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COMPLEX AI-BASED SYSTEMS AND THE FUTURES OF , HENRIK SINDING-LARSEN (ed) KNOWLEDGE AND RESPONSIBILITY IN PROFESSIONS ARTIFICIAL INTELLIGENCE AND LANGUAGE OLD QUESTIONS IN A NEW KEY

7/88

CompLex nr. 7/88

AI based systems and the future of language, knowledge and responsibility in professions A COST-13 project Secretariat: Swedish Center for Working Life Box 5606 S-l 1486 STOCKHOLM - Sweden

Henrik Sinding-Larsen (ed.) ARTIFICIAL INTELLIGENCE AND LANGUAGE Old questions in a new key

TANO OSLO © Tano A.S. 1988 ISBN 82-518-2550-4

Printed in Norway by Engers Boktrykkeri A/S, Otta Preface

This report is based on papers, presentations and ideas from the research seminar Artificial Intelligence and Language that took place at Hotel Lutetia, Paris 2.-4. November 1987. The seminar was organised by the research project AI-based Systems and the Future of Language, Knowledge and Responsibility in Professions. The seminar as well as the project was financed by a grant from the Commission of the European Communities through the research programme COST-13.

The project has been initiated and conducted by the following institutions: • Institute of informatics, University of Oslo • Swedish Centre for Working Life, Stockholm • Norwegian Research Institute for Computers and , University of Oslo • Austrian Research Institute for Artificial Intelligence, The project secretariat has been at the Swedish Centre for Working Life, Box 5606, S - 11486 Stockholm, Sweden. Project coordinator has been professor Kristen Nygaard, Institute of informatics, University of Oslo.

An important activity within the project has been to create a forum for collaboration among European researchers from different disciplines concerned with the impact of artificial intelligence on society and culture. Paris was the location for our third international seminar of this kind, the two first having been held in Vienna and London. The reports from these meetings are available as the following books:

Ernst Buchberger, Bo Goranzon, and Kristen Nygaard (eds.) Artificial Intelligence: Perspectives and Implications, CompLex 11/87, Oslo: The Norwegian University Press, 1987, ISBN: 82-00-07867-1

Ernst Buchbcrger, Bo Goranzon, and Kristen Nygaard (eds.) Artificial Intelligence: Perspectives of A! as a social technology. Complex 2/88 Oslo: Tano 1988

Bo Goranzon & Ingela Josefson (eds.) Knowledge, Skill, and Artificial Intelligence London:Springer Verlag (forthcoming May 1988) The research project has also taken the initiative to organize the international conference Culture, Language and Artificial Intelligence, Stockholm May 31.-June 4.1988.

The project members Henrik Sinding-Larsen (University of Oslo), Paul Henry (CNRS, Paris) and Ingela Josefson (Swedish Centre for Working Life) were responsible for the organisation of the Paris seminar while Henrik Sinding-Larsen has edited the report.

The report contains a selection of the presentations. Most of the papers have been written or rewritten after the seminar took place. Thus the report is more than just a summing up of what was said during our meeting. Important parts are reflections and after-thoughts inspired from three intense days of work in Paris.

The paper "Studying Cognition Today" by Daniel Andler (one of the seminar participants) was originally written for another occasion, and was not presented at the seminar. We have chosen to include it as an appendix because it provides a good background for an understanding of the other discussions. If the reader wants an overview of current discussion topics within and AI, we would recommend to start with this paper.

The seminar was an ambitious multi-disciplinary experiment. The participating researchers covered a wide range of disciplines: informatics, cognitive science, , , law, medicine, , archeology, , and literature. To some extent this made our already difficult topic even more difficult. But our aim was to elucidate fundamental aspects of human and social reality, and that will unavoidably complicated if one tries, as we did, to look critically at previous simplifications. The multi-disciplinary approach became at the same time our strength and our weakness. Our weakness because at times it was difficult to establish a common ground for the discussions. Our strength because we at the end could see ideas converge on a higher level.

It has been difficult to write an introduction and it has been difficult to establish a proper sequence for the papers. In many ways all of the papers are introductions to a new and relatively unexplored field and they could all have been placed first. This is how it must be at the present stage.

In reading the report, practitioners of AI may be disappointed if they look for ready-made methods and techniques that they can directly apply in their work. However, the distance from the reflections of this book to possible applications is short just as the distance from practical problems of AI to fundamental philosophical problems of language and knowledge is short. That is to say, the problems many practitioners of AI encounter in their work are of a fundamental, epistemological nature and they cannot be solved without a reflection of the kind this report is attempting to produce.

Our intended audience is not restricted to practitioners of AI, but includes all researchers concerned with the social, ethical, epistemological, and philosophical implications of this fascinating, new and powerful technology.

Kampen, April 10th 1988

Henrik Sinding-Larsen

Contents

Preface Henrik Sinding-Larsen: Introduction ...... 1

Language, cognition, and reality Elisabeth Leinfellner-Rupertsherger: Linguistics, Wittgensteinian Linguistic Philosophy, and Artificial Intelligence: Pros and Cons ...... 11 Ragnar Rommetveit: On Human Beings, Computers and Representational- Computational versus Hermeneutic-Dialogical Approaches to Human Cognition and Communication...... 47

Language and technology in the of culture Henrik Sinding-Larsen: A! and the externalisation of knowledge ...... 77 Henrik Sinding-Larsen: Notation and Music: The History of a Tool of Description and its Domain to be Described...... 91

Language and the computerization of knowledge Diane Berry: Implicit Knowledge and Expert Systems...... 115 Anna Hart: Theories of Knowledge and Misconceptions in A.1...... 137 Kristen Nygaard: The over-head foils from his talk, containing and conceptual clarifications about AI, language, and the professions...... 161 Paul Henry: Language,Speech, and AI-based Systems...... 171 Dr Gordon Jameson: The Place of Interactive Video in Teaching Systems ( Demonstration of a medical videodisc on the prevention of back injuries)...... 179 Julian Hilton: The Impact of Artificial Intelligence on the Future of Professions: Some Reflections...... 185

Appendix 1: Daniel Andler: Studying Cognition Today...... 201 A ppendix 2: List o f participants ...... 247

Henrik Sinding-Larsen Introduction: Artificial Intelligence - a challenge to our understanding of language, cognition, and reality.

I have a very intelligent vacuum-cleaner. It understands my intention to start every time I press the button with the word ’on'. And even better, when I tell it to stop by pressing the button with the word 'o ff, it understands that too.

To most of us statements like these sound rather ridiculous. We all "know" that a vacuum-cleaner, being a machine, reacts mechanically to our pressing of the two buttons and totally ignores what might be written on them. To talk about understanding in this case would at best be a far fetched metaphor. And to talk about a proper language understanding would hardly come to anybodies mind.

Let us now make the vacuum-cleaner a bit more complicated. Instead of two we provide it with three buttons and we call these buttons ’r , ’2 \ and ’3’. To start the machine we must first press ’1* and then ’2'. To stop the machine we must first press button ’1’ and then button ’3’ twice. And we may ask ourselves: Is there now any more reason to talk about linguistic understanding than in the first case? I guess most spontaneous answers to this question would simply be "no".

But let us make still further improvements on our vacuum-cleaner. We paste small pieces of white paper on the buttons to hide the numbers ’1 \ ’2 \ and *3’ and instead we label them ’o’, *n\ and T . This is very useful because now we don't need to remember arbitrary sequences of numbers but instead the familiar words ’on’ and ’off’. But what about the machine? Has this last move in any sense changed the machine ‘s capability to understand language? In my opinion the answer must still be no. But it has changed our capability for understanding the interaction with the machine, and this is a very significant and non-trivial change.

The keyboard I am currently touching while writing this text has 80 keys labeled with different characters. It would make no difference for the computer if all the keys were re­ labeled with numbers from 1 to 80. But that would make a great difference for me. The number of buttons and especially the number of acceptable sequences of buttons make it impossible for human beings to manipulate computing machinery without some kind of "linguistic organisation" of the interaction. -2 -

The history of the computer shows us how language metaphors like "programming language", "dialogue", "reserved word", "if-statements", "syntax" etc. rapidly became indispensable for us to make sense of this machine's behaviour. But still, it only says something about our understanding of the interaction and noting about the existence of a human-like linguistic competence in the computer.

What we may assert is that we apply a kind of linguistic perspective on our interaction with the computer, and that we try to construct the computer in ways that make particular linguistic perspectives appear appropriate. Different programming (as well as applications) could be regarded as different kinds of linguistic perspectives applied to computers for the double purpose of comprehension and construction.

May be at this point the reader will ask: "But what does he mean by 'language', 'linguistic', linguistic perspective', 'linguistic understanding' etc.?" - Good questions, but also difficult ones. If these questions could be answered with definitions that everybody would agree to, then both our seminar and this report would probably have been superfluous. Each contribution, in its own way, throws light on these most abstract of all phenomena: Language, cognition and knowledge.

The present introduction is not an attempt to introduce the main theme of every article. It is rather the result of a personal attempt to integrate and elaborate my impressions after having read through the texts. There are certainly many other ways of doing just that.

Elisabeth Lcinfellner-Rupertsberger shows in her article how central issues within philosophy and linguistics have gained new actuality through the advent of artificial intelligence. To map the different controversies she uses the famous scmiotic triangle of Ogden-Richard (cf. the appendix of her article). This is reasonable because however divergent the different theories about the nature of language may be, most researchers seem to agree that human linguistic activity involves at least three main groups of phenomena:

- Conscious subjects (often referred to with words like consciousness, thoughts, mental images, memory, intellect, , representations, sensations, etc.)

- External world (referred to with words like surrounding reality, physical environment, reality, or just the world) - 3-

- Signs (referred to with words like texts, utterances, statements etc.)

This last comer of the triangle is perhaps the most ambiguous one because it is sometimes identified with language as such and sometimes it is only identified with the perceivable manifestations of language. We could also regard texts as patterns that exist in the external world, but that, by means of language, play a mediating role between the individual's internal consciousness and their external environment; in particular when this environment consists of other conscious individuals. Here we already touch the basis of the controversy of what language is, what meaning is, and where it is located.

Leinfellner-Rupertsberger shows how different schools of linguistics and linguistic philosophy can be classified and understood through the way they give primacy to the different comers and edges of the semiotic triangle. Two main paradigms are discerned, one that gives primacy to the external world for determining our language, and another that gives primacy to our language for determining the external world (or at least our perception of it). We could also express it this way: one paradigm that implies that meaning stems from reference to a world that exists by itself and another that implies that meaning originates in language-intrinsic use (language games). The distinction is discussed in relation to Wittgenstein's early and later views of the .

In her paragraph 9.2 the distinction becomes directly relevant for a discussion of knowledge representation in Al-based systems. Through socialization into a language community we acquire knowledge about the world at the same time as we learn a language. Much of this coupling between knowledge and language is stable over time and becomes "petrified in semantic structures". The external reference paradigm may work fairly well for analysing this kind of language use. But there will also be language use that is less petrified, will) a more local validity and consistency in time and space. In this case it may be bener understood as a language game. As human beings we are able to live with apparent contradictions because we have a range of different ways of relating meaning to words. Computers cannot switch between paradigms of language and will probably always have to handle contradictions in a more burdensome way than we do.

