimov, at

<http://vadim.www.media.mit.edu/MAS862/Project.html>.

2. Marvin Minsky and Seymour A. Papert, Perceptrons: An Introduction to Computational Geometry (Cambridge, MA: MIT

Press, 1988).

3. The quoted text on the ʺtwo daughter sciencesʺ is from Seymour Papert, ʺOne AI or Many,ʺ Daedalus, Winter 1988.

ʺDr. Seymour Papert is a mathematician and one of the early pioneers of Artificial Intelligence. Additionally, he

is internationally recognized as the seminal thinker about ways in which computers can change learning. Born and

educated in South Africa where he participated actively in the anti‐apartheid movement, Dr. Papert pursued

mathematical research at Cambridge University from 1954 through 1958. He then worked with Jean Piaget at the

University of Geneva from 1958 through 1963. It was this collaboration that led him to consider using mathematics

in the service of understanding how children can learn and think. In the early 1960s, Papert came to MIT where, with Marvin Minsky, he founded the Artificial Intelligence Laboratory and coauthored their seminal work Perceptrons.ʺ

From the web page entitled ʺSeymour Papertʺ at <http://papert.www.media.mit.edu/people/papert/>.

4. ʺ[Marvin] Minsky was . . . one of the pioneers of intelligence‐based mechanical robotics and telepresence . . . In 1951

he built the first randomly wired neural network learning machine (called SNARC, for Stochastic Neural‐Analog

Reinforcement Computer), based on the reinforcement of simulated synaptic transmission coefficients. . . . Since the early 1950s, Marvin Minsky has worked on using computational ideas to characterize human psychological

processes, as well as working to endow machines with intelligence.ʺ From the brief academic biography of Marvin

Minsky at <http://minsky.www.media.mit.edu/people/minsky/minskybiog.html>.

5. Dr. Raj Reddy is dean of the School of Computer Science at Carnegie Mellon University and the Herbert A. Simon

University Professor of Computer Science and Robotics. Dr. Reddy is a leading AI researcher whose research

interests include the study of human‐computer interaction and artificial intelligence.

SUGGESTED READINGS

Abbott, E. A. Flatland: A Romance in Many Dimensions. Reprint. Oxford: Blackwell, 1962.

Abelson, Harold and Andrea disessa. Turtle Geometry: The Computer as a Medium for Exploring Mathematics. Cambridge, MA: MIT Press, 1980.

Abrams, Malcolm and Harriet Bernstein. Future Stuff. New York: Viking Penguin, 1989.

Adams, James L. Conceptual Blockbusting: A Guide to Better Ideas. Reading, MA: Addison‐Wesley, 1986.

–––––––. The Care and Feeding of ideas: A Guide to Encouraging Creativity. Reading, MA: Addison‐Wesley, 1986.

Adams, Scott. The Dilbert Future: Thriving on Stupidity in the 21st Century. New York: Harper Business, 1997.

Alexander, S. Art and Instinct. Reprint. Oxford: Folcroft Press, 1970.

Allen, Peter K. Robotic Object Recognition Using Vision and Touch. Boston: Kluwer Academic, 1987.

Allman, William F. Apprentices of Wonder: Inside the Neural Network Revolution. New York: Bantam Books, 1989.

Amit Daniel J. Modeling Brain Function: The World of Attractor Neural Networks. Cambridge: Cambridge University Press, 1989.

Anderson, James A. An Introduction to Neural Networks. Cambridge, MA: MIT Press, 1997.

Andriole, Stephen, ed. The Future of Information Processing Technology. Princeton, NJ: Petrocelli Books, 1985.

Antitbi, Elizabeth and David Fishlock. Biotechnology: Strategies for Life. Cambridge, MA: MIT Press, 1986.

Anton, John P Science and the Sciences in Plato. New York: EIDOS, 1980.

Ashby, W Ross. Design for a Brain. New York: John Wiley and Sons, 1960.

–––––––. An Introduction to Cybernetics. New.York: John Wiley and Sons, 1963.

Asimov, Isaac. Asimov on Numbers. New York: Bell Publishing Company, 1977.

–––––––. I, Robot. New York: Doubleday, 1950.

–––––––. Robot Dreams. New York: Berkeley Books, 1986.

–––––––. Robots of Dawn. New York: Doubleday and Company, 1983.

Asimov, Isaac and Karen A. Frenkel. Robots: Machines in Manʹs Image. New York: Harmony Books,1985.

Atkins, R. W. The Second Law. New York: Scientific American Books 1984.

Augarten, Stan. Bit by Bit: An Illustrated History of Computers. New York: Ticknor and Fields, 1984.

Austrian, Geoffrey D. Herman Hollerith: Forgotten Giant of Information Processing. New York: Columbia University Press, 1982.

Axelrod, Robert. The Evolution of Cooperation. New York: Basic Books, 1984.

Ayache, Nicholas and Peter T. Sander. Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception.

Cambridge, MA: MIT Pressʺ 1991.

Ayer, Alfred J. The Foundations of Empirical Knowledge. London: Macmillan and Company, 1964.

–––––––. Language, Truth and Logic. New York: Dover Publications, 1936.

–––––––, ed. Logical Positivism. New York: Macmillan, 1959.

Ayers, M. The Refutation of Determinism: An Essay in Philosophical Logic. London: Methuen, 1968.

Ayres, Robert U., et al. Robotics and Flexible Manufacturing Technologies: Assessment, Impacts, and Forecast. Park Ridge, NJ: Noyes Publications, 1985.

Babbage, Charles. Charles Babbage and His Calculating Engines. Edited by Philip Morrison and Emily Morrison. New York: Dover Publications, 1961.

–––––––. Ninth Bridgewater Treatise: A Fragment. London: Murray, 1838.

Babbage, Henry Prevost . Babbageʹs Calculating Engines: A Collection of Papers by Henry Prevost Babbage (Editor). Vol. 2.

Los Angeles: Tornash, 1982.

Bailey, James. After Thought: The Computer Challenge to Human Intelligence. New York: Basic Books, 1996.

Bara, Bruno G. and Giovanni Guida. Computational Models of Natural Language Processing. Amsterdam: North Holland, 1984.

Barnsley, Michael F. Fractals Everywhere. Boston: Academic Press Professional, 1993.

Baron, Jonathan. Rationality and Intelligence. Cambridge: Cambridge University Press, 1985.

Barrett, Paul H., ed. The Collected Papers of Charles Darwin. Vols. 1 and 2. Chicago: University of Chicago Press, 1977.

Barrow, John. Theories of Everything. Oxford: Oxford University Press, 1991.

Barrow, John D. and Frank J. Tipler. The Anthropic Cosmological Principle. Oxford: Oxford University Press, 1986.

Bartee, Thomas C., ed. Digital Communications. Indianapolis, IN: Howard W. Sams and Company, 1986.

Basalla, George. The Evolution of Technology. Cambridge: Cambridge University Press, 1988.

Bashe, Charles J., Lyle R. Johnson, John H. Palmer, and Emerson W. Pugh. IBMʹs Early Computers. Cambridge, MA: MIT Press, 1986.

Bateman, Wayne. Introduction to Computer Music. New York: John Wiley and Sons, 1980.

Baxandall, D. Calculating Machines and Instruments. Rev. ed. London: Science Museum, 1975. Original, 1926.

Bell, C. Gordon with John E. Mcnamara. High‐Tech Ventures: The Guide for Entrepreneurial Success. Reading, MA: Addison‐Wesley, 1991.

Bell, Gordon. ʺUltracomputers: A Teraflop Before Its Time.ʺ Science 256 (April 3, 1992).

Benedikt, Michael, ed. Cyberspace: First Steps. Cambridge, MA: MIT Press, 1992.

Bernstein, Jeremy. The Analytical Engine: Computers—Past, Present and Future. Revised ed. New York: William Morrow, 1981.

Bertin, Jacques. Semiology of Graphics: Diagrams, Networks, Maps. Madison: University of Wisconsin Press, 1983.

Beth, E. W Foundations of Mathematics. Amsterdam: North Holland, 1959.

Block, Irving, ed. Perspectives on the Philosophy of Wittgenstein. Cambridge, MA: MIT Press, 1981.

Block, Ned, Owen Flanagan, Guven Guzeldere, eds. The Nature of Consciousness: Philosophical Debates. Cambridge, MA: MIT Press, 1997.

Bobrow, Daniel G. and A. Collins, eds. Representation and Understanding. New York: Academic Press, 1975.

Boden, Margaret. Artificial Intelligence and Natural Man. New York: Basic Books, 1977.

–––––––. The Creative Mind: Myths & Mechanisms. New York: Basic Books, 1991.

Bolter, J. David. Turingʹs Man: Western Culture in the Computer Age. Chapel Hill: The University of North Carolina Press, 1984.

Boole, George. An Investigation of the Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities. 1854. Reprint. Peru, IL: Open Court Publishing, 1952.

Botvinnik, M. M. Computers in Chess: Solving Inexact Search Problems. New York: Springer‐Verlag, 1984.

Bowden, B. WI ed. Faster Than Thought. London: Pittman, 1953.

Brachman, Ronald J. and Hector J. Levesque. Readings and Knowledge Representation. Los Altos, CA: Morgan Kaufmann, 1985.

Brady, M., L. A. Gerhardt, and H. F. Davidson. Robotics and Artificial Intelligence. Berlin: Springer‐Verlag, 1984.

Brand, Stewart. The Media Lab: Inventing the Future at MIT. New York: Viking Penguin, 1987.

Briggs, John. Fractals: The Patterns of Chaos. New York: Simon and Schuster, 1992.

Brittan, Gordon G. Kantʹs Theory of Science. Princeton, NJ: Princeton University Press, 1978.

Bronowski, J. The Ascent of Man. Boston: Little, Brown and Company, 1973.

Brooks, Rodney A. ʺElephants Donʹt Play Chess.ʺ Robotics and Autonomous Systems 6 (1990).

–––––––. ʺIntelligence Without Representation.ʺ Artificial Intelligence 47 (1991).

–––––––.ʺNew Approaches to Robotics.ʺ Science 253 (1991).

Brooks, Rodney A. and Anita Flynn. ʺFast, Cheap and Out of Control: A Robot Invasion of the Solar System .ʺ Journal of the British Interplanetary Society 42 (1989).

Brooks, Rodney A., Pattie Maes, Maja J. Mataric, and Grinell More. ʺLunar Base Construction Robots.ʺ IROS, IEEE

International Workshop on Intelligence Robots and Systems, 1990.

Brown, John Seeley Seeing Differently: Insights on Innovation. Cambridge, MA: Harvard Business School Press, 1997.

Brown, Kenneth A. Inventors at Work: Interviews with 16 Notable American Inventors. Redmond, WA: Tempus Books of Microsoft Press, 1988.

Brumbaugh, R. S. Platoʹs Mathematical Imagination. Bloomington: Indiana University Press, 1954.

Bruner, Jerome S., Jacqueline J. Goodnow, and George A. Austin. A Study of Thinking. 1956. Reprint. New York: Science Editions, 1965.

Buderi, Robert. The Invention That Changed the World: How a Small Group of Radar Pioneers Won the Second World War and Launched a Technological Revolution. New York: Simon and Schuster, 1996.

Burger, Peter and Duncan Gillies. Interactive Computer Graphics: Functional, Procedural and Device‐Level Methods.

Workingham, UK: Addison‐Wesley Publishing Company, 1989.

Burke, James. The Day the Universe Changed. Boston: Little, Brown and Company, 1985.

Butler, Samuel. ʺDarwin Among the Machines.ʺ Canterbury Settlement. AMS Press, 1923, (Written in 1863 by the author of Erewhon.)

Buxton, H. W. Memoir of the Life and Labours of the Late Charles Babbage, Esq. ER. S. Edited by A. Hyman. Los Angeles: Tornash, 1988.

Byrd, Donald. ʺMusic Notation by Computer.ʺ Ph.D..dissertation, Indiana University Computer Science Department,

1984.

Bythell, Duncan. The Handloom Weavers: A Study in the English Cotton Industry During the Industrial Revolution.

Cambridge: Cambridge University Press, 1969.

Cairns‐Smith, A. G. Seven Clues to the Origin of Life. Cambridge: Cambridge University Press, 1985.

Calvin, William H. The Cerebral Code: Thinking a Thought in the Mosaics of the Mind. Cambridge, MA: MIT Press, 1996.

Campbell, Jeremy. The Improbable Machine. New York: Simon and Schuster, 1989.

Carpenter, Gail A. and Stephen Grossberg. Pattern Recognition by Self‐Organizing Neural Networks. Cambridge, MA: MIT Press, 1991.

Carroll, Lewis. Through the Looking Glass. London: Macmillan, 1871.

Cassirer, Ernst. The Philosophy of the Enlightenment. Princeton, NJ: Princeton University Press, 1951.

Casti, John L. Complexification: Explaining the Paradoxical World Through the Science of Surprise. New York: Harpercollins, 1994.

Cater, John P. Electronically Hearing: Computer Speech Recognition. Indianapolis, IN: Howard W. Sams and Company, 1984.

–––––––. Electronically Speaking: Computer Speech Generation. Indianapolis, IN: Howard W. Sams and Company, 1983.

Caudill, Maureen and Charles Butler. Naturally Intelligent Systems. Cambridge, MA: MIT Press, 1990.

Chaitin, Gregory J. Algorithmic Information Theory. Cambridge: Cambridge University Press, 1987.

Chalmers, D. J. The Conscious Mind. New York: Oxford University Press, 1996.

Chamberlin, Hal. Musical Applications of Microprocessors. Indianapolis, IN: Hayden Books, 1985.

Chapuis, Alfred and Edmond Droz. Automata: A Historical and Technological Study. New York: Griffon, 1958.

