Knowledge Management Tools (Resources for the Knowledge-Based Economy)

Knowledge Management Tools

Resources for the Knowledge-Based Economy KNOWLEDGE IN ORGANIZATIONS

Laurence Prusak KNOWLEDGE MANAGEMENT AND ORGANIZATIONAL DESIGN

Paul S. Myers KNOWLEDGE MANAGEMENT TOOLS

Rudy L. Ruggles, 111 THE STRATEGIC MANAGEMENT OF INTELLECTUAL CAPlTAL

David A. KIein

Knowledge Management Tools Rudy L. Ruggles I11

Editor

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Library of Congress Cataloging-in-Publication Data Knowledge management tools / Rudy L. Ruggles 111, editor. p. cm.-(Resources for the knowledge-based economy) A collection of articles from iournals and books published between 1964-1 995. Includes index. ISBN 0-7506-9849-7 (pbk.) 1. Information resources management. 2. Information science. I. Ruggles, Rudy L., 1966- . 11. Series. T58.64.K66 1997 00 1-dc21 96-36.542 CIP British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. The publisher offers special discounts on bulk orders of this book. For information, please contact: Manager of Special Sales Butterworth-Heinemann 313 Washington Street Newton, MA 021581626 Tel: 617-928-2500 Fax: 6 17-928-2620

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Table of Contents Acknowledgments

1 PART O N E

2

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Tools for Knowledge Management: An Introduction

1

9

Knowledge and Technology

Information Processing in Computer and Man (1 964)

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Simon Herbert

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Why Computers May Never Think Like People

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Hubert and Stuart Dreyfus

4 How Many Bulldozers for an Ant Colony?

51

Daniel Crevier PART T W O

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Knowledge Generation

77

Information Systems and the Stimulation of Creativity David Bawden

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The Light of Discovery

102

George Johnson PART THREE

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Knowledge Codification

119

Information Retrieval and Cognitive Authority Patrick Wilson

121

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Table of Contents

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Humans, Machines, and the Structure of Knowledge

145

Harry M . Collins PART FOUR

9

165

Knowledge Transfer

Collaborative Tools: A First Look

167

Michael Schrage

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Knowledge Synthesis and Computer-Based Communication 187 Systems: Changing Behaviors and Concepts Kathleen Vian and Robert Johansen

PART FIVE

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209

Implementation

Implementing and Integrating New Technical Processes and Tools 211 Dorothy Leonard-Barton

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Learning from Notes: Organizational Issues in Groupware Implementation 231 Wanda J. Orlikowski

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Cosmos vs. Chaos: Sense and Nonsense in Electronic Contexts 247 Karl Weick

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PART SIX

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Future: A Knowledge-Based System for Threat 261 Assessment I! J. delongh, K. J. Carden, and N . A. Rogers

What Next?

273

Webs of Cognition

275

Daniel McNeill and Paul Freiberger

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Into the Future Stan Franklin

287

Acknowledgments I would like to thank Larry Prusak for blazing the trail in the study of knowledge management, for his interest and guidance in my research, and for inviting me to edit this volume. All at Ernst & Young’s Center for Business Innovationsmhave been very supportive of this project, and I would particularly like to thank Suzanne Connolly for her assistance and hard work in getting permission for us to reprint these selections. Finally, I thank all of the authors and their copyright holders for allowing us to incorporate their ideas into this book, enabling us to share these ideas with others in the context of knowledge management.

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Management: An Introduction Rudy L. Ruggles I11

Man is a tool-using animal. . . Without tools he is nothing, with tools he is all. -Thomas Carlyle (Essayist & historian, 1795-1 88 1)

KNOWLEDGE MANAGEMENT For thousands of years, humans have been discussing the meaning of knowledge, what it is to know something, and how people can generate and share new knowledge. It is interesting to consider, therefore, that despite the pervasiveness of epistemological discussions throughout history, it is only in the past few years that the world of business has begun to recognize the importance of knowledge as a resource. Individual and organizational knowledge has been invisible on balance sheets, overlooked in reward and incentive systems, and allowed to flow out of companies en masse, unrecognized and uncaptured. Recently, however, knowledge has come into its own in the business world. Some companies, like Skandia AFS for example, have begun to produce supplementary annual reports reflecting their intellectual capital. Balanced scorecard performance measurement plans capture the value of intangibles and financials simultaneously to provide a more complete picture of organizational health. Some organizations, Ernst & Young LLP among them, have instituted Chief Knowledge Officers and whole knowledge management processes and infrastructures. Firms have realized that, while managing data and information is important, true competitive advantage lies in leveraging the unique, powerful knowledge of the organization. Knowledge management covers three main knowledge activities: generation, codification, and transfer. Other books in this series of readers cover these areas in more detail, but a brief description of each is in order here. Although there are many definitions of knowledge in the world, with several varieties represented 1

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even within this book, the working definition I use is that knowledge is a fluid mix of contextual information, values, experiences, and rules. It comes in many forms, including process knowledge (how-to), catalog knowledge (what is), and experiential knowledge (what was). All of these types are similarly generated, codified, and transferred. Knowledge generation includes all activities which bring to light knowledge which is “new,” whether to the individual, to the group, or to the world. It includes activities such as creation, acquisition, synthesis, fusion, and adaptation. Knowledge codification is the capture and representation of knowledge so that it can be re-used either by an individual or by an organization. Knowledge transfer involves the movement of knowledge from one location to another and its subsequent absorption. Generation, codification, and transfer all occur constantly, so management itself does not create these actions. The power of knowledge management is in allowing organizations to explicitly enable and enhance the productivity of these activities and to leverage their value for the group as well as for the individual. This compendium is an integral part of this series about knowledge. Combined, the books of this series reflect the many facets of knowledge management, expressed through the works of individuals from a wide variety of fields and disciplines. This volume has a very specific place in this collection: it reflects the wide variety of thoughts and perspectives on the use and usefulness of tools in managing knowledge in organizations. It is my intention, as the editor of this volume, for readers of this book to be able to make better decisions about how to gain greater results in managing their organization’s knowledge by effectively and appropriately leveraging the potential power of tools.

TOOLS Tools are defined, for the purposes of this work, as technologies which support the performance of activities or actions. As reflected by the quotation which begins this chapter, man is a tool-user from way back. According to anthropologist Jane Lancaster, “An estimation of two million years of tool use prior to handaxe cultures and Homo erectus is undoubtedly conservative.”’ In fact, human tool use has often been cited as one of the primary drivers of human evolution! Theorists and scientists from Engels to Darwin maintain that one of the main reasons that humans walk upright is because their hands were specialized over time primarily for the job of using tools, with ambulation relegated to the legs alone. It was mankind’s ability to create and use tools which leveraged the ability of the human mind, the only competitive advantage humans had over animals in the fight for survival. In turn, many scholars also link tool use to the evolution of the human cognitive and behavioral capacities as welL2 If utilizing tools to enable more ‘Lancaster, Jane B., “On the Evolution of Tool-Using Behavior,” Americun Anthropologist, 70, 1968, pp. 56-66. ’Kurland, Jeffrey A. and Stephen J. Beckerman, “Optimal Foraging and Hominid Evolution: Labor and Reciprocity,” American Anthropologist, 87, 1985, pp. 73-92.

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efficient manual activities has had these effects on humans so far, one wonders at what the evolutionary impact of tools enabling intellectual activities might be as this human-tool co-evolution moves into the knowledge age. In fact, humans have moved tools so far beyond the initial sticks and rocks that they are able to embed a certain amount of intelligence, and some would argue knowledge, into the tools themselves. With this rise in intelligence, comes a new relationship with such tools. “Interactive” has become a prime descriptor of the new generation of technology, indicating a move from humans using tools to humans conversing with tools. It is important to realize however, that this human-tool conversation does not itself create more efficiency, greater effectiveness, or better innovations, the primary objectives of most tools. This is particularly true in the world of computers. Many studies of workplace productivity show no real increase in efficiency or effectiveness due to the use of computers. Often, people spend a great deal of time and energy fitting their computers to their jobs and their jobs to their computers. The fascination with advancing technologies occasionally overshadows the human element in the interaction. This is what happens in the human-tool co-evolution when the humans evolve much more slowly than the tools. As expressed by Donna Haraway, author of A Cyborg Manifesto, “Our machines are increasingly lively, and we are increasingly inert.” Our challenge is to keep pushing the human capacity in parallel with the technological capabilities, so that neither will hold the other back for any length of time. This is the realm of knowledge tools.

KNOWLEDGE TOOLS Knowledge management tools are technologies, broadly defined, which enhance and enable knowledge generation, codification, and transfer. As with any tools, they are designed to ease the burden of work and to allow resources to be applied efficiently to the tasks for which they are most suited. It is important to note that not all knowledge tools are computer-based, as paper and pen can certainly be utilized to generate, codify, and transfer knowledge. For the purposes of this work however, the tools covered are primarily the technological ones due to their quick evolution, dynamic capabilities, and organizational impacts. They are also the most expensive tools, and are worthy of the closest scrutiny. True knowledge management tools are not data or information management tools with a 1990s title. They do different things. Data management tools allow organizations to generate, access, store, and analyze data, usually in the form of facts and figures, which can be considered “raw material.” Examples include data warehouses, data search engines, data modeling, and visualization tools. Information management tools enable the manipulation of information (i.e., data which informs in and of itself). Examples of these tools include automated information search and retrieval agents (or ‘bots), basic decision support technologies, many executive information systems, and document management technology. All may be useful for the jobs they do, but such tools do not capture the complexity of context and the richness of knowledge. While knowledge man-

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agement tools may indeed also handle data and information, the other types are not robust enough to truly facilitate knowledge management. Think about what it is to know a thing, versus simply having information about that thing. It is the difference between reading a description of the Mona Lisa and seeing the painting itself. Knowledge tools can help us see the paintings. In the world of knowledge management, the role of the tool in the work is an even more difficult concept than it would initially appear. The crux of decades of discussion as to whether or not technologies can be used to help manage knowledge was captured as recently as the March 25, 1996 Time magazine cover article: Can machines think? If one answers yes, or possibly yes, the tools themselves can (may) generate, codify, and transfer knowledge. If not, the role of the tool is purely enabler, with the onus on the humans to conduct knowledge activities. The debate over the answer to this question, usually the domain of the field of artificial intelligence (AI), is continued in this volume to the extent that it applies to knowledge management. This book also reflects my belief that no matter what an individual’s answer to this question, focusing on the technology as the key to managing knowledge organizationally or individually neglects the economic, political, and social issues which are the keys to effective knowledge management. Too often people look to technology to solve the hard questions, when in fact the tools are the easiest part. A Stradivarius violin sounds just as terrible as a dimestore fiddle in the hands of a novice. The key is putting the right tools in the hands of people who know how to use them. Anyone who has heard Itzhak Perlman or the London Symphony Orchestra knows the results can be quite incredible. Business is no different.

THE SELECTIONS I have assembled the enclosed articles and chapters from a wide variety of perspectives, including the fields of sociology, economics, computer sciences, and cognitive psychology, to create a discussion about knowledge tools. The first sections present issues and implications in various aspects of using tools in knowledge generation, codification, and transfer. Following these are descriptions of implementation and application considerations, and then a glance at what tools may look like in the future. These selected readings present a wide range of pertinent issues, creating a fertile field for continued discussion and deep and broad exploration.

Knowledge and Technology In the first section, three articles reflect the debate over whether technology can indeed duplicate, or at least replicate, human thought. The first selection comes from Nobel Laureate Herbert Simon, who has long defended the possibility, in fact the actuality, that computers process information in ways which paral-

Tools for Knowledge Management: An Introduction

5

lel human thought. Although the article is over 30 years old, its well thought through reasoning and straightforward approach present an interesting argument for the ability of the machine to duplicate human cognition, even when considering the significantly more primitive computing tools of the 1960s. Simon lays the groundwork for many of the arguments in this collection which stand in support of the ability of technological tools to capture, and in some cases replicate and even generate, knowledge. Hubert and Stuart Dreyfus’ article, “Why Computers May Never Think Like People,” serves as the appropriate counterpoint to the notion that technology can cogitate. Without the ability to make intelligent decisions, without being able to incorporate “know-how” along with “know-what,” computers can be n o more than conduits of human intelligence, devoid of context, distinctions, or true judgment. While technology may enable humans to manage their knowledge better, the Dreyfus’ maintain that knowledge can never reside within the machine. In their view, the answer to Time’s question is no, and the knowledge activities will always rest squarely with the humans. To provide a third perspective in this conversation, I have included a chapter from a book by Daniel Crevier, a chronicler of artificial intelligence’s history of successes and failures, discussing why machines have such a difficult time dealing with knowledge, despite their advancing processing power. By detailing the scale and scope of the human brain’s processing power, he paints a vivid picture of the gap which exists between today’s most sophisticated and powerful computers and the cerebral cortex. However, he balances the views of the section’s previous writers by allowing that this huge gap is being spanned, if slowly. It is this progress which makes the rest of the articles in this volume even more interesting.

Knowledge Generation The section on generation tools begins with an article by David Bawden on whether information systems can actually contribute to knowledge generation. While the direct case used concerns research and development, the concepts apply to all whose job includes generating new ideas. This article squarely addresses when information systems, and more generally information environments, can contribute to knowledge generation. The focus is on technology as enabler. This is a good example of people leading the knowledge work, utilizing the tools at hand to support their own internal generation processes. This is contrasted with the chapter from George Johnson’s book, Machinery of the Mind, which describes interesting cases where the tools, the computers, actually create new objects and ideas themselves. Stories such as these open the discussion as to what sorts of knowledge work can, or should, be automated. This, to me, leads to a deeper discussion important in understanding the human-tool relationship: Will people trust a machine’s creatiodsynthesidperspective? Instead of NIH (not invented here) syndrome, will we hear more about NIBH (not invented by human) syndrome? Many organizations are already trusting neural networks

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and genetic algorithms to solve optimization problems in operations, financial services, and environmental scanning, but how far will this trust extend? As tool sophistication increases it is likely that decisions about their use will focus less on whether technological tools should be used at all, but more on whether they will be used to augment (enable) or to automate (do) the work of people. The power of advanced technological capability can be alluring, but unless the result contributes value accepted by the whole system, the tool is useless.

Knowledge Codification One of the most hotly contested area of knowledge management involves codification, i.e., putting knowledge in various forms that can be leveraged and transferred. The field of artificial intelligence has long involved so-called “knowledge elicitation” and “knowledge engineering” in the development of expert systems. The section on codification begins with a chapter from Patrick Wilson’s book Second-Hand Knowledge describing the interesting question of cognitive authority, i.e., how much trust there is in the validity of the knowledge. Also, is all knowledge captured for later sorting or is it pre-screened? In either case, who determines knowledge quality? Wilson looks at these questions through the eyes of library science, a field which has long dealt with these exact concerns. The activities of the librarian (e.g., establishing information sorting, cataloging, and retrieval mechanisms) are now required far beyond the bounds of the library, coming to rest on the shoulders of most every member of the information economy. Workers today, faced with the internet, Lotus Notes databases, organizational knowledge maps, etc., realize that they have much to learn from library sciences’ experience with codified knowledge. In the second chapter in this section, an article by Harry Collins explains how utilizing the idea of behavior-specific action in captur ing knowledge can help us get closer to striking a balance between symbol-type (explicit) knowledge and so-called “encultured” (tacit) knowledge. While Wilson talks about how to organize what you know you have codified, Collins describes how to codify what you have a hard time even recognizing. Through greater understanding of the differences Collins points out, the place of technology in capturing and handling all types of knowledge becomes clearer.

Knowledge Transfer Knowledge transfer has received a tremendous amount of publicity recently with advances in groupware and networking tools, designed to enable the flow of knowledge among groups and individuals. The goal of such tools is ultimately shared memory and understanding. In fact, this is difficult to achieve because knowledge is “sticky,” alive, and rich. It is “sticky” because it is very tightly bound to the context which gives it meaning; without context it is information. Knowledge can be thought of as being alive in that it must be constantly attended

Tools for Knowledge Management: An Introduction

7

to as it is ever-changing and growing. It also dies, goes out of date, becomes irrelevant and must be discarded, but who is its rightful steward? Lastly, it is rich in

its multi-dimensionality, containing a tremendous amount of content, context, and experience. All three of these factors make it very difficult to distribute knowledge. Tools can help. Advanced knowledge tools allow some context to be captured along with content and they can create global work “spaces.” The sort of virtual work environments these capabilities allow have real advantages, but also major drawbacks. Michael Schrage, in a chapter drawn from his book Shared Minds, examines the idea of collaborative tools from the pen and paper level to the dynamic “groupware” level, examining the underlying strengths and weaknesses of tools built to share knowledge. Although he discusses how advanced technology can enable and enhance collaborative environments, he reminds us of a lesson that should run throughout this discussion of tools: The real purpose of design here is not to build knowledge tools but to build knowledge. Vian and Johansen’s paper reflects on how electronic communications can enhance knowledge transfer and synthesis. I have included this piece to illustrate, consistent with the message above, that more important than the technology itself is the new ways people work together via electronic media. By describing behavioral patterns, Vian and Johansen concentrate on the process of knowledge synthesis through transfer. This selection addresses the strong influence technological tools have on the way people interact with each other and how they perform their work. As they point out, ignoring either side of the human-tool (or socio-technical) co-evolution will likely cause failure, if not of the work itself, certainly of the use of the technology.

Implementation Having outlined the discussions around each of the various aspects of toolsupported knowledge management, the book goes on to deal with the implementation of such tools. This is the area where the most frequent problems with knowledge tools arise. Many times, organizations react to the idea of knowledge management by buying the tools, installing them, and then expecting overnight results. This “tool-centric” approach can waste a great deal of time and money. Groupware packages have seen the lion’s share of such treatment and, usually through no fault of the technology itself, have often been branded as failures. The four selections in this section were chosen to illustrate many of the pitfalls involved in implementing knowledge tools in organizations, offering guidance wherever possible. A chapter from Dorothy Leonard-Barton’s book, The Wellsprings of Knowledge, starts off this section by presenting a framework for managing knowledge tool implementation as an innovation project, and not just an execution of plans. Wanda Orlikowski describes the lessons learned by one organization as it implemented Lotus Notes, the most popular of the groupware packages. Karl Weick’s piece deepens the discussion with a look at how people try to retain meaning in electronic interactions when the “sense-making” tools they

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use in life differ from those needed to make sense in electronic contexts. The Future system story closes this section as an interesting study in the development of a knowledge-based system, its roll-out, and the results it produced. Taken together, these four pieces reflect aspects of implementation ranging from strategic to personal, all of which must be considered if tools are to successfully support knowledge management.

What Next? With the advent of more advanced technology, knowledge tools will become increasingly sophisticated. Two entertaining selections were chosen to represent some ideas for what knowledge tools might be able to do in the future. McNeill and Freiberger start this section with a chapter about how computers are being tasked with sorting through “fuzziness” of human thought. One of the key attributes of knowledge is its context-sensitivity, and action in the face of changing contexts requires judgment, an extremely “fuzzy” capability. Tools which can handle such context-sensitivity, perhaps based on the brain’s own neural mechanisms, may not be too far in the future. The last chapter, from Stan Franklin’s book Artificial Minds,explores the future of the mechanisms of the mind from the perspectives of physicists, roboticists, and biologists. These groups all contribute interesting notions to how the workings of the human mind might be duplicated and utilized. If knowledge is thought of as residing in the mind of a “knower,” producing artificial knowers will have interesting implications for knowledge management.

SUMMARY Tools and technologies are not the answer to the 5000-year-old questions surrounding knowledge. They can certainly facilitate the implementation of knowledge processes-the generation, transfer, and codification of knowledgeand in some cases they may be able to automate some kinds of knowledge work in these areas. Still, they must be taken in context and implemented as a part of the overall effort to leverage organizational knowledge through integration with the business strategy, the culture, the current processes, and the existing technologies. This book represents a dialog about how tools can facilitate the knowledge processes of an organization. These selections should help make discussible the many issues involved in choosing and using knowledge tools. Once aware of the pros and cons, strengths and weaknesses of knowledge tools, each person should be ready to begin to make informed decisions about how to incorporate such tools into his or her organization. Not an easy task, but worth the hard work if done knowledgeably.

PART ONE

Knowledge and Technology

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Computer and Man (1964) Simon Herbert

Organizing a computer to perform complex tasks depends very much more upon the characteristics of the task environment than upon the “hardware”-the specific physical means for realizing the processing in the computer. Thus, all past and present digital computers perform basically the same kinds of symbol manipulations. In programming a computer it is substantially irrelevant what physical processes and devices-electromagnetic, electronic, or what not-accomplish the manipulations. A program written in one of the symbolic programming languages, like ALGOL or FORTRAN,will produce the same symbolic output on a machine that uses electron tubes for processing and storing symbols, one that incorporates magnetic drums, one with a magnetic core memory, or one with completely transistorized circuitry. The program, the organization of symbol-manipulating processes, is what determines the transformation of input into output. In fact, provided with only the program output, and without information about the processing speed, one cannot determine what kinds of physical devices accomplished the transformations: whether the program was executed by a solid-state computer, an electron-tube device, an electrical relay machine, or a room full of statistical clerks! Only the organization of the processes is determinate. Out of this observation arises the possibility of an independent science of information processing. By the same token, since the thinking human being is also an information processor, it should be possible to study his processes and their organization independently of the details of the biological mechanisms-the “hardware”-that implement them. The output of the processes, the behaviour of Homo cogituns, should reveal how the information processing is organized, without necessarily providing much information about the protoplasmic structures or biochemical processes that implement it. From this observation follows the possibility of constructing and testing psychological theories to explain human thinking in terms of Reprinted with the permission of the copyright holder from American Scientist, vol. 52, no. 3, September 1964. Copyrighted 1964, by The Society of the Sigma Xi and reprinted by permission of the copyright ownec

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the organization of information processes; and of accomplishing this without waiting until the neurophysiological foundations at the next lower level of explanation have been constructed. Finally, there is a growing body of evidence that the elementary information processes used by the human brain in thinking are highly similar to a sub-set of the elementary information processes that are incorporated in the instruction codes of present-day computers. As a consequence it has been found possible to test information-processing theories of human thinking by formulating these theories as computer programs-organizations of the elementary information processes-and examining the outputs of computers so programmed. The procedure assumes no similarity between computer and brain at the “hardware” level, only similarity in their capacities for executing and organizing elementary information processes. From this hypothesis has grown up a fruitful collaboration between research in “artificial intelligence,” aimed at enlarging the capabilities of computers, and research in human cognitive psychology. These, then, are the three propositions on which this discussion rests: 1. A science of information processing can be constructed that is substantially independent of the specific properties of particular informationprocessing mechanisms. 2. Human thinking can be explained in information-processing terms without waiting for a theory of the underlying neurological mechanisms. 3. Information-processing theories of human thinking can be formulated in computer programming languages, and can be tested by simulating the predicted behaviour with computers.

LEVELS OF EXPLANATION No apology is needed for carrying explanation only to an intermediate level, leaving further reduction to the future progress of science. The other sciences provide numerous precedents, perhaps the most relevant being nineteenth-century chemistry. The atomic theory and the theory of chemical combination were invented and developed rapidly and fruitfully during the first three-quarters of the nineteenth century-from Dalton, through Kekuli, to Mendeleev-without any direct physical evidence for or description of atoms, molecules, or valances. To quote Pauling (1960): Most of the general principles of molecular structure and the nature of the chemical bond were formulated long ago by chemists by induction from the The study of the structure of molecules was great body of chemical facts originally carried on by chemists using methods of investigation that were essentially chemical in nature, relating to the chemical composition of substances, the existence of isomers, the nature of the chemical reactions in which a substance takes part, and so on. From the consideration of facts of this kind Frankland, Kekult!, Couper, and Butlerov were led a century ago to

...