Computer terms will always have a strict referential meaning in the program that defines them. The computer user therefore operates in an environment where the referential paradigm is true and hence this kind of linguistic perspective will be appropriate. One can only speculate on what the consequences will be of being brought up in a society where children get their primary language acquisition from interaction with computers. - 4-

To get a better understanding of language, Leinfellner-Rupertsberger points out that our future theories must combine more "edges" of the semiotic triangle than previous theories have tended to do.

Ragnar Rommetveit approaches many of the same issues as Leinfellner-Rupertsberger but his emphasis is somewhat different. To him the most characteristic aspect of human language is not to be found in any of the comers or edges of the semiotic triangle (or in their combination). It is to be found in subject-to-subject interaction, in intersubjectivity, in dialogue, in goal-oriented collective activity and not in a single individual. "The image of Man provided by mainstream cognitive science so far is in my view essentially that of an asocial, but highly complex computational and information processing device." (p.8 of his paper).

Rommetveit's discussion of the phrase "Mr. Smith is working in his garden" shows first how this utterance in ordinary language can have no literal meaning that will be impermeable to changes in the context or situation of interpretation. But this is not the most important finding. He argues that an enlarged context of meaning cannot simply be an extension of the material surroundings of the speaker or listener. The important "unit of interpretation" is more than an individual plus his context since ordinary language according to Rommetveit should be regarded as "dialogically constituted, i.e., as providing drafts of contracts concerning human , by definition negotiable and constantly subject to contextual specification." (his p.20)

The meaning of "Mr.Smith is working" should be regarded as the result of a negotiation between speakers about the sharing of their reality with all its cognitive, practical and ethical implications. "Qualifications for membership in our linguistic society have to do with capacity for participation and co-responsibility for what is meant in human dialogues." (his p.25)

If we accept this as a definition, then the question of whether computers can learn to understand language will have to get a negative answer unless we would grant computers full responsibilities as members of society.

The computer has raised series of questions that appear to be fundamental. Are language and linguistic competence fundamentally the same or different in men and computing machines? And what about the brain? Do neurons communicate linguistically or are "we", as conscious selves, the only entities capable of linguistic communication with other - 5 - beings? Are computers just an extension of our own linguistic competence or are they "linguistic beings" in their own right?

I believe these questions first and foremost have become important because they are metaphysically provocative. Sherry Turkle shows in her book "The Second Self' how this provocation was articulated most clearly by small children. However, it seemed that the provocation was not resolved with growing age, but rather transformed. The current philosophical debate is the adult version of the childish question "Is the computer really alive?"

The answer to these questions do not seem to be of great importance to the computer industry. Computers will probably have the same profound influence on human use of language whatever the conclusion will be in the philosophical debate on the ontological status of formal versus human languages.

Henrik Sinding-Larsen s first article "Information technology and the extemalisation of knowledge" attempts to place AI and computer technology within a larger framework of cultural evolution.

The idea of creating two sets of concepts, one with the prefix "artificial" and the other with the prefix "natural", is not new. But it is problematic, not only because of the idea of the artificial but as much because of the seeming innocence of the "natural". Already in the fourth century B.C. argued against the artificiality of written language which he thought might degrade our natural memory and thereby man's ability to attain true knowledge and wisdom; he argued against the very same form of writing that today is called natural language and that systems of so-called artificial intelligence try to simulate. With the same kind of reasoning our clothes can be regarded as artificial fur and lightbulbs as artificial sunlight. Artefacts as well as communicational invendons are inherendy human to such a degree that the behaviour of a Robinson Crusoe living as a lonely animal in the wilderness should be the best candidate to be labeled artificial.

Nevertheless, intuitively the division between natural and artificial makes sense, so we should not ignore it but rather explore with less normative concepts. "Artificial intelligence" is after all a product of man's natural abilities for manipulation. The article explores how the of externalisation of knowledge can be used to describe some of the processes that we usually call artificial. -6 -

Sinding-Larsen's second article "Notation and Music: The History of a Tool of Description and its Domain to be Described" explores one aspect of the first article in more detail. Musical notation is seen as an example of an information technology that for a long period coevolved with the domain of its description. This kind of historical study is important because the impacts of computers and AI that we can observe today will just be the very early stages of long-term processes.

Diane Berry discusses in her article the complex relationship between language and knowledge. The question is elucidated through a discussion of problems within knowledge engineering.

It is obvious that language plays an important part in much of what we call knowledge, but it is equally obvious that we possess knowledge that is of an implicit, non-verbal kind. Berry warns us that the relationship between knowledge and language is more complicated than what may be understood by the distinction between verbal and non­ verbal. She discusses and distinguishes knowledge that can always be verbalised, knowledge that once was verbalised but that no longer is, knowledge that never was verbalised but that can be demonstrated with body movements upon request, and knowledge that can neither be verbalised nor demonstrated but that still can be used in the performance o f tasks.

Berry shows that we cannot understand the relationship between knowledge and language independently of learning. In fact much of what we call explicit or verbalised knowledge exist and is used only in certain learning situations. Passing exams and repeating memorized text may in some cases be a ritual that the novices must pass before they are allowed to work where the real learning starts. Later when they are asked to explain how they do their job, even after many years of experience, they tend to repeat old phrases from the textbooks rather than try to formulate how they actually reason.

The textbook's explicit, verbal version of knowledge is often regarded as a ideal even when the expert knows that it does not work in practice. Experts may feel that they have to justify the possible discrepancies that exist between the textbooks and their behaviour, even when it is successful.

We may find a parallel situation in relation to language as such. On the one hand, computer programs are used to simulate human language, on the other hand computer programs are a kind of human language. Programming is a way of expressing ideas. It is part of our symbolic and thus, in a wide sense, linguistic behaviour. But a characteristic - 7 -

process starts when programmes and programming languages are used to simulate and analyse our traditionally spoken and written language. If "artificial languages" shall be able to approach natural languages it must be possible to analyse and make explicit the rules and functioning of natural language in terms of the artificial, i.e. the artificial becomes a m odel o f the natural. However, a possible next step is that the artificial becomes the model fo r the natural. This is precisely what happened with spoken language in in the encounter with writing.

Writing got a privileged place as linguistic object. Written grammars and dictionaries set the standards for spoken language. Natural variations of speech became deviations that had to be explained and justified instead of the other way around. For the moment our attention is directed towards how "artificial" computer languages deviate from natural language, but the situation may soon be that we have to justify why our "natural" language deviates from the computer-based standards.

Anna Hart discusses in her article the nature of professional knowledge. She gives examples of the subtle ways in which this knowledge is more complex than what is recognised by mainstream research in AI. Her case study of a mountain rescue team shows that the ability to recall or retrieve knowledge is dependent upon a series of circumstances. One is the emotional climate at the moment of knowledge retrieval. Hart's principal informant says about rescue operations: "It isn't the same if there's a real body lying out there. You can feel the atmosphere. A practice feels different. On a practice we make safe decisions. On a call-out we make daring decisions." The emotional climate influences the way we recall our knowledge as well as what we recall. This indicates that we, at least on some level, operate quite differently from computers.

Intuition is an emotional state. It is a feeling that something is right without being able to verbally state why. It represents a style or an ideal of knowledge that is very far from that which can be computerized. If expert systems will in the future be available in many situations where experts previously had to rely upon their intuition, maybe our general style of knowledge retrieval will change. Our ability to use emotion and intuition may, in many situations, decrease.

Kristen Nygaard's over-head foils contain succinct statements on many aspects of informatics in general and AI in particular. Of particular importance to the question of language is his idea of "designed extensions of a profession's (natural) language to provide a conceptual and operational integration of information technology in the profession". There is no doubt that concepts and expressions originating in computers - 8 - enter into both professional and everyday language. In addition there is a whole series of concepts in natural language that are specially made for the description and understanding of computers, e.g. the concept of artificial intelligence. Problems arise when the same concept is used to describe properties both of computers and human beings. Knowledge is such a concept, and Nygaard proposes to always use the expression "machine representable knowledge" to escape endless discussions of whether a machine can possess knowledge or not. This will not solve all the problems, but it is certainly important to linguistically accentuate the difference between humans and machines in many cases where traditional terminology tends to blur the distinction.

Gordon Jameson gave a demonstration of a of a medical videodisc on the prevention of back injuries. This was a very useful reminder, because it showed clearly that the interaction with a computer is more than linguistic in a strict sense. The user interface of future knowledge based systems is certainly to a much larger degree than today going to be an integration of text, voice, music, sound effects, colours, graphics, videos, stills etc. The discussion of knowledge, language, and computers will then probably have to be more complex than is the case today. The potential for storing and disseminating knowledge is obviously greatly enhanced with the use of several channels of communication. But there is also a danger. When a computer simulation of a teacher or a more comprehensive learning situation gets better then it may be easier to drop the contact with a human model altogether and thus reducing the importance of knowledge that can only be communicated in a situation containing "real" objects and persons.

Julian Hilton had the difficult task of summing up the seminar. His written version presented here is a kind of kaleidoscopic, logic programmed tour of the problems of AI. At first reading, I found it somewhat confusing, but then I realized that this confusion was in many ways inherent to the problem. It is an interesting way of demonstrating to whai extent AI raises a lot of questions whose answers are difficult to determine. It seems that in most cases a particular consequence of AI is as plausible as its opposite, and with our present state of knowledge, we are hardly able to weigh the alternatives against each other, indeed a strong argument for further research.

And that will also have to be the conclusion of the seminar. Language is in many ways a key issue in relation to computers. We need a better understanding of languages for the construction of computers capable of handling a wider range of tasks. We need a better understanding of the relation between language and knowledge to be able see the possibilities and not the least the limits of computer-mediated knowledge. The discussions at the seminar showed clearly how these questions had ramifications into all - 9 -

of the present disciplines, and that future research on language and computers will not be a task for narrow specialists.

The seminar was an interesting and fruitful experiment on a multi-disciplinary approach to a central issue in a field that to an increasing degree is characterised by specialisation.