Chermak, Christopher. Minimal Rationality. Cambridge, MA: MIT Press, 1986.

Chomsky, Noam. Cartesian Linguistics. New York: Harper and Row, 1966.

–––––––. Language and Min d. Enlarged edition. New York: Harcourt Brace Jovanovich, 1972.

–––––––. Language and Problems of Knowledge: The Managua Lectures. Cambridge, MA: MIT Press, 1988.

–––––––. Language and Thought. Wakefield, RI, and London: Moyer Bell, 1993.

–––––––. Reflections on Language. New York: Pantheon, 1975.

–––––––. Rules and Representation. Cambridge, MA: MIT Press, 1980.

–––––––. Syntactic Structures. The Hague: Mouton, 1957.

Choudhary, Alok N. and Janak H. Pattl. Parallel Architectures and Parallel Algorithms for Integrated Vision Systems.

Boston: Kluwer Academic, 1990.

Christensen, Clayton. The Innovatorʹs Dilemma: When New Technologies Cause Great Firms to Fail. Cambridge, MA: Harvard Business School Press, 1997.

Church, Alonzo. Introduction to Mathematical Logic. Vol. 1. Princeton, NJ: Princeton University Press, 1956.

Church, Kenneth W. Phonological Parsing in Speech Recognition. Norwell, MA: Kluwer Academic, 1987.

Churchland, P S. and T. J. Sejnowski. The Computational Brain. Cambridge, MA: MIT Press, 1992.

Churchland, Paul. The Engine of Reason, the Seat of the Soul. Cambridge, MA: MIT Press, 1995.

–––––––. Matters and Consciousness: A Contemporary Introduction to the Philosophy of Mind. Cambridge, MA: MIT Press, 1984.

–––––––. A Neurocomputational Perspective: The Nature of Mind and the Structure of Science. Cambridge, MA: MIT Press, 1989.

Clark, Andy Being There: Putting Brain, Body, and World Together Again. Cambridge, MA: MIT Press, 1997.

Clarke, Arthur C. 3001: The Final Odyssey. New York: Ballantine Books, 1997.

Coates, Joseph E, John B. Mahaffie, and Andy Hines . 2025: Scenarios of U.S. and Global Society Reshaped by Science and Technology. Greensboro, NC: Oak Hill Press, 1997.

Cohen, I. Bernard. The Newtonian Revolution. Cambridge: Cambridge University Press, 1980.

Cohen, John. Human Robots in Myth and Science. London: Allen and Unwin, 1966.

Cohen, Paul R. Empirical Methods for Artificial Intelligence. Cambridge, MA: MIT Press, 1995.

Cohen, Paul R. and Edward A. Feigenbaum. The Handbook of Artificial Intelligence, Vols. 3 and 4. Los Altos, CA: William Kaufmann, 1982.

Connell, Jonathan H. Minimalist Mobile Robotics: A Colony‐Style Architecture for an Artificial Creature. Boston: Academic Press, 1990.

Conrad, Michael and H. H. Pattee. ʺEvolution Experiments with an Artificial Ecosystem.ʺ Journal of Theoretical Biology 28 (1970).

Conrad, Michael et al. ʺTowards an Artificial Brain.ʺ Biosystems 23 (1989).

Cornford, Francis M. Platoʹs Cosmology. London: Routledge and Kegan Paul, 1937.

Crandall, B. C., ed. Nanotechnology: Molecular Speculations on Global Abundance. Cambridge, MA: MIT Press, 1997.

Crease, Robert P and Charles C. Mann. The Second Creation. New York: Macmillan, 1986.

Crick, Francis. The Astonishing Hypothesis: The Scientific Search for the Soul. New York: Charles Scribnerʹs Sons, 1994.

–––––––. Life Itself. London: Mcdonald, 1981.

Critchlow, Arthur J. Introduction to Robotics. New York: Macmillan Publishing Company, 1985.

Cullinane, John J. The Entrepreneurʹs Survival Guide: 101 Tips for Managing in Good Times and Bad. Homewood, IL: Business One Irwin, 1993.

Daedalus: Journal of the American Academy of Arts and Sciences. Artificial Intelligence. Winter 1998. Vol. 117.

Darwin, Charles. The Descent of Man, and Selection in Relation to Sex. Second ed. New York: Hurst and Company, 1874.

–––––––. The Expression of the Emotions in Man and Animals. 1872. Reprint. Chicago: University of Chicago Press, 1965.

–––––––. Origin of Species. Reprint. London: Penguin, 1859.

Davies, Paul. Are We Alone? Implications of the Discovery of Extraterrestrial Life. New York: Basic Books, 1995.

–––––––. The Cosmic Blueprint. New York: Simon and Schuster, 1988.

–––––––. God and the New Physics. New York: Simon and Schuster, 1983.

–––––––. The Mind of God. New York: Simon and Schuster, 1992.

–––––––. ʺA New Science of Complexity.ʺ New Scientist 26 (November 1988).

Davis, Philip J. and David Park, eds. No Way: The Nature of the Impossible. New York: W. H. Freeman, 1988.

Davis, Philip J. and Reben Hersh. Descartesʹ Dream: The World According to Mathematics. San Diego, CA: Harcourt Brace Jovanovich, 1986.

Davis, R. and D. B. Lenat. Knowledge‐Based Systems in Artificial intelligence. New York: McGraw‐Hill, 1980.

Dawkins, Richard. The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. New York: W. W. Norton and Company, 1986.

–––––––. ʺThe Evolution of Evolvability.ʺ Artificial Life, edited by Christopher G. Langton. Reading, MA: Addison-Wesley, 1988.

–––––––. The Extended Phenotype. San Francisco: Freeman, 1982.

–––––––. River out of Eden: A Darwinian View of Life. New York: Basic Books, 1995.

–––––––. ʺUniversal Darwinism.ʺ Evolution from Molecules to Men, edited by D. S. Bendall. Cambridge: Cambridge University Press, 1983.

–––––––. The Selfish Gene. Oxford: Oxford University Press, 1976.

Dechert, Charles R., ed. The Social Impact of Cybernetics. New York: Simon and Schuster, 1966.

Denes, Peter B. and Elliot N. Pinson. The Speech Chain: The Physics and Biology of Spoken Language. Bell Telephone Laboratories, 1963.

Dennett, Daniel C. Brainstorms: Philosophical Essays on Mind and Psychology. Cambridge, MA: MIT Press, 1981.

–––––––. Consciousness Explained. Boston: Little, Brown and Company, 1991

–––––––. Content and Consciousness. London: Routledge and Kegan Paul, 1969.

–––––––. Darwinʹs Dangerous Idea: Evolution and the Meanings of Life. New York: Simon and Schuster, 1995.

–––––––. Elbow Room: The Varieties of Free Will Worth Wanting. Cambridge, MA: MIT Press, 1985.

–––––––. The Intentional Stance. Cambridge, MA: MIT Press, 1987.

–––––––. Kinds of Minds: Toward an Understanding of Consciousness. New York: Basic Books, 1996.

Denning, Peter J. and Robert M. Metcalfe. Beyond Calculation: The Next Fifty Years of Computing. New York: Copernicus, 1997.

Depew, David J. and Bruce H. Weber, eds. Evolution at a Crossroads. Cambridge, MA: MIT Press, 1985.

Dertouzos, Michael. What Will Be: How the New World of Information Will Change Our Lives. New York: Harpercollins, 1997.

Dertouzos, Michael L. and Joel Moses Dertouzos. The Computer Age: A Twenty Year View. Cambridge, MA: MIT Press, 1979.

Descartes, R. Discourse on Method, Optics, Geometry, and Meteorology. 1637. Reprint. Indianapolis, IN: Bobbs‐Merrill, 1956.

–––––––. Meditations on First Philosophy. Paris: Michel Soly, 1641.

–––––––. Treatise on Man. Paris, 1664.

Devlin, Keith. Mathematics: The Science of Patterns. New York: Scientific American Library, 1994.

Dewdney, A. K. The Armchair Universe: An Exploration of Computer Worlds. New York: W. H. Freeman, 1988.

De Witt, Bryce and R. D. Graham, eds. The Many‐Worlds Interpretation of Quantum Mechanics. Princeton, NJ: Princeton University Press, 1974.

Diebold, John. Man and the Computer: Technology as an Agent of Social Change. New York: Avon Books, 1969.

Dixit, Avinash and Robert S. Pindyck. Investment Under Uncertainty. Princeton, NJ: Princeton University Press, 1994.

Dobzhansky, Theodosius. Mankind Evolving: The Evolution of the Human Species. New Haven, CT: Yale University Press, 1962.

Dodds, E. R. Greeks and the Irrational. Berkeley: University of California Press, 1951.

Downes, Larry, Chunka Mui, and Nicholas Negroponte . Unleashing the Killer App: Digital Strategies for Market Dominance. Cambridge, MA: Harvard Business School Press, 1998.

Drachmann, A. G. The Mechanical Technology of Greek and Roman Antiquity. Madison: University of Wisconsin Press, 1963.

Drexler, K. Eric. Engines of Creation. New York: Doubleday, 1986.

–––––––. ʺHypertext Publishing and the Evolution of Knowledge.ʺ Social Intelligence 1:2 (1991).

Dreyfus, Hubert. ʺAlchemy and Artificial Intelligence,ʺ Rand Technical Report, December 1965.

–––––––. Philosophic Issues in Artificial intelligence. Chicago: Quadrangle Books, 1967.

–––––––. What Computers Canʹt Do: The Limits of Artificial Intelligence. New York: Harper and Row, 1979.

–––––––. What Computers Still Canʹt Do: A Critique of Artificial Reason. Cambridge, MA: MIT Press, 1992.

–––––––, ed. Husserl, Intentionality & Cognitive Science. Cambridge, MA: MIT Press, 1982.

Dreyfus, Hubert L. and Stuart E. Dreyfus . Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York: The Free Press, 1986.

Drucker, Peter F. Innovation and Entrepreneurship: Practice and Principles. New York: Harper and Row, 1985.

Durrett, H. John, ed. Color and the Computer. Boston: Academic Press, 1987.

Dyson, Esther. Release 2.0: A Design for Living in the Digital Age. New York: Broadway Books, 1997.

Dyson, Freeman. Disturbing the Universe. New York: Harper and Row, 1979.

–––––––. From Eros to Gaia. New York: Harpercollins, 1990.

–––––––. Infinite in All Directions. New York: Harper and Row, 1988.

–––––––. Origins of Life. Cambridge: Cambridge University Press, 1985.

Dyson, George B. Darwin Among the Machines: The Evolution of Global Intelligence. Reading, MA: Addison‐Wesley, 1997.

Eames, Charles and Ray Eames. A Computer Perspective. Cambridge, MA: Harvard University Press, 1973.

Ebeling, Carl. All the Right Moves: A VLSI Architecture for Chess. Cambridge, MA: MIT Press, 1987.

Edelman, G. M. Neural Darwinism: The Theory of Neuronal Group Selection. New York: Basic Books, 1987.

Einstein, Albert. Relativity: The Special and the General Theory. New York: Crown, 1961.

Elithorn, Alick and Ranan Banerji. Artificial and Human Intelligence. Amsterdam: North Holland, 1991.

Enderle, G. Computer Graphics Programming. Berlin: Springer‐Verlag, 1984.

Fadiman, Clifton, ed. Fantasia Mathematica: Being a Set of Stories, Together with a Group of Oddments and Diversion, All Drawn from the Universe of Mathematics. New York: Simon and Schuster, 1958.

Fahlman, Scott E. NETL: A System for Representing and Using Real‐World Knowledge. Cambridge, MA: MIT Press, 1979.

Fant, Gunnar. Speech Sounds and Features. Cambridge, MA: MIT Press, 1973.

Feigenbaum, E. and Avron Barr, eds. The Handbook of Artificial Intelligence. Vol. 1. Los Altos, CA: William Kaufmann, 1981.

Feigenbaum, Edward A. and Julian Feldman, eds. Computers and Thought. New York: McGraw‐Hill, 1963.

Feigenbaum, Edward A. and Pamela Mccorduck . The Fifth Generation: Artificial Intelligence and Japanʹs Computer Challenge to the World. Reading, MA: Addison‐Wesley, 1983.

Feynman, Richard, ʺThereʹs Plenty of Room at the Bottom.ʺ Miniaturization, edited by H. D. Gilbert. New York: Reinhold, 1961.

Feynman, Richard P. Surely Youʹre Joking, Mr Feynman! New York: Norton, 1985.

–––––––. What Do You Care What Other People Think? New York: Bantam, 1988.

Feynman, Richard P. Robert B. Leighton, and Matthew Sands. The Feynman Lectures in Physics. Reading, MA: Addison‐Wesley, 1965.

Findlay, J. N. Plato and Platonism: An Introduction. New York: Times Books, 1978.

Finkelstein, Joseph, ed. Windows on a New World: The Third Industrial Revolution. New York: Greenwood Press, 1989.

Fischler, Martin A. and Oscar Firschein. Intelligence: The Eye, the Brain and the Computer. Reading, MA: Addison‐

Wesley, 1987.

–––––––, eds., Readings in Computer Vision: Issues, Problems, Principles, and Paradigms. Los Altos, CA: Morgan Kaufmann, 1987.

Fjermedal, Grant. The Tomorrow Makers: A Brave New World of Living Brain Machines. New York: Macmillan Publishing Company, 1986.

Flanagan, Owen. Consciousness Reconsidered. Cambridge, MA: MIT Press, 1992.

Flynn, Anita, Rodney A. Brooks, and Lee S. Tavrow ʺTwilight Zones and Cornerstones: A Gnat Robot Double Feature.ʺ A.I. Memo 1126. MIT Artificial Intelligence Laboratory, 1989.