Information Processing in Computer and Man (I 964)

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formulate the theory of valence and to write the first structural formulas for molecules, van? Hoff and le Be1 were led to bring classical organic stereochemistry into its final form by their brilliant postulate of the tetrahedral orientation of the four valence bonds of the carbon atom, and Werner was led to his development of the theory of the stereochemistry of complex inorganic substances. (pp. 3-4)

The history this passage outlines is worth pondering, because the last generation of psychologists has engaged in so much methodological dispute about the nature, utility, and even propriety, of theory. The vocal methodologically self-conscious, behaviourist wing of experimental psychology has expressed its scepticism of “unobserved entities” and “intermediate construct^."' Sometimes it has seemed to object to filling the thinking head with anything whatsoever. Psychologists who rejected the empty-head viewpoint, but who were sensitive to the demand for operational constructs, tended to counter the behaviourist objections by couching their theories in physiological language.2 The example of atomic theory in chemistry shows that neither horn of this dilemma need be seized. On the one hand, hypothetical entities, postulated because they were powerful and fruitful for organizing experimental evidence, proved exceedingly valuable in that science, and did not produce objectionable metaphysics. Indeed, they were ultimately legitimized in the present century by “direct” physical evidence. On the other hand, the hypothetical entities of atomic theory initially had no physical properties (other than weight) that could explain why they behaved as they did. While an electrical theory of atomic attraction predated valence theory, the former hypothesis actually impeded the development of the latter and had to be discredited before the experimental facts could fall into place. The valence of the mid-century chemist was a “chemical affinity” without any underlying physical mechanism. So it remained for more than half a century until the electron-shell theory was developed by Lewis and others to explain it. Paralleling this example from chemistry, information-processing theories of human thinking employ unobserved entities-symbols-and unobserved processes-elementary information processes. The theories provide explanations of behaviour that are mechanistic without being physiological. That they are mechanistic-that they postulate only processes capable of being effected by mechanism-is guaranteed by simulating the behaviour predicted on ordinary digital computers. . . . Simulation provides a basis for testing the predictions of the theories, but does not imply that the protoplasm in the brain resembles the electronic components of the computer.

A SPECIFIC INFORMATION-PROCESSINGTHEORY: PROBLEM SOLVING IN CHESS Information-processing theories have been constructed for several kinds of behaviour, and undertake to explain behaviour in varying degrees of detail. As a

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first example, we consider a theory that deals with a rather narrow and special range of human problem-solving skill, attempting to explain the macroscopic organization of thought in a particular task environment. Good chess players often detect strategies-called in chess “combinations”-that impose a loss of a piece or a checkmate on the opponent over a series of moves, no matter what the latter does in reply. In actual game positions where a checkmating possibility exists, a strong player may spend a quarter of an hour or more discovering it, and verifying the correctness of his strategy. In doing so, he may have to look ahead four or five moves, or even more.3 If the combination is deep, weaker players may not be able to discover it at all, even after protracted search. How do good players solve such problems? How do they find combinations? A theory now exists that answers these questions in some detail. First, I shall describe what it asserts about the processes going on in the mind of the chess player as he studies the position before him, and what it predicts about his progress in discovering an effective strategy. Then we can see to what extent it accounts for the observed facts. The actual theory is a computer program couched in a list-processing language, called Information Processing Language V (IPL-V). O u r account of the theory will be an English-language translation of the main features of the program.‘ The statement of the theory has five main parts. The first two of these specify the way in which the chess player stores in memory his representation of the chess position, and his representation of the moves he is considering, respectively. The remaining parts of the theory specify the processes he has available for extracting information from these representations and using that information: processes for discovering relations among the pieces and squares of the chess position, for synthesizing chess moves for consideration, and for organizing his search among alternative move sequences. We shall describe briefly each of these five parts of the theory. The theory asserts, first of all, that the human chess player has means for storing internally, in his brain, encoded representations of the stimuli presented to him. In the case of a highly schematized stimulus like a chess position, the internal symbolic structure representing it can be visualized as similar to the printed diagram used to represent it in a chess book. The internal representation employs symbols that name the squares and the pieces, and symbolizes the relations among squares, among pieces, and between squares and pieces. For example, the internal representation symbolizes rather explicitly that a piece on the King’s square is a Knight’s-move away, in a SSW direction, from a piece on the third rank of the Queen’s file. Similarly, if the King’s Knight is on the King’s Bishop’s Third square (KB3), the representation associates the symbol designating the Knight with the symbol designating the KB3 square, and the symbol designating the square with that designating the Knight. On the other hand, the representation does not symbolize directly that two pieces stand on the same diagonal. Relations like this must be discovered or inferred from the representation by the processes to be discussed below.

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Asserting that a position is symbolized internally in this way does not mean that the internal representations are verbal (any more than the diagrams in a chess book are verbal). It would be more appropriate, in fact, to describe the representations as a “visual image,” provided that this phrase is not taken to imply that the chess player has any conscious explicit image of the entire board in his “mind’s eye.” The chess player also has means for representing in memory the moves he is considering. He has symbol-manipulating processes that enable him, from his representations of a position and of a move, to use the latter to modifr the formerthe symbolic structure that describes the position-into a new structure that represents what the position would be after the move. The same processes enable him to “unmake” a move-to symbolize the position as it was before the move was considered. Thus, if the move that transfers the King’s Knight from his original square ( K N l ) to the King’s Bishop’s Third square (KB3) is stored in memory, the processes in question can alter the representation of the board by changing the name of the square associated with the Knight from K N l to KB3, and conversely for unmaking the move. The chess player has processes that enable him to discover new relations in a position, to symbolize these, and to store the information in memory. For example, in a position he is studying (whether the actual one on the board, or one he has produced by considering moves), he can discover whether his King is in check-attacked by an enemy man; or whether a specified piece can move to a specified square; or whether a specified man is defended. The processes for detecting such relations are usually called perceptual processes. They are characterized by the fact that they are relatively direct: they obtain the desired information from the representation with a relatively small amount of manipulation. The chess player has processes, making use o f the perceptual processes, that permit him to generate or synthesize for his consideration moves with specified properties-for example, to generate all moves that will check the enemy King. To generate moves having desired characteristics may require a considerable amount of processing. If this were not so, if any kind of move could be discovered effortlessly, the entire checkmating program would consist of the single elementary process: DISCOVER CHECKMATING MOVES. An example of these more complex, indirect processes is a procedure that would discover certain forking moves (moves that attack two pieces simultaneously) somewhat as follows. Find the square of the opposing Queen. Find all squares that lie a Knight’smove from this square. Determine for each of these squares whether it is defended (whether an opposing piece can move to it). If not, test all squares a Knight’smove away from it to see if any of them has a piece that is undefended or that is more valuable than a Knight. Finally, the chess player has processes for organizing a search for mating combinations through the “tree” of possible move sequences. This search makes use of the processes already enumerated, and proceeds as follows. The player generates all the checking moves available to him in the given position, and for each checking move, generates the legal replies open to his oppo-

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nent. If there are no checking moves, he concludes that no checkmating combination can be discovered in the position, and stops his search. If, for one of the checking moves, he discovers there are no legal replies, he concludes that the checking move in question is a checkmate. If, for one of the checking moves, he discovers that the opponent has more than four replies, he concludes that this checking move is unpromising, and does not explore it further. Next, the player considers all the checking moves (a) that he has not yet explored and (b) that he has not yet evaluated as “CHECKMATE,” or “ N O MATE.” He selects the move that is most promising-by criteria to be mentioned presently-and pushes his analysis of that move one move deeper. That is, he considers each of its replies in turn, generates the checking moves available after those replies, and the replies to those checking moves. He applies the criteria of the previous paragraph to attach “CHECKMATE” or “NO MATE” labels to the moves where he can. He also “propagates” these labels to antecedent moves. For example, a reply is labelled CHECKMATE if at least one of its derivative checking moves is CHECKMATE; it is labelled NO MATE if all the consequent checking moves are so labelled. A checking move is labelled CHECKMATE if all of the replies are so labelled; it is labelled N O MATE if at least one reply is so labelled. The most promising checking move for further exploration is selected by these criteria: that checking move to which there are the fewest replies receives first prioritys If two or more checking moves are tied on this criterion, a double check (check with two pieces) is given priority over a single check. If there is still a tie, a check that does not permit a recapture by the opponent is given priority over one that does. Any remaining ties are resolved by selecting the check generated most recently. A number of details have been omitted from this description, but it indicates the theory’s general structure and the kinds of processes incorporated. The theory predicts, for any chess position that is presented to it, whether a chess player will discover a mating combination in that position, what moves he will consider and explore in his search for the combination, and which combination (if there are several alternatives, as there often are) he will discover. These predictions can be compared directly with published analyses of historical chess positions or tape recordings of the thinking-aloud behaviour of human chess players to whom the same position is presented. Now it is unlikely that, if a chess position were presented to a large number of players, all of them would explore it in exactly the same way. Certainly strong players would behave differently from weak players. Hence, the informationprocessing theory, if it is a correct theory at all, must be a theory only for players of a certain strength. On the other hand, we would not regard its explanation of chess playing as very satisfactory if we had to construct an entirely new theory for each player we studied. Matters are not so bad, however. First, the interpersonal variations in search for chess moves in middle-game positions appear to be quite small for players at a common level of strength as we shall see in a moment. Second, some of the differences that are unrelated to playing strength appear to correspond to quite simple

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variants of the program altering, for example, the criteria that are used to select the most promising checking move for exploration. Other differences, on the other hand, have major effects on the efficacy of the search, and some of these, also, can be represented quite simply by variants of the program organization. Thus, the basic structure of the program, and the assumptions it incorporates about human information-processing capacities, provide a general explanation for the behaviour, while particular variants of this basic program allow specific predictions to be made of the behavioural consequences of individual differences in program organization and content. The kinds of information the theory provides, and the ways in which it has been tested, can be illustrated by a pair of examples. Adrian de Groot (1964) has gathered and analysed a substantial number of thinking-aloud protocols, some of them from grand masters. He uniformly finds that, even in complicated positions, a player seldom generates a “tree” of more than 50 or 75 positions before he chooses his move. Moreover, the size of the tree does not depend on the player’s strength. The thinking-aloud technique probably underestimates the size of the search tree somewhat, for a player may fail to mention some variations he has seen, but the whole tree is probably not an order of magnitude greater than that reported. In 40 positions from a standard published work on mating combinations where the information-processing theory predicted that a player would find mating strategies, the median size of its search tree ranged from 13 positions for twomove mates, to 53 for five-move mates. A six-move mate was found with a tree of 95 positions: and an eight-move mate with a tree of 108. (The last two mates, as well as a number of the others, were from historically celebrated games between grand masters, and are among the most “brilliant” on record.) Hence, we can conclude that the predictions of the theory on amount of search are quite consistent with de Groot’s empirical findings o n the behaviour of highly skilled human chess players. The second example tests a much more detailed feature of the theory. In the eight-move mate mentioned above, it had been known that by following a different strategy the mate could have been achieved in seven moves. Both the human grand master (Edward Lasker in the game of Lasker-Thomas, 1912) and the program found the eight-move mate. Examination of the exploration shows that the shorter sequence could only have been discovered by exploring a branch of the tree that permitted the defender two replies before exploring a branch that permitted a single reply. The historical evidence here confirms the postulate of the theory that players use the “fewest replies” heuristic to guide their search. (The evidence was discovered after the theory was constructed.) A second piece of evidence of the same sort has been found in a game between experts reported in Chess Life (December 1963). The winner discovered a seven-move mate, but overlooked the fact that he could have mated in three moves. The annotator of the game, a master, also overlooked the shorter sequence. Again, it could only have been found by exploring a check with two replies before exploring one with a single reply.

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The “fewest replies” heuristic is not a superficial aspect of the players’ search, nor is its relevance limited to the game of chess. Most problem-solving tasks-for example, discovering proofs of mathematical theorems-require a search through a branching “tree” of possibilities. Since the tree branches geometrically, solving a problem of any difficulty would call for a search of completely unmanageable scope (numbers like arise frequently in estimating the magnitude of such searches), if there were not at hand powerful heuristics, or rules of thumb, for selecting the promising branches for exploration. Such heuristics permit the discovery of proofs for theorems (and mating combinations) with the limited explorations reported here. The “fewest replies” heuristic is powerful because it combines two functions: it points search in those directions that are most restrictive for the opponent, giving him the least opportunity to solve his problems; at the same time, it limits the growth of the search tree, by keeping its rate of branching as low as possible. The “fewest replies” heuristic is the basis for the idea of retaining the initiative in military strategy, and in competitive activities generally, and is also a central heuristic in decision making in the face of uncertainty. Hence its appearance in the chess-playing theory, and in the behaviour of the human players, is not fortuitous.

PARSIMONIOUS AND GARRULOUS THEORIES Granting its success in predicting both some general and some very specific aspects of human behaviour in chess playing, like the examples just described, the theory might be confronted with several kinds of questions and objections. It somehow fails to conform to our usual notions of generality and parsimony in theory. First, it is highly specific-the checkmating theory purports to provide an explanation only of how good chess players behave when they are confronted with a position on the board that calls for a vigorous mating attack. If we were to try to explain the whole range of human behaviour, over all the enormous variety of tasks that particular human beings perform, we should have to compound the explanations from thousands of specific theories like the checkmate program. The final product would be an enormous compendium of “recipes” for human behaviour at specific levels of skill in specific task environments.6 Second, the individual theories comprising this compendium would hardly be parsimonious, judged by ordinary standards. We used about a thousand words above to provide an approximate description of the checkmate program. The actual program-the formal theory-onsists of about three thousand computer instructions in a list-processing language, equivalent in information content to about the same number of English words (It should be mentioned that the program includes a complete statement of the rules of chess, so that only a small part of the total is given over to the description of the player’s selection rules and their organization.)

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Before we recoil from this unwieldy compendium as too unpleasant and unaesthetic to contemplate, let us see how it compares in bulk with theories in the other sciences. With the simplicity of Newtonian mechanics (why is this always the first example to which we turn?), there is, of course, no comparison. If classical mechanics is the model, then a theory should consist of three sentences, or a couple of differential equations. But chemistyg and particularly organic chemistry, presents a different picture. It is perhaps not completely misleading to compare the question “How does a chess player find a checkmating combination?” with a question like “How do photoreceptors in the human eye operate?” or “How is the carbohydrate and oxygen intake of a rabbit transformed into energy usable in muscular contraction?’ The theory of plant metabolism provides a striking example of an explanation of phenomena in terms of a substantial number of complex mechanisms. Calvin and Bassham (1962), in their book on The Photosynthesis of Carbon Compounds, introduce a figure entitled “carbon reduction pathways in photosynthesis” with the statement: “We believe the principal pathways for photosynthesis of simple organic compounds from COz to be those shown in Figure 2.” (pp 8-1 1, italics ours.) The figure referred to represents more than 40 distinct chemical reactions and a corresponding number of compounds. This diagram, of course, is far from representing the whole theory. Not only does it omit much of the detail, but it contains none of the quantitative considerations for predicting reaction rates, energy balances, and so on. The verbal description accompanying the figure, which also has little to say about the quantitative aspects, or the energetics, is over two pages in length-almost as long as our description of the chess-playing program. Here we have a clearcut example of a theory of fundamental importance that has none of the parsimony we commonly associate with scientific theorizing. The answer to the question of how photosynthesis proceeds is decidedly long-winded-as is the answer to the question of how chess players find mating combinations. We are often satisfied with such long-winded answers because we believe that the phenomena are intrinsically complex, and that no brief theory will explain them in detail. We must adjust our expectations about the character of information-processing theories of human thinking to a similar level. Such theories, to the extent that they account for the details of the phenomena, will be highly specific and highly complex. We might call them “garrulous theories” in contrast with our more common models of parsimonious theories.

ELEMENTARY INFORMATION PROCESSES We should like to carry the analogy with chemistry a step further. Part of our knowledge in chemistry-and a very important part of the experimental chemist-consists of vast catalogues of substances and reactions, not dissimilar in bulk to the compendium of information processes we are proposing. But, as we come to understand these substances and their reactions more fully, a second level of theory emerges that explains them (at least their general features) in a more parsi-

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monious way. The substances, at this more basic level, become geometrical arrangements of particles from a small set of more fundamental substances-atoms and sub-molecules-held together by a variety of known forces whose effects can be estimated qualitatively and, in simple cases, quantitatively. If we examine an information-processing theory like the checkmating program more closely, we find that it, too, is organized from a limited number of building blocks-a set of elementary information processes-and some composite processes that are compounded from the more elementary ones in a few characteristic ways. Let us try to describe these building blocks in general terms. First, we shall characterize the way in which symbols and structures of symbols are represented internally and held in memory. Then, we shall mention some of the principal elementary processes that alter these symbol structures.n

Symbols, Lists and Descriptions The smallest units of manipulable information in memory are symbol tokens9 or symbol occurrences. It is postulated that tokens can be compared, and that comparison determines that the tokens are occurrences of the same symbol (symbol type), or that they are different. Symbol tokens are arranged in larger structures, called lists. A list is an ordered set, a sequence, of tokens. Hence, with every token on a list, except the last, there is associated a unique next token. Associated with the list as a whole is a symbol, its name. Thus, a list may be a sequence of symbols that are themselves names of lists-a list of lists. A familiar example of a list of symbols that all of us carry in memory is the alphabet. (Its name is “alphabet.”) Another is the list of days of the week, in order-Monday is next to Sunday, and so on. Associations also exist between symbol types. An association is a twotermed relation, involving three symbols, one of which names the relation, the other two its arguments. “The colour of the apple is red” specifies an association between “apple” and “red” with the relation “colour.” A symbol’s associations describe that symbol.

Some Elementary Processes A symbol, a list and an association are abstract objects. Their properties are defined by the elementary information processes that operate on them. One important class of such processes are the discrimination processes. The basic discrimination process, which compares symbols to determine whether or not they are identical, has already been mentioned. Pairs of compound structures-lists and sets of associations-are discriminated from each other by matching processes that apply the basic tests for symbol identity to symbols in corresponding positions in the two structures. For example, two chess positions can be discriminated by a matching process that compares the pieces standing on corresponding

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squares in the two positions. The outcome of the match might be a statement that “the two positions are identical except that the White King is on his Knight’s square in the first but on his Rook’s square in the second.” Other classes of elementary information processes are those capable of creating or copying symbols, lists and associations. These processes are involved, for example, in fixating or memorizing symbolic materials presented to the sense organs-learning a tune. Somewhat similar information processes are capable of modifying existing symbolic structures by inserting a symbol into a list, by changing a t e r n of an association (from “its colour is red” to “its colour is green”), or by deleting a symbol from a list. Still another class of elementary information processes finds information that is in structures stored in memory. We can think of such a process, schematically, as follows: to answer the question, “What letter follows ‘g’ in the alphabet?,” a process must find the list in memory named ”alphabet.” Then, another process must search down that list until (using the match for identity of symbols) it finds a “g.” Finally, a third process must find the symbol next to “g” in the list. Similarly, to answer the question, “what colour is the apple?” there must be a process capable of finding the second term of an association, given the first term and the name of the relation. Thus, there must be processes for finding named objects, for finding symbols on a list, for finding the next symbol on a list, and for finding the value of an attribute of an object. This list of elementary information processes is modest, yet provides an adequate collection of building blocks to implement the chess-playing theory as well as the other information-processing theories of thinking that have been constructed to date: including a general problem-solving theory, a theory of rote verbal learning, and several theories of concept formation and pattern recognition, among others.’O

Elementary Processes in the Chess Theory A few examples will show how the mechanisms employed in the chess-playing theory can be realized by symbols, lists, associations and elementary information processes. The player’s representation of the chess board is assumed to be a collection of associations: with each square is associated the symbol representing the man on that square, and symbols representing the adjoining squares in the several directions. Moves are similarly represented as symbols with which are associated the names of the squares from which and to which the move was made, the name of the piece moved, the name of the piece captured, if any, and so on. Similarly, the processes for manipulating these representations are compounded from the elementary processes already described. To make a move, for example, is to modify the internal representation of the board by deleting the association of the man to be moved with the square on which he previously stood, and creating the new association of that man with the square to which he moved; and, in case of a capture, by deleting also the association of the captured man with

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the square on which he stood. Another example: testing whether the King is in check involves finding the square associated with the King, finding adjoining squares along ranks, files and diagonals, and testing these squares for the presence of enemy men who are able to attack in the appropriate direction. (The latter is determined by associating with each man his type, and associating with each type of man the directions in which such men can legally be moved.) We see that, although the chess-playing theory contains several thousand program instructions, these are comprised of only a small number of elementary processes (far fewer than the number of elements in the periodic table). The elementary processes combine in a few simple ways into compound processes and operate on structures (lists and descriptions) that are constructed, combinatorially, from a single kind of building block-the symbol. There are two levels of theory: an “atomic” level, common to all the information-processing theories, of symbols, lists, associations and elementary processes, and a “macro-molecular” level, peculiar to each type of specialized human performance, of representations in the form of list structures and webs of associations, and of compound processes for manipulating these representations.

Processes in Serial Pattern Recognition A second example of how programs compounded from the elementary processes explain behaviour is provided by an information-processing theory of serial pattern recognition. Consider a sequence like: ABMCDMEFM

-.

An experimental subject in the laboratory, asked to extrapolate the series, will, after a little thought, continue: GHM, etc.

To see how he achieves this result, we examine the original sequence. First, it makes use of letters of the Roman alphabet. We can assume that the subject holds this alphabet in memory stored as a list, so that the elementary list process for finding the NEXT item on a list can find B, given A, or find S, given R, and so on. Now we note that any letter in the sequence, after the first three, is related to previous letters by relations NEXT and SAME. Specifically, if we organize the series into periods of three letters each: ABM CDM EFM

we see that:

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1. The first letter in each period is NEXT in the alphabet to the second letter in the previous period. 2. The second letter in each period is NEXT in the alphabet to the first letter in that period. 3. The third letter in each period is the SAME as the corresponding letter in the previous period. The relations of SAME and NEXT also suffice for a series like: AAA CCC

EEE

or for a number series like: 1 7 2 8 3 9 4 0

In the last case, the “alphabet” to which the relation of NEXT is applied is the list of digits, 0 to 9, and NEXT is applied circularly, i.e. after 9 comes 0 and then 1 again. Several closely related information-processing theories of human pattern recognition have been constructed using elementary processes for finding and generating the NEXT item in a list (see Feldman, Tonge and Kanter 1964; Laughery and Gregg 1962, and Simon and Kotovsky 1963). These theories have succeeded in explaining some of the main features of human behaviour in a number of standard laboratory tasks, including so-called binary choice tasks, and series-completion and symbol-analogy tasks from intelligence tests. The nature of the series-completion task has already been illustrated. In the binary choice experiment, the subject is confronted, one by one, with a sequence of tokens-each a “+” or “V,” say. As each one is presented to him, he is asked what the next one will be. The actual sequence is, by construction, random. The evidence shows that, even when the subjects are told this, they rarely treat it as random. Instead, they behave as though they were trying to detect a serial pattern in the sequence and extrapolate it. They behave essentially like subjects faced by the series-completion task, and basically similar information-processing theories using the same elementary processes can explain both behaviours.

A BROADER VIEW OF THINKING PROCESSES A closer look at the principal examples now extant of information-processing theories suggests that another level of theory is rapidly emerging, intermediate between the “atomic” level common to all the theories and the “macro-molecular” level idiosyncratic to each. It is clear that there is no prospect of eliminating all idiosyncratic elements from the individual theories. A theory to explain chessplaying performances must postulate memory structures and processes that are completely irrelevant to proving theorems in geometry, and vice versa.

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On the other hand, it is entirely possible that human performances in different task environments may call on common components at more aggregative levels than the elementary processes. This, in fact, appears to be the case. The first information-processing theory that isolated some of these common components was called the General Problem Solver (Newell and Simon 1964).