- 11-

LINGUISTICS, WITTGENSTEINI AN LINGUISTIC PHILOSOPHY, AND ARTIFICIAL INTELLIGENCES PROS AND CONS

Eli sabeth Lei nfel 1ner-Rupertsberger

O. INTRODUCTION Linguistic philosophy, and particularly Wittgenstein's philoso­ phy, have frequently -found admirers, if not followers, among researchers working in the computer sciences and A I , although there is no uniform reason for this admiration or allegiance. I just want to mention four examples. In 1963, Sayre and Crosson edited a bool- called The modeling of mind: computers and i n t e l l i ­ gence. They included a chapter "Remarks on mechanical mathema­ tics" which is an excerpt from W ittgenstein's Remarks on the f oundations of mathematics <1956). Dreyfus 1972 holds that AI is impossible since a computer lacks feelings, a body, and other ty p ic a lly human ch a ra cte ristics , and that computer lacks essential features of the semantics of natural languages, like context-dependency and openness or fuzziness; and he supports his view with Wittgenstein's philosophy. Wilks 1976, on the other hand, while e x p lic itly opposing Dreyfus, thinks that W ittgenstein‘s later philosophy may be claimed for the AI position, perhaps, we may add, even for the strong AI position, according to which a program is a form of mind. Indeed, W ilk s bool Grammar. mean ing.___and the machine analysis of language <1972) may be viewed as an attempt to "verify" Wittgenstein's later thesis according to which the meaning of a word is its use in the language by developing a specific system of semantic parsing. Zemanek 1978, too, considers Wittgenstein to be the "computer philosopher par excellence". According to Zemanek, the Tractarian view corresponds to the processes inside the computer, while Wittgenstein's later philosophy corresponds to the rela­ tionship between the computer and its user.* The following discussion is restricted to topics which have to do with language, particularly semantics. It is not an attempt to show that AI is bad and linguistics and philosophy good, vice versa.5* -12-

1. REMARKS ON WITTGENSTEIN'S EARLY AND LATE PHILOSOPHY As we may see already -from the preceding remarks, Wittgenstein's philosophy is not a monolithic block but fa lls into two main - but overlapping, intersecting - parts, the -first of which is identified with the logic-inspired Tractatus. and the second with his later, semantically oriented work, p a rtic u la rly the Philosophical investigations. How deep is this rift between Wittgenstein's early and late philosophy? We do riot really know. A different picture of Wittgenstein may emerge after the publica­ tion of the manuscripts still extant. It is, however, his published later writings which founded and continue to influence it, while the Tractatus fits into a particular philosophical school, namely the , Logical Empiricism, and its predecessors, Russell and Frege. But even Wittgenstein's later philosophy contains strong positivistic elements

2. PARADIGM (1): SYNTAX AS CALCULUS; MEANING IS REFERENTIAL AND TRUTH-FUNCTIONAL The approach here is referential and truth-functional; the name ■for the extended Vienna C ircle philosophy, "Logical Empiricism", points to the origin of paradigm (1). There is, on the one side, an emphasis on , on deduction. Language is viewed as something that is to be lo gically analyzed, or lo g ic a lly im­ proved, 10 or simply as something that is lo g ically inadequate: Everyday language is a part of the human organism and is no less complicated than it.

It is not humanly possible to gather immediately from it what the logic of language i s . 11 P articu larly the syntax, but also the semantic« of language — and sometimes even language itself (Montague's t i t l e "English as a formal language" exemplifies this view) - appear as a kind of calculus or algorithm. In Philosophical grammar. a work that lies between Tractatus and Philosophical investigations. Wittgen­ stein presents grammar as such a calculus which offers no surprises and needs no empirical justification.1* In linguistics this has dire consequences: for instance, once syntax is seen as a calculus, an algorithm of the tranformational kind, as a ready-made system that comes into being like Pallas Athena emerging from Ju p ite r's forehead, for the production of senten­ ces, it appears that the sentences thus produced are sentences by name only; in re a lity they are constructs which have to be matched against genuine empirically occurring (etic) sentences (utterances). (In Post's algorithm, which is the ancestor of TG, the constructs on the right hand of the rule are called "theo­ rems" . ) The meaning of terms or syntactic categories is to be explained referential1y , the meaning of sentences truth-fune - tio n ally. Again one is reminded of the two poles of Logical Empiricism. Under the influence of paradigm (1), lingu ists show no interest in statistical phenomena, and little interest in empirical language data. Linguistic variation does not seem to exist; if it is noticed at all it is relegated to the trash heap called "performance" or, sometimes, "style". We will find extreme formulations, for instance: for the syntactic or semantic analysis of modern languages we don't need a corpus;1* for grammatical descriptions it is an unnecessary ceremony to -15-

look at a corpus.1'* Here the linguist resembles the p o litic ia n who says to his/her aide: "Don't confuse me with the fa cts." But i-f one wants to preserve the notion that linguistics is, after all, an empirical science, and does not want to analyze a corpus, then there is only one solution le ft: to put the algorithm itself into the mind. Hence the em pirically as yet unsupported Carte­ sian mental ism of transformational grammars. On the empiristic side of Logical Empiricism and Wittgen­ stein's theory of meaning in Tractatus we see the following: semantics appears indeed as semantics of empirical meaning, but also, quite literally, as an afterthought to syntax, namely, as the empirical interpretation of a formal calculus or algorithm, e. g . , syntax as a calculus or algorithm. Or semantics accompa­ nies syntax one step behind, so to speak, as in model-theoretic semantics of the Montague type where each syntactic category is teamed up with a semantic reference object in a possible world; these possible worlds are all fic titio u s except one, that possible world which is also real. A semantics of this type is referential without being empirical. In the Vienna C irc le 's the meaning of non-logical terms was always seen as empirical meaning, be this the meaning of empirical or theoretical terms, the latter receiving meaning through reduction to empirical terms. However, the attempt to reduce the meanings of ail theoretical terms to empirical meanings was a failure. meaning is esta­ blished through the prin cip le of verifica tion : The meaning of a is the empirical procedures we would carry out to establish its truth or falsehood. Ur, as Wittgenstein formula­ ted in Tractatus: To understand a proposition CSatg.D means to know what is the case if it is t r u e .1®

The Wittgenstein of Tractatus agrees with Frege's principle of compositionality: The meaning of a proposition or sentence (W ittgenstein's term "S atz" is always translated as "proposi­ tion", although "sentence" would perhaps be more natural.) composed of the meanings of its terms: A proposition is understood by anyone who understands its constituents.1«* The view that semantics interprets a formal structure does make sense in science, for instance when a tree from a point as - 16-

a ■formal structure is interpreted as the hierarchical com- mand-structure in an organization, a lin g u is tic taxonomy, a lin g u is tic feature hierarchy, or a biological taxonomy. But in language, only relatively few structures may be successfully analyzed in such a way that syntax is kept separate -from seman­ tics. In AI, linguistics, and Cognitive Science, paradigm <1> shows up in the following intersecting hypotheses! the mind functions like a computer as the embodiment of two-valued logic and Boolean algebra; thinking is equated with logical manipula­ tions; the functions of our language. are basically logical; meanings are to ta lly definable; language may be captured in a transformational grammar as a derivational device, etc. This ends the exposition of paradigm (1) as such. However, p articu la rly the semantic elements of paradigm ( 1) come with an attached problem which we may name appropriately

3. PROBLEM QF PARADIGM (1): MEANINGLESS TERMS AND SENTENCES (PROPOSITIONS), or THE INSUFFICIENCY OF THE EMPIRISTIC CRITERION OF MEANING Here we are particu la rly interested in a critique of the semantic aspects of paradigm ( 1 ). In the framework of paradigm (1) in its extreme Vienna C ircle form we have to conclude that there are terms and senten­ ces that are meaningless, even though they appear meaningful to the everyday language user. This is a puzzling result. But if it is exclusively empirical reference and verification procedures which determine the meaning of terms (other than the purely logical terms) and sentences, then all terms which do not f u lf i ll either one of two c rite ria are meaningless and have to be eliminated. The criteria are: (1) a term is empirical or (2) can be reduced to an empirical term (terms). L in g u is tic a lly spea­ king, or, more general, from the standpoint of the "understan­ ding" paradigm, the yet to be discussed paradigm (2 ), this is quite unsatisfactory. This unsatisfactory result spreads to the semantics of sentences. If the Fregeari principle of composi ti onal i ty holds then there must be sentences without (empirical) meaning simply by virtue of the fact that they contain non-logical terms without (empirical) meaning. - 17-

□n the other hand, to postulate platon istic, -fictitious referents in possible worlds in order to make up for the lacking empirical referential objects does not seem to be a great i mprovement. There is an additional complication for sentences. Even if terms as the constituents of a sentence have themselves empirical reference and, thus, empirical meaning, this does not guarantee that the sentence as a whole has empirical meaning when we apply the principle of verification. There may be sentences, for instance predictions, where we can point to empirical referents but have at present no way of determining the empirical proce­ dures to ascribe some to these referents. For instance: one finds some subatomic p a rticle , theorizes about its possible properties, but knows of no experimental way to prove these properties. It has been said that the mere possibility of ve rific a tio n , for instance the p o s s ib ility of finding mountains on the dark side of the moon, is sufficient to establish empiri­ cal meaning. However, this is too sim plistic an example. It may turn out at a later time that in a certain case no experimental procedures are possible; then the question of whether this particular sentence has meaning has to be decided in the negati­ ve, even though at a certain time i t was thought that such procedures were possible and that the sentence under considera­ tion did have empirical meaning. In other words, there are no a priori c rite ria whether it w ill be possible or impossible to carry out experimental verification procedures. x'r To sum it up: Verification guarantees empirical meaning provided verification procedures can be actually carried out.

Possible verifications may turn out to be f i cti ti o l is . Besides empirical meaning one speculated about logical meaning: the meaning of a sentence is its logical truth value. But then logical meaning is nothing else but logical truth obtained through deduction in a calculus. Such a logical meaning is in no way suited to solve the puzzle of why there are terms and sentences that have meaning for the everyday language user and nevertheless do not have meaning re lative to a certain phi 1osophy. This r i f t between empirical meaning and logic which pervades the philosophy of the Vienna C ircle and Logical Empiricism froze -18- into what Quine has called the "two dogmas of empiricism". Here Wittgenstein's later philosophy and paradigm (2) come to the rescue, since they explain meaning without using the notions of reference, verification, and logical truth.

4. PARADIGM <2)* THE "UNDERSTANDING" PARADIGM 4.1 THREE PILLARS OF THE "UNDERSTANDING" PARADIGM 4.1.1 PILLAR (1): LANGUAGE IS NEITHER AN OBJECT TO BE LOGICALLY IMPROVED UPON, NOR IS IT A CALCULUS OR THE OBJECT OF A CALCULUS Here the linguist or the linguistic, Wittgensteinian philosopher analyzes language as it is, for instance a corpus of empirical evidence, rather than trying to determine more or less a priori how language is, or, even, ought to be. The linguist following paradigm (2) is not interested in algorithmic language production, but trie s to capture (l) either what happens when we understand a text, or, ( li ) when focussing on meaning as language-intrinsic use (see 4.1.3), tries to extract the semantic structure of a text without recurring to "the mind", p a rticu la rly the mind viewed as a lo g ic a lly , analyti­ ca lly operating system, but also - at least in prin cip le — without recurring to the synthetic, empirical world. Empirical text production is usually left to the psycholinguist, since linguists working under paradigm <2 ) prefer to analyze already existing texts. Many scholars in psychology, in AI, and certain linguistic schools follow paradigm (2)5 because of its overwhelming interest in understanding we may call this paradigm the " 1anguage-’under­ standing'" paradigm. 11’ Some linguistshave furiouslyrejected the view that lin g u is tic s should study language understanding;Fillmore, however, has stated e x p lic itly that issues in semantics that have no conceivable application to the process of understanding cannot be very important for semantic theory, and that many internal semantic problems might better be solved by looking at compre­ hension, even if this means mentalism (see 9.1) .30 Wittgenstein continued for a while after the Tractatus to see grammar and language31 as a sort of calculus; in Phi 1osoohi- cal grammar he hints at the empirico—referential roots of logic, its roots in the empirical worId; 23 in Philosophical investiga- - 19- 1

ti ons. -finally, he explicitly rejects the idea of grammar and language as c a lc u li. 23 His "castle in the a ir" , his pipedream had come to nothing. Philosophy, then, according to this later Wittgensteinian view, leaves language as it i s . 2* Logical analysis is seen as to be to ta lly unsatisfactory. The main source of our not-undet— standing is that we do not have a comprehensive view of the use of our words,=a or — since meaning is now defined as language use — of their meanings (see 4 .1 .3 ). Generally, in paradigm (2) semantics is seen as more important than either syntax or logic; the division between syntax and semantics is blurred, if not deemed insignifican t. Instead of TG -like or other algorithmic approaches to syntax the linguist may prefer semantic-based or semantically enriched case structures.