Fodor, Jerry A. The Language of Thought. Hassocks, UK: Harvester, 1975.

–––––––. ʺMethodological Solipsism Considered as a Research Strategy in Cognitive Psychology .ʺ Behavioral and Brain Sciences. Vol. 3, 1980.

–––––––. The Modularity of Mind. Cambridge, MA: MIT Press, 1983.

–––––––. Psychosemantics. Cambridge, MA: MIT Press, 1987.

–––––––. Representations: Philosophical Essays on the Foundations of Cognitive Science. Cambridge, MA: MIT Press, 1982.

–––––––. A Theory of Content and Other Essays. Cambridge, MA: MIT Press, 1990.

Fogel, Lawrence J., Alvin J. Owens and Michael J. Walsh. Artificial Evolution Through Simulated Evolution. New York: John Wiley and Sons, 1966.

Foley, James, Andries van Dam, Steven Feiner, and John Hughes. Computer Graphics: Principles and Practice. Reading, MA: Addison‐Wesley, 1990.

Forbes, R. J. Studies in Ancient Technology. 9 vols. Leiden, Netherlands: E, J. Brill, 1955–1965.

Ford, Kenneth M., Clark Glymour, and Patrick J. Hayes. Android Epistemology. Cambridge, MA: MIT Press, 1995.

Forester, Tom. Computers in the Human Context. Cambridge, MA: MIT Press, 1989.

–––––––. High‐Tech Society: The Story of the Information Technology Revolution. Cambridge, MA: MIT Press, 1987.

–––––––. The Information Technology Revolution. Cambridge, MA: MIT Press, 1985.

–––––––. The Materials Revolution. Cambridge, MA: MIT Press, 1988.

Forrest, Stephanie, ed. Emergent Computation. Amsterdam: North Holland, 1990.

Foster, Richard. Innovation: The Attackerʹs Advantage. New York: Summit Books, 1986.

Fowler, D. H. The Mathematics of Platoʹs Academy. Oxford: Clarendon Press, 1987.

Franke, Herbert W. Computer Graphics‐Computer Art. Berlin: Springer‐Verlag, 1985.

Franklin, Stan. Artificial Minds. Cambridge, MA: MIT Press, 1997.

Frauenfelder, Uli H. and Lorraine Kormisarevsky Tyler. Spoken Word Recognition. Cambridge, MA: MIT Press, 1987.

Freedman, David H. Brainmakers: How Scientists Are Moving Beyond Computers to Create a Rival to the Human Brain.

New York: Simon and Schuster, 1994.

Freeman, Herbert, ed. Machine Vision for Three‐Dimensional Scenes. Boston: Academic Press, 1990.

Freud, Sigmund. The Interpretation of Dreams. Reprint. London: Hogarth Press, 1955

–––––––. Jokes and Their Relation to the Unconscious. Vol. 8 of Standard Edition of the Complete Psychological Works of Sigmund Freud. 1905. Reprint. London: Hogarth Press, 1957.

Freudenthal, Hans. Mathematics Observed. Trans. Stephen Rudolfer and I. N. Bakei. New York: McGraw‐Hill, 1967.

Frey, Peter W. ed. Chess Skill in Man and Machine. New York: Springer‐Verlag, 1983.

Friend, David, Alan R. Pearlman, and Thomas D. Piggott. Learning Music with Synthesizers. Lexington, MA: Hal Leonard, 1974.

Gamow George. One Two Three . . . Infinity. Toronto: Bantam Books, 1961.

Gardner, Howard. The Mindʹs New Science: A History of the Cognitive Revolution. New York: Basic Books, 1985.

Gardner, Martin. Time Travel and Other Mathematical Bewilderments. New York: W. H. Freeman, 1988.

Garey Michael R. and David S. Johnson. Computers and Intractability. San Francisco: W. H. Freeman, 1979.

Gates, Bill. The Road Ahead. New York: Viking Penguin, 1995.

Gay, Peter. The Enlightenment: An Interpretation. Vol. 1, The Rise of Modern Paganism. New York, W. W. Norton, 1966.

–––––––. The Enlightenment: An Interpretation. Vol. 2, The Science of Freedom. New York, W. W. Norton, 1969.

Gazzaniga, Michael S. Mind Matters: How Mind and Brain Interact to Create Our Conscious Lives. Boston: Houghton-Mifflin Company, 1988.

Geissler, H. G. et al. Advances in Psychology. Amsterdam: Elsevier Science, B.V, 1983.

Geissler, Hans‐George, et al. Modern Issues in Perception. Amsterdam: North Holland, 1983.

Gelernter, David. Mirror Worlds: Or the Day Software Puts the Universe in a Shoebox . . . How It Will Happen and What It Will Mean. New York: Oxford University Press, 1991.

–––––––. The Muse in the Machine: Computerizing the Poetry of Human Thought. New York: The Free Press, 1994.

–––––––. Gell‐Mann, Murray The Quark and the Jaguar: Adventures in the Simple and the Complex. New York: W H.

Freeman, 1994.

–––––––. ʺSimplicity and Complexity in the Description of Nature.ʺ Engineering & Science 3, Spring 1988.

Ghiselin, Brewster. The Creative Process: A Symposium. New York: New American Library, 1952.

Gilder, George. Life After Television. New York: W. W. Norton and Company, 1994.

–––––––. The Meaning of Microcosm. Washington, D.C.: The Progress and Freedom Foundation, 1997.

–––––––. Microcosm: The Quantum Revolution in Economics and Technology. New York: Simon and Schuster, 1989.

–––––––. Telecosm. New York: American Heritage Custom Publishing, 1996.

Gillispie, Charles. The Edge of Objectivity. Princeton, NJ: Princeton University Press, 1960.

Glass, Robert L. Computing Catastrophes. Seattle, WA: Computing Trends, 1983.

Gleick, James. Chaos: Making a New Science. New York: Viking Penguin, 1987.

Glenn, Jerome Clayton. Future Mind: Artificial Intelligence: The Merging of the Mystical and the Technological in the 21st Century. Washington, D.C.: Acropolis Books, 1989.

Gödel, Kurt. On Formally Undecidable Propositions in ʺPrincipia Mathematicaʺ and Related Systems. New York: Basic Books, 1962.

Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison‐Wesley, 1989.

Goldstine, Herman. The Computer From Pascal to von Neumann. Princeton, NJ: Princeton University Press, 1972.

Goleman, Daniel. Emotional Intelligence: Why It Can Matter More Than IQ. New York: Bantam Books, 1995.

Good, I. J. ʺSpeculations Concerning the First Ultraintelligent Machine.ʺ Advances in Computers. Vol. 6. Edited by Franz L. Alt and Morris Rubinoff. Academic Press, 1965.

Goodman, Cynthia. Digital Visions: Computers and Art. New York: Harry N. Abrams, 1987.

Gould, Stephen J. Ever Since Darwin. New York: Norton, 1977.

–––––––. Full House: The Spread of Excellence from Plato to Darwin. New York: Crown, 1995.

–––––––. Henʹs Teeth and Horseʹs Toes. New York: Norton, 1983.

–––––––. The Mismeasure of Man. New York: Norton, 1981.

–––––––. Ontogeny and Phyloge ry. Cambridge, MA: Harvard University Press, 1977.

–––––––. ʺOpus 200.ʺ Natural History, August 199 1.

–––––––. The Pandaʹs Thumb. New York: Norton, 1980.

–––––––. Wonderful Life: The Burgess Shale and the Nature of History. New York: Norton, 1989.

Gould, Stephen J. and Elisabeth S. Vrba. ʺExaptation—A Missing Term in the Science of Form.ʺ Paleobiology 8:1, 1982.

Gould, Stephen J. and R. C. Lewontin. ʺThe Spandrels of San Marco and the Panglossian Paradigm: A Critique of the

Adaptationist Programme.ʺ Proceedings of the Royal Society of London, B 205 (1979).

Graubart, Steven R., ed. The Artificial Intelligence Debate: False Starts, Real Foundations. Cambridge, MA: MIT Press, 1990.

Greenberg, Donald, Aaron Marcus, Alan H. Schmidt, and Vernon Gorter . The Computer Image: Applications of Computer Graphics. Reading, MA: Addison‐Wesley, 1982.

Greenberger, Martin, ed. Computers and the World of the Future. Cambridge, MA: MIT Press, 1962.

Greenblatt, R. D. et al. The Greenblatt Chess Program. Proceedings of the Fall Joint Computer Conference. ACM, 1967.

Gribbin, J. In Search of Schrodingers Cat: Quantum Physics and Reality. New York: Bantam Books, 1984.

Grimson, W. Eric L. Object Recognition by Computer: The Role of Geometric Constraints. Cambridge, MA: MIT Press, 1990.

Grimson, W. Eric L. and Ramesh S. Patil, eds. AI in the 1980s and Beyond: An MIT Survey. Cambridge, MA: MIT Press, 1987.

Grimson, William Eric Leifur. From Images to Surfaces: A Computational Study of the Human Early Visual System.

Cambridge, MA: MIT Press, 1981.

Grossberg, Stephen, ed. Neural Networks and Natural Intelligence. Cambridge, MA: MIT Press, 1988.

Grossman, Reinhardt. Phenomenology and Existentialism: An Introduction. London Routledge and Kegan Paul, 1984.

Guillen, Michael. Bridges to Infinity: The Human Side of Mathematics. Los Angeles: Jeremy P. Tarcher, 1983.

Guthrie, W. K. C. A History of Greek Philosophy. 6 vols. Cambridge: Cambridge University Press, 1962–1981.

Hafner, Katie and John Markoff. Cyberpunk: Outlaws and Hackers on the Computer Frontier. New York: Simon and Schuster, 1991.

Halberstam, David. The Next Century. New York: William Morrow, 1991.

Hameroff, Stuart R., Alfred W Kaszniak, and Alwyn C. Scott, eds . Toward a Science of Consciousness: The First Tucson Discussions and Debates. Cambridge, MA: MIT Press, 1996.

Hamming, R. W. Introduction to Applied Numerical Analysis. New York: McGraw‐Hill, 1971.

Hankins, Thomas L. Science and the Enlightenment. Cambridge: Cambridge University Press, 1985.

Harel, David. Algorithmics: The Spirit of Computing. Menlo Park, CA: Addison‐Wesley, 1987.

Harman, Willis. Global Mind Change: The New Age Revolution in the Way We Think. New York: Warner Books, 1988.

Harmon, Paul and David King. Expert Systems: Artificial Intelligence in Business. New York: John Wiley and Sons, 1985.

Harre, Rom, ed. American Behaviorial Scientist: Computation and the Mind. Vol. 40, no. 6, May 1997.

Harrington, Steven. Computer Graphics: A Programming Approach. New York: McGraw‐Hill, 1987.

Harris, Mary Dee. Introduction to Natural Language Processing. Reston, VA: Reston, 1985.

Haugeland, John. Artificial Intelligence: The Very Idea. Cambridge, MA: MIT Press, 1985.

–––––––, ed. Mind Design: Philosophy, Psychology, Artificial Intelligence. Cambridge, MA: MIT Press, 1981.

–––––––, ed. Mind Design II: Philosophy, Psychology, Artificial Intelligence. Cambridge, MA: MIT Press, 1997.

Hawking, Stephen W. A Brief History of Time: From the Big Bang to Black Holes. Toronto: Bantam Books, 1988.

Hayes‐Rothʺ Frederick, D. A. Waterman, and D. B. Lenat, eds. Building Expert Systems. Reading, MA: Addison-Wesley, 1983.

Heisenberg, Werner. Physics and Beyond: Encounters and Conversations. New York: Harper and Row, 1971.

Hellemans, Alexander and Bryan Bunch. The Timetables of Science. New York: Simon and Schuster, 1988.

Herbert, Nick. Quantum Reality. Garden City, NY: Anchor Press, 1985.

Hildebrandt, Stefan and Anthony Tromba. Mathematics and Optimal Form. New York: Scientific American Books, 1985.

Hillis, W. Daniel. The Connection Machine. Cambridge, MA: MIT Press, 1985.

–––––––. ʺIntelligence as an Emergent Behavior; Or: The Songs of Eden,ʺ in S. R. Graubard, ed . The Artificial Debate: False Starts and Real Foundations. Cambridge, MA: MIT Press, 1988.

Hindle, Brooke and Steven Lubar. Engines of Change: The American Industrial Revolution, 1790–1860. Washington, D.C.: Smithsonian Institution Press, 1986.

Hoage, R. J. and Larry Goldman. Animal Intelligence: Insights into the Animal Mind. Washington, D.C.: Smithsonian Institution Press, 1986.

Hodges, Andrew. Alan Turing: The Enigma. New York: Simon and Schuster, 1983.

Hoel, Paul G., Sidney C. Port, and Charles J. Stone. Introduction to Stochastic Processes. Boston: Houghton‐Mifflin, 1972.

Hofstadter, Douglas R. Gödel, Escher Bach: An Eternal Golden Braid. New York: Basic Books, 1979.

–––––––. Metamagical Themas: Questing for the Essence of Mind and Pattern. New York: Basic Books, 1985.

Hofstadter, Douglas R. and Daniel C. Dennett. The Mindʹs I: Fantasies and Reflections on Self and Soul. New York: Basic Books, 1981.

Hofstadter, Douglas R., Gray Clossman, and Marsha Meredith. ʺShakespeareʹs Plays Werenʹt Written by Him, but by

Someone Else of the Same Name.ʺ Bloomington: Indiana University Computer Science Department Technical

Report 96, 1980.

Holland, J.H., K.J. Holyoke, R.E. Nisbett, and P.R. Thagard. Induction: Processes of Inference, Learning, and Discovery.

Cambridge, MA: MIT Press, 1986.