Means-End Analysis The General Problem Solver is a program organized to keep separate (1) problem-solving processes that, according to the theory, are possessed and used by most human beings of average intelligence when they are confronted with any relatively unfamiliar task environment, from (2) specific information about each particular task environment. The core of the General Problem Solver is an organization of processes for means-end analysis. The problem is defined by specifying a given situation (A), and a desired situation (B). A discrimination process incorporated in the system of means-end analysis compares A with B, and detects one or more differences (D) between them, if there are any. With each difference, there is associated in memory a set of operators, (0,)or processes, that are possibly relevant to removing differences of that kind. The means-end analysis program proceeds to try to remove the difference by applying, in turn, the relevant operators. Using a scheme of means-end analysis, a proof of a trigonometric identity like cos 8 tan 8 = sin 0 might proceed like this: The right-hand side contains only the sine function; the left-hand side other trigonometric functions as well. The operator that replaces tan by sinkos will eliminate one of these. Applying it we get cos 8 (sin Bkos 8 ) = sin 8. The left-hand side still contains an extraneous function, cosine. The algebraic cancellation operator, applied in the two cosines, might remove this difference. We apply the operator, obtaining the identity sin 8 = sin 8.

Planning Process Another class of general processes discovered in human problem-solving performances and incorporated in the General Problem Solver are planning processes. The essential idea in planning is that the representation of the problem situation is simplified by deleting some of the detail. A solution is now sought for the new, simplified, problem, and if one is found, it is used as a plan to guide the solution of the original problem, with the detail reinserted. Consider a simple problem in logic. Given: (1)“A,” (2) “not A or B,” ( 3 ) “if not C then not B”; to prove “C.” To plan the proof, note that the first premise contains A, the second A and B, the third, B and C, and the conclusion, C. The plan might be to obtain B by combining A with (AB),then to obtain C by combining B with (BC).The plan will in fact work, but requires (2) to be transformed

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into “ A implies B” and (3) into “B implies C,” which transformations follow from the definitions of “or” and “ i f . . . then.”

Problem-Solving Organization The processes for attempting sub-goals in the problem-solving theories and the exploration processes in the chess-playing theory must be guided and controlled by executive processes that determine what goal will be attempted next. Common principles for the organization of the executive processes have begun to appear in several of the theories. The general idea has already been outlined above for the chess-playing program. In this program the executive routine cycles between an exploration (search) phase and an evaluation (scan) phase. During the exploration phase, the available problem-solving processes are used to investigate sub-goals. The information obtained through this investigation is stored in such a way as to be accessible to the executive. During the evaluation phase, the executive uses this information to determine which of the existing sub-goals is the most promising and should be explored next. An executive program organized in this way may be called a search-scan scheme, for it searches an expanding tree of possibilities, which provides a common pool of information for scanning by its evaluative processes.” The effectiveness of a problem-solving program appears to depend rather sensitively on the alternation of the search and scan phases. If search takes place in long sequences, interrupted only infrequently to scan for possible alternative directions of exploration, the problem solver suffers from stereotypy. Having initiated search in one direction, it tends to persist in that direction as long as the sub-routines conducting the search determine, locally, that the possibilities for exploration have not been exhausted. These determinations are made in a very decentralized way, and without benefit of the more global information that has been generated. On the other hand, if search is too frequently interrupted to consider alternative goals to the one being pursued currently, the exploration takes on an uncoordinated appearance, wandering indecisively among a wide range of possibilities. In both theorem-proving and chess-playing programs, extremes of decentralized and centralized control of search have shown themselves ineffective in comparison with a balanced search-scan organization.

Discrimination Trees Common organizational principles are also emerging for the rote memory processes involved in almost all human performance. As a person tries to prove a theorem, say, certain expressions that he encounters along the way gradually become familiar to him and his ability to discriminate among them gradually improves. An information-processing theory (EPAM) was constructed several years

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ago to account for this and similar human behaviour in verbal learning experiments (e.g. learning nonsense syllables by the serial anticipation or paired associate methods) (see Feigenbaum 1964). This theory is able to explain, for instance, how familiarity and similarity of materials affect rates of learning. The essential processes in EPAM include processes for discriminating among compound objects by sorting them in a “discrimination tree”; and familiarization processes for associating pairs or short sequences of objects. Discrimination processes operate by applying sequences of tests to the stimulus objects, and sorting them on the basis of the test results-a sort of “twenty questions” procedure. The result of discrimination is to find a memory location where information is stored about objects that are similar to the one sorted. Familiarization processes create new compound objects o u t of previously familiar elements. Thus, during the last decade, the letter sequence “IPL” has become a familiar word (to computer programmers!) meaning “information processing language.” The individual letters have been associated in this word. Similarly, the English alphabet, used by the serial pattern-recognizing processes, is a familiar object compounded from the letters arranged in a particular sequence. All sorts of additional information can be associated with an object, once familiarized (for example, the fact that IPLs organize symbols in lists can be associated with “IPL”). Because discrimination trees play a central role in EPAM, the program may also be viewed as a theory of pattern detection, and EPAM-like trees have been incorporated in certain information-processing theories of concept formation. It also now seems likely that the discrimination tree is an essential element in problem-solving theories like GPS, playing an important role in the gradual modification of the subject’s behaviour as he familiarizes himself with the problem material.

CONCLUSION Our survey shows that within the past decade a considerable range of human behaviours has been explained successfully by information-processing theories. We now know, for example, some of the central processes that are employed in solving problems, in detecting and extrapolating patterns, and in memorizing verba I ma teria Is. Information-processing theories explain behaviour at various levels of detail. In the theories now extant, at least three levels can be distinguished. At the most aggregative level are theories of complex behaviour in specific problem domains: proving theorems in logic or geometry, discovering checkmating combinations in chess. These theories tend to contain very extensive assumptions about the knowledge and skills possessed by the human beings who perform these activities, and about the way in which this knowledge and these skills are organized and represented internally. Hence each of these theories incorporates a rather extensive set of assumptions, and predicts behaviour only in a narrow domain.

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At a second level, similar or identical information-processing mechanisms are common to many of the aggregative theories. Means-end analysis, planning, the search-scan scheme, and discrimination trees are general-purpose organizations for processing that are usable over a wide range of tasks. As the nature of these mechanisms becomes better understood, they, in turn, begin to serve as basic building blocks for the aggregative theories, allowing the latter to be stated in more parsimonious form, and exhibiting the large fraction of machinery that is common to all, rather than idiosyncratic to individual tasks. At the lowest, “atomic,” level, all the information-processing theories postulate only a small set of basic forms of symbolic representation and a small number of elementary information processes. The construction and successful testing of large-scale programs that simulate complex human behaviours provide evidence that a small set of elements, similar to those now postulated in information-processing languages, is sufficient for the construction of a theory of human thinking. Although none of the advances that have been described constitute explanations of human thought at the still more microscopic, physiological level, they open opportunities for new research strategies in physiological psychology. As the information-processing theories become more powerful and better validated, they disclose to the physiological psychologist the fundamental mechanisms and processes that he needs to explain. He need no longer face the task of building the whole long bridge from microscopic neurological and molecular structures to gross human behaviour, but can instead concentrate on bridging the much shorter gap from physiology to elementary information processes. The work of Lettvin, Maturana, McCulloch and Pitts on information processing in the frog’s eye (1959), and the work of Hubel and Wiesel on processing of visual information by the cat (1962) already provide some hints of the form this bridging operation may take.

NOTES 1. The best-known exponent of this radical behaviourist position is Professor B.F. Skinner. He has argued, for example, that “an explanation is the demonstration of a functional relationship between behavior and manipulable or controllable variables,” in Wann (1964), p. 102. 2. A distinguished example of such a theory is Hebb‘s (1949) formulation in terms of “cell assemblies.” Hebb does not, however, insist on an exclusively physiological base for psychological theory, and his general methodological position is not inconsistent with that taken here. See also Hebb (1958), Chap. 13. 3. A “move” means here a move by one player followed by a reply by his opponent. Hence to look ahead four of five moves is to consider sequences of eight or ten successive positions. 4. A general account of this program, with the results of some hand simulations, can be found in Simon and Simon (1962), pp. 425-9. The theory described there has subsequently been programmed and the hand-simulated findings confirmed on a computer.

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5. This is perhaps the most important element in the strategy. It will be discussed further later. 6. The beginnings of such a compendium have already appeared. A convenient source for descriptions of a number of the information-processing theories is the collection by Feigenbaum and Feldman (1964). 7. Of course, even Newtonian mechanics is not at all this simple in structure. See Simon (1947), pp. 888-905. 8. Only a few of the characteristics of list-processing systems can be mentioned here. For a fuller account, see Newell and Simon (1963), especially pp. 273-376,380 84,419-24. 9. Evidence as to how information is symbolized in the brain is almost non-existent. If the reader is assisted by thinking of different symbols as different macromolecules, this metaphor is as good as any. A few physiologists think it may even be the correct explanation. See Hyden (1961), pp. 18-39. Differing patterns of neural activity will d o as well. See Adey, Kador, Didio and Schindler (1963), pp. 259-81. 10. For examples, see Feigenbaum and Feldman (1964), Part 2. 11. Perhaps the earliest use of the search-scan scheme appeared in the Logic Theorist, the first heuristic theorem-proving program. See Newell and Simon (1964).

REFERENCES Adey, W. R., Kador, R. T., Didio, J. and Schindler, W. J. (1963) “Impedance Changes in Cerebral Tissue Accompanying a Learned Discriminative Performance in the Cat,” Experimental Physiology, 7. Calvin, M. and Bassharn, J. A. (1962) The Photosynthesis of Carbon Compounds. New York: W. A. Benjamin. De Groot, A. (1964) Thought and Choice in Chess. The Hague: Mouton. Feigenbaum, E. A. (1964) “The Simulation of Verbal Learning Behavior” in Feigenbaum and Feldman (1964). Feigenbaum, E. A. and Feldman, J. (eds) (1964) Computers and Thought. New York: McGraw-Hill. Feldman, J., Tonge, E and Kanter, H. (1963) “Empirical Explorations of Hypothesis-Testing Model of Binary Choice Behavior” in Hoggatt, A. C. and Balderston, E E. (eds) Symposium on Simulation Models. Cincinnati: South-Western Publishing. Hebb, D. 0. (1949) The Organization of Behavior. New York: Wiley. Hebb, D. 0. (1958) Textbook of Psychology. Philadelphia: Saunders. Hubel, D. H. and Wiesel, T. N. (1962) “Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex,” Journal of Physiology, 160, pp. 106-54. Hyden, Holger (1961) “Biochemical Aspects of Brain Activity” in Farber, S. M. and Wilson, R. H. L. (eds) Control of the Mind. New York: McGraw-Hill. Laughery, K. R. and G r e g , L. W. (1962) “Simulation of Human Problem-Solving Behaviour,” Psychometrika, 27. Lettvin, J. Y., Maturana, H. R., McCulloch, W. S. and Pitts, W. H. (1959) “What the Frog’s Eye Tells the Frog’s Brain,” Proceedings of the Institute of Radio Engineers, 47, pp. 1940-51.

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Newell, A. and Simon, H. A. (1963) “Computers in Psychology” in Luce, Bush and Galanter (eds) Handbook ofMathematical Psychology, vol. 1 . New York: Wiley. Newell, A. and Simon, H. A. (1964) “Empirical Explorations with the Logic Theory Machine: A Case Study in Heuristics” in Feigenbaum and Feldman (1964). Newell, A. and Simon, H. A. (1964) “GPS, A Program that Simulates Human Thought” in Feigenbaum and Feldman (1964). Pauling (1960) The Nature of the Chemical Bond. Ithaca: Cornell University Press, 3rd ed. Simon, H. A. (1947) “The Axioms of Newtonian Mechanics,” Phil. Mag., Ser. 7, 38 (December). Simon, H. A. and Kotovsky, K. (1963) “Human Acquisition of Concepts for Sequential Patterns,” Psychological Review, 70 (November), pp. 534-46. Simon, H. A. and Simon, P. A. (1962) “Trial and Error Search in Solving Difficult Problems: Evidence from the Game of Chess,” Behavioral Science, 7 (October). Wann, T. W. (ed.) (1964) Behaviorism and Phenomenology. Chicago: University of Chicago Press.

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3 Why Computers May Never Think Like People Hubert and Stuart Dreyfus

Scientists who stand at the forefront of artificial intelligence (AI) have long dreamed of autonomous “thinking” machines that are free of human control. And now they believe we are not far from realizing that dream. As Marvin Minsky, a well-known A1 professor at MIT, recently put it: “Today our robots are like toys. They do only the simple things they’re programmed to. But clearly they’re about to cross the edgeless line past which they’ll do the things we are programmed to.” Patrick Winston, Minsky’s successor as head of the MIT A1 Laboratory, agrees: “Just as the Wright Brothers at Kitty Hawk in 1903 were on the right track to the 747s of today, so artificial intelligence, with its attempt to formalize common-sense understanding, is on the way to fully intelligent machines.” Encouraged by such optimistic pronouncements, the U.S. Department of Defense (DOD) is sinking millions of dollars into developing fully autonomous war machines that will respond to a crisis without human intervention. Business executives are investing in “expert” systems whose wisdom they hope will equal, if not surpass, that of their top managers. And A1 entrepreneurs are talking of “intelligent systems” that will perform better than we can-in the home, in the classroom, and at work. But no matter how many billions of dollars the Defense Department or any other agency invests in AI, there is almost no likelihood that scientists can develop machines capable of making intelligent decisions. After 25 years of research, A1 has failed to live up to its promise, and there is no evidence that it ever will. In fact, machine intelligence will probably never replace human intelligence simply because we ourselves are not “thinking machines.” Human beings have an intuitive intelligence that “reasoning” machines simply cannot match. Military and civilian managers may see this obvious shortcoming and refrain from deploying such “logic” machines. However, once various groups have From “Why Computers May Never Think Like People,” Dreyfus, Hubert and Stuart, Technology Review. v. 89, p. 42 (20) January, 1986. Reprinted with permission from MIT’s Technology Review Magazine, copyright 1986.

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invested vast sums in developing these machines, the temptation to justify this expense by installing questionable A1 technologies will be enormous. The dangers of turning over the battle-field completely to machines are obvious. But it would also be a mistake to replace skilled air-traffic controllers, seasoned business managers, and master teachers with computers that cannot come close to their level of expertise. Computers that “teach“ and systems that render “expert” business decisions could eventually produce a generation of students and managers who have no faith in their own intuition and expertise. We wish to stress that we are not Luddites. There are obvious tasks for which computers are appropriate and even indispensable. Computers are more deliberate, more precise, and less prone to exhaustion and error than the most conscientious human being. They can also store, modify, and tap vast files of data more quickly and accurately than humans can. Hence, they can be used as valuable tools in many areas. As word processors and telecommunication devices, for instance, computers are already changing our methods of writing and our notions of collaboration. However, we believe that trying to capture more sophisticated skills within the realm of electronic circuits-skills involving not only calculation but also judgment-is a dangerously misguided effort and ultimately doomed to failure.

ACQUIRING HUMAN KNOW-HOW Most of us know how to ride a bicycle. Does that mean we can formulate specific rules to teach someone else how to do it? How would we explain the difference between the feeling of falling over and the sense of being slightly off-balance when turning? And do we really know, until the situation occurs, just what we would d o in response to a certain wobbly feeling? No, we don’t. Most of us are able to ride a bicycle because we possess something called “know-how,” which we have acquired from practice and sometimes painful experience. That know-how is not accessible to us in the form of facts and rules. If it were, we could say we “know that” certain rules produce proficient bicycle riding. There are innumerable other aspects of daily life that cannot be reduced to “knowing that.” Such experiences involve “knowing how.” For example, we know how to carry on an appropriate conversation with family, friends, and strangers in a wide variety of contexts-in the office, at a party, and on the street. We know how to walk. Yet the mechanics of walking on two legs are so complex that the best engineers cannot come close to reproducing them in artificial devices. This kind of know-how is not innate, as is a bird’s skill at building a nest. We have to learn it. Small children learn through trial and error, often by imitating those who are proficient. As adults acquire a skill through instruction and experience, they do not appear to leap suddenly from “knowing that”-a knowledge guided by rules-to experience-based know-how. Instead, people usually pass through five levels of skill: novice, advanced beginner, competent, proficient, and expert. Only when we understand this dynamic process can we ask how far the computer could reasonably progress.

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During the novice stage, people learn facts relevant to a particular skill and rules for action that are based on those facts. For instance, car drivers learning to operate a stick shift are told at what speed to shift gears and at what distancegiven a particular speed-to follow other cars. These rules ignore context, such as the density of traffic or the number of stops a driver has to make. Similarly, novice chess players learn a formula for assigning pieces point values independent of their position. They learn the rule: “Always exchange your pieces for the opponent’s if the total value of the pieces captured exceeds that of pieces lost.” Novices generally do not know that they should violate this rule in certain situations. After much experience in real situations, novices reach the advanced-beginner stage. Advanced-beginner drivers pay attention to situational elements, which cannot be defined objectively. For instance, they listen to engine sounds when shifting gears. They can also distinguish between the behavior of a distracted or drunken driver and that of the impatient but alert driver. Advanced-beginner chess players recognize and avoid overextended positions. They can also spot situational clues such as a weakened king’s side or a strong pawn structure. In all these cases, experience is immeasurably more important than any form of verbal description. Like the training wheels on a child’s first bicycle, initial rules allow beginners to accumulate experience. But soon they must put the rules aside to proceed. For example, at the competent stage, drivers no longer merely follow rules; they drive with a goal in mind. If they wish to get from point A to point B very quickly, they choose their route with an eye to traffic but not much attention to passenger comfort. They follow other cars more closely than they are “supposed” to, enter traffic more daringly, and even break the law. Competent chess players may decide, after weighing alternatives, that they can attack their opponent’s king. Removing pieces that defend the enemy king becomes their overriding objective, and to reach it these players will ignore the lessons they learned as beginners and accept some personal losses. A crucial difference between beginners and more competent performers is their level of involvement. Novices and advanced beginners feel little responsibility for what they do because they are only applying learned rules; if they foul up, they blame the rules instead of themselves. But competent performers, who choose a goal and a plan for achieving it, feel responsible for the result of their choices. A successful outcome is deeply satisfying and leaves a vivid memory. Likewise, disasters are not easily forgotten.

THE INTUITION OF EXPERTS The learner of a new skill makes conscious choices after reflecting on various options. Yet in our everyday behavior, this model of decision making-the detached, deliberate, and sometimes agonizing selection among alternatives-is the exception rather than the rule. Proficient performers d o not rely on detached deliberation in going about their tasks. Instead, memories of similar experiences in

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the past seem to trigger plans like those that worked before. Proficient performers recall whole situations from the past and apply them to the present without breaking them down into components or rules. For instance, a boxer seems to recognize the moment to begin an attack not by following rules and combining various facts about his body’s position and that of his opponent. Rather, the whole visual scene triggers the memory of similar earlier situations in which an attack was successful. The boxer is using his intuition, or know-how. Intuition should not be confused with the reenactment of childhood patterns or any of the other unconscious means by which human beings come to decisions. Nor is guessing what we mean by intuition. To guess is to reach a conclusion when one does not have enough knowledge or experience to do so. Intuition or know-how is the sort of ability that we use all the time as we go about our everyday tasks. Ironically, it is an ability that our tradition has acknowledged only in women and judged inferior to masculine rationality. While using their intuition, proficient performers still find themselves thinking analytically about what to do. For instance, when proficient drivers approach a curve on a rainy day, they may intuitively realize they are going too fast. They then consciously decide whether to apply the brakes, remove their foot from the accelerator, or merely reduce pressure on the accelerator. Proficient marketing managers may intuitively realize that they should reposition a product. They may then begin to study the situation, taking great pride in the sophistication of their scientific analysis while overlooking their much more impressive talent-that of recognizing, without conscious thought, the simple existence of the problem. The final skill level is that of expert. Experts generally know what to do because they have a mature and practiced understanding. When deeply involved in coping with their environment, they do not see problems in some detached way and consciously work at solving them. The skills of experts have become so much a part of them that they need be no more aware of them than they are of their own bodies. Airplane pilots report that as novices they felt they were flying their planes, but as experienced pilots they simply experience flying itself. Grand masters of chess, engrossed in a game, are often oblivious to the fact that they are manipulating pieces on a board. Instead, they see themselves as participants in a world of opportunities, threats, strengths, weaknesses, hopes, and fears. When playing rapidly, they sidestep dangers as automatically as teenagers avoid missiles in a familiar video game. One of us, Stuart, knows all too well the difference between expert and merely competent chess players; he is stuck at the competent level. He took up chess as an outlet for his analytic talent in mathematics, and most of the other players on his college team were also mathematicians. At some point, a few of his teammates who were not mathematicians began to play fast five- or ten-minute games of chess, and also began eagerly to replay the great games of the grand masters. But Stuart and his mathematical colleagues resisted because fast chess didn’t give them the time to figure out what to do. They also felt that they could learn nothing from the grandmaster games, since the record of those games seldom if ever provided specific rules and principles.

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Some of his teammates who played fast chess and studied grand-mastergames absorbed a great deal of concrete experience and went on to become chess masters. Yet Stuart and his mathematical friends never got beyond the competent level. Students of math may predominate among chess enthusiasts, but a truck driver is as likely as a mathematician to be among the world’s best players. Stuart says he is glad that his analytic approach to chess stymied his progress because it helped him to see that there is more to skill than reasoning. When things are proceeding normally, experts do not solve problems by reasoning; they do what normally works. Expert air-traffic controllers do not watch blips on a screen and deduce what must be going on in the sky. Rather, they “see” planes when they look at their screens and they respond to what they see, not by using rules but as experience has taught them to. Skilled outfields do not take the time to figure out where a ball is going. Unlike novices, they simply run to the right spot. In The Bruin, Richard Restak quotes a Japanese martial artist as saying, “There can be no thought, because if there is thought, there is a time of thought and that means a flaw. . . If you take the time to think, ‘I must use this or that technique,’ you will be struck while you are thinking.” We recently performed an experiment in which an international chess master, Julio Kaplan, had to add numbers at the rate of about one per second while playing five-second-a-move chess against a slightly weaker but master-level player. Even with his analytical mind apparently jammed by adding numbers, Kaplan more than held his own against the master in a series of games. Deprived of the time necessary to see problems or construct plans, Kaplan still produced fluid and coordinated play. As adults acquire skills, what stands o u t is their progression from the analytic behavior of consciously following abstract rules to skilled behavior based on unconsciously recognizing new situations as similar to remembered ones. Conversely, small children initially understand only concrete examples and gradually learn abstract reasoning. Perhaps it is because this pattern in children is so well known that adult intelligence is so often misunderstood. By now it is evidence that there is more to intelligence than calculative rationality. In fact, experts who consciously reason things out tend to regress to the level of a novice or, at best, a competent performer. One expert pilot described an embarrassing incident that illustrates this point. Once he became an instructor, his only opportunity to fly the four-jet KC-135s at which he had once been expert was during the return flights he made after evaluating trainees. He was approaching the landing strip on one such flight when an engine failed. This is technically an emergency, but an experienced pilot will effortlessly compensate for the pull to one side. Being out of practice, our pilot thought about what to do and then overcompensated. He then consciously corrected himself, and the plane shuddered violently as he landed. Consciously using rules, he had regressed to flying like a beginner. This is not to say that deliberative rationality has no role in intelligence. Tunnel vision can sometimes be avoided by a type of detached deliberation. Focusing on aspects of a situation that seem relatively unimportant allows another perspective to spring to mind. We once heard an Israeli fighter pilot recount how

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deliberative rationality may have saved his life by rescuing him from tunnel vision. Having just vanquished an expert opponent, he found himself taking on another member of the enemy squadron who seemed to be brilliantly eluding one masterful ploy after another. Things were looking bad until he stopped following his intuition and deliberated. He then realized that his opponent’s surprising manuevers were really the predictable, rule-following behavior of a beginner. This insight enabled him to vanquish the pilot.