4.1.2 PILLAR (2): THE REDUCTION OF THINKING TO LANGUAGE In his later philosophy, Wittgenstein explicitly reduces thought to language; the relations R3 and R«, (Fig. ( 1 ) of the appendix), taken together, degenerate at first into an identity relation; in f in e . they are eliminated entirely in favor of the situation depicted in Fig. (2). Furthermore, he rejects traditional mentalism in the form that there are non-physiological, Cartesian mental objects besides language: "The purpose of language is to express thoughts." So presumably the purpose of every sentence is to express a thought. Then what thought is expressed, for example, by the sentence " I t 's raining"? - Thinking, according to Wittgenstein, is not some incorporeal, non-empirical process which lends lif e and sense to speaking; it is something physical; accordingly, he rejects the notion of ex tra lin g u istic thinking and of an "inner" mental languaqe as a substitute for ordinary language. 27 As regards the problem of language and thought, Wittgenstein assumed a very positivistic attitude. One may call his attitude even behavioristic, although he himself rejected behaviorism. 30 (Wittgenstein obviously held that behaviorism is not interested in mental processes at a ll; he himself, however, did not want to deny that there are such processes.2’ ) Once reference is rejected as the source of meaning, the reduction of thought -7.0-

to language encourages the view of language as a self-contained, s e lf-s u ffic ie n t system, a 1anque in the strictest of de Saussu- rian senses. Here Wittgenstein painted himself into a corner, as it were, since this view has severe shortcomings which are pointed out below (see sections 6 and 12). Therefore, if we look at Wittgenstein's philosophical goals we may indeed say that his views are "anthropocentric")30 but Wittgenstein frequently failed when he attempted to philosophi­ cally formulate this intention, and this is one of the reasons why many of his views can be interpreted as being close to AI.

4.1.3 PILLAR <3>: THE REDUCTION OF MEANING TO, OR ITS EXPLANA­ TION AS, LANGUAGE-INTRINSIC USE What happens to meaning? That the em pirlco-referential view has its problems and may be only part of the whole picture had been realized by Frege as well as by the early Wittgenstein in Tractatus. Once thinking has been reduced to language, meaning may be negatively defined as something that does not exist as a mental entity; meanings are not mental objects: When I think in language, there aren’t meanings' going through my mind in addition to the verbal expression; the language is itself the vehicle of thought. 31 And: There is a lack of clarity about the role of lmaginabi1ltv in our investigation. Namely about the extent to which it ensures that a proposition CSatz 3 has sense [ Sinn 3. 33

It is no more essential to the understanding of a prop osi­ tion CSatz 3 that one should imagine anything in connexion with i t , than that one should make a sketch from i t . 33 This is quite the opposite to Tractatus where Wittgenstein states that a thought is a sentence (proposition) with sense - sentence and sense are two different things in Tractatus. On should take notice here that Wittgenstein does not say that there are no mental processes; he says that they are lin g u is tic and at the same time something physical. There exists, then, a mental language; but, on his view, this mental language is identical with language as it is. P ositively, meaning is defined as use in the language. The seeds of meaning as language-intrinsic use may be found in Tractatus when Wittgenstein introduces the Fregean notion of -21-

sense (Sinn)s The sense CSinnJ of a proposition CSatz 3 is its agreement and disagreement with p o s s ib ilitie s of existence and non-ex x stence of states of a H a i r s . 34 Frege's sense is a forerunner of meaning as language-intrinsic use. This might puzzle the reader who remembers that Frege was a platonist and that Frege's notion of sense (Sinn) is a platonis- tic concept. However, once we slough off Platonism from sense - and mentalism, too - we are le ft with meaning as language-intrin­ sic use. Put simplys Fregean sense enables us to understand language items without considering the referents; and so does meaning as language-intrinsic language use.3“ Wittgenstein himself did realize that the concept of language use has the same systematic function as Frege's concept of sense. After discus­ sing Frege's objections to formalism in mathematics, he continues with remarks on Frege's and his own solution to Frege's problems Without a sense, or without the thought, a proposition would be an utterly dead and trivial thing. And further it seems clear that no adding of inorganic signs can make the proposition live . And the conclusion which one draws from this is that what must be added to the dead signs in order to make a liv e proposition is something immaterial, with properties different from all mere signs.

But if we had to name anything which is the lif e of the sign, we should have to say that it was its use. 36 Besides the chess simile (see section 12), the continuation of th is quote is the strongest evidence imaginable for our thesis that Wittgenstein conceives of use as 1anguage-intrinsic use, that he knows, in general, no other context than the lin g u istic context, and that the pragmatic lnterpretation of his later philosophy where "context" is taken to mean the cultural and social context is in re a lity a development away from Wittgen­ stein's original ideas, a very important development, I would lik e to add, but nevertheless a development rather than an interpretation. 37 In the continuation of the above discussion, Wittgenstein at f ir s t rejects the view that a painted or modelled image as replacement of a mental image - Frege's "thought" — gives life to the dead sign. And why should i t , he asks, and continues, discussing "use": The mistake we are liab le to make could be expressed thus: We are looking for the use of a sign, but we look for -22-

it as though it were an object co-existina with the sign. (One of the reasons for this mistake is again that we are looking for a "thing corresponding to a substantive.")

The sign (the sentence) gets its significance from the system of signs, from the language to which it belongs, t . . . 3 But one is tempted to imagine that which gives the sentence lif e as something in an occult sphere [the mind, etc. ELI, accompanying the sentence. But whatever accompa­ nied it would for us just be another sig n .3“ Itshould be pointed out that the "sloughing off" of Platonism from the concept of sense (Sinn) occurs already in Tractatus: Man possesses the capacity of constructing languages, in which every sense LSinn 3 can be expressed, but without having an idea how and what each word means — just as one speaks without knowing how the single words are produced.3'5’ In combination with Wittgenstein's strong rejection, or at least severe restrictio n of the referential view of language (which he ascribes to Augustine), where words are names for objects in the empirical world, where each word has its meaning, and where words are put together to form sentences which have meaning if their truth values can be determined, we are well on our way to the situation pictured in Fig. (2) of the appendix. The most striking of Wittgenstein's examples - though not all - are based on use as 1 anguage-intrinsic use. I will give a few quotes to illu s tra te the points How do I know that this colour is red? - It would be an answer to says "I have learnt English".'*0 (instead of having learned the term "red" by having a red thing pointed out.) You learned the concept pain’ when you learned language.'*1 Wittgenstein therefore compares words to chessmens we play chess without attaching a separate meaninq to the chessmens all we have is the rules of how to move them. For a more thorough discussion of the chess simile and of rules see sections 11.2 and 12. Meaning in use as opposed to referential and tru th — functional meaning changes certain aspects of philosophical problems: Essence is expressed by grammar.Aa and Grammar te lls us what kind of object anythinq is. (Theology as grammar.)'*3 In principle all this amounts to a view of language where the meaning = use of one term is defined, or given, through -23-

co-occurring terms; in the appendix, Fig. (2), this self-suf­ ficie n t language is represented as a reflexive relation

5. ADVANTAGES OF PARADIGM (2) FOR AI (AND LINGUISTICS) It is obvious that paradigm (2) - or at least certain aspects of it - seems to be ready-made for research in AI (and lin g u is tic s ). There are two main reasons for this: (i) Once thought is reduced to language, one can dispense with the troublesome attempt to find a computer equivalent of thinking or of a model of thinking that is extra-linguistic. Language is readily accessible and the "intelligence" part of "Artificial Intelligence" seems to be more accessible, too, since the structures of thought, memory, etc. are reduced to language structures. Even though this Wittgensteinian reduction of thinking to language may be massively incorrect, a large part for instance of memory and AI research proceeds as if it were correct; we only have to think of Schank's (1982) and, to a certain extent, Tu lving 's (1983) book, or of Scragg's (1976) paper on memory structures which look just like any semantic network. Since language structures are the result of dialogical language developments, the mental structures offered in such approaches would be a kind of "think-tank". In a sense, th is notion has been anticipated by all those who, like Humboldt, consider language as a nation's mental treasury. -24-

Meaning as language-intrinsic use offers the equally attractive possibility to define linguistic structures through other linguistic structures, even though the hope to semantically exhaust a language in such a way is surely utopian. For ling u istics, there is the additional bonus that paradigm (2 ) severs the ties between psychology and lin g u istics and main­ tains linguistics as an autonomous science, provided this is deemed important. However, no paradigm without its attached problem:

3 6. PROBLEM OF PARADIGM (2): NO CONNECTION TO THE EMPIRICAL WORLD Once we are defining meaning exclusively as 1anguage-intrinsic use, there is no way out into the empirical world; there is no longer the p o ssibility of learning meanings through ostension, and, generally, of relating language to the empirical world. (Wittgenstein misunderstands the "biology" of ostension when he states that even ostension presupposes a priori lin g u is tic knowledge.*A> I do not want to deny that "meaning in use" also may mean use in a pragmatic situation, in a language game whose objective is naming, etc. However, the main thrust of Wittgen­ stein's argument points into the direction of language-intrinsic use, since the referential function of language is e x p lic itly re je c te d .O n c e this stance is taken, many philosophical and lin g u is tic problems become intractable, for instance, generic reference (sloppy identity). In Philosophical investigations. Wittgenstein remarked that it was his philosophical goal to show the f ly the way out of the f ly traps'*" and language may become such a f ly trap. (I have the feeling that postmodern philosophers encourage us to stay in the fly trap - and be happy.) And epistemological1y it doesn’t re a lly matter whether we are caught in the logical m irror, or in the fly -tra p . A common argument is here that Wittgenstein meant by "use in the language" not "use in the language" but language use in a pragmatic setting, and that his notions of and fuzzy meanings are not only anti-logic, but also (or: therefore) pragmatic.‘*l? This case seems to be illustrated by Wittgenstein when he says that two people who do not speak the same language -25-

understand one another through the way they act which is common to both of them.00 And one may be tempted to quote Wittgen- stei n 's C . . . 3 to imagine a language means to imagine a form of life.0 * and Here the term "language-game" is meant to bring into prominence the fact that the apeaking of language is part of an a c tiv ity , or of a form of l i f e . “3 in support of this position. However, as Hunter has painted out, "form of life " does not mean much more than engaging in patterned self-sufficient linguistic behavior.®’ And even if "form of lif e " were not to be understood in such a way, pragmatic embedding alone s t i ll would not guarantee meaning, be this empirical meaning or meaning as use in the language; the best way to show this is by distinguishing two kinds of pragmatic embeddings

return to the referential view as already indicated above. And then we are back to square one, that is , paradigm (1). We seem to have gone in circle s. Even if we do not think that there is an abyss between the early and the late Wittgenstein, there seems to be an abyss between paradigms ( 1 ) and (2 ) and we are left with the contention that we either have empirical reference and empirical referential meaning or language-intrinsic meaning as meaning in use.