Hookway, Christopher, ed. Minds, Machines, and Evolution: Philosophical Studies. Cambridge: Cambridge University Press, 1984.

Hopper, Grace Murray and Steven L. Mandell. Understanding Computers. Second ed. St. Paul, MN: West Publishing Co., 1987.

Horn, Berthold Klaus Paul. Robot Vision. Cambridge, MA: MIT Press, 1986.

Horn, Berthold K. P. and Michael J. Brooks. Shape from Shading. Cambridge, MA: MIT Press, 1989.

Hsu, F. Two Designs of Functional Units for VLSI Based Chess Machines. Technical Report. Computer Science Department, Carnegie Mellon University, 1986.

Hubel, David H. Eye, Brain, and Vision. New York: Scientific American Library, 1988.

Hume, D. Inquiry Concerning Human Understanding. 1748. Reprint. Indianapolis, IN: Bobbs‐Merrill, 1955.

Hunt, V. Daniel. Understanding Robotics. San Diego, CA: Academic Press, 1990.

Huxley, Aldous. Brave New World. New York: Harper, 1946.

Hyman, Anthony. Charles Babbage: Pioneer of the Computer. Oxford: Oxford University Press, 1982.

Inose, Hiroshi and John R. Pierce. Information Technology and Civilization. New York: W. H. Freeman, 1984.

Jacobs, Francois. The Logic of Life. New York: Pantheon Books, 1973.

James, Mike. Pattern Recognition. New York: John Wiley and Sons, 1988.

James, William. The Varieties of Religious Experience. New York: Collier Books, 1961.

Jamieson, Leah H., Dennis Gannon, and Robert J. Douglas. The Characteristics of Parallel Algorithms. Cambridge, MA: MIT Press, 1987.

Johnson, Mark and George Lakoff. Metaphors We Live By. Chicago: University of Chicago Press, 1980.

Jones, Steve. The Language of Genes: Solving the Mysteries of Our Genetic Past, Present, and Future. New York: Anchor Books, 1993.

Jones, W. T. Kant and the Nineteenth Century. Vol. 4 of A History of Western Philosophy Second ed. New York: Harcourt Brace Jovanovich, 1975.

–––––––. The Twentieth Century to Wittgenstein and Sartre. Vol. 5 of A History of Western Philosophy. Second ed. New York: Harcourt Brace Jovanovich, 1975.

Joy, Kenneth I., Charles W. Grant, Nelson L. Max, and Lansing Hatfield. Tutorial: Computer Graphics: Image Synthesis.

Washington, D.C.: Computer Society Press, 1988.

Judson, Horace F. The Eighth Day of Creation. New York: Simon and Schuster, 1979.

Jung, Carl. Memories, Dreams, Reflections. Rev. ed. Edited by Aniela Jaffe, and translated by Richard and Clara Winston. New York: Pantheon Books, 1961.

Jung, Carl, et al. Man and His Symbols. Garden City, NY: Doubleday, 1964.

Kaku, Michio. Hyperspace: A Scientific Odyssey Through Parallel Universes, Time Warps, and the 10th Dimension. New York: Anchor Books, 1995.

–––––––. Visions: How Science Will Revolutionize the 21st Century. New York: Doubleday, 1997.

Kant, Immanuel. Prolegomena to Any Future Metaphysics. Indianapolis, IN: Bobbs‐Merrill, 1950.

Kasner, Edward and James Newman. Mathematics and the Imagination. New York: Simon and Schuster, 1940.

Kauffman, Stuart A. ʺAntichaos and Adaptation.ʺ Scientific American, August 1991.

–––––––. At Home in the Universe: The Search for the Laws of Self‐Organization and Complexity. New York: Oxford University Press, 1995.

–––––––. The Origins of Order: Self‐Organization and Selection in Evolution. Oxford: Oxford University Press, 1993.

–––––––. ʺThe Sciences of Complexity and ʹOrigins of Order.ʹʺ Santa Fe Institute, 1991, technical report 91‐04‐021.

Kaufmann, William J. and Larry L. Smarr. Supercomputing and the Transformation of Science. New York: Scientific American Library, 1993.

Kay, Alan C. ʺComputers, Networks and Education.ʺ Scientific American, September 1991.

Kelly, Kevin. Out of Control: The New Biology of Machines, Social Systems and the Economic World. Reading, MA: Addison‐Wesley, 1994.

Kent, Ernest W. The Brains of Men and Machines. Peterborough, NH: BYTE/McGraw‐Hill, 1981.

Kidder, Tracy. The Soul of a New Machine. London: Allen Lane. 1982.

Kirk, G. S., J. E. Raven, and M. Schofield. The Presocratic Philosophers. Cambridge: Cambridge University Press, 1983.

Kleene, Stephen Cole. Introduction to Metamathematics. New York: D. Van Nostrand, 1952.

Kline, Morris. Mathematics and the Search for Knowledge. Oxford: Oxford University Press, 1985.

Klivington, Kenneth A. The Science of Mind. Cambridge, MA: MIT Press, 1989.

Klix, Friedhart, ed. Human and Artificial Intelligence. Amsterdam: North Holland, 1979.

Knorr, Wilbur Richard. The Ancient Tradition of Geometric Problems. Boston: Birkhauser, 1986.

Kobayashi, K i. Computers and Communications: A Vision of C & C. Cambridge, MA: MIT Press, 1986.

Kohonen, Teuvo. Self‐Organization and Associative Memory. Berlin: Springer‐Verlag, 1984.

Kosslyn, Stephen M. Image and Brain: The Resolution of the Imagery Debate. Cambridge, MA: MIT Press, 1996.

Koza, John R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.

Krauss, Lawrence N. The Physics of Star Trek. New York: Basic Books, 1995.

Kullander, Sven and Bole Larsson. Out of Sight! From Quarks to Living Cells. Cambridge: Cambridge University Press, 1994.

Kuno, Susumu. Functional Syntax: Anaphora, Discourse, and Empathy. Chicago: University of Chicago Press, 1987.

Kurzweil, Raymond. The Age of Intelligent Machines. Cambridge, MA: MIT Press, 1990.

–––––––. The Age of Spiritual Machines: When Computers Exceed Human Intelligence. New York: Viking Penguin, 1999.

–––––––. ʺWhen Will HAL Understand What We Are Saying? Computer Speech Recognition and Understanding.ʺ

Chapter in HALʹs Legacy: 2001ʹs Computer as Dream & Reality. Edited by David G. Stork. Cambridge, MA: MIT Press, 1996.

–––––––. The 10% Solution for a Healthy Life: How to Eliminate Virtually All Risk of Heart Disease and Cancer. New York: Crown Publishers, 1993.

Lammers, Susan. Programmers at Work: Interviews. Redmond, WA: Microsoft Press, 1986.

Landes, David S. Revolution in Time: Clocks and the Making of the Modern World. Cambridge, MA: Harvard University Press, 1983.

Landreth, Bill. Out of the Inner Circle: A Hackerʹs Guide to Computer Security. Bellevue, WA: Microsoft Press, 1985.

Langley, Pat, Herbert A. Simon, Gary L. Bradshaw, and Jan M. Zytkow. Scientific Discovery: Computational Explorations of the Creative Process. Cambridge, MA: MIT Press, 1987.

Langton, Christopher G., ed. Artificial Life: An Overview. Cambridge, MA: MIT Press, 1997.

Lasserre, Francois. The Birth of Mathematics in the Age of Plato. New York: World Publishing Co., 1964.

Latil, Pierre de. Thinking by Machine: A Study of Cybernetics. Boston: Houghton‐Mifflin. 1956.

Laver, Murray Computers and Social Change. Cambridge: Cambridge University Press, 1980.

Lea, Wayne A., ed. Trends in Speech Recognition. Englewood Cliffs, NJ: Prentice‐Hall, 1980.

Leavitt, Ruth, ed. Artist and Computer. Morristown, NJ: Creative Computing Press, 1976.

Lee, Kai‐Fu and Raj Reddy. Automatic Speech Recognition: The Development of the SPHINX Recognition System. Boston: Kluwer, 1989.

Lee, Thomas F. The Human Genome Project: Cracking the Genetic Code of Life. New York: Plenum Press, 1991.

Leebaert, Derek, ed. Technology 2001: The Future of Computing and Communications. Cambridge, MA: MIT Press, 1991.

Leibniz, Gottfried Wilhelm. Philosophical Writings. Ed. G. H. R. Parkinson. London and Toronto: J. M. Dent and Sons, 1973.

Leibniz, Gottfried Wilhelm and Samuel Clarke. The Leibniz‐Clarke Correspondence. Ed. H. G. Alexander. Manchester, UK: Manchester University Press, 1956.

Lenat, Douglas B. ʺThe Heuristics of Nature: The Plausible Mutation of DNA.ʺ Stanford Heuristic Programming Project, 1980, technical report HPP‐80‐27.

Lenat, Douglas B. and R. V Guha. Building Large Knowledge‐Based Systems: Representation and Inference in the CYC

Project. Reading, MA: Addison‐Wesley, 1990.

Leontief, Wassily W. The Impact of Automation on Employment, 1963–2000, Institute for Economic Analysis, New York University, 1984.

Leontief, Wassily W. and Faye Duchin, eds. The Future Impact of Automation on Workers. Oxford: Oxford University Press, 1986.

Lettvin, J. Y., U. Maturana, W. McCulloch, and W. Pitts. ʺWhat the Frogʹs Eye Tells the Frogʹs Brain.ʺ Proceedings of the IRE, 47 (1959). Levy, Steven. Artificial Life: The Quest for a New Creation. New York: Pantheon Books, 1992.

–––––––. Hackers: Heroes of the Computer Revolution. Garden City, NY: Anchor Press/Doubleday, 1968.

Lewin, Roger. Complexity: Life at the Edge of Chaos. New York: Macmillan, 1992.

–––––––. In the Age of Mankind: A Smithsonian Book of Human Evolution. Washington, D.C.: Smithsonian Books, 1988.

–––––––. Thread of Life: The Smithsonian Looks at Evolution. Washington, D.C.: Smithsonian Books, 1982.

Lieff, Jonathan D. (M.D.) Computer Applications in Psychiatry. Washington, D.C.: American Psychiatric Press, 1987.

Lloyd, G. E. R. Aristotle: The Growth and Structure of His Thought. Cambridge: Cambridge University Press, 1968.

–––––––. Early Greek Science: Thales to Aristotle. New York: W. W. Norton, 1970.

Locke, John. Essay Concerning Human Understanding. London, 1690.

Lord, Norman W. and Paul A. Guagosian. Advanced Computers: Parallel and Biochip Processors. Ann Arbor, MI: Ann Arbor Science, Butterworth Group, 1983.

Lowe, David G. Perceptual Organization and Visual Recognition. Boston: Kluwer Academic, 1985.

Lubar, Steven. Infoculture: The Smithsonian Book of Information Age Inventions. Boston: Houghton‐Mifflin Company, 1993.

Luce, R. D. and H. Raiffa. Games and Decisions. New York: John Wiley and Sons, 1957.

Lucky, Robert W. Silicon Dreams: Information, Man, and Machine. New York: St. Martinʹs Press, 1989.

Macey, Samuel L. Clocks and the Cosmos: Time in Western Life and Thought. Hamden: Archon Books, 1980.

Maes, Pattie. Designing Autonomous Agents. Cambridge, MA: MIT Press, 1991.

Magnenat‐Thalmann, Nadia and Daniel Thalmann. Computer Animation: Theory and Practice. Tokyo: Springer‐Verlag, 1985.

Malcolm, Norman. Ludwig Wittgenstein: A Memoir, with a Biographical Sketch by Georg Henrik Von Wright. Oxford: Oxford University Press, 1958.

Mamdam, E. H. and B. R. Gaines. Fuzzy Reasoning and Its Applications. London: Academic Press, 1981.

Mandelbrot, Benoit B. The Fractal Geometry of Nature. New York: W. H. Freeman, 1988.

–––––––. Fractals: Form, Chance, and Dimension. San Francisco: W. H. Freeman, 1977.

Mander, Jerry. In the Absence of the Sacred: The Failure of Technology and the Survival of the Indian Nations. San Francisco: Sierra Club Books, 1992.

Margulis, Lynn and Dorion Sagan. Microcosmos: Four Billion Years of Evolution from Our Microbial Ancestors. New York: Summit Books, 1986.

Markle, Sandra and William Markle. In Search of Graphics: Adventures in Computer Art. New York: Lothrop, Lee and Shepard Books, 1985.

Markoff, John. ʺThe Creature That Lives in Pittsburgh.ʺ New York Times, April 21, 1991.

Markov, A. The Theory of Algorithms. Moscow: National Academy of Sciences, USSR, 1954.

Marr, D. Vision. New York: W H. Freeman, 1982.

Martin, James and Steven Oxman. Building Expert Systems: A Tutorial. Englewood Cliffs, NJ: Prentice‐Hall, 1988.

Martin, William A., K. W Church, and R. S. Patil. ʺPreliminary Analysis of a Breadth‐First Parsing Algorithm: Theoretical and Experiential Results.ʺ Cambridge, MA: MIT Laboratory for Computer Science, 1981.

Marx, Leo. The Machine in the Garden: Technology and the Pastoral ideal in America. London: Oxford University Press, 1964.

Mason, Matthew T. and Kenneth Salisbury, Jr. Robot Hands and the Mechanics of Manipulation. Cambridge, MA: MIT

Press, 1985.

Massaro, D. W, et al. Letter and Word Perception: Orthographic Structure and Visual Processing in Reading. Amsterdam: North Holland, 1980.

Mathews, Max V. The Technology of Computer Music. Cambridge, MA: MIT Press, 1969.