IS INTELLIGENCE BASED ON FACTS? Digital computers, which are basically complicated structures of simple onoff switches, were first used for scientific calculation. But by the end of the fifties, researchers such as Allen Newell and Herbert Simon, working together at the Rand Corp., began to exploit the idea that computers could manipulate general symbols. They saw that one could use symbols to represent elementary facts about the world and rules to represent relationships between the facts. Computers could apply these rules and make logical inferences about the facts. For instance, a programmer might give a computer rules about how cannibals like to eat missionaries, and facts about how many cannibals and missionaries must be ferried across a river in one boat that carries only so many people. The computer could then figure out how many trips it would take to get both the cannibals and the missionaries safely across the river. Newell and Simon believed that computers programmed with such facts and rules could, in principle, solve problems, recognize patterns, understand stories, and indeed do anything that an intelligent person could do. But they soon found that their programs were missing crucial aspects of problem solving, such as the ability to separate relevant from irrelevant operations. As a result, the programs worked in only a very limited set of cases, such as in solving puzzles and proving theorems of logic. In the late sixties, researchers at MIT abandoned Newell and Simon’s approach, which was based on imitating peoples’ reports of how they solved problems, and began to work on any processing methods that could give computers intelligence. They recognized that to sole “real-world” problems the computer had to somehow simulate real-world understanding and intuition. In the introduction to Semantic Information Processing, a collection of his students’ Ph.D. theses, Marvin Minsky describes the heart of the MIT approach: “If w e . . . ask. . . about the common everyday structures-that which a person needs to have ordinary common sense-we will find first a collection of indispensable categories, each rather complex: geometrical and mechanical properties of things and of space; uses and properties of a few thousand objects; hundreds of ‘facts’ about hundreds of people; thousands of facts about tens of people; tens of facts about thousands of people; hundreds of facts about hundreds of organizations . . . I therefore feel that a machine will quite critically need to acquire on the order of a hundred thousand elements of knowledge in order to be-

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have with reasonable sensibility in ordinary situations. A million, if properly organized, should be enough for a very great intelligence.” However, Minsky’s students encountered the same problem that had plagued Newell and Simon: each program worked only in its restricted specialty and could not be applied to other problems. Nor did the programs have any semantics-that is, any understanding of what their symbols meant. For instance, Daniel Bobrow’s STUDENT program, which was designed to understand and solve elementary algebraic story problems, interpreted the phrase “the number of times I went to the movies” as the product of the two variables “number of” and “I went to the movies.” That’s because, as far as the program knew, “times” was a multiplicative operator linking the two phrases. The restricted, ad hoc character of such work is even more striking in a program called ELIZA, written by MIT computer science professor Joseph Weizenbaum. Weizenbaum set out to show just how much apparent intelligence one could get a computer to exhibit without giving it any real understanding at all. The result was a program that imitated a therapist using simple tricks such as turning statements into questions: it responded to “I’m feeling sad” with “Why are you feeling sad?” When the program couldn’t find a stock response, it printed out statements such as “Tell me about your father.’’ The remarkable thing was that people were so easily fooled by these tricks. Weizenbaum was appalled when some people divulged their deepest feelings to the computer and asked others to leave the room while they were using it. One of us, Hubert, was eager to see a demonstration of the notorious program, and he was delighted when Weizenbaum invited him to sit at the console and interact with ELIZA. Hubert spoiled the fun, however. He unintentionally exposed how shallow the trickery really was by typing, “I’m feeling happy,” and then correcting himself by typing, “No, elated.” At that point, the program came back with the remark, “Don’t be so negative.” Why? Because it had been programmed to respond with that rebuke whenever there was a “no” in the input.

MICROWORLDS VERSUS THE REAL WORLD It took about five years for the shallowness of Minsky’s students’ programs to become apparent. Meanwhile, Hubert published a book, What Computers Can’t Do, which asserted that A1 research had reached a dead end since it could not come up with a way to represent general common-sense understanding. But

just as What Computers Can’t Do went to press in 1970, Minsky and Seymour Papert, also a professor at MIT, developed a new approach to AI. If one could not deal systematically with common-sense knowledge all at once, they asked, then why not develop methods for dealing systematically with knowledge in isolated sub-worlds and build gradually from that? Shortly after that, MIT researchers hailed a computer program by graduate student Terry Winograd as a “major advance” in getting computers to understand human language. The program, called SHRDLU, simulated on a TV screen a ro-

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bot arm that could move a set of variously shaped blocks. The program allowed a person to engage in a dialogue with the computer, asking questions, making statements, and issuing commands within this simple world of movable blocks. The program relied on grammatical rules, semantics, and facts about blocks. As Winograd cautiously claimed, SHRDLU was a “computer program which ‘understands’ language in a limited domain.” Winograd achieved success in this restricted domain, or “microworld,” because he chose a simple problem carefully. Minsky and Papert believed that by combining a large number of these microworlds, programmers could eventually give computers real life understanding. Unfortunately, this research confuses two domains, which we shall distinguish as “universe” and “world.” A set of interrelated facts may constitute a “universe’’ such as the physical universe, but it does not constitute a “world” such as the world of business or theater. A “world” is an organized body of objects, purposes, skills, and practices that make sense only against a background of common human concerns. These “sub-worlds” are not isolable physical systems. Rather, they are specific elaborations of a whole, without which they could not exist. If Minsky and Papert’s microworlds were true subworlds, they would not have to be extended and combined to encompass the everyday world, because each one would already incorporate it. But since microworlds are only isolated, meaningless domains, they cannot be combined and extended to reflect everyday life. Because scientists failed to ask what a “world” is, another five-year period of A1 research ended in stagnation. Winograd himself soon gave up the attempt to generalize the techniques SHRDLU used. “The A1 programs of the late sixties and early seventies are much too literal built up of the bricks and mortar provided by the words.” From the late seventies to the present, A1 has been wrestling unsuccessfully with what is called the common-sense knowledge problem: how to store and gain access to all the facts human beings seem to know. This problem has kept A1 from even beginning to fulfill the predictions Minsky and Simon made in the mid-sixties: that within 20 years computers would be able to do everything humans can.

CAN COMPUTERS COPE WITH CHANGE? If a machine is to interact intelligently with people, it has to been endowed with an understanding of human life. What we understand simply by virtue of being human-that insults make us angry, that moving physically forward is easier than moving backward-all this and much more would have to be programmed into the computer as facts and rules. As A1 workers put it, they must give the computer our belief system. This, of course, presumes that human understanding is made up of beliefs that can be readily collected and stored as facts. Even if we assume that this is possible, an immediate snag appears: we cannot program computers for context. For instance, we cannot program a computer to know simply that a car is going “too fast.” The machine must be programmed

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in a way free of interpretation-we must stipulate that the car is going “20 miles a n hour,” for example. Also, computers know what to d o only by reference to precise rules, such as “shift to second at 20 miles an hour.” Computer programmers cannot use common-sense rules, such as “under normal conditions, shift to second a t about 20 miles an hour.” Even if all the facts were stored in a context-free form, the computer still couldn’t use them because it would be unable to draw on just the facts or rules that are relevant in each particular context. For example, a general rule of chess is that you should trade material when you’re ahead in the value of the pieces on the board. However, you should not apply that rule if the opposing king is much more centrally located than yours, or when you are attacking the enemy king. And there are exceptions to each of these exceptions. It is virtually impossible to include all the possible exceptions in a program and d o so in such a way that the computer knows which exception to use in which case, In the real world, any system of rules has to be incomplete. The law, for instance, always strives for completeness but never achieves it. “Common law” helps, for it is based more on precedents than on a specific code. But the sheer number of lawyers in business tells us that it is impossible to develop a code of law so complete that all situations are unambiguously covered. To explain our own actions and rules, humans must eventually fall back on everyday practices and simply say, “This is what one does.” In the final analysis, all intelligent behavior must hark back to our sense of what we are. We can never explicitly formulate this in clear-cut rules and facts; therefore, we cannot program computers to possess that kind of know-how. Nor can we program them to cope with changes in everyday situations. A1 researchers have tried to develop computer programs that describe a normal sequence of events as they unfold. One such script, for instance, details what happens when someone goes to a restaurant. The problem is that so many unpredictable events can occur-one can receive an emergency telephone call or run into an acquaintance-that it’s virtually impossible to predict how different people will respond. It all depends on what else is going on and what their specific purpose is. Are these people there to eat, to hobnob with friends, to answer phone calls, or to give the waiters a hard time? To make sense of behavior in restaurants, one has to understand not only what people typically d o in eating establishments but why they d o it. Thus, even if programmers could manage to list all that is possibly relevant in typical restaurant dining, computers could not use the information because they would have no understanding of what is actually relevant to specific customers.

THINKING WITH IMAGES, NOT WORDS Experimental psychologists have shown that people actually use images, not descriptions as computers do, to understand and respond to some situations. Humans often think by forming images and comparing them holistically. This proc-

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ess is quite different from the logical, step-by-step operations that logic machines perform. For instance, human beings use images to predict how certain events will turn out. If people know that a small box is resting on a large box, they can imagine what would happen if the large box were moved. If they see that the small box is tied to a door, they can also imagine what would result if someone were to open the door. A computer, however, must be given a list of facts about boxes, such as their size, weight, and frictional coefficients, as well as information about how each is affected by various kinds of movements. Given enough precise information about boxes and strings, the computer can deduce whether the small box will move with the large one under certain conditions. People also reason things out in this explicit, step-by-step way-but only if they must think about relationships they have never seen and therefore cannot imagine. At present, computers have difficulty recognizing images. True, they can store an image as a set of dots and then rotate the set of dots so that a human designer can see the object from any perspective. But to know what a scene depicts, a computer must be able to analyze it and recognize every object. Programming a computer to analyze a scene has turned o u t to be very difficult. Such programs require a great deal of computation, and they work only in special cases with objects whose characteristics the computer has been programmed to recognize in advance. But that is just the beginning of the problem. The computer can make inferences only from lists of facts. It’s as if to read a newspaper you had to spell out each word, find its meaning in the dictionary, and diagram every sentence, labeling all the parts of speech. Brains do not seem to decompose either language or images this way, but logic machines have no choice. They must break down images into the objects they contain-and then into descriptions of those objects’ features-before drawing any conclusions. However, when a picture is converted into a description, much information is lost. In a family photo, for instance, one can see immediately which people are between, behind, and in front of which others. The programmer must list all these relationships for the computer, or the machine must go through the elaborate process of deducting these relationships each time the photo is used. Some A1 workers look for help from parallel processors, machines that can do many things at once and hence make millions of inferences per second. But this appeal misses the point: that human beings seem to be able to form and compare images in a way that cannot be captured by any number of procedures that operate on descriptions. Take, for example, face recognition. People can not only form an image of a face, but they can also see the similarity between one face and another. Sometimes the similarity will depend on specific shared features, such as blue eyes and heavy beards. A computer, if it has been programmed to abstract such features from a picture of a face, could recognize this sort of similarity. However, a computer cannot recognize emotions such as anger in facial expressions, because we know of no way to break down anger into elementary sym-

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bols. Therefore, logic machines cannot see the similarity between two faces that are angry. Yet human beings can discern the similarity almost instantly. Many A1 theorists are convinced that human brains unconsciously perform a series of computations to perceive such subtleties. While no evidence for this mechanical model of the brain exists, these theorists take it for granted because it is the way people proceed when they are reflecting consciously. To such theorists, any alternative explanation appears mystical and therefore anti-scientific. But there is another possibility. The brain, and therefore the mind, could still be explained in terms of something material. But it does not have to be an information-processing machine. Other physical systems can detect similarity without using any descriptions or rules at all. These systems are known as holograms.

IS THE MIND LIKE A HOLOGRAM? An ordinary hologram works by taking a picture of an object using two beams of laser light, one of which is reflected off the object and one of which shines directly onto film. When the two beams meet, they create an interference pattern like that produced by the waves from several pebbles thrown into a pond. The light waves form a specific pattern of light and dark regions. A photographic plate records this interference pattern, thus storing a representation of the object. In ordinary light, the plate just looks blurry, a uniform silvery gray. But if the right frequency of light is projected into it, the recorded pattern of light and dark shapes the light into a replica of the object. This replica appears three-dimensional: we can view different sides of it as we change position. What first attracted neuropsychologists to the hologram was that it really is holistic: any small piece of the blur on the photographic plate contains the whole scene. For example, if you cut one corner off a hologram of a table and shine a laser beam through what remains, you do not see an image of a table with a corner missing. The whole table is still there but with fuzzier edges. Certain areas of the brain also have this property. When a piece is cut out, a person may lose nothing specific from vision, for example. Instead, that person may see everything less distinctly. Holograms have another mind like property: they can be used for associative memory. If one uses a single hologram to record two different scenes and then bounces laser light off one of the scenes, an image of the other will appear. In our view, the most important property of holograms is their ability to detect similarity. For example, if we made a hologram of this page and then made a hologram of one of the letters on the page, say the letter F, shining a light through the two holograms would reveal an astonishing effect: a black field with bright spots wherever the letter F occurs on the page. Moreover, the brightest spots would indicate the Fs with the greatest similarity to the F we used to make our hologram. Dimmer spots would appear where there are imperfect or slightly rotated versions of the E Thus, a hologram can not only identify objects; it can also recognize similarity between them. Yet it employs no descriptions or rules.

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The way a hologram can instantly pick out a specific letter on a page is reminiscent of the way people pick out a familiar face from a crowd. It is possible that we distinguish the familiar face from all the other faces by processing rules about objectively identifiable features. But we would have to examine each face in the crowd, detect its features, and compare them with lists of our acquaintances’ features. It is much more plausible that our minds work on some variation of the holistic model. While the brain obviously does not contain lasers or use light beams, some scientists have suggested that neurons could process incoming stimuli using interference patterns like those of a hologram. However, the human mind seems to have an ability that far transcends current holographic techniques: the remarkable ability to recognize whole meaningful patterns without decomposing them into features. Unlike holography, our mind can sometimes detect faces in a crowd that have expressions unlike any we have previously seen on those faces. We can also pick out familiar faces that have changed dramatically because of the growth of a beard or the ravages of time. . We take no stand on the question of whether the brain functions holographically. We simply want to make clear that the information-processing computer is not the only physical system that can exhibit mind like properties. Other devices may provide closer analogies to the way the mind actually works. Given the above considerations, what level of skill can we expect logic machines to reach? Since we can program computers with thousands of rules combining hundreds of thousands of features, the machines can become what might be thought of as an expert novices in any well-structured and well-understood domain. As long as digital computers’ ability to recognize images and reason by analogy remains a vague promise, however, they will not be able to approach the way human beings cope with everyday reality. Despite their failure to capture everyday human understanding in computers, A1 scientists have developed programs that seem to reproduce human expertise within a specific, isolated domain. The programs are called expert systems. In their narrow areas, such systems perform with impressive competence. In his recent book on “fifth-generation” computers, Edward Feigenbaum, a professor at Stanford, spells out the goal of expert systems: “In the kind of intelligent systems envisioned by the designers of the Fifth Generation, speed and processing power will be increased dramatically. But more important, the machines will have reasoning power: they will automatically engineer vast amounts of knowledge to serve whatever purpose human beings propose, from medical diagnosis to product design, from management decisions to education.” The knowledge engineers claim to have discovered that all a machine needs to behave like an expert in these restricted domains are some general rules and lots of very specific knowledge. But can these systems really be expert? If we agree with Feigenbaum that “almost all thinking that professionals do is done by reasoning,” and that each expert builds up a “repertory of working rules of thumb,” the answer is yes. Given their speed and precision, computers should be as good as or better than people at following rules for deducing conclusions. Therefore, to build an expert system, a programmer need only extract those rules and program them into a computer.

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JUST HOW EXPERT ARE EXPERT SYSTEMS? However, human experts seem to have trouble articulating the principles on which they allegedly act. For example, when Arthur Samuel at IBM decided to write a program for playing checkers in 1947, he tried to elicit “heuristic” rules from checkers masters. But nothing the experts told him allowed him to produce master play. So Samuel supplemented these rules with a program that relies blindly on its memory of past successes to improve its current performance. Basically, the program chooses what moves to make based on rules and a record of all past positions. This checkers program is one of the best expert systems ever built. But it is no champion. Samuel says the program “is quite capable of beating any amateur player and can give better players a good contest.” It did once defeat a state champion, but the champion turned around and defeated the program in six mail games. Nonetheless, Samuel still believes that chess champions rely on heuristic rules. Like Feigenbaum, he simply thinks that the champions are poor at recollecting their compiled rules: “The experts do not know enough about the mental processes involved in playing the game.” INTERNIST-1 is an expert system highly touted for its ability to make diagnoses in internal medicine. Yet according to a recent evaluation of the program published in The New England Journal of Medicine, this program misdiagnosed 18 o u t of a total of 43 cases, while clinicians at Massachusetts General Hospital misdiagnosed 15. Panels of doctors who discussed each case misdiagnosed only 8. (Biopsies, surgery, and post-mortem autopsies were used to establish the correct diagnosis for each case.) The evaluators found that “the experienced clinician is vastly superior to INTERNIST-1, in the ability to consider the relative severity and independence of the different manifestations of disease and to understand the . . . evolution of the disease process.” The journal also noted that this type of systematic evaluation was “virtually unique in the field of medical applications of artificial intelligence.” In every area of expertise, the story is the same: the computer can do better than the beginner and can even exhibit useful competence, but it cannot rival the very experts whose facts and supposed rules it is processing with incredible speed and accuracy. Why? Because the expert is not following any rules? While a beginner makes inferences using rules and facts just like a computer, the expert intuitively sees what to do without applying rules. Experts must regress to the novice level to state the rules they still remember but no longer use. No amount of rules and facts can substitute for the know-how experts have gained from experience in tens of thousands of situations. We predict that in no domain in which people exhibit such holistic understanding can a system based on rules consistently do as well as experts. Are there any exceptions? At first glance, at least one expert system seems to be as good as human specialists. Digital Equipment Corp. developed R1, now called XCON, to decide how to combine components of VAX computers to meet consumers’ needs. However, the program performs as well as humans only because there are so many pos-

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sible combinations that even experienced technical editors depend on rule-based methods of problem solving and take about 10 minutes to work o u t even simple cases. It is no surprise, then, that this particular expert system can rival the best specialists. Chess also seems to be an exception to o u r rule. Some chess programs, after all, have achieved master ratings by using “brute force.” Designed for the world’s most powerful computers, they are capable of examining about 10 million possible positions in choosing each move. However, these programs have an Achilles’ heel: they can see only about four moves ahead for each pieces. So fairly good players, even those whose chess rating is somewhat lower than the computers, can win by using long-range strategies such as attacking the king side. When confronted by a player who knows its weakness, the computer is not a master-level player. In every domain where know-how is required to make a judgment, computers cannot deliver expert performance, and it is highly unlikely that they ever will. Those who are most acutely aware of the limitations of expert systems are best able to exploit their real capabilities. Sandra Cook, manager of the Financial Expert Systems Program at the consulting firm SRI International, is one of these enlightened practitioners. She cautions prospective clients that expert systems should not be expected to perform as well as human experts, nor should they be seen as simulations of human expert thinking. Cook lists some reasonable conditions under which expert, or rather “competent,” systems can be useful. For instance, such systems should be used for problems that can be satisfactorily solved by human experts at such a high level that somewhat inferior performance is still acceptable. Processing of business credit applications is a good example, because rules can be developed for this task and computers can follow them as well as and sometimes better than inexperienced humans. Of course, there are some exceptions to the rules, but a few mistakes are not disastrous. On the other hand, no one should expect expert systems to make stock-market predictions because human experts themselves cannot always make such predictions accurately. Expert systems are also inappropriate for use on problems that change as events unfold. Advice from expert systems on how to control a nuclear reactor during a crisis would come too late to be of any use. Only human experts could make judgments quickly enough to influence events. It is hard to believe some A1 enthusiasts’ claim that the companies who use expert systems dominate all competition. In fact, a company that relies too heavily on expert systems faces a genuine danger. Junior employees may come to see expertise as a function of the large knowledge bases and masses of rules on which these programs must rely. Such employees will fail to progress beyond the competent level of performance, and business managers may ultimately discover that their wells of true human expertise and wisdom have gone dry.

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COMPUTERS IN THE CLASSROOM Computers pose a similar threat in the classroom. Advertisements warn that a computer deficiency in the educational diet can seriously impair a child’s intellectual growth. As a result, frightened parents spend thousands of dollars on home computers and clamor for schools to install them in the classroom. Critics have likened computer salespeople to the encyclopedia peddlers of a generation ago, who contrived to frighten insecure parents into spending hundreds of dollars for books that contributed little to their offsprings’ education. We feel that there is a proper place for computers in education. However, most of today’s educational software is inappropriate, and many teachers now use computers in ways that may eventually produce detrimental results. Perhaps the least controversial way computers can be used is as tools. Computers can sometimes replace teaching aids ranging from paintbrushes, typewriters, and chalkboards to lab demonstrations. Computer simulations, for instance, allow children to take an active and imaginative role in studying subjects that are difficult to bring into the classroom. Evolution is too slow, nuclear reactions are too fast, factories are too big, and much of chemistry is too dangerous to reproduce realistically. In the future, computer simulations of such events will surely become more common, helping students of all ages in all disciplines to develop their intuition. However, since actual skills can be learned only through experience, it seems only common sense to stick to the world of real objects. For instance, basic electricity should be taught with batteries and bulbs. Relying too heavily on simulations has its pitfalls. First of all, the social consequences of decisions are often missing from simulations. Furthermore, the appeal of simulations could lead disciplines outside the sciences to stress their formal, analytic side at the expense of lessons based on informal, intuitive understanding. For example, political science departments may be tempted to emphasize mathematical models of elections and neglect the study of political philosophies that question the nature of the state and of power. In some economics departments, econometrics-which relies heavily on mathematical modelshas already pushed aside study of the valuable lessons of economic history. The truth is that no one canvasses the dynamic relationships that underlie election results or economies with anything like the accuracy of the laws of physics. Indeed, every election campaign or economic swing offers vivid reminders of how inaccurate predictions based on simulation models can be. On balance, however, the use of the computer as a tool is relatively unproblematic. But that is not the case with today’s efforts to employ the computer as tutor or tutee. Behind the idea that computers can aid, or even replace, teachers is the belief that teachers’ understanding of the subject being taught and their profession consists of knowing facts and rules. In other words, the teacher’s job is to convey specific facts and rules to students by drill and practice or by coaching. Actually, if our minds were like computers, drill and practice would be completely unnecessary. The fact that even brilliant students need to practice when learning subtraction suggests that the human brain does not operate like a com-

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puter. Drill is required simply to fix the rule in human memory. Computers, by contrast, remember instantly and perfectly. Math students also have to learn that some features such as the physical size and orientation of numbers are irrelevant while others such as position are crucial. In this case, they must learn to “decontextualize,” whereas computers have no context to worry about. There is nothing wrong with using computers as drill sergeants. As with simulation, the only danger in this use stems from the temptation to overemphasize some skills at the expense of others. Mathematics might degenerate into addition and subtraction, English into spelling and punctuation, and history into dates and places. A1 enthusiasts believe that computers can play an even greater role in teaching. According to a 1984 report by the National Academy of Sciences, “Work in artificial intelligence and the cognitive sciences has set the stage for qualitatively new applications of technology to education.” Such claims should give us pause. Computers will not be first-rate teachers unless researchers can solve four basic problems: how to get machines to talk, to listen, to know, and to coach. “We speak as part of our humanness, instinctively, on the basis of past experience,” wrote Patrick Suppes of Stanford University, one of the pioneers in computer-aided instruction, in a 1966 Scientific American article. “But to get a computer to talk appropriately, we need an explicit theory of talking.” Unfortunately, there is no such theory, and if our analysis of human intelligence is correct, there never will be. The same holds true for the problem of getting computers to listen. Continuous speech recognition seems to be a skill that resists decomposition into features and rules. What we hear does not always correspond to the features of the sound stream. Depending on the context and our expectations, we hear a stream of sound as “I scream,” or “ice cream.” We assign the space or pause in one of two places, although there is no pause in the sound stream. One expert came up with a sentence that illustrates the different ways we can hear the same stream of sound: “It isn’t easy to wreck a nice beach.” (Try reading that sentence out loud.) Without the ability to coach, a computer could hardly substitute for an inexperienced teacher, let alone a Socrates. “Even if you can make the computer talk, listen, and adequately handle a large knowledge data base, we still need to develop an explicit theory of learning and instruction,” Suppes writes. “In teaching a student, young or old, a given subject matter, a computer-based learning system can record anything the student does. It can know cognitively an enormous amount of information about the student. The problem is how to use this information wisely, skillfully, and efficiently to teach the student. This is something that the very best human tutors do well, even though they do not understand at all how they d o it.” While he recognizes how formidable these obstacles are, Suppes persists in the hope that we can program computers to teach. However, in our view, expertise in teaching does not consist of knowing complicated rules for deciding what tips to give students, when to keep silent, when to intervenealthough teachers may

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have learned such rules in graduate school. Rather, expert teachers learn from experience to draw intuitively and spontaneously on the common-sense knowledge and experience they share with their students to provide the tips and examples they need. Since computers can successfully teach only novice or, at best, competent performance, they will only produce the sort of expert novices many feel our schools already graduate. Computer programs may actually prevent beginning students from passing beyond competent analysis to expertise. Instead of helping to improve education, computer-aided instruction could easily become part of the problem. In the air force, for instance, instructors teach beginning pilots a rule for how to scan their instruments. However, when psychologists studied the eye movements of the instructors during simulated flight, the results showed that the instructors were not following the rule they were teaching. In fact, as far as the psychologists could determine, the instructors were not following any rules a t all. Now suppose that the instrument-scanning rule goes into a computer program. The computer monitors eye movements to make sure novices are applying the rule correctly. Eventually, the novices are ready, like the instructors, to abandon the rules and respond to whole situations they perceive as similar to others. At this point, there is nothing more for the computer to teach. If it is still used to check eye movements, it would prevent student pilots from making the transition to intuitive proficiency and expertise. This is no mere bogeyman. Expert systems are already being developed to teach doctors the huge number of rules that programmers have “extracted” from experts in the medical domain. One can only hope that someone has the sense to disconnect doctors from the system as soon they reach the advanced-beginner stage.