7. PARADIGM (1) + PARADIGM (2) = ? In a next step I will connect the two paradigms in a way in which Wittgenstein might have connected them. Wittgenstein hints at such a possibility in Philosophical investigations when he says: One thinks that language learning consists in giving names to objects. Vi2 ., to human beings, shapes, colours, to pains, moods, to numbers, etc. To repeat - naming is something lik e attaching a label to a thing. One can say that this is preparatory to the use of a word. But what is it a preparation for?BB (Compare also the notion that one sometimes explains the use of a name by pointing at its bearer; see section 1 ). I think it is possible to show in a model "what it is a preparation for". L in g u is tic a lly speaking, there is a developmental and a diachronic side to the model which connects paradigm ( 1 ) and (2 ). The developmental aspect may be roughly formulated as follows: Sentential token constituents with referential meaning which denote (represent) some s ta tis tic a lly invariant states, aspects, etc. of the world p e trify, as it were, in our memory when occurring repeatedly in certain linguistic contexts. In other words: the sentential token constituents become petrified contexts in memory for one another; once this has happened, meaning as intra -1 ing u istic language use and types appear. This remembrance of words past, to echo Proust, substitutes for possible worlds. There is a definition aspect to this process — here we agree with ("odor. But for us, the elements of the definition are not "real" as they are for Fodor; they are just remembered. The diachronic side of our two-paradigm modelstipulates that from pragmatic contexts and pragmatically and re ferential1y established meanings arise other semantic properties - meanings -27-

in use - which again may p e trify as the so-called syntax of a 1anguage. On the diachronic side, the two-paradigm model explains why syntax and semantics of natural languages cannot be clearly separated; it explains phenomena like grammaticalization (Marou- zeau), justifies Jakobson's attempt to semantically explain the Russian case system, etc. Its developmental aspect is suppor­ ted by research in psycholinguistics and psychology. The two-pa­ radigm model may be applied to research in philosophy of science and AI. Leinfellner 197B and Leinfellner / Leinfellner 1977 and 1978 develop the philosophical and linguistic basis for such a view. In lin g u is tic s , a sim ilar view is being held also by Talmy BivOn 1979 and 1982.=** For psychology and memory research, I refer particularly to Tu lving 's (1983) work. In Tu lving 's model, episodic and semantic memory share the essential features of referential meaning and meaning as language-intrinsic use. In A I , semantic networks that have an episodic and a conceptual part conform, to a large extent, to the lin g u is tic model which integrates the two para— di gins.

8. THE EPISTEMOLOGICAL PLACE OF GENERALIZED FRAMES FROM A WITTGENSTEIN IAN POINT OF VIEW I also believe we are now able to give the concept of frame an epistemological place and also a place in Wittgensteinian philosophy. A frame is the lin g u is tic expression of something like a language game in the sense of Wittgenstein. A language game is a ling u istic stereotype as element of a "form of life " (see section 6 for the language-intrinsic aspect of "form of life"): . . . the term "language game" is meant to bring into promi­ nence the fact that the speaking of language is part of an a c tiv ity , or of a form of life .® 7. The language game, and the frame concept with i t , sit in the border area between referential meaning and meaning as language-intrinsic use; a frame is one of the links, so to speak, since it is neither totally "concrete", nor is it totally "abstract". A frame is not an individual referential linguistic -28-

expression (token) for an individual empirical occurrence with immediate referential meaning; nor is it a semantic structure in the meaning-in-use paradigm. But it mediates between them.

9. ENSUING PROBLEMS FOR CLASSICAL BEHAVIORISM, POSITIVISM, CLASSICAL LINGUISTIC , AND AI 9.1 PROBLEM (1> r THE NEED FOR SOME FORM OF MENTAL ISM Since we introduced memory as an important factor into our two-paradigm model, we need some form Df mentalism. Wittgenstein rejected traditional mentalism as we have already seen, but he did not reject thinking in language as a physical process. We might add that he considered Verstehen not as a psychological act. However, the total reduction of thinking to language is most likely empirically wrong. From empirical research we know that there exists a patterned neuronal communication, a "language of the brain", which necessarily differs from each and any known natural language, has to differ by necessity, because otherwise we are le ft with an extreme Whorfian hypothesis: that every language community has its own incomparable way of thinking which is incompatible with other ways of thinking. If we need a simile for this relationship between the silent brain language and our natural languages, we may compare the neuronal language to the machine language of a computer, and our natural languages to some of our user-friendly programming languages. Like all similes (and metaphors) so also this one, is to be taken "cum grano salis" (I suggest a large dose). This is one of the reasons why we can't as yet say that AI provides us with a complete model of human thinking. For something to be a model we must know what it is a model of — which we don't as far as neuronal communication is concerned. But AI offers somevery serviceable models of natural languages. The idea of a mental language is old. Ockham talks about the model of a conceptualistic mental language; he even specula­ tes about the properties of such a mental language: mental terms are pictorial representations of things; syntactic features of terms that have no impact on representation and truth—values are stripped away, for instance grammatical gender and synonyms. =** -29-

9.2 PROBLEM <2>« KNOWLEDGE STRUCTURES AND SEMANTIC STRUCTURES Another problem appears that is , in my eyes, rathertroublesome for A I , p a rtic u la rly knowledge-representing systems. It is the problem of how much knowledge is already petrified in semantic structures, of how much is not, and of how to distinguish these two forms of knowledge. This entire complex is also a problem for empirical lin g u is tics where it appears under the key-words of "dictionary" and "encyclopedia". And it seems to me that the problem may be tackled more easily from the diachronic stand­ point, since then one does not have to draw sharp boundaries between knowledge petrified as language structures and knowledge structures per se; but W ittgenstein's and particular A I's approa­ ches are essentially synchronic, static. We need, then, also fuzzy knowledge structures, not only fuzzy semantic structures. Before guoting Wittgenstein, I'll illustrate the dilemma with an example: The sentence (1) The fishermen went whaling but caught afish instead, looks "fishy"; intuitively, it seems to be unacceptable. But <2) The hunters went rabbitting but shot a bird instead, even though it is very similar, seems (intuitively) acceptable. The reason for this is guite simple: in the f ir s t example there is a clash between knowledge as p e trified in language and present-day knowledge: according to the semantic structures of English (and German) whales are still considered to be fish (excepting probably Moby Dick); therefore, on this view, the token "instead" is inappropriate. The sentence "The fisherman want whaling but caught a fish instead" seems to be "synonymous" wi th (3) The fishermen went fishing but caught a fish instead. No such conflict arises in (2). However, once the knowledge that whales are mammals w ill have pe trified in the semantic structure of English, the inappropriateness of "instead" in (1) will disappear. The problem is played down for instance by Goodwin / Hein;0'' but I feel it is important, since semantically petrified know­ ledge structures and newer knowledge structures which contradict one another may exist side by side. There is nothing that really forbids us to call the earth both fla t and a spheroid; but there is a semantic incompatibility relation between (solid) rubber -30-

ba l1 and f 1 at. In alchemy, a salamander may liv e , quite unnatu­ rally, in the fire; in "real life" he may live in the forest; etc. This means that knowledge structures and linguistic struc­ tures are frequently identified with one another; and that there is in the language community a learning process whereby certain semantic structures are changed to accommodate new knowledge; this does not necessarily mean that old structures disappear. Wittgenstein perceived the identification of semantic structure and knowledge structure as the "normal" case: It is only in normal cases that the use of a word is clearly prescribed; we know, are in no doubt, what to say in this or that case. The more abnormal the case, the more doubtful it becomes what we are to say. And if things were quite different from what they actually are - if there were for instance no characteristic expression of pain, of fear, of joy; if rule became exception and exception rule; or if both became phenomena of roughly equal frequency - this would make our normal lanquage games lose their point.*”0 Mere, it seems, there is a difference of how to approach this problem in A I , ling u istics, and epistemology, respectively. In A I , we can either concentrate on very general knowledge which would, presumably, coincide with ling u istic structures, provided we disregard troublesome examples like the whalefish and the rising of the sun, which are, anyway, perhaps too specific to be included. This general knowledge would be of the sort of which Wittgenstein says that one learns the color red while learning a language or, aa in the passage just quoted, where the knowledge of certain feelings is treated as knowledqe petrified in lan­ guage. Or we concentrate on very specific knowledge, for instance knowledge of how one proceeds in a restaurant etc. - as one AI researcher said: "With spoon in hand, this must be the eating frame". In linguistics, we ordinarily disregard knowledqe structures which are not yet petrifie d, although it has been attempted to step beyond those boundaries. The eptstemological aspect is best dealt with from the diachronic standpoint of the two-paradiqm model. It is intere­ sting that theories develop alonq the same lines as languages.*1 - 31-

10. PROJECTION OF LANGUAGE INTO THE WORLD Ancwfier problem is epi stemol ogi cal 1 y relevant. It p arallels, in a sense, the problem of theory-1 adenness in philosophy of science. How much does language, or previous theories in the sciences, influence our view of the world, and what is it that we really represent? Since the meeting where th is paper was presented also dealt with the topic of re sp o n sibility I want to point out something here which is not obvious at once when one thinks of AI and respo n sibility, even though it is more obvious when looking at language and re spon sibility. In our epistemological diagram, Fig. (I) in the appendix, we see an edge, R », leading from 1anguage to the empirical world, and th is means, of course, the justifiable or not-justifiable projection of linguistic struc­ tures into structures of the world. It is here were the so-called critique of language - a term which Mauthner introduced into philosophical usage with his three volumes of Beitraae zu einer K ritik der Sprar.he ('Contributions towards a c ritiq u e of language') gets its leverage. Both Mauthner and the Wittgenstein of Philosophical i nvestigations saw a critiq u e of language as the main goal of phi 1o s o p h y . W e may ask ourselves whether critique of language and the resulting respon sibility are relevant also for research in AI. In a very general way they are. I ’m pretty sure that AI has sinned by projecting language into the world or mind (R« and Rfc of Fig. (1 ), appendix) just as much as, for instance, , when he took his categories from the Greek language and presentedthem as ontological categories. On the other hand, we do see attempts to emancipate Al-structures from language structures. Forinstance, in semantic networks one normally represents both nouns and verbs as nodes and not nouns as nodes and verbs as edges as is suggested by a Carnapian logical analysis of n-place predicates in natural languages. This is a decision which allows us to get around the lndoeuropean sentence structure of subject-predicate. We must bear in mind that there are languages where it re a lly doesn't make any sense to discriminate between subject and predicate, and there are philosophical analyses to support the just mentioned Al-view , for instance R ussell's. If we would not analyze in such a way we would again fall prey to all sorts of untenable hypotheses, for -32-

instance, the (strong) Whorfian hypothesis: there would be AI structures attempting to model memory which would show a sub- ject-predicate structure, and others which wouldn’t, depending on which language we have just taken as the basis. And we could be justly accused of ethnocentrism if we would present memory structures which are taken from any specific language. Finally, responsibility rests also on the guestion of rules. Me w ill make a few more e xp licit remarks on rules below. Who made the rules? Did they originate from language use, customs, and forms of lif e , or evolutionary processes, or are they introduced by "creators" of languages, by grammarians, scribes, and translators?