Mayr, Ernst. Animal Species and Evolution. Cambridge, MA: Harvard University Press, 1963.

–––––––. Toward a New Philosophy of Biology. Cambridge, MA: Harvard University Press, 1988.

Mayr, Otto. Authority, Liberty, and Automatic Machinery in Early Modern Europe. Baltimore, MD: Johns Hopkins University Press, 1986.

Mazlish, Bruce. The Fourth Discontinuity: The Co‐Evolution of Humans and Machines. New Haven, CT: Yale University Press, 1993.

McClelland, James L. and David E. Rumelhart. Parallel Distributed Processing Explorations in the Microstructure of Cognition Volume 1. Cambridge, MA: MIT Press, 1986.

–––––––. Parallel Distributed Processing: Explorations in the Microstructure of Cognition Volume 2. Cambridge, MA: MIT

Press, 1986.

McCorduck, Pamela. Aaronʹs Code: MetaArt, Artificial Intelligence, and the Work of Harold Cohen. New York: W. H.

Freeman, 1991.

–––––––. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. San Francisco: W.

H. Freeman, 1979.

McCulloch, Warren S. An Account of the First Three Conferences of Teleological Mechanisms. Josiah Macy, Jr. Foundation, 1947.

–––––––. Embodiments Of Mind. Cambridge, MA: MIT Press, 1965.

McLuhan, Marshall. The Medium Is the Message. New York: Bantam Books, 1967.

–––––––. Understanding Media: The Extension of Man. New York: McGraw‐Hill, 1964.

Mcrae, Hamish. The World in 2020: Power Culture, and Prosperity. Cambridge, MA: Harvard Business School Press, 1994.

Mead, Carver, Analog VLSI Implementation of Neural Systems. Reading, MA: Addison‐Wesley, 1989.

Mead, Carver and Lynn Conway. Introduction to VLSI Systems. Reading, MA: Addison‐Wesley, 1980.

Meisel, William S. Computer‐Oriented Approaches to Pattern Recognition. New York: Academic Press, 1972.

Mel, Bartlett W. Connectionist Robot Motion Planning: A Neurally‐Inspired Approach to Visually‐Guided Reaching. Boston: Academic Press, 1990.

Metropolis, N. J. Howlett, and Gian‐Carlo Rota, eds. A History of Computing in the Twentieth Century. New York: Academic Press, 1980.

Miller, Eric, ed. Future Vision: The 189 Most Important Trends of the 1990s. Naperville, IL: Sourcebooks Trade, 1991.

Minsky, Marvin. Computation: Finite and Infinite Machines. Englewood Cliffs, NJ: Prentice‐Hall, 1967.

–––––––. ʺA Framework for Representing Knowledge.ʺ In The Psychology of Computer Vision, edited by P. H. Winston.

New York: McGraw‐Hill, 1975.

–––––––. The Society of Mind. New York: Simon and Schuster, 1985.

–––––––, ed. Robotics. New York: Doubleday, 1985.

–––––––, ed. Semantic Information Processing. Cambridge, MA: MIT Press, 1968.

Minsky, Marvin and Seymour A. Papert. Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT

Press, 1969 (revised edition, 1988).

Mitchell, Melanie. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press, 1996.

Mohr, Richard R. The Platonic Cosmolity. Leiden, Netherlands: E. J. Brill, 1985.

Moore, Thomas J. Lifespan: New Perspectives on Extending Human Longevity. New York: Simon and Schuster, 1993.

Moore, Walter. Schrodinger: Life and Thought. Cambridge: Cambridge University Press, 1989.

Moravec, Hans. Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press, 1988.

Morgan, Christopher R, ed. The ʺByteʹ Book of Computer Music. Peterborough, NH: Byte Books, 1979.

Morowitz, Harold J. and Jerome L. Singer. The Mind, the Brain, and Complex Adaptive Systems. Reading, MA: Addison-Wesley, 1995.

Morris, Desmond. The Naked Ape: A Zoologistʹs Study of the Human Animal. New York: McGraw‐Hill, 1967.

Morse, Stephen S., ed. Emerging Viruses. Oxford: Oxford University Press, 1997.

Mumford, Lewis. The Myth of the Machine: Technics and Human Development. New York: Harcourt Brace and World, 1967.

Murphy, Pat. By Natureʹs Design. San Francisco: Chronicle Books, 1993.

Murray, David W. and Bernard E Buxton. Experiments in the Machine Interpretation of Visual Motion. Cambridge, MA: MIT Press, 1990.

Myers, Terry, John Laver, and John Anderson, eds. The Cognitive Representation of Speech. Amsterdam: North Holland, 1981.

Naisbitt, John. Global Paradox: The Bigger the World Economy, the More Powerful Its Smallest Players. New York: William Morrow, 1994.

Naisbitt, John and Patricia Aburdene. Megatrends 2000: Ten New Directions for the 1990s. New York: William Morrow, 1990.

–––––––. Re‐Inventing the Corporation: Transforming Your Job and Your Company for the New Information Society. New York: Warner Books, 1985.

Nayak, P. Ranganath and John M. Ketteringham . Breakthroughs! How the Vision and Drive Of Innovators in Sixteen Companies Created Commercial Breakthroughs That Swept the World. New York: Arthur D. Little, 1986.

Negroponte, Nicholas. Being Digital. New York: Alfred A. Knopf, 1995.

–––––––. ʺProducts and Services for Computer Networks,ʺ Scientific American, September 1991.

Neuberger, A. R The Technical Arts and Sciences of the Ancients. London: Methuen, 1930.

Newell, Allen. Intellectual Issues in the History of Artificial Intelligence. Pittsburgh, PA: Carnegie Mellon University, 1982.

–––––––. The Unified Theories of Cognition. Cambridge, MA: Harvard University Press, 1990.

Newell, Allen and Herbert A. Simon. Human Problem Solving. Englewood Cliffs, NJ: Prentice‐Hall, 1972.

Newell, Allen, et al. ʺSpeech Understanding Systems: Final Report of a Study Group.ʺ Computer Science Department,

Carnegie Mellon University, Pittsburgh, May 1971.

Newmeyer, Frederick J. Linguistic Theory in America. Second ed. Orlando, FL: Academic Press, 1986.

Newton, Isaac. Philosophiae Naturalis Principia Mathematica. Third ed. Cambridge, MA: Havard University Press, 1972.

Original, 1726.

Nierenberg, Gerard. The Art of Creative Thinking. New York: Simon and Schuster, 1982.

Nilsson, Lennart. The Body Victorious: The Illustrated Story of Our Immune System and Other Defenses of the Human Body.

Trans. Clare James. New York: Delacorte Press, 1985.

Nilsson, Nils J. Principles of Artificial Intelligence. Los Altos, CA: Morgan Kaufmann, 1980.

Nilsson, Nils J. and Bonnie Lynn Webber. Readings in Artificial Intelligence. Los Altos, CA: Morgan Kaufmann, 1985.

Nocera, Joseph. A Piece of the Action: How the Middle Class joined the Money Class. New York: Simon and Schuster, 1994.

Norretranders, Tor. The User Illusion: Cutting Consciousness Down to Size, New York: Viking, 1998.

OʹKeefe, Bernard J. Nuclear Hostages. Boston: Houghton‐Mifflin Company, 1983.

Oakley, D. A., ed. Brain and Mind. London and New York: Methuen, 1985.

Oliver, Dick. Fractalvision: Put Fractals to Work for You. Carmel, IN: Sams Publishing, 1992.

Ornstein, Robert. The Evolution of Consciousness: Of Darwin, Freud, and Cranial Fire; the origins of the Way We Think. New York: Prentice‐Hall Press, 1991.

–––––––. The Mind Field. London: Octagon Press, 1976.

–––––––. Multimind: A New Way of Looking at Human Behavior. Boston: Houghton‐Mifflin, 1986.

–––––––. On the Experience of Time. London: Penguin Books, 1969.

–––––––. The psychology of Consciousness. Second ed. New York: Harcourt Brace Jovanovich, 1972.

–––––––, ed. The Nature of Human Consciousness: A Book of Readings. New York: Viking, 1973.

Ornstein, Robert and Paul Ehrlich. New World, New Mind: Moving Toward Conscious Evolution. New York: Doubleday, 1989.

Ornstein, Robert and D. S. Sobel. The Healing Brain. New York: Simon and Schuster, 1987.

Ornstein, Robert and Richard F. Thompson. The Amazing Brain. Boston: Houghton‐Mifflin, 1984.

Osherson, Daniel N., Michael Stob, and Scott Weinstein. Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. Cambridge, MA: MIT Press, 1986.

Ouellette, Pierre. The Deus Machine. New York: Villard Books, 1994.

Owen, G. The Universe of the Mind. Baltimore, MD: Johns Hopkins University Press, 1971.

Pagels, Heinz R. The Cosmic Code: Quantum Physics as the Language of Nature. New York: Bantam Books, 1983.

–––––––. The Dreams of Reason: The Computer and the Rise of the Sciences of Complexity. New York: Bantam Books, 1988.

–––––––. Perfect Symmetry: The Search for the Beginning of Time. New York: Bantam Books, 1986.

Papert, Seymour. The Childrenʹs Machine: Rethinking School in the Age of the Computer. New York: Basic Books, 1993.

–––––––. Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books, 1980.

Pascal, Blaise. Pensees. New York: E. R Dutton, 1932. Original, 1670.

Paul, Gregory S. and Earl D. Cox. Beyond Humanity: Cyberevolution and Future Minds. Rockland, MA: Charles River Media, 1996.

Paul, Richard P. Robot Manipulators: Mathematics, Programming, and Control. Cambridge, MA: MIT Press, 1981.

Paulos, John Allen. Beyond Numeracy: Ruminations of a Numbers Man. New York: Alfred A. Knopf, 1991.

Pavlov, I. P. Conditioned Reflexes. London: Oxford University Press, 1927.

Peat, F. David. Artificial Intelligence: How Machines Think. New York: Baen Enterprises, 1985.

–––––––. Synchronicity: The Bridge Between Matter and Mind. Toronto: Bantam Books, 1987.

Peitgen, H. O., D. Saupe, et al. The Science of Fractal Images. New York: Springer‐Verlag, 1988.

Peitgen, H. O., and P. H. Richter. The Beauty of Fractals: Images of Complex Dynamical Systems. Berlin: Springer‐Verlag, 1986.

Penfield, W. The Mystery of the Mind. Princeton, NJ: Princeton University Press, 1975.

Penrose, R. and C. J. Isham, eds. Quantum Concepts in Space and Time. Oxford: Oxford University Press: 1986.

Penrose, Roger. The Emperorʹs New Mind: Concerning Computers, Minds, and the Laws of Physics. New York: Oxford University Press, 1989.

–––––––. Shadows of the Mind. Oxford: Oxford University Press, 1994.

Pentland, Alex P., ed. From Pixels to Predicates: Recent Advances in Computational and Robotic Vision. Norwood, NJ: Ablex Publishing Corporation, 1986.

Peterson, Dale. Genesis II: Creation and Recreation with Computers. Reston, VA: Reston Publishing Co., 1983.

Petroski, Henry. To Engineer Is Human: The Role of Failure in Successful Design. New York: St. Martinʹs Press, 1985.

Piaget, Jean. The Psychology of Intelligence. London: Routledge and Kegan Paul, 1967.

Pickover, Clifford A. Computers and the Imagination: Visual Adventures Beyond the Edge. New York: St. Martinʹs Press, 1991.

Pierce, John R. The Science of Musical Sound. New York: Scientific American Books, 1983.

Pines, David, ed. Emerging Syntheses in Science. Reading, MA: Addison‐Wesley, 1988.

Pinker, Steven. How the Mind Works. New York: W. W. Norton and Company, 1997

–––––––. The Language Instinct. New York: William Morrow, 1994.

–––––––. Language Learnability and Language Development. Cambridge, MA: Harvard University Press, 1984.

–––––––. Learnability and Cognition: The Acquisition of Argument Structure. Cambridge, MA: MIT Press, 1989.

–––––––. ed. Visual Cognition. Cambridge, MA: MIT Press, 1984

Pinker, Steven and J. Mehler, eds. Connections and Symbols. Cambridge, MA: MIT Press, 1988.

Plato. Epinomis. The Loeb Classical Library. Ed. W. R. M. Lamb. Vol. 8. New York: G. P. Putnamʹs Sons, 1927.

–––––––. Protagoras and Meno. Baltimore, MD: Penguin Books, 1956.

–––––––. Timaeus. Indianapolis, IN: Bobbs‐Merrill, 1959.

Pollock, John. How to Build a Person: A Prolegomenon. Cambridge, MA: MIT Press, 1989.

Poole, Robert M. The incredible Machine. Washington, D.C.: The National Geographic Society, 1986.

Poppel, Ernst. Mindworks: Time and Conscious Experience. Boston: Harcourt Brace Jovanovich, 1988.

Popper, Karl and John Eccles. The Self and Its Brain. Berlin, London: Springer‐Verlag, 1977.

Posner, Michael I. and Marcus E. Raichle. Images of Mind. New York: Scientific American Library, 1994.

Potter, Jerry L., ed. The Massively Parallel Processor. Cambridge, MA: MIT Press, 1985.

Poundstone, William. Prisonerʹs Dilemma. New York: Doubleday, 1992.

–––––––. The Recursive Universe: Cosmic Complexity and the Limits of Scientific Knowledge. New York: William Morrow, 1985.

Pratt, Vernon. Thinking Machines: The Evolution of Artificial Intelligence. New York: Basil Blackwell, 1987.

Pratt, William K. Digital Image Processing. New York: John Wiley and Sons, 1978.

Price, Derek J. de Solla. Gears from the Greeks: The Antikythera Mechanism—A Calendar Computer from Circa 80 B.C. New York: Science History Publications, 1975.