CAN CHILDREN LEARN BY PROGRAMMING? The concept of using computers as tutees also assumes the informationprocessing model of the mind. Adherents of this view suppose that knowledge consists of using facts and rules, and that therefore students can acquire knowledge in the very act of programming. According to this theory, learning and learning to program are the same thing. Seymour Papert is the most articulate exponent of this theory. He is taking his LOGO program into Boston schools to show that children will learn to think more rigorously if they teach a literal-minded but patient and agreeable studentthe computer. In Papert’s view, programming a computer will induce children to articulate their own program by naming the features they are selecting from their environment, and by making explicit the procedures they are using to relate these features to events. Says Papert: “ I have invented ways to take educational advantage of the opportunities to master the art of deliberately thinking like a computer,

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according, for example, to the stereotype of a computer program that proceeds in a step-by-step, literal, mechanical fashion.” Papert’s insistence that human know-how can be analyzed has deep roots in our “rationalistic” Western tradition. We can all probably remember a time in school when we knew something perfectly well but our teacher claimed that we didn’t know it because we couldn’t explain how we got the answer. Even Nobel laureates face this sort of problem. Physicist Richard Feynman had trouble getting the scientific community to accept his theories because he could not explain how he got his answers. In his book Disturbing the Universe, physicist and colleague Freeman Dyson wrote, “The reason Dick’s physics were so hard for the ordinary physicists to grasp was that he did not use equations . . . He had a physical picture of the way things happen, and the picture gave him the solutions directly with a minimum of calculation. It was no wonder that people who spent their lives solving equations were baffled by him. Their minds were analytical; his was pictorial.” While Papert tries to create a learning environment in which learners constantly face new problems and need to discover new rules, Timothy Gallwey, the author of Znner Tennis, encourage learners to achieve mastery by avoiding analytic thinking from the very start. He would like to create a learning environment in which there are no problems a t all and so there is never any need for analytic reflection. Our view lies in between. At any stage of learning, some problems may require rational, analytic thought. Nonetheless, skill in any domain is measured by the performer’s ability to act appropriately in situations that might once have been problems but are no longer problems and so do not require analytic reflection. The risk of Gallwey’s method is that it leaves the expert without the tools to solve new problems. But the risk of Papert’s approach is far greater: it would leave the learner a perpetual beginner by encouraging dependence on rules and analysis.

A1 ON THE BATTLEFIELD The Department of Defense is pursuing a massive Strategic Computing Plan (SCP) to develop completely autonomous land, sea, and air vehicles capable of complex, far-ranging reconnaissance and attack missions. SCP has already spent about $145 million and received approval to spend $150 million in fiscal 1986. To bolster support for this effort, the DOD’s Defense Advanced Research Projects Agency (DARPA) points to important advances in AI-expert systems with common sense and systems that can understand natural language. However, no such advances have occurred. Likewise, computers are no more able today to deal intelligently with “uncertain data” than they were a few years ago when our computerized ballistic-missile warning system interpreted radar reflections from arising moon as an enemy attack. In a recent report evaluating the SCP, the congressional Office of Technology Assessment cautioned, “Unlike the Manhattan Project or the Manned Moon

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Landing Mission, which were principally engineering problems, the success of the DARPA program requires basic scientific breakthroughs, neither the timing nor the nature of which can be predicted.” Even if the Defense Department invests billions of dollars in AI, there is almost no likelihood that this state of affairs will change. Yet once vast sums of money have been spent, there will be a great temptation to install questionable AIbased technologies in a variety of critical areas-from battle management to “data reduction” (figuring out what is really going on given noisy, contradictory data). Military commanders now respond to a battlefield situation using common sense, experience, and whatever data are available. The frightening prospect of a fully computerized and autonomous defense system is that the expert’s ability to use intuition will be replaced by merely competent decision making. In a crisis, competence is just not good enough. Furthermore, to justify its expenditures to the public, the military may feel compelled to encourage the civilian sector to adopt similar technologies. Full automation of air-traffic control systems and of skilled factory labor are both real possibilities. Unless illusions concerning A1 are dispelled, we are risking a future in which computers make crucial military and civilian decisions that are best left to human judgment. Knowledgeable A1 practitioners have learned from bitter experience that the development of fully autonomous war machines is unlikely. We hope that military decision makers o r the politicians who fund them will see the light and save U.S. taxpayers’ money by terminating this crash program before it is too late.

THE OTHER SIDE OF THE STORY At this point the reader may reasonably ask: If computers used as logic machines cannot attain the skill level of expert human beings, and if the “Japanese challenge in fifth-generation systems” is a false gauntlet, then why doesn’t the public know that? The answer is that A1 researchers have a great deal at stake in making it appear that their science and its engineering offspring--expert systems-are on solid ground. They will do whatever is required to preserve this image. When public television station KCSM in Silicon Valley wanted to do a program on A1 to be aired nationally, Stanford A1 expert John McCarthy was happy to take part. So was a representative of IntelliCorp, a company making expert systems that wished to air a promotional film. KCSM also invited one of us, Hubert, to provide a balanced perspective. After much negotiating, an evening was finally agreed upon for taping the discussion. That evening the producer and technicians were standing by at the studio and Hubert had already arrived in San Mateo when word came that McCarthy would not show up because Hubert was to be o n the program. A fourth partici-

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pant, expert-systems researcher Michael Genesereth of Stanford University, also backed out. All of us were stunned. Representatives from public TV’s NOVA science series and CBS news had already interviewed Hubert about AI, and he had recently appeared on a panel with Minsky, Papert, philosopher John Searle of Berkeley, and McCarthy himself at a meeting sponsored by the New York Academy of Sciences. Why not on KCSM? It seems the “experts” wanted to give the impression that they represented a successful science with marketable products and didn’t want to answer any potentially embarrassing questions. The shock tactic worked. The station’s executive producer, Stewart Cheifet, rescheduled the taping with McCarthy as well as the demo from IntelliCorp, and he decided to drop the discussion with Hubert. The viewers were left with the impression that A1 is a solid, ongoing science which, like physics, is hard as work solving its quite manageable current problems. The public’s chance to hear both sides was lost and the myth of steady progress in A1 was maintained. The real story remained to be told, and that is what we have tried to do here.

an Ant Colony? Daniel Crevier

It should be clear by now that intelligence defies the heartiest effort to define it. Yet it is equally clear that an essential ingredient of an intelligent system is its ability to manipulate information. Indeed, this is the only function common to brains

and computers. The essential ingredients of information are bits. Just as matter ultimately consists in atoms, all the information that reaches us through our senses can be broken down into little pieces of “yes” and “no.” These particles make up any conversation, scenery, spanking, or caress we experience. To see how this is possible, consider the spectacle of the sun setting into the ocean. Delicate hues play on clouds and water; a royal alley of gold leads to the sun, and waves twinkle like stars in the reflected light. Yet our brain, which lets us appreciate this beauty, has no direct contact with it. Locked up inside our skull, our thinking organ is just as removed from the sea as would be a computer shuttered in the basement of the Pentagon. In order to appreciate this scene, our brain must reassemble the raw elements of data that our senses supply it as nerve impulses corresponding to yes or no bits of information. In the case of sight, perception happens as follows: The cornea, a transparent lens on the front of the eye, projects an image of the scene onto the back of the ocular globe. In this respect, the eye works much like a camera, where a lens projects the image on photographic film. The retina, which plays the role of film in our eyes, is a sheet of nervous tissue covering the back of the ocular globe. It contains many light-sensitive nerve cells called “receptors.” Some of these receptors tell other neurons, through a sequence of nerve pulses, how bright the projection of the image is at the location of the receptor. Some other receptors signal how much red, green, or blue the image contains at their locations. (All the delicate hues of pink, orange, and indigo in the sunset correspond to varying mixtures of these three primary colors.) The image is turned into discrete pulses in two ways:

From “How Many Bulldozers for an Ant Colony?” (281-311) from Al: The Tumultuous History of the Seurch for Artificial Intelligence by Daniel Crevier. Copyright (c) 1993 by Daniel Crevier. Reprinted by permission of BasicBooks, a division of HarperCollins Publishers, Inc.

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first, spatially, by becoming an array of dots, with each dot corresponding to the location of a receptor cell; second, in the domain of brightness and color. Colors become mixtures of discrete hues, and brightnesses translate into more or less rapid firings of nerve cells (the larger the brightness, the faster the firing rate). In similar ways, nerve cells in our ears turn the sounds we hear into pulse trains. Sensor cells in our skin do the same for sensations of heat, cold, and pressure. Thus, our brain is constantly bombarded with trains of pulses telling it what our senses perceive. Intelligence has to do with our ability to manipulate these bits of information and use them to make sense of the world. Animals manipulate the information from their senses in a manner that does not let them generate more than immediate reactions to perceived threats or inducements. We, however, get more mileage out of the information we extract from our surroundings. We can refine it into knowledge and use it for long-range planning and abstract reasoning. Nevertheless, our brains do work on the same principles as those of animals. Dissection shows that any differences between our brains and those of most mammals lie in the size of the structures present and in their complexity. Thus, one can logically conclude that our capabilities for planning and abstract thought are built on the same basic skills that allow animals to react to their environment. Further, this extra power probably stems from the additional abilities for processing information that the more elaborate structure of our brains allow. Thus, intelligence has to do with how much information one can manipulate in a given time (say, per hour or per second). Since one measures information in bits, one aspect of intelligence, in its most elementary form, is bits per second of raw processing power. If one compares the brain to a telephone switching station, this power would correspond to the number of phone lines the station can switch in a given time. Of course, there is more to intelligence than raw power, but let us not worry about this aspect of the problem right now. Let us just recognize that no matter how superbly structured and programmed the switching station is, it will simply not do its job if it can’t process enough connections in a given time. In the first part of this chapter I shall try to answer the following questions: How many bits of information can the brain manipulate per unit of time, and how close do our present computers come to this benchmark? I shall then look back at the history of computer development, and try to extrapolate how long it will take for our machines to rise to the level of our brains. Finally, I shall acknowledge that raw processing power is not the only ingredient required for intelligence, and discuss whether software powerful enough to emulate the human mind can be developed for the computers of the future.

THE HUMAN CORTEX AS CIRCUIT BOARD The exposed human brain is certainly not an impressive sight: about three pounds of soft, jellylike, grayish tissue. This mushy texture long prevented anatomists from cutting the brain into clean thin slices suitable for microscopic obser-

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vation. Further, the uniform color of the material kept them from seeing structural details. It was only in the late nineteenth century that different hardening and coloring processes, among them the still-used Golgi stain, enabled anatomists to study the fine texture of neural tissue. It came as no surprise that, like other organs, the brain is made up of cells. They come in varying sizes and shapes, and neuroanatomists called them “neurons.” One feature of the neurons, however, did astonish early researchers, including the Spaniard Santiago Ram6n y Cajal and the Italian Camillo Golgi, developer of the staining process. They were astonished by the intricacy and extensiveness with which these cells connected to each other, each sending out literally thousands of tendrils that link it to as many other neurons. They make up a network of such Byzantine complexity that Golgi, for one, firmly believed it formed one continuous tissue extending throughout the brain. He defended this point of view, called “reticularism,” in his 1906 Nobel address.’ Later observations-as science progressed in its usual tedious, prodding path-proved him wrong. Indeed, as we will see, the gaps between neurons play a crucial role in the workings of the brain. Early in this century, researchers started to distinguish elements of order in the apparent chaos of brain structure. First, investigators realized that, although neurons can differ from each other as much as a honey-suckle bush does from a sequoia tree, they come in a small number of different shapes and sizes. Only seven kinds of cells with similar exterior morphology exist throughout the cortex (the largest structure in the brain). Moreover, cells very similar to these make up the brains of mammals, from the higher primates down to the puny mouse. The nuts and bolts of our most abstract thoughts are thus the same ones that support the mouse’s instinctive reactions. The cortex, the brain structure responsible for our perceptions, motor responses, and intellectual functions, is a thin sheet of nerve cells, about six millimeters (a quarter of an inch) in thickness. Its surface area is that of a square twenty inches on a side: roughly the space that IBM’s first personal computers used to take up on a desk. To fit it into our skulls, Nature has had to fold the cortex; hence, the furrowed look of the naked brain. The cortex comprises six distinct layers, caused by an uneven distribution of neurons of different types. The thicknesses and makeup of the layers vary over the area of the cortex. However, except in the visual part, the number of cells per unit area remains fairly constant at 146,000 per square millimeter. (Multiplying this figure by the area of the cortex produces an estimated total number of neurons in it of about 30 billion, or 3 x 1O’O.) The average distribution of types of cells throughout the cortex also remains constant. The density of cells per unit area is likewise the same for all mammals. What distinguishes us from the mouse is the area of our cortex, and not the kinds or density of the cells in it. To an engineer’s eye, the cortex presents striking similarities with a structure universally present in computers: the printed circuit board, a flat, thin support holding integrated circuit chips, which serve as processing elements. The board allows the chips to talk to each other through conductive paths buried in distinct layers over its thickness. Strangely enough, a typical board comprises six layers,

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just like the cortex. Each chip on the board is made up of microscopic elements (transistors, capacitors, diodes) of about the size of a neuron, and performs a specific function within the computer. It turns out that one can also divide the cortex into chips, after a fashion. Experiments conducted on animals by probing the sensory cortex with a microelectrode can detect firing impulses from single neurons. If you move the electrode to and fro, in a direction perpendicular to the surface of the cortex, you will meet only nerve cells that process one kind of stimulus. For example, if buried into the visual part of the cortex, the probe may only respond if you shine a light in the left eye, and not on the right. However, if you probe in a nonperpendicular direction by slanting the needle, you will meet neatly separated regions which respond to the left or the right eye, alternatively. It is as if the cortex were divided horizontally into different modules, each extending throughout its depth. Why did Nature design our cortex as a circuit board? We can conjecture that, in both brain and board, it is necessary to separate the closely interacting processing elements from the cabling connecting faraway parts of the network. As in a circuit board, the cabling of the brain lies underneath the computing units. Large neurons present in the cortex, called “pyramidal cells,’’ send nervous fibers downward, out of the cortex, toward other regions of the brain or cortex. Covered with an insulating greasy layer of myelin, these fibers make up a whitish tissue, very different in appearance from the gray color of the cortex itself. Only about one cell in a hundred extends beyond the cortex, yet the volume of white matter in the brain is larger than that of gray matter. Having the “white cables” travel through the gray matter would have interfered with the direct communication between adjacent gray cells: this is probably why Nature kept them out of the cortex. The similarity in structure between the cortex and a circuit board may have yet another reason, related to ease of design. It is already quite complicated to lay out the chips and connecting paths on a flat surface. Doing it in three dimensions would be a combinatorial problem of monstrous proportions. Perhaps Nature was no more willing to face this difficulty than human engineers are! Whatever the reason for it, one cannot contemplate the convergent evolution of brain and circuit boards without wondering. Let us go back to the basic building block of the brain, the neuron. Its anatomy can tell us more about the amount of computing performed by the brain. Extending from the cell body of the neuron are different appendages. On the input side, the dendrites look like the branches of a tree. They connect with sensor cells, or other neurons, and receive their electric pulses. The meeting points between dendrites and appendages of other cells are actually gaps, called “synapses.” Although researchers had long suspected their existence, they could not prove it before the invention of electron microscopy in the 1950s. Direct observations of synapses then struck a final blow to the theory of nervous system continuity, or reticularism, which Golgi had defended until his death in 1926. When strong enough, nerve pulses can cross the synaptic gap between cells. Pulses usually increase the electric potential of the receiving cell, which encourages this cell to generate a pulse of its own, or “fire.” Sometimes, however, arriv-

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ing pulses decrease this potential, and discourage the receiving cell from firing. The cell body sums (or otherwise combines) the membrane potentials and fires at a rate that is an s-shaped function of this sum. The pulses generated by the cell body travel along a wire-like appendage of the neuron called the “axon.” It may be short or very long: axons sometimes bundle together to form a nerve, which can be as long as your arm. They also form the “white cabling” of the brain I have mentioned. The axon eventually branches out into another treelike network of fibers. These pass along signals to other cells, or activate muscles. The input and output ramifications of the neuron are its most striking characteristic. Extrapolations from counts on electron micrographs show there are from 1014 to 1015 synapses in the cortex. This means that, on the average, each neuron receives signals from about 10,000 other cells and sends its own messages to as many others. In this respect, the brain differs markedly from electronic circuits: on a circuit board, one component typically makes contact with fewer than five others. However, what computers lose in connectivity, they make up for in speed. In the brain, the pulses traveling from neuron to neuron are local imbalances in salt concentrations moving a t relatively low speed-exactly how fast depends on the diameter of the nerve fibers, but it is in the order of 100 feet per second. This is why sensory stimuli take from 10 to 100 milliseconds to reach the cortex. In a computer, however, pulses moving from chip to chip are pure electromagnetic fields. They travel at two thirds of the speed of light-about seven million times faster than nerve pulses!

The Brain’s Processing Power Although our knowledge of the brain’s structure is progressing, it remains sketchy. Recently bold-hearted scientists have tried to use this scanty evidence to estimate the amount of raw computing going on in the brain. I shall examine two such tries-by Jacob T. Schwartz at New York University and by Hans Moravec at Carnegie Mellon University. It will come as no surprise that these professors achieved wildly different results. In fact, the very divergence of these estimates is a good illustration of how little we really know about the brain. Yet, because of the accelerating pace of technological development, even guesses as poor as these provide useful estimates of when we will be able to beat Nature at brain building. I shall start with the work of Jacob T. Schwartz, a professor at NYU’s Courant Institute of Mathematical Sciences.* Schwartz estimates that since a neuron can fire about one hundred times per second, it sends information to other neurons at a rate of about one hundred bits per second. The amount of information processed inside the neuron is, however, much larger. To decide, every one hundredth of a second, whether to fire, the average neuron has first to combine all the signals it receives from ten thousand other neurons. The cell must then establish whether this total is large enough for it to fire. The decision to fire is complex, especially since some of the messages received from other neurons may inhibit firing rather than promote it. Schwartz estimates that to reach this decision, the neuron

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must-for each firing, at each synapseperform the equivalent of calculations involving forty bits. Since these operations involve intermediate steps, let’s assume that to simulate them, we have to manipulate one hundred bits of information per synapse, per firing. It is then a straightforward affair to work out the overall amount of information processed by one neuron in one second: 100 bits per synapse per firing per neuron x 100 firings per second per synapse x 10,000 synapses per neuron = 100 million bits per second per neuron. From there, we get an estimate for the processing power of the entire cortex: 100 million bits per second per neuron x 3 x 1O1O neurons in the cortex = 3 x 1OIx bits per second of information processing. Extrapolating to the entire brain, a total of about 1019bits per second results. Thus we have our first estimate: Schwartz’s estimate of brain power: l o t 9 bits per second. Schwartz, however, puts a very strong qualifier on this figure. He points out that computation rates, many orders of magnitude lower, might suffice to represent the logical operations of the brain. Indeed, it is fairly safe to suppose that what really matters to our thought processes is not the internal mechanics of a neuron, but how it looks like to other neurons. This may be considerably more simple than the neuron’s internal structure would show. Thus, stick-figure models of neurons may be enough to simulate the brain accurately. Moreover, our brain is built to accommodate a very large amount of redundancy, and much of its complexity may be due to the constraints limiting its growth and evolution (see the section entitled “Avoiding Nature’s Mistakes” later in this chapter). Hans Moravec calculates the information-processing power of the brain in a manner different from Schwartz’s, concentrating on the retina, the paper-thin layer of nerve cells and photoreceptors in the back of the eye.3 After performing a certain amount of massaging on the information provided by the receptors, the nerve cells send the results of their calculations to the brain through the optic nerve. Such is the structure of the retina that, in effect, it makes up an extension of the brain. Yet, contrary to most brain structures, the functions the retina performs are well understood. They are similar to those of artificial vision systems processing TV images. Of course, we know exactly how much computing power these operations require. Further, and by no coincidence, the resolution (number of receptors) of the fovea, the high-resolution part of the retina, is about equivalent to that of a television image. On that basis, Moravec estimates the processing power of the retina at about one billion operations per second. He then proceeds to extrapolate from this figure the computing power of the entire brain, and is faced with a dilemma. The brain has about 1,000 times as many neurons as the retina, but its volume is 100,000 times as large. Which figure correctly accounts for the larger computing power of the brain? We can attribute the excess volume to three factors. First, the connections between neurons in the brain are longer: the required cabling takes up most of the space in the brain. Next, there are more connections per neuron in the brain. Finally, the brain contains nonneural tissues, such as the greasy myelin sheath of many nerve fibers. Of these three factors, only one-the excess of connections per neuron-entails an increase in complexity. Following Moravec, we shall thus take the Solomonic deci-

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sion of awarding the brain a computing power 10,000 times that of the retina. There follows an information-processing capability on the order of l O I 3 calculations, or about 10“ bits, per second-which is Moravec’s estimate of brain power. According to him, the brain is thus 100,000 times slower than Schwartz’s estimate of l O I 9 bits per second. Moravec’s procedure has one crucial advantage: since it sidesteps the need to rate the unknown factors required to adjust Schwartz’s estimate, we no longer need to guess the effective (“stick figure”) processing power of an individual neuron, and we are also spared the need to assess the unnecessary complexity with which evolutionary constraints burdened our brains. Moravec’s estimate probably lies closer to the truth. How do computers fare compared with the processing power of the brain? Not well at all. The fastest computer in existence in 1989, the Cray-3, could process only 10” bits per second. By Moravec’s estimate, it is therefore 1,000 times weaker than the human brain, or at about the level of the laboratory rat, with its brain of 65 million neurons. Further, the Cray-3 is much too expensive to serve in A1 work. Researchers must make do with machines like the Sun-4 workstation. At 2 x lo8 bits per second, the Sun-4 is 500,000 times less powerful than a human brain-but it would evenly match the 100,000 neurons of a snail!