11. PROBLEMS OF OUR MODEL FOR All FUZZ INESS OF MEANINGS AND EMERGING RULES The two problems I'm going to discuss concern features of natural languages that should be incorporated into AI research as far as language is concerned, and aren t, or perhaps can't be, incorporated. Wittgenstein extensively discusses these two topics; but he provides no method of dealing with them.

11.1 PROBLEM (1): FUZZINESS OF MEANING It should be clear by now that meanings as 1anguage-intrlnsic use must be fuzzy. Wittgenstein introduces fuzziness with his notion of family resemblances The tokens of one lexeme which occur in different 1anguage games have meanings which exhibit family resemblance; and this family resemblance, like all similarity relations, is partim-transitive (non-transitive) only. However, in AI language understanding systems one generally proceeds as if semantic fuzziness did not exist. When we subordinate some lexeme under some node in a semantic network we act as if this was the only possible c 1assification for this particular item. In AI, the only exception I know of is Freksa's work. In lin g u is tics , one works here with fuzzy sets or polythe- tic classes.*3 -3 3-

11.2 PROBLEM (2)i EMERGING RULES This fuzziness of meaning has a bearing on semantic rules which we may extend to lin g u is tic rules in general. Synchronical1y , Wittgenstein solved the problem by assuming that rules do not govern a language game in it s entirety; there are "holes", so to speak, where rules don't apply.*"» This resembles the lin g u istic notion of statistically applied rules. Wittgenstein also tried to formulate the rule problem from a more diachronic standpoint. In Philosophical grammar, he went as far as to say that grammar is a p rio ri and that it needs no empirical ju s tific a tio n (see section 2 ); rules, then, exist diachronical1y a p rio ri. This is o.k. for formal systems, but not in agreement with the way rules develop in actual human linguistic practice. Therefore, in Philosophical investigations Wittgenstein retracted the view that rules are a priori. We often "make up the rules as we go along". A!S And: Grammar does not te ll us how language must be construc­ ted in order to fulfil its purpose, in order to have such-and-such an effect on human beings. It only describes and in no way explains the use of signs. Wittgenstein starts from the rule paradox: rules govern seman­ tic s , but in order to be understood themselves, other semantic rules have to exist already.*'5’ There is an infinite regress of rules. However, once we correlate lin g u is tic a c tiv itie s and lin g u istic customs which govern them, those correlations that are more frequent become the rules or object of ru le s .6" Lanquaqe use and rules develop simultaneously. That is, in our terms, more frequent uses enter into rules. In linguistics, we may capture this process by ordering statistically different instan­ ces of a certain rule along a time axis. Me may start with the rule when it has zero probability at time t x and then continue with the same rule but ris in g probabi1 ities along the time axis. Only then when the p roba bilities are f a irly high, people w ill see the emergence of rules. This is a very important feature for Wittgenstein’s philoso­ phy and for lin g u is tic s , since i t has to do with actual language behavior. Whether it is possible or theoretically fruitful to imitate the emergence of rules in AI research remains to be seen. -34-

12. EVALUATION OF MEANING IN USE FOR AI; THE CHESS SIMILE I believe that Mittgensteinian meaning in use, despite its seeming advantage for A I , leads to a major problem. We have seen that Wittgenstein did not reject a physicalistic mental ism where thinking is language, but like classical structuralism of the Bloomfield type he identified language more or less with that aspect of language which we hear, with its outside expression or even with its traces, writing. In AI, for obvious reasons, one does not look at human physical mental communication, but on language as it appears on the outside, in accordance with Wittgenstein's later philosophy. In this respect, language research in AI has developed along the same lines as Wittgen­ ste in's later philosophy; and the old mistakes were repeated over and over. The identification of language with its phonemic or qraphe- mic structure is most unfortunate. (i) We act here as 1 f the tokens of phonemic or graphemic shapes are identical with the meanings, since the meaning of a term as intral m g u is tic use is defined through other, contextual terms which are "the (narrowed down) language" when Wittgenstein speaks of "use in the lan­ guage".*’ This identification is not restricted to philosophy and linguistics, but may also be found in psychology and related disciplines. ’’0 If this would be true, intelligence and artifi­ cial intelligence as an imitation of natural intelligence would lie in the traces of linguistic behavior rather than in behavior and its neurophysiological foundation. ’ 1

stein's later views of language, and particularly for meaning as 1 anguage-intrinsic usejT3 but it is the wrong simile for language as it is, which, according to the Wittgenstein of Phi 1osophical investigations, is to be the object of philosophy. Moreover, the chess-game simile and the explication of meaning as language-in­ trinsic use clash with Wittgenstein's concomitant view that neither grammar nor language is a calculus

it is", that is, parole. While one "understands" - "understand" here in the sense of 'can imitate, -follow the procedures' (or, in semantic All ‘has semantically parsed') - an algorithm on paper simply because it is a two-dimensional geometric object - therefrom the importance of rules concerning "left of", "right of", "bottom", "top”, of rules that forbid the occurrence of the same token of a graphemic shape at both sides of a particular sign that has a particular graphemic shape - one "understands" (again in quotation marks) a chess game simply because it is a three-dimensional geometric object. The rules that govern our manipulations of the chessmen (= words, terms and contextual words, terms as language-intrinsic meanings) are in re a lity not semantic rules but rules that concern our perception of the situation on the chessboard, the geometric position of the chessmen, etc. hutatis mutandis, these considerations can be applied to transformational grammar, Montague semantics, and meanings as 1anguage—in trin s ic use. For instance, a grammar as an algorithm needs rules about "top", "bottom", "left", "right", e t c ., and so does Montague semantics. The chessmen in the sim ile, then, are both words and meanings rolled in one. Both de Saussur'e and Wittgenstein conceive of the chessmen and what they stand for as types, rather than tokens, since they are interpreted functionally; but “language as it is" contains no types. Alas, it seems to me that semantic networks in AI fare no better than Wittgenstein's meanings as 1anguage-intrinsic use: meaning as the function that enables us to comprehend texts is s t i ll in the eye of the beholder, the language user.00 There is a way out? but it would sever the ties between the semantics of natural languages and semantic AI: We retreat to an extreme extension of the position expressed by Wittgenstein in the passage quoted in section 4.1.3: We may construct languages, sentences, and texts which do have sense or meaning as lan­ guage-intrinsic use, but which cannot be understood by a human being, in the common-sense interpretation of "understand".

13. RESULT We have shown the advantages of a Wittgensteinian reduction of thought to language and other of his discussions for A I , and it is probably these advantages which have induced certain resear- -37-

chera in AI, for instance Wilks, to make the Wittgenstein of Philosophical investigations the patron saint of (semantic) Al-research - almost. However, AI-researchers (and others, for instance linguists and psychologists) have often focussed on rather doubtful aspects of Wittgenstein's laterphilosophy and have neglected other aspects that might be more important. For instance, instead of discussing - in agreement with Wittgen­ s tein's view that everything mental is physical - neuronal communication in combination with lin g u is tic behavior, they have focussed on the reduction thesis, with emphasis on the graphemic traces of language. We may now make a lis t of what we should learn from Wittgen— steinian philosophy and Linguistic (Analytic) Philosophy in general, and what we should reject. First the cons or "rejects": thought is to be reduced to language: semantically, language resembles a game of chess: there is no referential meaning. Now the pros: logic is not a (or the) tool for the analysis of natural language(s); language and qrammar are not calcu li; meanings are fuzzy; rules emerge: the mental is physical.

Department of Linguistics

NOTES

1Zemanek 1978:114, 117, 120.

=C f., e. g ., Dresher / Hornstein 1976.

3 i use "term" (or "word") unspecified either for a type tlexeme) or a token; if it becomes necessary to distinguish between types and tokens this will be painted out specifically. On my view, meaning and sense are not to be interpreted as en tities; instead, they are to be introduced functionally, for instance: A term "x" has empirico-referenti al meaning if its bearer can be pointed out, that is, if we can form the tr ip le , where x is the empirical referential object, "x " the referential term, and R a meaning relation.

■•PI: section 40.

aPl:section 43.

*Pl:section 43. -38-

TPI:section 44.

“ TLP:3 .3j cf. also TLP:3.326; NB:23 Oct. 1914, 20 June 1915, 22 June 1915, 11 September 1916.

^TLP« 3.328.

*°For an illu s tra tio n of how logic might "improve" or be “better" than ordinary language, see the discussion of the of i st in ordinary language and the demand that there should be a logical grammar which excludes such at TLP:3.323-3.325.

»‘TLPi4.002.

,aPG:sections 71, 133.

,3Hermodson 197B:10.

l *Itkonen 1965:65.

TLP:4.024. Frege maintained that the meaninq of a sentence is its truth-value.

*-TLP:4.024.

l-TC f., however, the a rtic le "V e rifikation" by Lenzen:

In der Tat ist die Frage, ob eine Aussaqe S ven fizierbar is t, allein von der logischen Struktur von S abhàngig - nàmlich genau dann, wenn S die Gestalt eines singularen Satzes Oder eines Existenzsatzes hat (Lenzen 1980:672).

‘ ■For this and related questions, see Juhos 1959-1960.

‘ ’ The term is taken from Wilks 1975.

aoF i l l more 1975»137.

aiPG:section 140.

” PG 1978:204.

■*PI i sect i on 81.

a*PI: section 124.

aaP I: section 122.

3*PI«section 501.

*TPIisections 339-344.

a*Plisection 307. Wittgenstein's accounts of how to learn a language are pretty much in the behavioristic vein; c f ., for instance, PI:sections 29, 208, 495.

a,PI:section 308. -39-

3°Qbermeier 19B3s339.

31 PIisect1 on 329.

3aPIs section 395.

33PI:section 396.