Prigogine, Ilya. The End of Certainty: Timeʹs Flow and the Laws of Nature. New York: Simon and Schuster, 1997.

Prueitt, Melvin L. Art and the Computer. New York: McGraw‐Hill, 1984.

Prusinkiewicz, Przemyslaw and Aristid Lindenmayer. The Algorithmic Beauty of Plants. New York: Springer‐Verlag, 1990.

Rabiner, Lawrence R. and Ronald W. Schafer. Digital Processing of Speech Signals. Englewood Cliffs, NJ: Prentice‐Hall, 1978.

RACTER. The Policemanʹs Beard Is Half Constructed: Computer Prose and Poetry by RACTER. [William Chamberlain and Joan Hall. I New York: Warner Books, 1984.

Radford, Andrew Transformational Syntax: A Studentʹs Guide to Chomskyʹs Extended Standard Theory. Cambridge: Cambridge University Press, 1981.

Raibert, Marc H. Legged Robots That Balance. Cambridge, MA: MIT Press, 1986.

Randell, Brian, ed. The Origins of Digital Computers: Selected Papers. New York: Springer‐Verlag, 1975.

Raphael, Bertram. The Thinking Computer: Mind Inside Matter. San Francisco: W. H. Freeman, 1976.

Rasmussen, S., et al. ʺComputational Connectionism Within Neurons: A Model of Cytoskeletal Automata Subserving

Neural Networks,ʺ Emergent Computation. Edited by Stephanie Forrest. Cambridge, MA: MIT Press, 1991.

Raup, David M. Extinction: Bad Genes or Bad Luck? New York: W. W. Norton, 1991.

Rawlings, Gregory J. E. Moths to the Flame: The Seductions of Computer Technology. Cambridge, MA: MIT Press, 1996.

Ree, Jonathan. Descartes. New York: Pica Press, 1974.

Reichardt, Jasia. Robots: Fact, Fiction and Prediction. Middlesex, UK: Penguin Books, 1978.

Reid, Robert H. Architects of the Web: 1,000 Days That Built the Future of Business. New York: John Wiley and Sons, 1997.

Restak, Richard M. (M.D.) The Brain. Toronto: Bantam Books, 1984.

Rheingold, Howard. Virtual Reality. New York: Summit Books, 1991.

Rich, Elaine. Artificial Intelligence. New York: McGraw‐Hill, 1983.

Rich, Elaine and Kevin Knight. Artificial Intelligence. Second ed. New York: McGraw‐Hill, 1991.

Ringle, Martin D., ed. Philosophical Perspectives in Artificial Intelligence. Brighton, Sussex: Harvester Press, 1979.

Roads, Curtis, ed. Composers and the Computer. Los Altos, CA: William Kaufmann, 1985.

–––––––, ed. The Music Machine: Selected Readings from ʺComputer Music Journal.ʺ Cambridge, MA: MIT Press, 1988.

Roads, Curtis and John Strawn. Foundations of Computer Music. Cambridge, MA: MIT Press, 1989.

Robin, Harry and Daniel J. Kevles. The Scientific Image: From Cave to Computer. New York: Harry N. Abrams, 1992.

Rock, Irvin. Perception. New York: Scientific American Books, 1984.

Rogers, David F. and Rae A. Ernshaw, eds. Computer Graphics Techniques: Theory and Practice. New York: Springer-Verlag, 1990.

Rose, Frank. Into the Heart of the Mind: An American Quest for Artificial Intelligence. New York: Vintage Books, 1984.

Rosenberg, Jerry M. Dictionary of Artificial Intelligence and Robotics. New York: John Wiley and Sons, 1986.

Rosenblatt, Frank. Principles of Neurodynamics. New York: Spartan, 1962.

Rosenfield, Israel. The Invention of Memory: A New View of the Brain. New York: Basic Books, 1988.

Rothchild, Joan, ed. Machina ex Dea: Feminist Perspectives on Technology. New York: Pergamon Press, 1982.

Rothschild, Michael. Bionomics: The Inevitability of Capitalism. New York: Henry Holt and Company, 1990.

Rucker, Rudy. Infinity and the Mind. Boston: Birkhauser, 1982.

–––––––. Mind Tools: The Five Levels of Mathematical Reality. Boston: Houghton‐Mifflin Company, 1987.

–––––––. Software. Middlesex, UK: Penguin Books, 1983.

Rumelhart, D. E., J. L. Mcclelland, and the PDP Research Group. Parallel Distributed Processing. Vols. 1 and 2.

Cambridge, MA: MIT Press, 1982.

Russell, Bertrand. The ABC of Relativity. Fourth ed. 1925. Reprint. London: Allen and Unwin, 1985.

–––––––. The Autobiography of Bertrand Russell: 1872–1914. Toronto: Bantam Books, 1967.

–––––––. The Autobiography of Bertrand Russell: 1914–1944. Toronto: Bantam Books, 1968.

–––––––. A History of Western Philosophy. New York: Simon and Schuster, 1945.

–––––––. Introduction to Mathematical Philosophy. New York: Macmillan, 1919.

–––––––. Mysticism and Logic. New York: Doubleday Anchor Books, 1957.

–––––––. The Principles of Mathematics. Reprint. New York: W. W. Norton & Company, 1996.

–––––––. The Problems of Philosophy. New York: Oxford University Press, 1959.

Russell, Peter. The Global Brain: Speculations on the Evolutionary Leap to Planetary Consciousness. Los Angeles: J. R

Tarcher, 1976.

Sabbagh, Karl. The Living Body. London: MacDonald & Company, 1984.

Sacks, Oliver. The Man Who Mistook His Wife for a Hat and Other Clinical Tales. New York: Harper and Row, 1985.

Sagan, Carl. Contact. New York: Simon and Schuster, 1985.

–––––––. The Dragons of Eden: Speculations on the Evolution of Human Intelligence. New York: Ballantine Books, 1977.

–––––––. ed. Communication with Extraterrestrial Intelligence. Cambridge, MA: MIT Press, 1973.

Sambursky, S. The Physical World of the Greeks. London: Routledge and Kegan Paul, 1963. Original, 1956.

Sanderson, George and Frank Mcdonald, eds. Marshall Mcluhan: The Man and His Message. Golden, CO: Fulcrum, 1989.

Saunders, Peter I. ʺThe Complexity of Organisms.ʺ Evolutionary Theory: Paths into the Future, edited by J. W Pollard.

New York: John Wiley and Sons, 1984.

Savage, John E., Susan Magidson, and Alex M. Stein. The Mystical, Machine: Issues and Ideas in Computing. Reading, MA: Addison‐Wesley, 1986.

Saxby Graham. Holograms: How to Make and Display Them. London: Focal Press, 1980.

Sayre, Kenneth M. and Frederick J. Crosson. The Modeling of Mind: Computers and Intelligence. New York: Simon and Schuster, 1963.

Schank, Roger. The Creative Attitude: Learning to Ask and Answer the Right Questions. New York: Macmillan Publishing Company, 1988.

–––––––. Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge: Cambridge University Press, 1982.

–––––––. Tell Me a Story: A New Look at Real and Artificial Memory. New York: Charles Scribnerʹs Sons, 1990.

Schank, Roger C. and Kenneth Mark Colby, eds. Computer Models of Thought and Language. San Francisco: W. H.

Freeman, 1973.

Schank, Roger [with Peter G. Childers]. The Cognitive Computer: On Language, Learning, and Artificial Intelligence.

Reading, MA: Addison‐Wesley, 1984.

Schilpp, R A., ed. The Philosophy of Bertrand Russell. Chicago: Chicago University Press, 1944.

Schon, Donald A . Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions.

San Francisco: Jossey‐Bass, 1987.

Schorr, Herbert and Alain Rappaport, eds. Innovative Applications of Artificial Intelligence. Menlo Park, CA: AAAI Press, 1989.

Schrodinger, Erwin. What Is Life? Cambridge: Cambridge University Press, 1967.

Schull, Jonathan. ʺAre Species Intelligent?ʺ Behavioral and Brain Sciences 13:1 (1990).

Schulmeyer, G. Gordon. Zero Defect Software. New York: McGraw‐Hill, 1990.

Schwartz, Lillian F. The Computer Artistʹs Handbook: Concepts, Techniques, and Applications. New York: W. W. Norton and Company, 1992.

Searle, John R. ʺMinds, Brains, and Programs.ʺ The Behavioral and Brain Sciences. Vol. 3. Cambridge: Cambridge University Press, 1980.

–––––––. Minds, Brains and Science. Cambridge, MA: Harvard University Press, 1985.

–––––––. The Rediscovery of the Mind. Cambridge, MA: MIT Press, 1992.

Sejnowski, T. and C. Rosenberg. ʺParallel Networks That Learn to Pronounce English Text.ʺ Complex Systems 1 (1987).

Serra, Jean, ed Image Analysis and Mathematical Morphology. Vol. 1. London: Academic Press, 1988.

–––––––. ed. Image Analysis and Mathematical Morphology. Vol. 2: Theoretical Advances. London: Academic Press, 1988.

Shapiro, Stuart D., ed. Encyclopedia of Artificial Intelligence. 2 vols. New York: John Wiley and Sons, 1987.

–––––––, Sharples, M. D., et al. Computers and Thought: A Practical Introduction to Artificial Intelligence. Cambridge, MA: MIT Press, 1989.

Shear, Jonathan, ed. Explaining Consciousness—The ʺHardʺ Problem. Cambridge, MA: MIT Press, 1995–1997.

Shortliffe, E. MYCIN: Computer‐Based Medical Consultations. New York: American Elsevier, 1976.

Shurkin, Joel. Engines of the Mind: A History of the Computer. New York: W. W. Norton, 1984.

Siekmann, Jorg and Graham Wrightson. Automation of Reasoning 1: Classical Papers on Computational Logic 1957–1966.

Berlin: Springer‐Verlag, 1983.

–––––––. Automation of Reasoning 2: Classical Papers on Computational Logic 1967–1970. Berlin: Springer‐Verlag, 1983.

Simon, Herbert A. Models of My Life. New York: Basic Books, 1991.

–––––––. The Sciences of the Artificial. Cambridge, MA: MIT Press, 1969.

Simon, Herbert A. and Allen Newell. ʺHeuristic Problem Solving: The Next Advance in Operations Research.ʺ

Operations Research. Vol. 6. 1958.

Simon, Herbert A. and L. Siklossy, eds. Representation and Meaning: Experiments with Information Processing Systems.

Englewood Cliffs, NJ: Prentice‐Hall, 1972.

Simpson, George Gaylord. The Meaning of Evolution. The New American Library of World Literature. New York: A Mentor Book, 1951.

Singer, C., E. J. Holmyard, A. R. Hall, and T. I. Williams, eds. A History of Technology. 5 vols. Oxford: Oxford University Press, 1954–1958.

Singer, Michael A. The Search for Truth. Alachua, FL: Shanti Publications, 1974.

Slater, Robert. Portraits in Silicon. Cambridge, MA: MIT Press, 1987.

Smith, John Maynard. Did Darwin Get It Right? Essays on Games, Sex and Evolution. New York: Chapman and Hall, 1989.

Smullyan, Raymond. Forever Undecided: A Puzzle Guide to Gödel. New York: Alfred A. Knopf, 1987.

Solso, Robert L. Mind and Brain Sciences in the 21st Century. Cambridge, MA: MIT Press, 1997.

Soltzberg, Leonard J. Sing a Song of Software: Verse and Images for the Computer‐Literate. Los Altos, CA: William Kaufmann, 1984.

Soucek, Branko and Marina Soucek. Neural and Massively Parallel Computers: The Sixth‐Generation. New York: John Wiley and Sons, 1988.

Spacks, Barry. The Company of Children. Garden City, NY: Doubleday and Company, 1969.

Spinosa, Charles, Hubert L. Dreyfus, and Fernando Flores. Disclosing New Worlds: Entrepreneurship, Democratic Action, and the Cultivation of Solidarity. Cambridge, MA: MIT Press 1997.

Stahl, Franklin W. The Mechanics of Inheritance. Englewood Cliffs, NJ: Prentice‐Hall, 1964, 1969.

Stein, Dorothy. Ada: A Life and a Legacy. Cambridge, MA: MIT Press, 1985.

Sternberg, Robert J., ed. Handbook of Human Intelligence. Cambridge: Cambridge University Press, 1982.

Sternberg, Robert J. and Douglas K. Detterman, eds. What is intelligence? Contemporary Viewpoints on its Nature and Definition. Norwood, NJ: Ablex Publishing Corporation, 1986.

Stewart, Ian. Does God Play Dice? New York: Basil Blackwell, 1989.

Stock, Gregory. Metaman: The Merging of Humans and Machines into a Global Superorganism. New York: Simon and Schuster, 1993.

Stork, David G. HAL Legacy: 2001ʹs Computer as Dream and Reality. Cambridge, MA: MIT Press, 1996.

Strassmann, Paul A. Information Payoff: The Transformation of Work in the Electronic Age. New York: The Free Press, 1985.

Talbot, Michael. The Holographic Universe. New York: Harpercollins, 1991.

Tanimoto, Steven L. The Elements of Artificial Intelligence: An Introduction Using LISP. Rockville, MD: Computer Science Press, 1987.

Taylor, E Sherwood. A Short History of Science and Scientific Thought. New York: W. W. Norton and Company, 1949.

Taylor, Philip A., ed. The Industrial Revolution in Britain: Triumph or Disaster? Lexington, MA: Heath, 1970.

Thearling, Kurt. ʺHow We Will Build a Machine That Thinks.ʺ A Workshop at Thinking Machines Corporation, August 24–26, 1992.