The Size of Human Memory As any computer enthusiast knows, the rate at which a given machine calculates is but one measure of its power. Another crucial question is, How much information can the computer hold in memory? Similarly, in respect to the human mind, how well we think is very much a function of how much we know. As it turns out, it is possible to estimate how much memory we need to function in the world. There are three ways to go about this.‘ One is to repeat what I have just done for the brains calculating power: that is, examine the brain’s anatomy and work out estimates from hardware considerations. A more direct method is to survey the knowledge of an average adult. Third, we could also deduce how much adults know from how fast they can learn, and how long they live. To start with the first approach, what happens in our brain when we remember something? Scientists are still very much in the dark about memory. They know plenty about the periphery of brain operation, such as how we perceive the world through our senses, or how we activate our muscles to act on the world. What happens between, though, remains very much a conjecture. Researchers do not really know how we settle on one particular response to a perception, or how we store the memories on which we base this decision. One can make plausible assumptions, though. Consider a mouse that flees in response to the snarl of a cat: this instinctive reaction mechanism probably resembles our own. First, sensor cells in the mouse’s ears send nerve pulses to other neurons in the brain, which start firing in response. Thus, an activation pattern of neurons assembles in the

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mouse’s brain. Eventually, further waves of activation reach the neurons controlling the legs, which send the mouse running. The snarl of the cat thus corresponds to an activation pattern of neurons in the mouse’s brain. A human brain would represent it in the same way. What happens, then, when we remember the snarl of a cat in response to another cue, such as the sight of an angry cat? It is logical to assume that at least some of the neurons that fired when we last perceived a snarl become active again. By this token, a memory is also a neural activation pattern. Can we identify in the brain the elements responsible for eliciting such an activation? What can cause a certain group of neurons, among the billions present in the brain, to become active all of a sudden? All evidence points to the synapses, these microscopic gaps between neural terminations. The average neuron makes contact with ten thousand others through synapses of various conductivities. A synapse can let through more or less of the nerve pulses emitted by the source neuron. Of the ten thousand downstream neurons, those that are more likely to fire in response are those with the more conductive synapses. Thus, one can assume that highly conductive synapses connect the neurons representing an angry cat to those representing the snarl of a cat. Hence, synapses probably store the information causing the recall of memories. If this is the case, we can estimate the capacity of the brain for encoding memories as follows: Assume that the degree of conductivity of a synapse can have sixteen values. Then the synapse can store four bits of information, since a sequence of four bits can represent the numbers from 0 to 15. The l o J ssynapses in the brain would then hold room for 4 x 1OIs bits. Does this mean the brain can actively use that many bits of information? Probably not. Synapses are just the mechanism that induces patterns of neural activation in response to stimuli or other activation patterns. There are, however, many fewer neurons than synapses. If a memory item corresponds to a group of neurons firing together, then there will be fewer such items than synapses also. In the past few years, A1 researchers have devoted much attention to studying networks of artificial neurons. Experimental results, as well as mathematical theory, show that the number of bits one can store in such a net depends on the number of neurons in it. Further, an artificial net can store typically fewer bits than there are neurons in it. For example, a type of neural net known as a Hopfield network, containing n neurons, has a storage capacity of 0 . 1 5 Assuming ~~ a similar ratio for the brain, we end up with a capacity of about 15 billion bits of usable memory. A more direct way of finding out how much each of us knows is the game of twenty questions. It involves two people. Player A thinks of a subject, and player B must find out what it is by asking questions that A will only answer with yes o r no. A wins if B can’t guess the subject in twenty questions or less. The target item must be clearly identifiable and known to both players. Facts that one must deduce from other primary information-like “300,286 is the product of 482 by 6 2 3 ” - d o not count. It turns out that a good player can usually come to the answer in just about twenty questions-this fact reveals how many items you have to sift through. The first question lets you partition the other player’s memory in

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two groups: the items corresponding to a yes answer, and those corresponding to no. The next question divides one of these groups in two again, and so on. Since twenty partitions are required for you to end up with a single fact, the number of items to choose from is clearly 220,or about 1,000,000.We must, however, correct this figure because players will typically limit their choices to neutral items of mutual knowledge, such as Marilyn Monroe or the Eiffel Tower. Items known to A only, or too sensitive for casual evocation, will be avoided. It is not farfetched to multiply by another factor of 2 to compensate for this effect: we are now up to 2,000,000 items. A knottier issue concerns the hidden information corresponding to unconscious or informal knowledge. For example, how much memory does a recipe for how to tie shoelaces, say, take up? What about the knowledge that enables us to interpret body language or voice inflections in people? Such knowledge will never appear as an item of the game. Psychologists consider that most of our mental activities occur at the unconscious level. Are most of o u r memories, then, also unconscious? Not necessarily. There are two reasons for a mental activity to remain unconscious: one is the repression of painful associated emotions (“I desire mother, but it is forbidden!”); the other reason, which probably accounts for many more cases, is that there is simply not enough room in a person’s awareness for all his or her mental activities. For example, consider your behavior when you drive while carrying on a conversation. You will steer left or right, watch for other cars, slow down or speed up as required without any conscious decision. That does not mean you are ignorant of the technicalities of driving, such as being able to stop by stepping on the brake pedal. You are simply too busy to pay attention to these details. Thus, much unconscious behavior uses facts that could come to full awareness in the twenty-question game. So one might reasonably argue that it is not most of our memories that are unconscious, merely just a good deal! Let us boldly “guesstimate” again and multiply by 2 to compensate for this effect: we are now up to 4,000,000 items of memory in a typical human being. An important question remains. What I have called an “item” represents much more than one bit. Typically, it would correspond to an entire data structure: if player A says, for example, “1 was thinking of Abraham Lincoln,” to how many bits of information does the Abraham Lincoln structure correspond? First, the sequence “Abraham Lincoln” contains 14 characters plus 1 blank, which probably requires the equivalent of 15 bytes of storage space in the brain. (A byte is a set of eight bits. Digital computers use bytes to represent characters.) The sequence of phonemes corresponding to the pronunciation of the name probably requires about as much. A few years after history classes, most of us probably remember only a sketchy biography of the sixteenth president: “He taught himself law and campaigned against slavery as a congressman. His election as president caused the Civil War. Lincoln led the North to victory, emancipated the slaves, and delivered the Gettysburg Address. He was assassinated in 1865 when attending a play.” Our internal representation of this information is certainly very different from the string of 265 characters it takes to write it down or store it in a computer. There is no reason, however, to believe that this representation requires

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much less information in our brain. If this were so, we would probably have developed a much more concise language and writing. Similarly, if we needed much more information than a few hundred bytes to store these characters, it would mean that our speech is much more efficient than our thinking: this is hard to believe. Let us, therefore, accept that this short biography of Lincoln requires the equivalent of about 265 bytes of storage in o u r brain. What about Lincoln’s face? Even if we cannot visualize him precisely in our mind’s eye, we could certainly recognize his photograph among thousands of others. How many bits of information does one need to store a recognizable likeness of a face? Not much, it turns out. Digitizing TV images into arrays of numbers, with varying resolutions, shows that an image of 20 x 20 pixels (dots) of varying gray levels provides a very recognizable likeness. With 4 bits per dot, it would take 200 bytes to store such a picture, which brings the size of our Lincoln data structure to 495 bytes. Yet another item of our internal representation of Lincoln is the emotional aura surrounding assassinated presidents. Something in the data structure must be pointing to the emotions awe or sadness. We also require pointers to other items of information: the words congressman or Civil War refer us to other complete data structures. Further, we know that Lincoln was a man and a politiciun-categories that contain still more information we could tap if required. We can estimate how many bytes these relations require by reference to conventional computer data bases. Some kinds of them, such as relational data bases or the data structures used by the LISP language, require pointers between data items. These pointers serve much the same purpose as in our human memory model above. In a LISP data structure, as much memory is reserved for the pointers as for the data. Thus, let us assume that in the brain, the amount of memory required for relationships between items is equal to the memory required for the data items themselves. How much information does one require, finally, for the data structure “Abraham Lincoln”? The 495 bytes above correspond to about 4,000 bits of direct information. Doubling this for pointers brings us to 8,000 bits for one item of the twenty-question game. For the 4 million items of information the game and other considerations show we hold in memory, the total would amount to about 32 billion bits. In view of the number of approximations and informed guesses involved, this figure is in surprising agreement with the one we got by assuming 0.15 bits per neuron, or 15 billion bits. Looking at how we gather information provides yet another cross-check on this figure. As witnessed by the helplessness of a newborn baby, little of our abilities are inborn. From walking to the multiplication table, we must painfully learn virtually all of the skills and knowledge we need to function in the world. Yet, in less than twenty years, we learn the basic material that will support us in life. If we knew how fast we can absorb new information, we could estimate how much of it the basic “human knowledge base” contains. Certainly, we do not commit new information to memory as fast as our senses feed it to us. The optic nerve, for one, sends over a hundred million bits of

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information to the brain at each second. Yet we interpret and remember only a tiny fraction of this information. Consider what happens when you try to learn a page of text by heart. At a resolution equivalent to 300 dots per inch, your optic nerve can send over to the brain the entire contents of that page in about a second. Yet, if you glance a t an open book for a second, and then read the page in your mind’s eye, you’ll discover that you can retain at most a few words. Further, rather than corresponding to the image of the words, which requires thousands of bits per letter to describe, your memory will be a highly abstracted description of the words you recognized while glancing at them. This encoding probably requires only a few bits to store a letter. In fact, experiments on memorizing random sequences of syllables show that we can absorb new information only a t rates of 100 bits per second.6 Learning at 100 bits per second means memorizing an entire page of text (about 400 words) in less than three minutes. At that rate, an actor could memorize his lines by reading them once aloud! Yet, even at such breathtaking speed, twenty years of continuous learning a t eight hours a day would let you digest only 21 billion bits of information. We thus have four estimates of the size of human memory. Assessing it from the number of synapses leads to the astronomical figure of 4 million billion bits. But as I pointed out, there is no direct link between the capacity of synapse storage and the number of explicit items represented in the brain. The other three estimates give much lower values: considerations from the number of neurons in the brain and neural-net theory yield 15 billion bits. The twenty-question game leads to 32 billion bits. Learning rate and duration give 21 billion bits. The relatively close agreement of these three estimates lets one hope that the true value lies somewhere in the range they define. Thus, I shall settle on 20 billion bits (2.5 gigabytes) as an estimate of the memory capacity of the brain. This is still a lot of information: it corresponds to slightly more than a million pages of printed text or twenty-five hundred books like this one. Can computers store that much information? Yes: by this yardstick, our machines have already overtaken us. The Cray-2 supercomputer, built in 1985, already had 32 billion bits of memory capacity.’ Even A1 research budgets allow scientists to come close: As I write this the typical A1 workstation offers about 200 million bits of random access memory, only 100 times less than the brain. . . . the Cyc common-sense knowledge base will be slightly smaller than our estimate of the brain’s capacity (about 8 billion bits instead of 20). A1 workstations will have that much random access memory a t their disposal in a very few years.

REACHING HUMAN EQUIVALENCE Despite this essential parity of the board in memory, machines still process information thousands of times more slowly than we do. Because of its myriads of cells working together, it is tempting to compare the brain to an ant colony. Computer engineers, by contrast, like to think of their mainframe machines as bulldoz-

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ers shoveling about mountains of data through their unique central processors. Puny bulldozers, indeed, since it would take thousands of them to match the ant colonies in our skulls. This fact certainly explains many of the disappointments A1 researchers have met. If the brain is a jet engine, they have to make do with the equivalent of bicycles! Rather than uncover the secrets of intelligence, they must spend most of their time programming around the weaknesses of their machines. Yet, as we will see next, engineers are closing the gap. Soon computers will approach the power of the human brain.

The Fifth Generation of Computers As I described . . . ,the first generation of computers was based on vacuum tubes: orange-hot filaments glowed in various computing machines from 1943 to 1959. Even during those years, progresses in vacuum tube technology cut down by a factor of 20 the time needed to perform an addition. The gain in cost per unit of computing power was even more impressive. In 1943, it cost about one hundred dollars to buy one bit per second of computing power. Sixteen years later, it cost less than ten cents. Generation 2, based on single transistors, accounted for most of the new machines until 1971. From then on, computers were built out of integrated circuits: silicon chips containing first a few, then hundreds, and finally thousands of microscopic elements. These formed the third generation of computers, and lasted until 1980. Around that year, it became possible to put the entire processing unit of a computer on a single chip; and by 1985, these microprocessor chips contained up to a quarter of a million elements. Thus was born the fourth generation of computers. As I write this in the early 1990s, the upward spiral of computer power continues to accelerate. If you’ve ever pondered the economics of replacing an aging computer, you may have felt a kinship with the future space traveler faced with the star ship problem: a better time to leave is always next year, because by then ships will be faster and will get you to your destination sooner. So is it with computers: next year’s machine will, on the average, offer 50 percent more computing power than this year’s for the same price. Let me demonstrate this tendency by focusing on two particular machines.s The Zuse-2, the first electromechanical computer built in 1939 by the German engineer Konrad Zuse, would then have cost about $90,000 in today’s money and took 10 seconds to multiply two numbers. By contrast, the Sun-4 workstation, introduced in 1987, cost $10,000 and can multiply two numbers in 400 nanoseconds. In raw power, measured by the admittedly crude yardstick of the time required to multiply two numbers, the Sun-4 is 25 million times faster than its predecessor. If we consider the cost per unit of computing power, the comparison is even more favorable: it costs 225 million times less to do a multiplication with the Sun-4 than with the Zuse-2.

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To understand the staggering implication of these figures, consider what similar improvements would bring about if applied to automobiles. A luxury car of 1938-say, a Cadillac-would have cost about $30,000 in today’s money. It reached a top speed of 60 miles an hour and traveled about 15 miles on a gallon. If today’s Cadillac were to the 1938 car what the Sun-4 is to the Zuse-2, it would cost only $3,300, run at twice the speed of light, and do 3 billion miles per gallon! No fairy waved a wand suddenly to induce these changes. If one compares the relay-activated Zuse-2 to electronic machines introduced right after it, and progresses on to the Sun-4, there are no abysmal drops in price or dramatic improvements in performance anywhere. Instead, a smooth evolutionary process is revealed. Hans Moravec courageously calculated and plotted the cost per unit of computing power of sixty-seven consecutive machines, starting with a mechanical calculator built in 1891.9 These data points clearly show that, for the past sixty years, the cost of computing has decreased by a constant factor of 2 every other year. As a result, the mainframes of the 1970s are the desktoppers of today. Mainframes of the 1960s can now be stored in a single chip, and many electronic wristwatches contain more elements than these early machines did! Although speculators who blindly extrapolate stock prices from past tendencies usually end up broke, there are sound arguments for applying yesterday’s trends to tomorrow’s computers. The staggering progress of the past sixty years stems from profound structural processes in the evolution of the technology. Since we are also dealing with the behavior of an industry, many of these arguments are economic rather than technical. In fact, one could even argue that the brainequivalence problem is essentially economic. Indeed, it is now technically possible to build a machine with the raw computing power of the brain if we connect a thousand Cray-3 supercomputers. And, even though managing such interconnection, and programming it to perform like a human brain, would still raise formidable problems, the raw power would be available for us to experiment with. Before we could do that, however, we would have to deal with the small matter of finding the twenty billion dollars this network would cost. Thus, the problem of building human-equivalent hardware boils down to reducing the cost of processing power to affordable levels. Let me examine, therefore, why we can expect the sixty-year-old trend of decreasing prices to continue. First, the regularity of the price curve is to a large extent the result of a selffulfilling prophecy. Manufacturers, aware of the tendency, plan the introduction of new products accordingly; hence, the absence of drastic jumps in cosdperformance ratios. Manufacturers introducing a product well ahead of the competition in performance have no incentive to reduce prices drastically, even if their costs are much lower. Instead, they pamper their profits for a while, until competition forces them to accept lower prices. Competition in the computer industry is fierce. To grab a share of a $150billion world computer market, companies are willing to scrambIe.l0 For this reason, the number of people developing computers, and the resources at their disposal, are on the rise. Since computer companies spend a constant fraction of their revenues on research and development, resources for computer development

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grow about as fast as the computer market. They pushed ahead by about 15 percent a year since 1960. Since this growth is much faster than that of the economy, it will slow down eventually. (Otherwise, a continued growth would lead to the impossible situation of everybody developing or building computers.) Even if the number of people and dollars devoted to computer development levels off, the total intellectual resources available for this activity would still increase exponentially. The reason is because computers are largely designed by other computers. Indeed, involving computers in their own conception can have dramatic effects. Consider the problem of planning the paths of metallic traces on printed circuit boards. In the assembled board, these traces connect together the pins of different processing chips. They all have to fit in the restricted area available on the board, while maintaining minimum distances between each other. Typically, out of a multitude of possible combinations of paths, only a few satisfy these constraints. In the 1960s and 1970s, laying o u t these paths with pencil and ruler used to take months. Worse, changing a design after testing took almost as long as starting anew. Nowadays, computers perform this layout automatically in a matter of hours. Similar gains occurred in implementing those procedures at the chip level: integrated circuits are also designed by computers. In coming years, computers ever more powerful will gradually assume a larger part of the design and construction of their successors, further speeding up the design (or, reproduction) cycle. Economies of scale should also speed up the rate of price decrease. Present computers typically contain only one, expensive, processing unit. Future machines, however, will consist of thousands, and eventually millions, of identical components which will serve as both memory and processors. Manufacturing these components in such large quantities will give rise to economies of scale comparable to those affecting memory chips. Since there are many memory chips in a computer, they come down in price faster than processing chips. Recognizing these new economics, the Defense Advanced Research Projects Agency’s goal is to double the pace of cost reductions in coming years. From now on, instead of multiplying computer power by 2 every other year, the U.S. government hopes to double it every year. But even if we devote ever larger resources to perfect electronic components, little will result if Nature does not cooperate. Aren’t we coming up against basic natural barriers that cannot be overcome? Won’t we soon bump our noses on the outer limits of computation? One obvious boundary which is fast approaching is that of the speed of light-or, as computer scientists sometimes call it, the “Einstein bottleneck.” In a conventional computer equipped with a single processing unit, information flows between the memory and the lone processor as acrobats leap-fly between swings. Infinitesimal errors in timing lead to murderous crashes, and the entire computer must operate like a finely tuned clock. Indeed, an electronic master clock beats time, keeping all components in lockstep, as inexorably as the drummer in a slave ship. For the drummer to be obeyed, there should be time enough for one beat to reach all parts of the computer well before the clock generates the next beat. The

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beats are electric signals: the fact that they travel at close to the speed of light, but no faster, imposes a limit on the frequency of the clock. For example, the time required for light to travel the entire width of a l-foot-wide computer is 1.76 nanoseconds. This implies a maximum clock frequency of 568 million beats per second (megahertz, in computerese). Many desktop computers already operate within a factor of 10 of that limit. One solution would be to keep walking on the path we have so profitably followed since the invention of the transistor: that of miniaturization. Let us make the components smaller and cram them in a tighter space. The signals will have less distance to travel, and we can then speed up the clock. Alas, this approach immediately bumps into another obstacle: heat removal. Not only will crowding more components in a smaller space increase the amount of heat generated, but having them work faster will also increase the heat generated per component. Evacuating this extra heat to keep the machine from melting down requireswhen it is possible at all-technological prowesses. These complications just about cancel any economies brought about by the extra miniaturization. Nature herself presents us with a way out of this blind alley. Compared with those of computers, the components of the human brain operate at positively slumbering rates. A neuron will generate about a hundred impulses per second, as opposed to the millions of beats per second of a digital computer’s clock. Being so sluggish, each neuron generates little heat, and we normally find it easy to keep a cool head. Yet the brain packs about a thousand times the information-processing capability of our fastest computers. This performance is due to the different mode of operation of the brain. Von Neumann’s suggestion to break up computers into a memory and CPU initially offered obvious advantages. It was possible to manufacture the memory as many low-cost, identical cells. Since there was only one processor, it could be as complex as required to perform a variety of logic or arithmetic functions. Further, this layout reduced programming to the relatively simple task of issuing a single string of instructions to the one processing unit. Unfortunately the setup also introduced a major inefficiency that became clear when memories increased in size to billions of bits. So huge is the memory of a modern computer that it compares to a large city. (Indeed, it could store the names and addresses of all inhabitants of New York or Los Angeles.) A metropolis of its own, the processing unit also houses millions of elements. The problem is that only one road connects these two cities. It takes a long time to travel and allows through only a very few bits of information at a time. Running a program in a von Neumann computer is like moving your household from New York to Los Angeles in the following senseless fashion. First load the TV set in your car, drive it to Los Angeles, and come back. Take the laundry iron, drive to LA, and come back. Take the coffee pot, drive to LA, and so on. To enhance matters a bit, computer manufacturers have recently tried flying between the cities instead of driving: they have improved the data-transfer rates between the parts of a computer. Unfortunately, this amounts to speeding up the circuitry, and soon bumps into the speed-of-light and heat dissipation limitations I men-

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tioned. An analogue to the obvious solution-using a van to move all items of your household at once-is not possible in a computer. Each bit transferred between processing unit and memory requires a separate wire, and there is a limit to how many of these can be crammed into a machine. Over the years, manufacturers have widened the data path from 8 bits at a time to 32, and even to 128 for large machines. A small improvement, this amounts to little more than letting you move both the coffee pot and laundry iron together! In terms of my two-city analogy, the solution adopted by our brains is surprising. It consists in moving Los Angeles to New York and mingling the two cities so you don’t have to move at all! In my fable, New York plays the role of processing unit, and Los Angeles, that of memory. It turns out that the brain does not make any difference between these two functions: each neuron serves as both a memory and a processing unit. In the brain, there is indeed no clearly identified center of intelligence comparable to the processing unit of a von Neumann computer. For example, despite long-standing efforts, neurologists have never been able to pinpoint a center of consciousness. The neurosurgeon Wilder Penfield suggested it might lie in the combined action of the upper brain stem and various areas of the cerebral cortex.” Others pointed out that consciousness has to do with laying down and recalling memories of the world. In this case, the hippocampus, which plays a central role in this function, might qualify for the seat of consciousness.’* Another view holds that our ability to communicate makes up our most obvious mark of intelligence: the language centers, located in the left cerebral cortex, would then bear the palm.I3 There are, however, good reasons to believe that, although various parts of the brain handle special functions, consciousness arises from the combined operation of many areas.I4 Likewise, long-term memories, once laid down, appear distributed throughout large areas of the brain. What are the advantages of this distributed configuration over the von Neumann architecture? For starters, it allows the brain to perform a myriad of operations concurrently. This is how your brain can analyze the torrent of information your eyes send it (millions of bits per second), and let you instantly recognize what you’re looking at. Roughly speaking, your brain separates the image into hundreds of thousands of dots, each separately analyzed by several neurons. A pure von Neumann machine would, by contrast, slowly process each dot in succession. Computer scientists call “parallel processing” the simultaneous application to a single task of many processors, be they neurons or computers. In addition to the speedup inherent in getting more workers on the job, applying parallel processing to computers offers another potential advantage: it amounts to nothing less than breaking the light-speed barrier. Since processors in a parallel machine work separately, they are no longer enslaved to the drumbeat of a central clock. These semi-independent units could now be made as small and quick as we want them. Through such parallelism, Nature will allow us to keep on increasing the power of our computers at a steady rate for a long time to come. Eventually, individual processors will reach microscopic dimensions. The emerging science of nanote~hnology’~ will soon let us build structures in which every atom plays its assigned role.