3<*TLP:4.2.

3“ A sim ilar view is expressed by Dummett 1971:38f.

3*BBB 1965«4.

*^For such pragmatic developments of Wittgenstein's philosophy see Bivon 1982 and Rommetveit 1987; cf. also some of the articles in Haller 19B1.

3-BBB 1965s5.

3"TLPs4.002| cf. also Hunter 1971a:282ff; Hunter 1967.

4°PIssection 381.

■*l PI s sec t i on 3B4j cf. also PGssections 105, 106.

PIssection 371.

*3PI:section 373.

•“•Pis section 290.

•"Hunter 197 lb :390.

•‘ PIssections 30, 32. ■*^Cf., e. g. , PIssection 27.

*"PI:section 308.

♦’ For this view, cf. Givön 1982:108ff. aoPI:section 206.

" ‘PIssection 19.

“aPI:section 23.

°3Hunter 1971a:286ff.

3*There is an empirical linguistic universal called "displace­ ment" pertaining to ); see Hockett 1963:11.

““PIssection 26; cf. also PIssection 264. a*First expressed in Leinfellner / Leinfellner 1977; E. L e in fe ll- ner 1978; Leinfellner / Leinfellner 1978; Givön 1979; Glvön 1982. -40-

a^PI:section 23; c-f. Marras 1987.

“"Ockham, Summa logicae 1:1, 3.

“’’’Goodwin / Hein 1982.

*0PIisectiDn 142; c-f. also Plisection 141.

•‘For an extensive discussion of this topic see Leinfellner / Leirffellner 197B:passim.

*aTLP:4.0031; for Mauthner see Leinfellner-Rupertsberqer (in press), section 4.

*3Freksa 1982a, b; Zadeh 1978« A1trnann 1972.

*4F'l:section B4.

•aPI:section B3.

••PI:section 496.

*TP1: section 201.

**PI: sections 142-145, 198, 199.

•’ See Anscombe 1981 for this problem.

7°For a discussion in psychology, see, e. g ., JuliA 1983. ylCf. Julia 1983:X Iff.

TaPIisecti on 199.

^3PIisections 31, 200| PG:section 140.

, '*de Saussure 1959: 19.

TaPI:sectlon 199.

‘7'*PItslip (b) under section 151.

7Tde Saussure 1959:22f.

’’■PI: sect i on 31. r’ Cf. Goodwin / Hein 1982i275.

■°Dennett and Searle have used the thought experiments of the spaceship and of the Chinese learning game, respectively, to emphasize this point; see also Leinfellner 1969:229 for a less fancy but equally valid presentation of the same criticism which may be captured as follows: If the stone of Rosette and other hieroglyhphic documents would not have been connected to a known language, the meaning of these documents would be forever closed to us. Formthis point, the views expressed in Obermeier 1983 seem to be overly optimistic. -4 1-

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APPENDIX

Generalized epistemologlca1 relations

Fig. (1)

Note: The following classifica tio n is neither meant to be exhaustive; nor is i t claimed that the assignment of a certain research area to a certain relation is absolute and "monothetic" (as opposed to "polyth etic").

Rt: empiristic epistemology; empirical structures are represented as linguistic structures; passive subject: Wittgenstein; logic mirrors the world.

Rz: psychology/physiology; psychological-physiological representations of parts of the external world for instance as brain—wave patterns; Ockham's mental 1anguage.

R3: biology of language; sociolinguistics etc.; pragmatics of language.

Ra + R3: evolutionary epistemology.

R«: projection of language structures into the world; lin g u is tic (and lo g ica l) ap n o n s m ; Whorf; Sapir; Mauthner; Hamann; the later Wittgenstein.

extreme subjective apriorism; Fichte's idealism; world is only a concept, a representation, or sensations (Duhem). -46-

♦ ft»: moderate apriorism; Kant s idealism; Poincares conven­ tional 1 sm.

Cf . ft«! projection of language structures into sub­ je c t's inte llect etc.; logic of languaqe as theory of thinkinq (psychologism); pragmatics.

+ ft't: These two relations, taken together, may be seen as an identity relation; identification of thought and languaqe; reduction of thought to language (Mauthner, later Wittgenstein); this may result in Fig. (2).

In CJgden-fti char d ' s famous semi otic triangle the edges are not directed; therefore, we can’t really identify them in our Fig. (1). The triangle s (network’s) nodes however correspond to our nodes.

structure of spoken and w r11ten 1anguage ( langue)

Fig. (2) - 4 7 -

On human beings, computers, and representational-computational versus

hermeneutic-dialogical approaches to human cognition and

communication.

Ragnar Rommetveit

University of Oslo

1. Introduction: On "anthropological thought" provoked by novel

information technology in a bimodal world of scholars.

What is Man?

This is in Martin Buber's opinion Immanuel Kant's basic philosophical question and the point of origin for what Buber calls serious

"anthropological thought". Such thought, he argues, is suppressed during periods of "normal", tradition-bound research and "epocks of habitation"

in philosophy. Its core question - "What is Man?" - may at such times be considered futile or even nonsensical because some preliminary and partial answer to it is embedded in the prevailing mode of thought and tacitly taken for granted. Thus, Man becomes a comprehensible species along with other species in Aristotle's self-contained and geocentric world of "things", whereas she/he is made "the horizon and the dividing line of spiritual and physical nature" in the philosophy of Aquinas and in Dante's

La Divina Commedia. Complacency with such solutions do not last long, however, and various ramifications of the Kantian riddle are pursued in novel ways by genuinely "homeless" thinkers such as Augustine, Pascal,

Kierkegaard and Wittgenstein.

Modern scholars trying to gain some insight into the riddles of - 46- human language and thought are - qua scientists - doomed to deal with only restricted aspects of Man. We have - to quote Buber (1962, p. 688) -

"...to divide Man into departments which can be treated singly in a less powerful, less binding way". But one recent and particularly intriguing ramification of the Kantian problem has to do with scientific- technological progress within highly specialized fields and, more specifically: with the emergence of modern information technology.

Borrowing a metaphor from Wittgenstein (1968, p. 212) we may venture to claim that novel aspects of Man "dawn" these days in comparisons between Man and Computer.

The Computer may within a broad cultural-evolutionary perspective be conceived of as signalling a fascinating novel stage in a long-term process of externalization of human knowledge (See Sinding-Larsen, this volume). Its most unique and intriguing feature - at least in comparison to written language - is the possibility it provides for externalization of human reasoning og knowledge processes. This, in turn, provides for a novel stance for scholars engaged in "anthropological thought".

The English word HUMAN has by modern representatives of an

"autonomous" sematics been explicated in terms of zoological taxonomy and conceived of as a semantic universal or "marker" in systematic mapping of the meanings of a whole set of related words (Katz and Fodor,

1963). This seems indeed plausible in view of the Aristotelian tradition and, as Habermas (1970, p. 137) remarks, "...after three hundred years of science and criticism of religion...". What distinguishes Man from Beast in view of biologically founded , moreover, is above all her/his brain capacity, intelligence, and reason. What happens when the Computer enters the scene of "anthropological thought", however, is that Man's reasoning is gauged against the performance of her/his own invention. The result is - at least superficially and at first glance - - 49-

provoking: We are apparently inferior to our own invention, the

Computer, with respect to intellectual capacities such as, e.g., "memory"

and complex algorithmic "reasoning".

The novel information technology also provides an opportunity for

computer simulation of human cognition. This particular development

may be interpreted as a scientific-technological extension of an analytic-

rationalist philosophical tradition concerned with formal features of

"pure" and "de-contextualized" human reason (see Winograd, 1985).

Questions concerning in what sense and to what extent human knowlede

and thought are "machine representable" (see Nygaard, this volume) can

therefore hardly be disentangled from perennial epistemological and

ontological issues. Current polarization of opinions about computer

silumation of cognition and application of computer terminology in

psychological accounts of human reasoning, it seems, tends to corroborate

and sustain the schizm between natural scientific explanation and

humanistic understanding of Man in Western philosophy.

The bimodal world of scholars described by C.P. Snow in his book

The Two Cultures (Snow, 1959) is indeed hardly anywhere more cogently

revealed than in current competing paradigms for studies of human

cognition and communication. Man is a biological organism, part of n a tu re, and as such subject to natural scientific explanation; yet in certain significant respects comprehensible only from within a taken-for- granted, subjectively meaningful and partially shared world. And real integration of natural scientific explanation and humanistic understanding, it is argued, can hardly ever be attained.

Donald Hebb, the outstanding cognitive psychologist and one of the precursors of modern cognitive science, thus conceives of science and literature as two entirely different ways of knowing human beings. The very notion of a simultaneously scientific and humanistic psychology is hence in his opinion absurd (Hebb, 197<>, p. 74): - 5 0 -

A science imposes limits on itself and makes its progress

by attacking only those problems that it is fitted to

attack by existing knowledge and methods...... The o th er

way of knowing about human beings is the intuitive

artistic insight of the poet, novelist, historian, dramatist

and biographer. This alternative to psychology is a valid

and deeply penetrating source of light on man, going

directly to the heart of the matter.

The prospect of a synthesis of humanistic and natural scientific knowledge advocated by philosophers of the Frankfurt school (see Apel,

1968; and Habermas, 1968) has thus been ignored or rejected as an entirely futile venture, not only by the majority of cognitive

psychologists, but also by mainstream linguistics. The issue of potentially unacknowledged hermeneutic components within transformational- generative linguistics, it appears, has indeed been resolved once and for ever by a simple and frank declaration of dependence upon "the intuition of the native speaker-listener." Since the linguist is supposed to be in possession of this intuition himself, he can simply take it for granted as a source of revelation and forget about it. After having put it in brackets together with pragmatics and other intellectually disturbing entities, he is free to engage in enquiries into meaning and mind of a truly natural scientific nature. This, in brief, seems to be 's position.

He maintains (Chomsky, 1980, p. 12 and p. U5):

....no new problems of principle arise in the study of

languages that are not familiar in the "hard" sciences....

1 see no reason not to take our theories to be tentatively

true at the level of description at which we are

working,....hoping ultimately to find neural and

hirv~h»»mi/-al with th*» nronerties expressed in - 5 1 -

Donald Hebb's and Noam Chomsky's views on what constitutes scientific knowledge are firmly rooted in traditional analytic-rationalist philosophy. While praising and trusting the intuitions of the creative artist and the native speaker-listener they both ardently oppose attempts at humanistic infiltration of their scientific trades.

What are the interesting alternatives to such orthodox analytic- rationalist positions within currect philosophy of science? To what extent is the novel, hybrid and rapidly expanding, movement labeled "cognitive science" concerned with the gulf between the analytic-rationalist and hermeneutic-dialectic philosophical traditions? These are some of the issues I want to discuss very briefly in order to explore in some more detail current accounts of everyday human discourse qua transmission of propositional content based upon models of individual "mental representations" of meaning.