Thomas, Abraham. The Intuitive Algorithm. New Delhi: Affiliated East‐West PVT, 1991.

Thomis, Malcolm I. The Luddites: Machine Breaking in Regency England. Hamden, CT: Archon Books, 1970.

Thorpe, Charles E. Vision and Navigation: The Carnegie Mellon Navlab. Norwell, MA: Kluwer Academic, 1990.

Thurow, Lester C. The Future of Capitalism: How Todayʹs Economic Forces Shape Tomorrowʹs World. New York: William Morrow, 1996.

Time‐Life Books. Computer Images. Alexandria, VA: Time‐Life Books, 1986.

Tjepkema, Sandra L. A Bibliography of Computer Music: A Reference for Composers. Iowa City: University of Iowa Press, 1981.

Toepperwein, L. L., et al. Robotics Applications for industry: A Practical Guide. Park Ridge: Noyes Data Corporation, 1983.

Toffler, Alvin. Powershift. New York: Bantam Books, 1990.

–––––––. The Third Wave: The Classic Study of Tomorrow. New York: Bantam Books, 1980.

Toffoli, Tommaso and Norman Margolis. Cellular Automata Machines: A New Environment for Modeling. Cambridge, MA: MIT Press, 1987.

Torrance, Stephen B., ed. The Mind and the Machine: Philosophical Aspects of Artificial Intelligence. Chichester, UK: Ellis Horwood, 1986.

Traub, Joseph E, ed. Cohabiting with Computers. Los Altos, CA: William Kaufmann, 1985.

Truesdell, L. E. The Development of Punch Card Tabulation in the Bureau of the Census, 1890–1940. Washington, D.C.: Government Printing Office, 1965.

Tafte, Edward R. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press, 1983.

–––––––. Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press, 1997.

Turing, Alan. ʺComputing Machinery and Intelligence.ʺ Reprinted in Minds and Machines, edited by Alan Ross Anderson. Englewood Cliffs, NJ: Prentice‐Hall, 1964.

–––––––. ʺOn Computable Numbers, with an Application to the Entscheidungsproblemʺ Proceedings, London Mathematical Society, 2, no. 42 (1936).

Tarkle, Sherry. The Second Self: Computers and the Human Spirit. New York: Simon and Schuster, 1984.

Tye, Michael. Ten Problems of Consciousness: A Representational Theory of the Phenomenal Mind. Cambridge, MA: MIT

Press, 1995.

Ullman, Shimon. The Interpretation of Visual Motion. Cambridge, MA: MIT Press, 1982.

Usher, A. R A History of Mechanical Inventions. Second ed. Cambridge, MA: Harvard University Press, 1958.

Vaina, Lucia and Jaakko Hintikka, eds. Cognitive Constraints on Communication. Dordrecht, Netherlands: Reidel, 1985.

Van Heijenoort, Jean, ed. From Frege to Gödel. Cambridge, MA: Harvard University Press, 1967.

Varela, Francisco J., Evan Thompson, and Eleanor Rosch. The Embodied Mind: Cognitive Science and Human Experience.

Cambridge, MA: MIT Press, 1991.

Vigne, V. ʺTechnological Singularity.ʺ Whole Earth Review, Winter 1993.

von Neumann, John. The Computer and the Brain. New Haven, CT: Yale University Press, 1958.

Waddington, C. H. The Strategy of the Genes. London: George Allen and Unwin, 1957.

Waldrop, M. Mitchell. Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Simon and Schuster, 1992.

–––––––. Man‐Made Minds: The Promise of Artificial Intelligence. New York: Walker and Company, 1987.

Waltz, D. ʺMassively Parallel AI.ʺ Paper presented at the American Association of Artificial Intelligence (AAAI) conference, August 1990.

Waltz, David. Connectionist Models and Their Implications: Readings from Cognitive Science. Norwood, NJ: Ablex, 1987.

Wang, Dr. An. Lessons: An Autobiography. Reading, MA: Addison‐Wesley, 1986.

Wang, Hao. A Logical Journey: From Gödel to Philosophy. Cambridge, MA: MIT Press, 1996.

Warrick, Patricia S. The Cybernetic Imagination in Science Fiction. Cambridge, MA: MIT Press, 1980.

Watanabe, Satoshi. Pattern Recognition: Human and Mechanical. New York: John Wiley and Sons, 1985.

Waterman, D. A. and F. Hayes‐Roth, eds, Pattern‐Directed Inference Systems. Out of print.

Watson, J. B. Behaviorism. New York: Norton, 1925.

Watson, J. D. The Double Helix. New York: Atheneum, 1968.

Watt, Roger. Understanding Vision. London: Academic Press, 1991.

Webber, Bonnie Lynn and Nils J. Nilsson, eds. Readings in Artificial Intelligence. Los Altos, CA: Morgan Kaufmann, 1981.

Weinberg, Steven. Dreams of a Final Theory. New York: Pantheon Books, 1992.

–––––––. The First Three Minutes: A Modem View of the Origin of the Universe. New York: Pantheon Books, 1977.

Weiner, Jonathan. The Next One Hundred Years. New York: Bantam Books, 1990.

Weinstock, Neal. Computer Animation. Reading, MA: Addison‐Wesley, 1986.

Weiss, Sholom M. and Casimir A. Kulikowski. A Practical Guide to Designing Expert Systems. Totowa, NJ: Rowman and Allanheld, 1984.

Weizenbaum, Joseph. Computer Power and Human Reason. San Francisco: W H. Freeman, 1976.

Werner, Gerhard. ʺCognition as Self‐Organizing Process.ʺ Behavioral and Brain Sciences 10, 2:183.

Westfall, Richard. Never at Rest: A Biography of Isaac Newton. Cambridge: Cambridge University Press, 1980.

White, K. D. Greek and Roman Technology. London: Thames and Hudson, 1984.

Whitehead, Alfred N. and Bertrand Russell. Principia Mathematica. 3 vols. Second ed. Cambridge: Cambridge University Press, 1925–1927.

Wick, David. The Infamous Boundary: Seven Decades of Heresy in Quantum Physics. Boston: Birkhauser, 1995.

Wiener, Norbert. Cybernetics: or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press, 1965.

–––––––. God and Golem, Inc.: A Comment on Certain Points Where Cybernetics Impinges on Religion. Cambridge, MA: MIT

Press, 1985.

Wills, Christopher. The Runaway Brain: The Evolution of Human Uniqueness. New York: Basic Books, 1993.

Winkless, Nels and Then Browning. Robots on Your Doorstep: A Book About Thinking Machines. Portland, OR: Robotics Press, 1978.

Winner, Langdon. Autonomous Technology: Technics‐Out‐of‐Control as a Theme in Political Thought. Cambridge, MA: MIT

Press, 1977.

Winograd, Terry. Understanding Computers and Cognition. Norwood, NJ: Ablex, 1986.

–––––––. Understanding Natural Language. New York: Academic Press, 1972.

Winston, Patrick Henry. Artificial Intelligence. Reading, MA: Addison‐Wesley, 1984.

–––––––. The Psychology of Computer Vision. New York: McGraw‐Hill, 1975.

Winston, Patrick Henry and Richard Henry Brown, eds. Artificial Intelligence: An MIT Perspective. Vol. 1. Cambridge, MA: MIT Press, 1979.

–––––––, eds. Artificial Intelligence: An MIT Perspective. Vol. 2. Cambridge, MA: MIT Press, 1979.

Winston, Patrick Henry. and Karen A. Prendergast. The AI Business: Commercial Uses of Artificial Intelligence.

Cambridge, MA: MIT Press, 1984.

Wittgenstein, Ludwig. Philosophical Investigations. Oxford: Blackwell, 1953.

–––––––. Tractatus Logico‐Philosophicus. London: Routledge and Kegan Paul, 1961.

Yavelow, Christopher. Macworld Music and Sound Bible. San Mateo, CA: IDG Books Worldwide, 1992.

Yazdani, M. and A. Narayanan, eds. Artificial Intelligence: Human Effects. Chichester, UK: Ellis Horwood, 1984.

Yovits, M. C. and S. Cameron, eds. Self‐Organizing Systems. New York: Pergamon Press, 1960.

Zadeh, Lofti. Information and Control. Vol 8. New York: Academic Press, 1974.

Zeller, Eduard. Plato and the Older Academy. Reprint ed. New York: Russell and Russell, 1962.

Zue, Victor W, Francine R. Chen, and Lori Lamel. Speech Spectrogram Reading: An Acoustic Study of English Words and Sentences. Cambridge, MA: MIT Press. Lecture Notes and Spectrograms, July 26–30, 1982.

WEB LINKS

The following is a catalog organized by subject of World Wide Web sites relevant to topics in the book. Remember

that compared to books listed in a bibliography, web sites are not nearly as long lasting. These sites were all verified when the book went to press, but inevitably some will become inactive. The Web, unfortunately, is littered with

nonfunctioning sites.

SITES RELEVANT TO THE BOOK

Web site for the book The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil:

<http://www.penguinputnam.com/kurzweil>

To e‐mail the author:

raymond@kurzweiltech.com

To download a copy of Ray Kurzweilʹs Cybernetic Poet:

<http://www.kurzweiltech.com>

This bookʹs publisher, Viking:

<http://www.penguinputnam.com>

For publications of Ray Kurzweil:

Go to <http://www.kurzweiltech.com> or <http://www.kurzweiledu.com> and then select ʺPublicationsʺ

WEB SITES FOR COMPANIES FOUNDED BY RAY KURZWEIL

Kurzweil Educational Systems, Inc. (creator of print‐to‐speech reading systems for persons with reading disabilities and visual impairment):

<http://www.kurzweiledu.com>

Kurzweil Technologies, Inc. (creator of Ray Kurzweilʹs Cybernetic Poet and other software projects):

<http://www.kurzweiltech.com>

The dictation division of Lernout & Hauspie Speech Products (formerly Kurzweil Applied Intelligence, Inc.), creator of speech recognition and natural language software systems:

<http://www.lhs.com/dictation/>

The overall Lernout & Hauspie web site: <http://www.lhs.com/>

Kurzweil Music Systems, Inc., creator of computer‐based music synthesizers, sold to Young Chang in 1990:

<http://www.youngchang.com/kurzweil/index.html>

Textbridge Optical Character Recognition (OCR). Formerly Kurzweil OCR from Kurzweil Computer Products, Inc.

(sold to Xerox Corp. in 1980): <http://www.xerox.com/scansoft/textbridge/>

ARTIFICIAL LIFE AND ARTIFICIAL INTELLIGENCE RESEARCH

The Artificial Intelligence Laboratory at Massachusetts Institute of Technology (MIT):

<http://www.ai.mit.edu/>

Artificial Life Online:

<http://alife.santafe‐edu>

Contemporary Philosophy of Mind: An Annotated Bibliography:

<http://ling.ucsc.edu/~chalmers/biblio.html>

Machine Learning Laboratory, the University of Massachusetts, Amherst:

<http://www‐ml.cs.umass.edu/>

The MIT Media Lab:

<http://www.media.mit.edu/>

SSIE 58OB: Evolutionary Systems and Artificial Life, by Luis M. Rocha, Los Alamos National Laboratory:

<http://www.c3.lanl.gov/~rocha/ss504_02.html>

Stewart Deanʹs Guide to Artificial Life:

<http://www.webslave.dircon.co.uk/alife/intro.html>

ASTRONOMY/PHYSICS

American Institute of Physics:

<http://www.aip.org/history/einstein/>

International Astronomical Union (IAU):

<http://www.intastun.org/>

Introduction to the Big Bang Theory:

<http://www.bowdoin.edu/dept/physics/astro.1997/astro4/bigbang.ht ml>

BIOLOGY AND EVOLUTION

American Scientist Article: Reward Deficiency Syndrome:

<http://www.amsci.org/amsci/Articles/96Articles/Blum‐full.html>

Animal Diversity Web Site, the Museum of Zoology at the University of Michigan:

<http://www.oit.itd.umich.edu/projects/ADW/>

Charles Darwinʹs Origin of Species:

<http://www.literature.org/Works/Charles‐Darwin/origin/>

Evolution and Behavior:

<http://ccp.uchicago.edu/~jyin/evolution.html>

The Human Genome Project:

<http://www.nhgri.nih.gov/HGP/>

Information Processing in the Human Body:

<http://vadim.www.media.mit.edu/MAS862/Project.html>

Thomas Ray/Tierra:

<http://www.hip.atr.co.jp/~ray/>

The Visible Human Project:

<http://www.nlm.nih.gov/research/visible/visible human.html>

BRAIN IMAGING RESEARCH

Brain Research Web Page, Jeffrey H. Lake Research:

<http://www.brainresearch.com/>

Applications of brain research:

<http://www.brainresearch.com/apps.html>

Amiram Grinvaldʹs web site: Imaging the Brain in Action:

<http://www.weizmann.ac.il/brain/grinvald/grinvald.htm>

The Harvard Brain Tissue Resource Center:

<http://www.brainbank.mclean.org:8080>

The Mclean Hospital Brain Imaging Center:

<http://www.mclean.org:8080/>

Optical Imaging, Inc., Home Page:

<http://opt‐imaging.com/>

Research Imaging Center: Solving the Mysteries of the Mind, University of Texas Health Science Center at San

Antonio:

<http://biad63.uthscsa.edu/>

Visualization and Analysis of 3D Functional Brain Images, by Finn A rup Nielsen, Institute of Mathematical

Modeling, Section for Digital Signal Processing, former Electronics Institute, Technical University of Denmark:

<http://hendrix.ei.dtu.dk/staff/students/fnielsen/thesis/finn/fin n.html>

Weizmann Institute of Science:

<http://www.weizmann.ac.il/>

The Whole Brain Atlas:

<http://www.med.harvard.edu/AANLIB/home.html>

COMPUTER BUSINESS/MEDICAL APPLICATIONS

Automated Highway System DEMO; National AHS Consortium Home Page:

<http://monolith‐mis.com/ahs/default.htm>

Biometric (The Face Recognition Home Page):

<http://cherrykist.re.kr/center/html/sites.html>

Face Recognition Homepage:

<http://www.cs.rug.nl/~peterkr/FACE/face.html>

The Intelligent Vehicle Initiative: Advancing ʺHuman‐Centeredʺ Smart Vehicles:

<http://www.tfhrc.gov/pubrds/pr97‐10/p18.htm>

Kurzweil Educational Systems, Inc.:

<http://www.kurzweiledu.com/>

Kurzweil music (Welcome to Kurzweil Music Systems):

<http://www.youngchang.com/kurzweil/index.html>

Laboratory for Financial Engineering at MIT:

<http://web.rnit.edu/lfe/www/>

Lernout & Hauspie Speech Products:

<http://www.lhs.com>

Medical Symptoms Matching Software:

<http://www.ozemail.com.au/~lisadev/sftdocpu.htm>

Miros Company Information:

<http://www.miros.com,/About_Miros.htm>

Synaptics, Inc.:

<http://www.synaptics.com/>

Systran:

<http://www.systransoft.ccm/>

COMPUTERS AND ART/CREATIVITY

Arachnautʹs Lair ‐ Electronic Music Links:

<http://www.arachnaut.org/music/links.html>

Artspace: Computer Generated Art:

<http://www.uni.uiuc.edu/~artspace/compgen.html>

BRUTUS.1 Story Generator:

<http://www.rpi.edu/dept/ppcs/BRUTUS/brutus.html>

But Is It Computer Art?:

<http://www.cs.swarthmore.edu/~binde/art/index.html>

Computer Artworks, Ltd.:

<http://www.artworks.co.uk/welcome.htm>

Computer Generated Writing:

<http://www.notam.uio.no/~mariusw/c‐g.writing/>

Northwest Cyberartists: Time Warp of Past Events:

<http://www.nwlink.com/cyberartists/timewarp.html>

Music Software:

<http://www.yahoo.com/Entertainment/Music/Software/>

An OBS Cyberspace Extension of Being Digital, by Nicholas Negroponte:

<http://www.obs‐us.com/obs/english/books/nn/bdintro.htm>

Ray Kurzweilʹs Cybernetic Poet:

<http://www‐kurzweiltech.com>

Recommended Reading, Computer Art:

<http://ananke.advanced.org/3543/resourcessites.html>

Virtual Muse: Experiments in Computer Poetry:

<http://camel.conncoll.edu/ccother/cohar/programs/index.html>

COMPUTERS AND CONSCIOUSNESS/SPIRITUALITY

Considerations on the Human Consciousness:

<http://www.mediacom,it/~v.colaciuri/consc.htm>

Extropy Online, Arterati on Ideas, by Natasha Vita More; Vingeʹs View of the Singularity:

<http://www.extropycom/~exi/eo/articles/vinge.htm>

God and Computers:

<http://web.mit.edu/bpadams/www/gac/>

Kasparov vs. Deep Blue: The Rematch:

<http://www.nytimes.com/partners/microsites/chess/archive8.html>

Online papers on consciousness, compiled by David Chalmers:

<http://ling.ucsc.edu/~chalmers/mind.html>

Toward a Science of Consciousness 1998 ʺTucson III,ʺ Conference, The University of Arizona, Tucson, Arizona.

Support provided by the Fetzer institute and the Institute of Noetic Sciences:

<http://www.zynet.co.uk/imprint/Tucson/>

COMPUTING SCIENCE RESEARCH

Defining Virtual Reality, Industry Consortium in the Institute for Communication Research, Department of

Communication, Stanford University:

<http://www.cyborganic.com/people/jonathan/Academia/Papers/Web/de fining‐v.html>

Computer Games: Past, Present, Future:

<http://www.bluetongue.com/~pang/DRAFT.html>

The Haptics Community Web Page:

<http://haptic.mech.nwu.edu>

Modeling and Simulation: Linking Entertainment and Defense:

<http://www.nap.edu/readingroom/books/modeling/index.html>

Physics News Update Number 219 ‐ The Density of Data. A link to Lambertus Hesselinkʹs research on crystal

computing:

<http://www.aip.org/enews/physnews/1995/split/pnu2l9‐2.htm>

Student cracks encryption code. A link to an article in USA Today on how Ian Goldberg, the graduate student from the University of California, cracked the 40‐bit encryption code:

<http://www.usatoday.com/life/cyber/tech/ct718.htm>

Autonomous Agents

Agent Web Links:

<http://www.cs.bham.ac.uk/~amw/agents/links/index.html>

Computer Vision

Computer Vision Research Groups:

<http://www.cs.cmu.edu/~cil/v‐groups.html>

DNA Computing

ʺDNA‐based computers could race past supercomputers, researchers predict.ʺ A link to an article in the Chronicle of Higher Education on DNA computing, by Vincent Kiernan:

<http://chronicle.com/data/articles.dir/art‐44. dir/issue14.dir/14a0230 l.htm>

Explanation of Molecular Computing with DNA, by Fred Hapgood, Moderator of the Nanosystems Interest Group at

MIT:

<http://www.mitre.org/research/nanotech/hapgood_on_dna.html>

The University of Wisconsin: DNA Computing:

<http://corninfo.chem.wisc.edu/writings/DNACOMPUTING.html>

Expert Systems/Knowledge Engineering

Knowledge Engineering, Engineering Management Graduate Program at Christian Brothers University: Online

Resources to a Variety of Links:

<http://www.cbu.edu/~pong/engm624.htnl>

Genetic Algorithms/Evolutionary Computation

The Genetic Algorithms Archive at the Navy Center for Applied Research in Artificial Intelligence:

<http://www.aic.nrl.navymil/galist/>

The Hitchhikerʹs Guide to Evolutionary Computation, Issue 6.2: A List of Frequently Asked Questions (FAQ), edited

by Jörg Heitkötter and David Beasley:

<ftp://ftp.cs.wayne.edu/pub/EC/FAQ/www/top.htm>

The Santa Fe Institute:

<http://www.santafe.edu>

Knowledge Management

ATM Links (Asynchronous Transfer Mode):

<http://www.ee.cityu.edu.hk/~splam/html/atmlinks.html>

Knowledge Management Network:

<http://kmn.cibit.hvu.nl/index.html>

Some Ongoing KBS/Ontology Projects and Groups:

<http://www.cs.utexas.edu/users/mfkb/related.html>

Nanotechnology

Eric Drexlerʹs web site at the Foresight institute (includes the complete text of Engines of Creation):

<http://www.foresight.org/EOC/index.html>

Richard Feymnanʹs talk, ʺThereʹs Plenty of Room at the Bottomʺ:

<http://nano.xerox.com/nanotech/feynman.html>

Nanotechnology: Ralph Merkleʹs web site at the Xerox Palo Alto Research Center:

<http://sandbox.xerox.com/nano>

Microelectromechanical Systems and Fluid Dynamics Research Group Professor Chih‐Ming Hoʹs Laboratory,

University of California at Los Angeles:

<http://ho.seas.ucla.edu/new/main.htm>

Nanolink: Key Nanotechnology Sites on the Web:

<http://sunsite.nus.sg/MEMEX/nanolink.html>

Nanothinc:

<http://www.nanothinc.com/>

NEC Research and Development Letter: A summary of Dr. Sumio Iijimaʹs research on nanotubes:

<http://www.labs.nec.co.jp/rdletter/letter01/index1.html>

An Overview of the Performance Envelope of Digital Micromirror Device (DMD) Based Projection Display System by

Dr. Jeffrey Sampsell of Texas Instruments. A link to a paper describing the creation of micromirrors in a tiny, high-resolution projector:

<http://www.ti.com/dlp/docs/it/resources/white/overview/over.shtm l>

Small Is Beautiful: A Collection of Nanotechnology Links:

<http://science.nas.nasa.gov/Groups/Nanotechnology/nanotech.html>

Center for Nanoscale Science and Technology at Rice University:

<http://cnst.rice.edu/>

The Smart Matter Research Group, Xerox Palo Alto Research Center:

<http://www.parc.xerox.com/spl/projects/smart‐matter/>

Richard Smalleyʹs home page:

<http://cnst.rice.edu/reshome.html>

Neural Implants/Neural Prosthetics

Membrane and Neurophysics Department, the Max Planck Institute for Biochemistry:

<http://mnphys.biochem.mpg.de/>

ʺNeural Prosthetics Come of Age as Research Continues,ʺ by Robert Finn, in the Scientist. A link to an article on the use of neural prosthetics in helping patients with neurological disorders:

<http://www.the‐scientist.library.upenn.edu/yr1997/sept/research_ 970929.html>

Physics of Computation‐Carver Meadʹs Group:

<http://www.pcmp.caltech.edu/>

Neural Nets

Brainmaker/California Scientificʹs home page:

<http://www.calsci.com/>

Hugo de Garisʹs web site on Brain Builder Group:

<http://www.hip.atr.co.jp/~degaris>

IEEE Neural Network Council Home Page:

<http://www.ewh.ieee.org/tc/nnc/>

Neural Network Frequently Asked Questions:

<ftp://ftp.sas.com/pub/neural/FAQ.html>

PROFIT Initiative at MITʹs Sloan School of Management:

<http://scanner‐group.mit.edu/>

Quantum Computing

The Information Mechanics Group/Lab for Computer Science at MIT:

<http://www‐im.lcs.mit.edu/>

Quantum computation/cryptography at Los Alamos National Laboratory:

<http://qso.lanl.gov/qc/>

Physics and Media Group at the MIT. Media Lab:

<http://physics.www.media.mit.edu/home.html>

Quantum Computation at IBM:

<http://www.research.ibm.com/quantuminfo/>

Supercomputers

Accelerated Strategic Computing Initiative:

<http://www.llnl.gov/asci>

Lawrence Livermore National Laboratory/University of California for the U.S. Department of Energy:

<http://www.llnl.gov/>

NEC Begins Designing Worldʹs Fastest Computer:

<http://www.nb‐pacifica.com/headline/necbeginsdesigningwo_1208.sh tml>

FUTURE VISIONS

ACM 97 ʺThe Next 50 Yearsʺ (Association for Computing Machinery):

<http://research.microsoft.com/acm97/>

The Extropy Site (a web site and on‐line magazine covering a wide range of advanced. and future technologies)

<http://www.extropy.org>

SETI Institute web site:

<http://www.seti.org>

WTA: The World Transhumanist Association:

<http://www.transhumanism.com/>

HISTORY OF COMPUTERS

Advances of the 1960s:

<http://www.inwap.com/reboot/alliance/1960s.txt>

BYTE Magazine‐December 1996/Cover Story/Progress and Pitfalls:

<http://www.byte.com/art/9612/sec6/art3.htm>

History of Computing: IEEE Computer Society:

<http://www.computer.org/50/>

The Historical Collection, the Computer Museum History Center:

<http://www.tcm.org/html/history/index.html>

Intel Museum Home Page: What is Mooreʹs Law?:

<http://www.pentium.com/intel/museum/25anniv/hof/moore.htm>

SPACEWAR: Fanatic Life and Symbolic Death Among the Computer Bums, by Stewart Brand:

<http://www.baumgart.com/rolling‐stone/spacewar.html>

Timeline of Events in Computer History, from the Virtual History Museum Group:

<http://video.cs.vt.edu:90/cgi‐bin/Showmap>

Chronology of Events in the History of Computers:

<http://www3.islandnet.com/~kpolssori/comphist.htm>

Unisys History Newsletter:

<http//www.cc.gatech.edu/services/unisys‐folklore./>

INDUSTRIAL REVOLUTION AND LUDDITES/NEOLUDDITE MOVEMENT

Anarcho‐Primitivist, anticivilization, and neo‐Luddite articles:

<http://elaine.teleport.com/~jaheriot/anarprim.htm>

Whatʹs a Luddite?:

<http://www.bigeastern.com/ludd/nl_whats.htm>

Luddites On‐Line:

<http://www.luddites.com/index2.html>

The Unabomber Manifesto by Ted Kaczynski:

<http://www.soci.niu.edu/~critcrim/uni/uni.txt>

[Index omitted]

Document Outline

Inside Jacket (Front and Back Flaps)

A Note to the Reader

Contents

Acknowledgements

Prologue: An Inexorable Emergence

Chapter One: The Law of Time and Chaos

Chapter Two: The Intelligence of the Universe

Chapter Three: Of Mind and Machines

Chapter Four: A New Form of Intelligence on Earth

Chapter Five: Context and Knowledge

Chapter Six: Building New Brains...

Chapter Seven: ...And Bodies

Chapter Eight: 1999

Chapter Nine: 2009

Chapter Ten: 2019

Chapter Eleven: 2029

Chapter Twelve: 2099

Epilogue: The Rest of the Universe Revisited

Time Line

How To Build an Intelligent Machine In Three Easy Steps

Glossary

Notes

Prologue: An Inexorable Emergence

Chapter One: The Law of Time and Chaos

Chapter Two: The Intelligence of the Universe

Chapter Three: Of Mind and Machines

Chapter Four: A New Form of Intelligence on Earth

Chapter Five: Context and Knowledge

Chapter Six: Building New Brains...

Chapter Seven: ...And Bodies

Chapter Eight: 1999

Chapter Nine: 2009

Chapter Ten: 2019

Chapter Eleven: 2029

Chapter Twelve: 2099

Epilogue: The Rest of the Universe Revisited

Time Line

How To Build an Intelligent Machine in Three Easy Paradigms

Suggested Readings

Web Links

Index [omitted]