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By common agreement among computer scientists, fifth-generation machines are those that implement parallel processing in an extensive way. A few of these machines are now in existence: for example, the Connection Machine, built by Thinking Machines, Inc., of Cambridge, Massachusetts, with 250,000 processors. As I said. . . , machines based on neural networks, in which microscopic components will emulate the neurons of our brains, are being contemplated.

Duplicating the Brain’s Processing Power I can now attempt to answer the question raised earlier: How long before we close the gap in processing power between computers and the human brain? I have summarized in tables 4-1 and 4-2 the earlier estimates about the brain’s computing power and information-storage capacity. Since we still know little about how the brain works, different avenues of investigation lead to extremely different results. The two estimates I cited for the information-processing capacity of the brain (table 4-1) differ by a factor of 100,000. My estimates for the memory capacity of the brain (table 4-2) do not fare any better, being six orders of magnitude apart. Our mightiest computers offer only an insignificant fraction whichever value we adopt for the brain’s processing power. How long will it take for the upward spiral of hardware progress to close this gap? Various answers appear in tables 4-3 and 4-4. Despite the arguments for a speedup in the rate of computer improvement, I have taken the conservative view that the sixty-year-old tendency of doubling every other year persists. I have taken for benchmarks in tables 4-3 and 4-4 the Cray-3 supercomputer, built in 1989, and the Sun-4 workstation, built in 1987. These tables list the years in which machines of a cost equivalent to the Cray-3 (about $10 million) and the S u n 4 (about $10,000) should reach human equivalence. The “best case” columns correspond to the weaker estimates of brain-processing power and memory in tables 4-1 and 4-2; the “worst case” columns correspond to the stronger estimates.

TABLE 4-1 Two Estimates of the Computing Power of the Brain Argument

Estimate

Detailed modeling of neurons

1019bits per second

(Schwartz)

Comparison of the retina with similar hardware

10“ bits per second

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TABLE 4-2 Various Estimates of the Information Storage Capacity of the Brain Argument

Estimate

Raw synapse storage

4 x 10” bits

Neural-net theory and number of neurons

15 x lo9 bits

20-question game

32 x lo9 bits

Human learning rate and duration

21 x lo9 bits

TABLE 4-3 Estimates for the Year in Which Supercomputers Will Reach Human Equivalence Best Case

Worst Case

Processing power

2009

2042

Memory

1989

2023

TABLE 4-4 Estimates for the Year in Which Desktop Computers Will Reach Human Equivalence Best Case

Worst Case

Processing power

202s

2058

Memory

2002

2037

The large discrepancies between estimates make remarkably little difference on dates. According to table 4-3, if the weak estimate is right, supercomputers will attain human equivalence in the year 2009. If the strong estimate holds, this sets us back only thirty-three years, to 2042! Indeed, if computer power doubles every other year, thirty-three years is all it takes to improve by a factor of 100,000. Also, as is clear in both tables, the roadblock is processing power, since we will always reach the required memory about twenty years earlier. From the first line of table 4-3, supercomputers will attain human equivalence around 2025,* give or take seventeen years. According to table 4-4, desktop machines will have to wait until 2041, with the same error margin. ‘Recent developments indicate that this date may be advanced: the September 1992 issue of Spec(the journal of the Institute of Electrical and Electronic Engineers) noted that “engineers.. . expect teraflops machines by 1996. . . . Price estimates exceed U.S. $100 million” (page 40). A teraflops is the approximate equivalent of our weaker estimate for brain power. This power, howtrum

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After these dates, we can expect our machines to become more clever than we are. We have already done Nature one better for all physical abilities of the human body. Our machines are stronger, faster, more enduring, and more accurate than we are. Some of them have sharper eyesight or hearing. Others survive in environments that would crush or suffocate us. Shouldn’t we expect to improve upon our mental abilities just as well?

Avoiding Nature’s Mistakes Not only can we build into our machines the strength of our minds, with eventually much to spare, but we can also avoid duplicating the many weaknesses and inefficiencies of our brains. Indeed, when building artificial minds, we enjoy much more freedom that Nature had in building us. First, we are free of the limitations on material and structure imposed on biological organisms. Living cells must grow, reproduce, repair themselves, and move over to their proper positions in the body early in life. They must constantly absorb nutrient material from their environment and evacuate waste. Most of their internal structure and functions serve these ends. An artificial neuron, however, would not have to perform any of these tasks. Its function would reduce to generating electric signals similar to those of a biological neuron. Thus, we can expect the structure of an artificial neuron to be much more simple than that of a natural one. Further, it could use materials that transmit impulses millions of times faster than protoplasm and process signals that much faster. Yet another limitation our machines will dispense with has to do with blueprints. Nature’s blueprint for our bodies, the DNA molecule, does not contain enough information to specify the connections of each neuron. Instead, Nature must make do with general instructions issued to entire classes of brain cells. What we know of neuroembryology shows that in the early stages of life, brain cells emit filaments that travel through brain tissue. These filaments eventually form the dendrites and axon of the adult cell. They travel more or less at random, until their ends meet cells of a kind that chemically attracts them. The filaments then bind to the cells in connections that become synapses. To understand the limitations this mode of construction places on the brain’s performance, consider the following fable, which I have called “Harry’s Plight.” Harry, an electronics engineer, has just taken charge of a new computer assembly plant in the remote country of Ogomongo. Harry has accepted a mission no one else in the company wants: to design a computer that the unskilled workers of Ogomongo can assemble easily from component chips. To Harry’s dismay, it soon becomes clear that the Ogomongans are incapable of reading a connection diagram. Neither can they tell apart chip models, except by their colors. Since difever, will come at ten times our target price of $10 million. Further, the degree of specialization of these early teraflops machines makes it unlikely that any amount of programming could endow them with intelligence.

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ferentiating the pins on the chips is also a little hard for Ogomongans, Harry has to settle for mounting instructions that typically read: “Connect any pin of a green chip to any pin of a yellow chip.” It is now Harry’s considerable challenge to design chips of a kind that will, when connected in this haphazard way, produce a computer. To increase the chance that compatible pins on different chips will connect, Harry first increases the number of pins per chip. Second, he adds some intelligence inside the chips and decrees that each newly assembled computer will undergo a “running in” period of a month. During this time, each chip sends out, through each of its pins, exploratory, low voltage pulses. It also listens to pulses emitted by other chips. Each pin of each kind of chip emits a characteristic pulse pattern, enabling chips on each side of a connection to check the validity of this link. Specially designed internal mechanisms break off connections of the wrong kind. Connections of the right kind are maintained and strengthened. Much to the surprise of Harry’s colleagues, and somewhat to his own, this Rube Goldberg procedure eventually does produce a working computer. There is only one snag: the machine is a hundred times bulkier and more expensive than a conventional number cruncher. Harry’s company wants to close the plant. The Ogomongans, however, feel it improves their international image, and insist on buying it. Since Ogomongo sits on newly discovered oil fields amounting to half the world’s reserves, they can well afford to. This is all fantasy, of course-but any resemblance to existing biological processes is intentional! In addition to the indiscriminate assembly of its parts, the brain suffers froin their lack of reliability. A neuron is a complex, delicately balanced mechanism, and we all lose hundreds of thousands every day. Yet we do not feel any the worse for this loss because the brain compensates for it by having a large amount of redundancy in its circuits. Computers also benefit from redundancy, and engineers are now finding ways to let their machines tolerate minor component failures. Yet they must pay a price: making computers more resilient requires more components. For this reason, building an intelligent machine out of parts more simple and robust than neurons would increase its performance. The brain evolved through a process of small-scale, local changes spanning millions of years. It embodies many elegant features that an intelligent designer would not disown. The layered, circuit-board-like structure of the cortex is a prime example. Yet the brain’s overall architecture expanded gradually, without benefit of advance planning, and it shows. In many respects, the brain is like a Midwestern country schoolhouse turned into a major city high school. It started with a one-room cabin with a wood stove, spacious enough to accommodate the children of the first few settlers. Then came the railway station: the school needed another room to handle the suddenly doubled population. Over the years, classrooms multiplied. To keep a studious atmosphere, workers had to pare precious square feet from each room to set up linking corridors. Later, installing indoors plumbing and electricity required major surgery, which gave the principal a severe headache. When it became necessary to add a second floor on a structure never meant for one, the mayor and the city engineer almost came to blows. The aldermen, leery of raising taxes for a whole new building, finally overruled the engineer

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and hired a contractor themselves. Twenty years later, congested plumbing and air conditioning, corridor traffic jams, and wavering lights prevented any further expansion of the school. The city council voted the site into a park and erected a new school elsewhere. Evolution does not have the option of starting over, and our brains still contain the original cabin cum wood stove. Lemon-sized, it grows o u t of the upper end of the spinal cord. The reticular formationI6 is in fact the brain of our reptilian ancestors. Programmed to stake out a territory and attack prey or enemies, it holds our darker instincts. Wrapped around the reptilian brain is the limbic system, or old mammalian brain: this is the school’s second floor. Developed from centers that govern smell in primitive mammals, the limbic system is the seat of emotions. It enabled our warm-blooded ancestors of a hundred million years ago to care for their young. Its programming often contradicts the reptilian brain, and many of our internal conflicts have no other origin. The cerebral cortex holds our higher reasoning functions and forms the outer layer of the brain. It talks to the inner parts through many nerve fibers, which somehow coordinate its action with theirs. The cortex has no equivalent in our fictional country school. At that level, a human architect trying to design a better brain probably would have started over. Our old friend the retina offers a striking example of how Nature evolves impressively elaborate fixes to make up for no longer adequate structures. Evolution hit upon the retina’s peculiar layout early in the development of vertebrates, and its unthinking mechanisms later locked it into place. As you recall, the retina includes photoreceptors, which turn light into nerve pulses, and layers of nerve cells that preprocess the image. These cells pack the number-crunching power of a modern mainframe computer. Nature made the early mistake of placing them up front, so light must pass through the cell layers to reach the photoreceptors. This arrangement put a major design constraint o n the data-processing part of the retina: just imagine IBM trying to make their computers perfectly transparent. Yet Nature rose to precisely that challenge in evolving our eyes: the nerve cells in the retina are transparent. There is yet another difficulty: the nerve cells’ position forces the optic nerve to pass through the photoreceptive layer to reach the brain, creating a blind spot in our field of vision. We do not see it because, through more sleight of hand, our brain interpolates from neighboring parts of the image and covers up the blind spot. “What if it wasn’t an early blunder?” you may ask. “Couldn’t a constraint we do not realize make this roundabout design the only possible one?” It seems not, because the independently evolved octopus and squid do have their photoreceptors up front. In a classic paper in Science, the eminent biologist Franqois Jacob maintained that evolution is not a rational designer but a thinker. He illustrated his point with many more examples of biology mixing slapdash foundations with prodigies of workmanship.” Many find this iconoclastic view of evolution shocking. Indeed, imperfection in Nature’s creations contradicts many an ecologist’s view of cosmic order. Personally, 1 find Nature’s ability to make up for its mistakes and keep forging ahead more impressive than the blunders themselves. And who knows: perhaps

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creating o u r brains was a crucial step in this self-correcting process? Now that we realize our imperfections, we may help weed them out of the next batch of intelligent beings. As in our other duplications of natural functions, we will probably discover in building artificial minds that it pays to design a little differently than Nature. Much as airplanes have wings but do not flap them, intelligent machines will operate on the same principles as their natural equivalents, but exploit these principles better. Streamlined, robust, and faster, they may well surpass our minds the way airliners do sparrows.

SOFTWARE: THE STRUGGLE TO KEEP UP So far I have compared the brain to a telephone switching station and looked only at how fast it can switch lines. I have neglected the fact that the switching station has to be wired to make the right connections. Since there is a lot more to intelligence than simple line switching, it is time to ask this question: If we d o develop, in the early part of the next century, hardware powerful enough to process as many bits per second as the brain does, will we be able to program intelligence into this hardware? In other words, will software progress follow hardware development? If the past is any indication, hardware and software development are closely linked. In general, software needs can provide the motivation for and point the way to appropriate directions in hardware development. Conversely, weaknesses in hardware can not only act as a powerful brake on software development but also divert it into blind alleys. There is no question that the relative inadequacy of early computers hindered early progresses in artificial intelligence. Marvin Minsky recalled for me how early researchers (himself included) would toil for years over a program, only to see it founder over lack of memory.18 For example, Ross Quillian, the inventor of semantic nets, never could test his theory on word disambiguation simply because the computers of the mid-1 960s couldn’t hold enough word definitions (see chapter 4). We also saw in chapter 6 that the advent of expert systems had to await the availability of computers with enough memory to hold large amounts of knowledge and the programs needed to quickly sift through it. Early A1 work was deliberately performed in toy task that did not require much special knowledge. This mind-set became so ingrained that researchers didn’t always realize that they were programming around their machines’ weaknesses instead of addressing the real issues. Consider SHRDLU, the talkative block-manipulating program that made up the wonder hack of the early 1970s. . . . Carl Hewitt, who invented the PLANNER language used for SHRDLU, pointed out the following to me:

PLANNER performed so well because, and we weren’t so conscious of it in those days, by working on only one aspect of a problem at a time it accommodated itself to the very small machine memories we had then. When it ex-

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plored possible solutions to a problem, it went down one single branch and only used the amount of storage needed for that one branch. I f that solution didn’t work out, it would backtrack, recover all the storage, and try another branch.l 9 Despite such craftiness, Marvin Minsky told me, SHRDLU still required a formidable amount of memory by the standards of the time. Without belittling the role of MIT’s researchers, I might add, DARPA’s financial largesse probably counted as much as their genius in the success of such projects. “The MIT A1 Laboratory,” Patrick Winston remembered, ”took delivery of the first megabyte memory. It cost us a million dollars, a dollar a byte.” He added ruefully, “It is strange to think that I carry ten megabytes in my portable PC these days.”2o Re-examining the history of A1 in the light of unrealized hardware constraints can lead to interesting revisions of accepted explanations for why the field took certain orientations. For example, although the demise of neural networks in the 1960s is widely attributed to Minsky and Papert’s implacable criticism of Perceptrons in their book of that same title, Carnegie Mellon’s James McClelland, a major contributor to the revival of this field in the mid-l980s, suggested an alternative explanation to me. He pointed out that most research on neural networks involves simulating them on digital computers:

I don’t believe it was that book per se which discouraged Perceptron research in the 1960s. I think what actually happened is that the world wasn’t ready for neural networks. A certain scale of computation is necessary before simulations show that neural networks can do some things better than conventional computers. The computing power available in the early sixties was totally insufficient for this.2’ Patrick Winston also believed that hardware limitations have often led researchers to make the wrong turns in their paths towards progress: “We are discovering that a lot of ideas we once rejected on the grounds of computational impracticality have become the right way after all.” He explained how conventional robots control their movements by constantly recalculating the control signals they send to their arms. Recent experimental robots, however, can use their increased memories and parallel processors to learn gradually by experience which efforts to exert under given circumstances. “This idea had been rejected twenty years ago,” continued Winston, “and a lot of the efforts that went into motion dynamics and the mathematical approach now seem somewhat misplaced. In my view, one of the milestones of A1 research over the last five years is the realization that we can do things on vastly parallel computers that we couldn’t do before.” However, Winston was quick to point out that hardware progress will not solve all of AI’s problems. Raising a cautioning finger, he added:

Don’t infer from what I said that we should just stop software research for twenty years and wait for the hardware to catch up. In fact, I’m a little schizophrenic on the subject of hardware. I’m saying, on the one hand, that

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the availability of better hardware allows the discovery of new ways of doing things. At the same time, I think we could do a whole lot more with the hardware we’ve got. In fact, I believe it would take us ten years worth of current software research to do hardware bad.

Minsky was, for his part, convinced that if hardware had constituted a bottleneck until the 1970s, the shoe was now on the other foot: that, in the 1980s, software turned into a millstone around A15 neck: “The machines right now could be as smart as a person if we knew how to program them.” Minsky’s former student David Waltz later elaborated on this point for me: In the old days, machine memories were too small to hold the knowledge researchers wanted to pour into them. Now it’s the other way around: you aren’t ever going to fill the new machines with hand code. Nowadays almost all research on learning is really aimed at making use of hardware in a better way. Ideally, you should only have to hand-code certain initial circumstances into the machine. You would then feed it experience in some form, which would allow the computer to acquire new knowledge on its own.22

CONCLUSION Thus it would appear that A1 software scientists have stepped into sevenleague boots too large for them. Their hardware colleagues have outfitted them with machinery they can’t quite handle. Will A1 software developers, then, remain hopelessly behind? Probably not: throughout the history of computer science, hardware and software development have kept leapfrogging each other. Software developers, periodically overwhelmed by hardware suddenly grown ten times as powerful, soon push it to its limits and start clamoring for more speed and memory. Because of the subject matter’s complexity, it hadn’t happened before in AI. Yet there is n o reason to believe that A1 software won’t take the lead again, and it may already have happened in areas of A1 other than symbolic reasoning. For example, in the Autonomous Land Vehicle project, which fell short of its objectives . . . , more powerful vision hardware might have made all the difference. Finally, although most of the A1 programs described in this book ran on computers with about as much processing power as a snail’s brain, these programs appeared much brighter than any snail. If A1 software researchers could cajole that much performance o u t of such puny hardware, what will they not achieve with machines a million times as powerful? And, when this day arrives, what may lie in store for humankind? H o w will we fare in a world containing machines intellectually equal, if not superior, to most human beings is the subject of my next, and final chapter.

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NOTES 1. Jean Pierre Changeux: L‘Homme neuronal (Paris: Fayard, 1983). 2. Jacob T. Schwartz, “The New Connectionism: Developing Relationships Between Neuroscience and Artificial Intelligence,” in Stephen R. Graubard, ed., The Artificial Intelligence Debate: False Starts, Real Foundations (Cambridge: MIT Press, 1988), p. 126. 3. Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge and London: Harvard University Press, 1988), pp. 57-60. 4. W. Daniel Hillis, “Intelligence as Emergent Behavior; or, The Songs of Eden,” in Graubard, Artificial Intelligence Debate. 5. J. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proceedings of the National Academy of Sciences, U.S.A.. vol. 79, April 1982, pp. 2554-58. 6. Allen Newell and Herbert Simon, Human Problem Solving (Englewood Cliffs, N.J.: Prentice-Hall, 1972), p. 793. In this section of their book, Newell and Simon estimate the time it takes to store knowledge into long-term memory at five to ten seconds per chunk, but leave undefined the size of a chunk in bits. However, Newell’s Soar program models the psychological mechanism of learning by chunk creation: Soar encodes chunks as if-then rules which occupy about 1000 bits of memory. Putting these two figures together yields a human learning rate of about 100 bits per second. 7. IEEE Scientific Supercomputer Subcommittee, “Supercomputer Hardware: An Update of the 1983 Report’s Summary and Tables,” I E E E Computer, November 1989, p. 64. 8. Moravec, Mind Children, pp. 174-77. 9. [bid., p. 64. 10. Data extrapolated from IEEE Scientific Supercomputer Subcommittee, “The Computer Spectrum: A Perspective on the Evolution of Computering,” IEEE Computer, November 1989, p. 57. 11. W. Penfield and H. Jasper, ”Highest Level Seizures,” Research Publications of the Association for Research and Mental Diseases, New York 26 (1947):252-71; quoted in R. Penrose, The Emperor’s New Mind (New York: Oxford University Press, 1989), p. 493. 12. H. O’Keefe, “Is Consciousness the Gateway to the Hippocampal Cognitive M a p ? ” in D. A. Oakley, ed., Brain and Mind (London and New York: Methuen, 1985); quoted in Penrose, The Emperor’s New Mind, p. 495. 13. J. C. Eccles, The Understanding o f t h e Brain (New York: McGraw-Hill, 1973);quoted in Penrose, Emperor’s New Mind, p. 496. 14. Penrose, Emperor’s New Mind, pp. 492-500. See also Francis Crick and Christ of Koch, “The Problem of Consciousness,” Scientific American, September 1992, pp. 152-9. 15. K. Eric Drexler, Engines of Creation: The Coming Era of Nanotechnology (New York: Anchor Books [Doubleday], 1986). 16. David Ritchie, The Binary Bruin: Artificial Intelligence in the Age of Electronics (Boston: Little, Brown, 1984). 17. Franqois Jacob, “Evolution and Tinkering,” Science 196 (1977):1161-66.

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18. Interview with Marvin Minsky, 13 May 1991. All other personal quotes of Minsky’s in this chapter are from this interview. 19. Interview with Carl Hewitt, 13 May 1991. 20. Interview with Patrick Winston, 15 May 1991. All other personal quotes of Winston’s in this chapter are taken from this interview. 21. Interview with James McClelland, 20 May 199 1 . 22. Interview with David Waltz, 15 May 1991.

PART TWO

Knowledge Generation

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5 Information Systems and the Stimulation of Creativity David Bawden

The nature of scientific creativity from the point of view of information provision is discussed, and the contributions of current information systems assessed. The changes necessary to enable “formal” libraryhnformation channels to play a fuller part in stimulating creativity are discussed. They include: representation of information for detection of analogies, patterns and exceptions; interdisciplinary information, and the role of reviews; creation of an information-rich environment, including peripheral material; extension of browsing capabilities, in both printed and computerized systems; direct involvement of information users; serendipitous use of literature; individually oriented information access; integration of information systems into formal creativity stimulation techniques. The implications of new information technology, particularly for the convergence of formal and informal communication channels, are considered. The necessity for research (theoretical and practical) on these topics is pointed out. A bibliography of 103 references attempts to draw together some of the scattered relevant literature.

INTRODUCTION The literature of creativity, scientific and otherwise is vast, but relatively little of it refers explicitly to information gathering and processing. Such mention as is to be found is often rather negative, suggesting that information provision

through formal channels is of little importance for creative advances; some writers have even gone so far as to suggest that it may actually be detrimental. Similarly, although library and information workers appear to assume implicitly that they make some contribution to the creativity and problem-solving abilities of their clients, there is little discussion of this aspect in the professional literature. This paFrom David Bawden, “Information Systems and the Stimulation of Creativity,’’ Volume 12, 1986. Reprinted with kind permission of Bowker-Saur, a division of Reed Elsevier (UK)Ltd. and the Institute of Information Scientists.

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per is intended as a modest attempt a t redressing the imbalance, and emphasising the actual and potential value of information services for these purposes. If the content and its treatment appear somewhat diffuse, then that is a reflection of the nature of the subject. Brittain has suggested that studies of the way in which information users process information supplied to them, merging it with existing knowledge and utilizing it for creative problem solving, has a potential future relevance to information science.20This is a much more ambitious objective than that which is aimed a t here, but perhaps more limited background studies like this one will pave a way towards it. The paper is divided into four sections: Section 1-Background, Section 2-Nature of creativity, Section 3-Information provision for creativity, Section 4-Conclusions. After a short background section, the main points of the paper come in the two main Sections (2 and 3). First (in Section 2), some aspects of scientific creativity, and formal creativity stimulation techniques, per se, are discussed with a view to isolating factors of relevance to information provision. Second (in Section 3), some thoughts on the sort of information systems able to offer real support to creative activity are outlined. Some conclusions are attempted in Section 4. The bibliography is not comprehensive (it could not be so in this subject area without excessive length), but is intended to include sufficient material to enable those interested to obtain an entry into relevant aspects of a highly scattered literature.

1. Background Before going further, it may be worthwhile commenting on the importance of the topic. Major conceptual advances, in both pure and applied research are of course very rare. In pharmaceutical research, for example, many drugs are discovered which have some incremental improvement in properties over those already available, but the number which are genuinely novel in concept (beta-blockers, H2 receptor antagonists etc.) is very small. Such advances are crucial to the longterm success of all research effort, academic or industrial, and it would be unfortunate, to say the least, if information services had no contribution to make to them. A few words on the scope of the paper are in order. The discussion will be centred around creativity in the context of research and development in science and technology, although reference will be made, where appropriate, to concepts from other fields of study. This restriction is unfortunate in some ways, particularly when one of the major themes of the discussion is the importance of crossdisciplinary contact, but it is necessary to keep the length of the paper, and the spread of the arguments, within bounds. Creativity will be considered as related

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to the initial stages of concept formation and problem solving. In industrial research terms, this is the process of invention, rather than innovation (by which an invention is transformed into a marketable product), or diffusion (by which an invention or innovation spreads through a community of potential users). The role of information transfer in innovation (broadly defined) has been extensively studied and reported in past years, and will be referred to here when relevant: useful introductions to this literature are.46,5n-59*60 Little attention has been paid to the specific concept of information for invention, however, though the importance has been frequently recognized. “Invention,” says Hill, “is but one stage of an innovation, but what an important one! It is the creative step, a quality which distinguishes it from the other components of innovation, equally important though they may be.”60 Creativity is of course a highly individual process, but creative individuals work, to some extent, within a social and organizational framework. It will therefore be necessary to consider information provision in aiding creativity at an organizational level, while not forgetting that a concentration on the information needs of the individual, and the matching of the style of information provision to the individual’s thought and work patterns, is arguably more important here than in any other context.

2. Nature of creativity This section has four subsections:

2.1. Definitions. 2.2. Specific aspects of creativity. 2.3. Formal creativity stimulation techniques. 2.4. Summary. 2.1. Definitions

Karl Popper has remarked that the subject of scientific creativity is vast, and also dangerous, because so much nonsense has been written about it.54The literature on creativity is indeed vast, with very many books and articles written in the past twenty five years. For example the Social Science Citation Index showed over 1500 articles with the term “creativity” in the title, from 1972 to mid-1986, while the US “Books in Print” database had over 300 items with the word “creativity” in the title. There are almost as many definitions of creativity as authors discussing the topic-the following are given as exemplifying the rather pragmatic approach used in this paper. Creativity, in art or in science, consists in the ability to present information in a light which had not appeared before, but which nevertheless adds to a coherent pattern already publicly available.’2

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[Creativity involves] the relating of things or ideas which were previously unrela ted.s2 [Creativity involves] perceiving significantly new patterns in bits of knowledge-data and theories-already available.s4 The essence of creativity in problem solving is the ability to break through constraints imposed by habit and tradition, so as to find “new” solutions to problems.s3 Originality often consists in finding connections or analogies between two or more objects or ideas not previously shown to have any bearing on each othetsS It is worth noting the recurrence of two themes in these definitions: the relating of ideas generally held to be quite distinct, and the recognition, or creation, of patterns. Also, the information processing aspect of creativity is brought out clearly. 2.2. Specific aspects

Five topics seem to recur in discussions of scientific creativity. These are:

(i) the role of chance in discovery; (ii) the great value of analogies; (iii) the importance of careful examination of exceptions to, and inconsistencies within, the accepted scheme of things; (iv) the damaging effect of commonly-held ideas which are in fact false; (v) the importance of inter-disciplinary research. These will now be examined briefly in turn. (i) The importance of chance observations in scientific research has been emphasised, with numerous e ~ a m p l e s . ~ ~Of . ~ course, ~ ~ ’ ~ ’ as Medawar points out, there is an inherent bias in our view of the importance of chance, since we cannot know how frequently bad luck robs us of a discovery, or solution to a problem, close at hand.57 The most thorough examination of the chance factor has been made by Austin, who distinguishes four kinds of chance, of relevance to scientific creativity:’

Chance 1: “Blind luck,” unattributable to any actions or qualities of the recipient. Chance 2: “Happy accidents,” when unconnected events impinge upon the matter in hand. Favoured by exposure to seemingly unconnected facts and experiences. Chance 3: “Prepared mind,” “Pasteur principle.” New relationships are perceived because of exposure to many facts related to the problem in hand. Chance 4: Chance favouring the particular individual, because of distinctive knowledge, interests or lifestyle, seemingly far removed from the problem a t hand.

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Foskett notes that “An information service can at times become itself a stimulus to creativity by providing a user with what is pertinent even though it may seem irrelevant. Chance favours the prepared mind, and such serendipitous information is often the sort that leads to scientific revolution^."^^ The important point is that the more information which has been assimilated, the more likely it is that a fortuitous chance observation will be utilized. This is not necessarily restricted to closely-focused “relevant” information; on the contrary, the information may be apparently divorced from the problem at hand. This latter factor appears to be particularly important for major conceptual advances. One particularly relevant form of chance, of course, is the accidental finding of something of interest in the literature while looking for something entirely different. Ways of assisting serendipitous use of the literature will be considered later. (ii) Reasoning by analogy plays an important part in scientific thought,” and, as is the case with chance, much anecdotal material is available to illustrate the point. A fascinating example comes from the world of library science. Ranganathan derived the initial concept of his Colon Classification after seeing a demonstration of a toy erector set in Selfridge’s department store.19 Another example comes from the work of Cowan in hypothesising an explanation for some druginduced hallucinations, sparked off by the analogy between the simple geometric patterns seen in the early stages of hallucination, and similar patterns seen in fluid c o n ~ e c t i o n The . ~ ~ initial concept of two histamine receptors, which provoked work leading to the discovery of the highly important H2 receptor antagonist drugs, was sparked by analogies with other substances acting at two sites with different effects.38Many other examples may be given. One particularly interesting point to note is that the language used for describing the concepts under consideration can greatly affect the readiness with which analogies can be detected. Formal languages, such as mathematical notation or chemical structure diagrams, are particularly powerful for displaying analogous situations. Natural language is likely to be poor, particularly when analogies are sought across disciplinary boundaries, because of the different specialized terminologies likely to be used. A good example of this is the difficulty of finding analogous legal precedent, using “conventional” retrieval systems.’02 (iii) The saying “science makes progress by the careful study of anomalous results” has become a truism39and again, anecdotal support is available for the value of noting exceptions and inconsistencies in the prevailing world view. For example, Charles Darwin’s son noted his father’s “special instinct” for noting exceptions, even when not particularly striking.s This factor has been recognized as a very general source of inspiration for creativity, in both science and the humanities. “The creative writer,” says Foskett, “looks for flaws in the paradigm, for gaps and inconsistencies in our general picture of reality,”13 while Dainton suggests that important new experiments may be suggested by “going back to the libraries to find and to ponder observations seemingly a t complete variance with existing ideas.”15

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The practicability of drawing particular attention to exceptions and inconsistencies in practical information systems will be discussed below. (iv) The potential danger of false knowledge, particularly of the “it is known to be impossible” kind, in preventing experiments (actual and gedanken) being done, has been commented on by a number of authors. Klingsberg gives the example of the noble gases, for which it was “known” that closed shells of electrons made compound formation i m p o ~ s i b l e .Many ~~ thousands of experimentalists could have done the work leading to the discovery of their compounds, but would have regarded it as a waste of time. It has even been suggested that if Newton and Leibnitz had known that continuous functions are not necessarily differentiable, the calculus would not have been invented.34 Beveridge points out the particular danger of false information in the literature, presenting a barrier to new ideas:$ but Garfield suggests that the danger is exaggerated, showing that scientists of Nobel class, presumably highly creative individuals, make as much use of the literature as their less exalted peers.29The nature of their literature use is of course unknown, and the true extent of inhibition of creativity by false ideas in the commonly accepted corpus of knowledge remains a matter for conjecture. Outdated knowledge is also, in a sense, false. The ability to forget, i.e. to shed past ideas and habits, has been held to be one of the most important aspects of i n n ~ v a t i o n . ~ ~ (v) The importance of interdisciplinary contact has been emphasised by several authors. Hill summarizes the issue in saying that “the benefit of cross-fertilization and the fact that the best site for the proliferation of new ideas is at the interface of two scientific disciplines are well known”60:the same point is made by Dainton, who notes the important discoveries which may stem from expertise in one area being applied elsewhere, giving as example Blackett’s work on palaeomagnetism following a career in cosmic ray Beveridge remarks that the greater our store of knowledge, the more likely that significant combinations will be discovered, the more so if there is a breadth of knowledge extending into related or even distant areas. Scientists who have made important original contributions have often had wide interests, or have changed subjects. The “outsider’s” view may be of most value in an older wellworked field of study. In an active, rapidly developing field, the close-in relevance of expert knowledge is appropriate.ss Interdisciplinary research may bring its own problems. Synge even suggests that those who make major advances in science are often regarded as interlopers or amateurs by their colleagues, because their experiences in entirely different fields of science, or of everyday life, causes them to see the problems in an entirely different way.42

2.3. Formal creativity stimulation techniques A number of formalized techniques for stimulation of creativity have been devised and promulgated, both for groups and individuals. They are described in

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detail in numerous books and articles (see for example1s~’n~S1sZ~S3~s4). After attaining great popularity some years ago, their appeal has somewhat diminished, but a consideration of them may still be of relevance in identifying factors of particular importance for creativity. All that will be attempted here is a summary of four of the more widelyknown techniques, emphasising those points relevant to the topic of this paper. (i) Synetiw. Defined as “the joining together of different and apparently irrelevant elements” to solve a problem, synetics is based on the use of analogical thinking, to seek similarities between apparently different things. Very far from obvious links (“metaphors”) are deliberately sought, in order to test assumptions about possible solutions. Application of the technique may require the involvement of specialists in fields far removed from the problem under investigation. (ii) Brainstorming. The aim here is to produce “checklists of ideas,” which may have relevance to solving a problem. Unconventional ideas are sought, and the brainstorming technique relegates evaluation and criticism of the ideas produced to a later stage, in order to encourage maximum freedom in generating concepts. (iii) Morphological analysis. This is a tool for ensuring that all information potentially relevant to the solution of a problem is systematically examined. All the information available is grouped into “attributes” of concepts, or things, and all combinations examined. Even illogical combinations are considered, since they may suggest feasible alternatives. The exhaustive nature of the process reduces the risk of novel combinations (i.e. solutions) being overlooked. The systematic and objective synthesis of possible solutions avoids the constraints of preconceived ideas. (iv) Lateral thinking. This term, devised by De Bono, describes a number of techniques for restructuring conceptual patterns and creating new ones, essentially looking at familiar data in new ways. This may involve using incorrect information as a step towards a correct solution, and may deliberately seek out irrelevant information. The most extreme form of this latter is the use of random stimulation. This may include exposure to ideas from a completely different field, by totally cross-disciplinary discussions, or by entirely random browsing in a library. Roe has described the university lecturer who “makes a practice of noting down the registration numbers of cars parked outside the library, and consulting the books shelved at those numbers, [rarely failing] to find something of interest.”lo3De Bono suggests the “formal” introduction of randomness by random selection of an article from a book or journal, or a word from a dictionary, as a stimulus to problem solving. Alternatively, one may use different aspects of relevant material as alternative entry points to begin problem solving. In these techniques we find strong echoes of factors already noted (analogies, cross-disciplinary data), as well as specific and unconventional ways of dealing with information (systematic combination of concepts, temporary acceptance of apparently nonsensical, or downright false, information, formal randomness, etc.).

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2.4. Summary

This brief survey of some aspects of scientific creativity, and creativity stimulation techniques, has identified a number of points clearly related to information provision. These are listed in Table 5-1. They include aspects related to the sort of information provided (e.g. interdisciplinary), the way in which it is provided (e.g. to identify analogies), and the way in which it is handled (e.g. temporary acceptance of false information). Some approaches to the inclusion of such requirements within information systems will be discussed in the next section.

TABLE 5-1 Some Aspects of Information Handling to Aid Creativity Recognizinglcreating patterns Identifying analogies Interdisciplinary contact Dealing with false knowledge Identifying exceptiondinconsistencies Favouring chance Random stimulation of ideas Temporary suspension of evaluation

3. Information provision for creativity This section has three subsections: 3.1. General aspects. 3.2. Type of information required. 3.3. Organization and retrieval of information.

3.1. General As was noted in the introduction, there is surprising.j little to be .aund in the literature of innovation and creativity on the role of information, as understood by the library/information community. Hill points out the paradox that, while case studies show that information is the key to successful innovation, very few of the monographs and reports written on the topic have even an index entry for information.60 He shows that in studies of the “conception phase” of industrial innovations, literature use is usually quoted in less than 10% of cases as the source of an idea. He suggests that this may be misleadingly low, for reasons including variability of literature usage between subject areas, unwillingness to give too much credit to prior art for fear of invalidating patents, (one must suspect that an element of personal and professional self-aggrandisement may lead to the same effect in the academic world) the fact that a conceptual jump may result from lit-

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erature read and assimilated much earlier, and (among the more literature-conscious) the very routineness of information gathering, which may therefore go unremarked. This last point suggests an interesting analogy with a study of the UK public library system:’ which showed that a number of prominent politicians, labour leaders etc., who were known from diaries, letters etc. to have relied heavily on public libraries for self-education, consistently failed to mention this, later in life, as an influence on their development. It may simply be the fate of information providers to go unrecognized, or a t least unacknowledged by, through familiarity, their most regular users. The fact is, however, that formal information services do not appear to be regarded as very useful for concept formation, the discouraging summary by Rothwell and Robertson being typical: “in ‘idea generation,’ of course, and barring accidental retrieval, library and SDI systems will play a relatively minor role.” There are naturally countervailing views, most commonly from within the information community, of which the following are typical: “access to pertinent literature” is among factors enhancing scientific creativ-

it^.^ “Library and information services make their own unique contribution to creativity. ” 9 “A continuing flow of information [is] essential both for creative advances and also for the consolidation which underpins them.”16 “Information searching is a creative activity, is proactive as well as, or more than reactive.”” These seem however to have the appearance of articles of faith, and few authors have attempted detailed discussion of specific information requirements for conceptual advance. Of those who have, most have concentrated on accidental, “serendipitous” retrieval, and have equated creative literature use with browsing; for example: “By definition, inspiration is not the end product of direct retrieval-it happens, it is happened upon, not least in the course of unstructured, creative browsing.”3s Garfield makes the distinction between information recovery (routine) and information discovery ( c r e a t i ~ e )He . ~ refutes ~ the idea that use of the literature inhibits creative work, by giving evidence to show that creative scientists are avid literature users. Synge gives examples of the attitude that too much familiarity with the literature inhibits c r e a t i ~ i t y Referring .~~ to his own experience, he suggests that immersion in too narrow an area may be harmful-wide reading, and personal contacts across a wider range of disciplines is advantageous. Beveridge also gives examples to support this, quoting Byron, “To be perfectly original one should think much and read little, and this is impossible, for one must have read before one has learnt to think.” He suggests that overuse of the literature is a problem only to those with a wrong attitude of mind. Preparation is essential, and reading may be a stimulus to originality via significant analogies and generalization^.^^

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Kasperson, studying this particular topic as part of a wider survey of the psychological make-up of scientific w0rkers,2~J~ found that scientists rated as “creative” differed little from their “uncreative” peers in their assessment of the value of published literature, but made much more extensive use of people as sources of information. These studies did not however examine the way in which the literature was used, and one may hypothesise that significant differences could be found by a detailed qualitative study. We shall now go on to consider first the kind of information necessary for creativity stimulation, and second the way it should best be organized and retrieved. There is inevitably some overlap between the two categories.

3.2. Trpe of information required Four kinds of information in particular can be identified as especially relevant for aiding the creative process These are:

(i) interdisciplinary information; (ii) peripheral information; (iii) speculative information; (iv) exceptions and inconsistencies. These will now be considered in turn. (i) Interdisciplinary infomation. The value of interdisciplinary information for creativity has been emphasised above, and it is plain that this is one problem which must be tackled if information systems are to make any real contribution to this area. The effective transfer of information across disciplinary boundaries is never an easy task, even for “informal,” and hence inherently more flexible, information transfer mechanisms. The problems of communication between individuals in the field of “water studies,” for instance, where cloud physicists and microbiologists may share common concerns, have been well described.44 Nonetheless, the rewards are potentially so great that the attempt is made, as is suggested by Kasperson’s work which showed that creative scientists place particular value on interdisciplinary personal contact^.^^^'^ The problems of formal information services in interdisciplinary areas are, of course, correspondingly greater. One based admittedly on a rather small survey, showed that 14 out of 22 American scientists working in interdisciplinary fields rated the information services available to them as unsatisfactory, compared with only 1 out of 55 scientists working in an established discipline. The corresponding figures for a number of countries combined were 68 out of 146 and 26 out of 286. Thus, well over half the interdisciplinary workers were conscious of a lack in information provision, which, given the general apathy of the scientific community towards quality of information services, indicates serious failings indeed. The causes of these problems were readily definedz’.43,62: scatter of publications in the primary literature, and hence in secondary services; abstracting and

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indexing from a single disciplinary viewpoint; the jargon barrier between disciplines; etc. It is also clear from surveys that it is interdisciplinary journals whose subscriptions are discontinued first in times of financial stringency. The solutions are less easy to find. New journals for interdisciplinary areas are constantly being created, while a number of cross-disciplinary “mission oriented” secondary services have come into being recently: those covering environmental energy:‘ water resources,6S and coffee66are good examples. These latter are, of course, of great value to workers in the fields concerned, but their number will necessarily be constrained by economics. There is also the consideration that journals and secondary services may arise only when a meeting point of two disciplines is “hardening” into a sub-discipline in its own right, and thereby becoming as impenetrable to outsiders as the original disciplines. This is the process which Pachevsky denotes as the emergence of a “complex” scientific discipline from an “interdisciplinary” a ~ e a . 4 ~ What are particularly desirable are information services which aim to cover a field of study for the benefit of non-specialists. An interesting example is Oceanographic Literature Review,67which includes (highly selectively) major papers of general interest, in fields sometimes far removed from oceanography. Many of its readers rely on it, not for their main area of work, but for fields of peripheral interest. This service is available only in printed form, and is primarily designed for browsing, of which more will be said later. The editor holds that “many problems are solved only by connecting the unconnected, and that selected literature reviews can put the pieces on the table,” and that publications like this will become increasingly common67: it certainly appears to be a promising approach. The journal Interdisciphny Science Reviews is an interesting example of an attempt to review interdisciplinary topics in a way suitable for audiences of all subject backgrounds. The importance of reviews in information transfer generally has been s t r e ~ s e d ~ ~and * ’ they ~ * ~are ~ of obvious and particular importance in conveying information across disciplinary boundaries. Many existing review journals emphasise their intention of serving the non-specialist reader, but this is easier said than done, particularly when the reader may have an entirely different subject orientation: Epstein reminds us that simple explanations of scientific theories may inthat Garfield’s call for volve more work than the original d e ~ e l o p m e n tIt. ~seems ~ an upgrading in the prestige awarded to scientific reviewing, and the support provided for it, are particularly relevant here.’O Taking a rather different tack, it seems an attractive proposition to make use of automated aids to computerized retrieval, so that the “jargon” of one field could be automatically converted to that of another, thereby removing one barrier to making use of cross-disciplinary material. There would remain the problem that the concepts chosen for abstracting and indexing might be inappropriate, and access to a machine-readable full-text database would be essential. Whether vocabulary translation techniques of sufficient generality and subtlety could, in any event, be devised is a matter for future research.

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Finally we should note the importance of informal interdisciplinary contacts, inside and outside the scientist’s own organization. These will be considered later, with other “informal” information transfer systems. (ii) Peripheral information. Another point to emerge in the consideration of creativity above was the value of seemingly peripheral, or even irrelevant, material, the “information around the fringe that will lead to new ideas,” as Martin puts it.71 Dijkhuis makes essentially the same point: “the informational spark which is really efficient for the creativity of R and D tends to be of a general, blurred and not immediately applicable nature.”72 One way of meeting this requirement is to limit deliberately the precision of information searching, agreeing with Martin that “in the areas of generating ideas, precision in searching is another block, another stop to people finding out ways to improve and extend . . . we must not teach innovators to search with precision but to search loosely in the area r e q ~ i r e d . ” This ~ ’ amounts to encouraging browsing, which will be discussed later. An essential point, of course, is to ensure that sufficiently wide ranging material is included in information sources (as in Oceanographic Literature Review), and that material when included is not categorized too closely. As long ago as 1914, Armstrong was railing against having any arrangement into sections in the Chemical Society’s abstracts, so that “we may have, in fact be forced to have, the whole of the subject matter under our noses.”73 With the growth of literature in the interim, such heroic measures have become totally impracticable, but on a smaller scale this author can confirm that the highly miscellaneous “History, Education and Documentation” section of Chemical Abstracts produces intriguing surprises. The advantages of broad categorization for browsing will be considered later. A general conclusion stemming from the recognition of the importance of peripheral, apparently irrelevant, material for creativity stimulation must be that information services within research organizations should provide as informationrich an environment as possible, deliberately going beyond the bounds of perceived relevance to their organization’s interests. The latter idea goes against much of the conventional wisdom of information service management, and would be particularly hard to argue for in the present economic climate, since it could involve provision of books, journals, and other information resources viewed as, at best, peripheral. The choice of such material would of course, not be arbitrary, and would involve at least as much care and skill as conventional resource acquisition. Truly random information input, as recommended in the lateral thinking approach, must be left to controlled creativity stimulation exercises, which will be considered below. (iii) Speculation. One of the keynotes of the formal creativity stimulation techniques discussed above was the importance attached to free speculation and idea generation. Though such speculations flow freely through informal communications channels, they are, on the whole, barred from formal information transfer systems. Dijkhuis regrets this, pointing out the tremendous freedom to express conjecture in the literature of seventeenth century science, one of the most highly creative of periods.72

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The Elsevier journal Speculations in Science and Technology prints articles on speculative ideas which may not be supported by any currently accepted theoretical background or experimental evidence, together with comments, which may be either critical or supportive. The main value of such a journal is in stimulating discussion, starting from an unorthodox viewpoint, which may help to clarify the issues involved. Other journals do this to a lesser extent, by publishing “target” papers with comments and rejoinders, but the degree of speculation involved is much more closely controlled. An example of almost uncontrolled speculation is given by the Daedalus column in the weekly magazine N e w Scientist.87 This column is devoted to (in the words of its author) “ingenious and novel concepts [which] fall in that uneasy no man’s land between the clearly possible and the clearly fantastic.” Despite the intentional “flight of fancy” nature of the column, experience has shown that almost 20% of its ideas are later seriously suggested as practical, patented, actually implemented, or shown to have been done already. They are also used by academic departments as studies for students. It is rather debatable to what extent such material belongs in the formal literature, with its slowness of publication and limited possibilities for interaction, rather than in the informal communication area. However, with increasing use of electronic means of communication, the boundaries between the two are likely to become blurred, and this will be discussed later. (iv) Exceptions and inconsistencies. Some means of drawing attention to exceptions and inconsistencies in existing knowledge is also a desirable attribute for information systems aiding creativity. One of the most obvious cases is a gap in existing knowledge, and it is a truism that it is essentially impossible to search the literature for what is not known. One interesting venture has been the Encyclopaedias of Ignorance, in which the authors of the articles discuss unsolved problems in a particular subject area.Z3 Because of the rapid progress in many fields, such information on ignorance would become dated very rapidly. Indeed, by the time a problem has been clearly formulated, it is probably close to solution (except in mathematics!). Periodical surveys of gaps in knowledge in particular subject areas, where the means of solving the problem are not apparent, seem a useful and feasible concept. Something of the