Such accounts, I shall argue, are symptomatic of a pervasive lack of concern with human intersubjectivity and inherently collective and social- interactional aspects of language and mind within mainstream cognitive psychology. 1 shall hence explore a prospective hermeneutic-dialogical approach to human cognition and communication as a constructive alternative to computational-representational models and, finally, briefly c o m m e n t upon machine representation of human knowledge and use of information technology from a hermeneutic-dialogical point of view.

2. On mainstream American psycholinguistics, cognitive psychology

and cognitive science.

The Harvard-MIT brand of psycholinguistics came into being as the love child of early transformational-generative linguistics and American individual (as opposed to social) cognitive psychology. In its early phase, experimental research aimed primarily at assessing the "psychological - 5 2 - reality" of stipulated linguistic structures. Mainstream American psycholinguistics was thus initially characterized by a programmatic disregard of context and considerable faith in experiments on isolated sentences as an avenue to insight into linguistic competence. The predicament of the subject in such experiments resembles that of the

Virgin Mary when she received words from God about the miracle that was going to happen and, being unable to understand what was meant,

"preserved the words in her heart". Belief in such rather peculiar experimental situations as optimal conditions for assessing the psychological reality of linguistic structures, however, was induced by comparing sentences detached from meaningful human communication to physical bodies falling in vacuo. And Chomsky was hailed as the Newton of a novel science of language and mind (Fodor and Garrett, 1966).

Hermeneutic scholars and philosophers, from Schleiermacher and

Dilthey to Gadamer and Ricoeur, have been profoundly concerned with the peculiar subject-subject relation in assessment of linguistically mediated meaning as opposed to the subject-object relation in natural scientific search for causal , with the embeddedness of such meaning in contexts of human communication, and with inherently social and collective features of language. These are precisely the issues that were consistently evaded and/or deliberately put into brackets in mainstream

American lingustics and psycholinguistics more than twenty years ago.

However, Chomsky had then not yet explicitly expressed his hope of ultimate verification of his theories about deep linguistic structures in future neurological and biological research. Most linguists and psychologists who adopted his formally impressive and abstract terminology were probably unaware of such underlying natural scientific aspirations, and some of them were in their own research indeed seriously concerned with problems of lineuistic meaning as traditionally, yet very -5 3 -

loosely, defined. One such follower was George Lakoff, a linguist whose

genuinely hermeneutic interests and talents are revealed in studies of

metaphors we live by (see Lakoff and Johnson, 1980). He reports in a

fairly recent debate with his former master on the research of a whole

group of first-generation transformational-generative linguists as follows

(Lakoff, 1980, p. 23): "What we found was that meaning and use

(communicative function) effected virtually every rule of syntax".

Chomsky's reply to Lakoff, however, is that (Chomsky, 1980, p. 46) "...his remarks betray very serious misunderstanding of the work he is discussing."

Serious misunderstanding between master and followers within transformational-generative linguistics are in my opinion what we should expect, given Chomsky's initial ambivalent and evasive attitude toward problems of meaning and use as traditionally pursued within linguistics and theory of literature. And the most visible change within the entire fields of linguistics and psycholinguistics during the last two decades is a definite shift of interest in the direction of pragmatic and jointly semantic-pragmatic problems.

Such a shift implies theoretical expansion, but not necessarily paradigmatic change. Meaning-in-use within actual contexts of human communication, one may for instance argue, could in principle be adequately conceptualized by adding auxiliary hypotheses about inferential processes and social interaction to pre-established models of individual cognition and linguistic competence. This, it seems to me, has been the strategy adopted within most variants of theory, text linguistics, and pragmatically oriented psycholinguistics.

The kind of genuinely social-interactional and collective features of language hermeneutic-dialectic philosophers wonder about can hardly be assessed at all by such a strategy. Mainstream cognitive psychology and psycholinguistics remain therefore in my view still imprisoned within a

thoroughly Cartesian outlook on language and mind, despite definite signs of an "anti-Cartesian revolution" in currect epistemological debate

(Rorty, 1980). Truth conditions on existential claims in linguistics, for

instance, are stated in terms of individual psychological states and

processes, and theories of sentence comprehension are still expressed in terms of "...functions from sentences onto internal representations"

(Fodor, Garrett, Walker and Parkes, 1980, p. 278).

Current cognitive science may perhaps most appropriately be described as an interdisciplinary movement. It provides a meeting place

for scholars from a whole range of disciplines such as computer science and artificial intelligence, linguistics, cognitive psychology, neuro­ science, and philosophy. The leaders of the movement seem to aspire toward a unified science of cognition and argue that human intelligence and potential computer intelligence may be very much alike in crucial aspects, at least when viewed and compared from the proper philosophical position (Andler, 1986, p. 1).

The image of Man provided by mainstream cognitive science so far is in my view essentially that of an asocial, but highly complex computational and information processing device. The very comparison between Man and computer, however, has brought to the foreground the subject-to-subject issue of hermeneutics in a novel and interesting way.

One very simple but important implication of the subject-to-subject relation or intersubjectivity of human meaning is that a person whose

behaviour we have predicted can act contrary to our - otherwise: perfectly correct - prediction simply because we inform her or him about

it. This may be considered, as Herbert Feigl (1953, p. 418) once wrote, a

"notorius difficulty" we hardly ever can evade in social scientific research. But if we look at the other side of the coin, we find the very - 5 5 -

crux of the critical-emancipatory of Apel and Habermas.

The moral imperative of humanistic-emancipatory psychology may thus be

expressed in an anti-positivist eleventh commandment: "Thou shalt not

seek such knowlege about thine brother that cannot be transformed into

self-insight in him".

Such entities as my prediction about a fellow human being's future

behaviour or m^ (let us assume: correct) belief about what is worrying him

belong clearly in the subject-to-subject or intersubjective domain of

meaning, yet constitute according to Pylyshyn (1980) indeed the proper

domain for cognitive science. Adopting a "computational view" of human

cognition, he conceives of mental activity as sequences of

transformations of internal representational states. What makes it

possible to view computation and cognition as processes of fundamentally

the same type, he argues, is the fact that (Pylyshyn, 1980, p. 113) "...both

are physically realized and both are governed by rules and

representations". Reaction time is accordingly often used in empirical

research on human cognitive processes as an index of what might be called "computational complexity" and, under certain conditions,

interpreted as corresponding to number of transformations or operations carried out.

Such a computational view of cognition entails what Fodor (1980, p.

63) refers to as a "representational theory of mind" and has indeed, as

Pylyshyn himself remarks, for several decades been part and parcel of individual information processing models within mainstream American cognitive psychology, including psycholinguistics. The most interesting novel feature of Pylyshyn's conceptualization of cognition as computation, however, is in my view the distinction he proposes between "cognitively penetrable functions" on the one hand and a "functional architecture" on th e o th er. - 5 6 -

Cognitive functions are penetrable, he maintains, if they can be influenced by such purely cognitive factors as goals, beliefs, tacit knowledge, and so on. They correspond to what Andler calls "personal processes", i.e., processes which concern and have significance for the human being as a whole. And (Andler, 1986, p. 5):

— though they need not be consciously performed, they

can be described and identified by the agent (as such, not

qua scientist), and in many cases actually brought to

consciousness while they are being performed.

The functional architecture, on the other hand, is by Pylyshyn defined as that part of the system which is cognitively impenetrable, fixed in certain respects, and presumedly universal to the species. When conceived of in dynamic terms, it may be described as a pattern of processes instantiating causal physical or biological laws as opposed to human agency and reason. Such processes are by Andler called

"subpersonal processes". They elude our attempts (as simple human agents) at recognition, monitoring, and instruction and are (Andler, 1986, p. 6) "...as radically inaccessible to inspection and volition as the release of hormones or the constriction of capillaries".

3. On critique of mainstream models of human cognition and

communication and current search for alternative paradigms.

Pylyshyn conceives of cognitive science as the study of cognitively penetrable functions and therefore, one might conjecture, as a potentially emancipatory science in the sense of Apel and Habermas. Such a definition of the field implies that Chomsky's theories, for instance, in view of the kind of ultimate validation of them he hopes for in the far future, must be considered cleverly disguised biology rather than cognitive science. It also implies that true cognitive scientists, in order - 5 7 -

to assess and describe the mental representations of minds other than

their own, have to be seriously concerned with issues of human

subjectivity and intersubjectivity. As Cummins (1983) has phrased it,

other minds are "inferentially characterizable" only under the attribution

of meaning which they themselves stipulate.

But how can the cognitive scientist as an observer of a fellow human

being gain access to such meaning? And if he manages in some way, what

is the relation between such intersubjectively assessed meaning and the

Cartesian mental representations he stipulates? Does it make sense at all

to assume - as most cognitive psychologists and many psycholinguists

seem to do - that we have "internal representations" of objects and even

of word meanings?

Such questions have for a long time been pursued by hermeneutic-

dialectic philosophers. They are currently raised and reformulated by

philosophers with an analytic-rationalist background as well, for instance

by Richard Rorty (1980) in his general critique of Cartesian epistemology

and by Barwise and Perry (1983) in their attempt at developing a formal

semantics focussing on social situations rather than on reference and

sense. And similar, but again slightly differently formulated questions, are pursued by cognitive scientists revolting against the representational- computational approach to human cognition. One of them is Terry

Winograd, who once impressed his colleagues within artificial intelligence research by his computer simulation of language comprehension. He has since then become more and more discontent with the representational theory of mind and increasingly worried by what he describes as "the error of reification of cognitive representations" in cognitive science (see

Winograd, 1980, pp. 226-227).

Pylyshyn, himself a mainstream cognitive scientist, has to admit that (Pylyshyn, 1980, p. 159) - 5 8 -

— the whole issue of meaning and reference remains

largely shrowded in mystery, yet that part of the

problem of representation infects all approaches to

m representation...

What is at stake is thus nothing less than the philosophical foundation of mainstream cognitivism. Pleas for paradigmatic change, moreover, grow out of discontent with dubious philosophical assumptions embedded in representational-computational models such as, for instance, a conceptualization of intersubjectively accessible meaning in terms internalized Fregean sense.

Frege was a mathématicien and, as such, concerned with language as an abstract system and legitimately engaged in normative idealization of that system. He located sense, not in the individual mind, but in a stipulated Platonic domain between the outer world of referents and human organisms. This is particularly evident in the passage of his work

"Funktion, Begriff, Bedeutung11 where he invites us to conceive of sense as analogous to the objective image (of the moon) inside a telescope, not to the retinal image inside the human observer (see Frege, 1969, p. **5).

Frege was thus not at all concerned with natural language in contexts of human communication, but with idealized or "eternal" sentences. These are sentences which are supposed to make the same claim about the world no matter who utters them or when and hence to be fully understood even in Virgin Mary type of situations.

Assessment of linguistically mediated meaning with no concern for contexts of use, however, appears to be a futile venture once we descend from domains of Platonic idealization to verbal communication among mortal human beings. Frege's idealized world can be exhaustively analysed in terms of context-free data or atomic facts and captured in a semantically closed language. What is essentially wrong with much - 5 9 -

current artificial intelligence research is according to Dreyfus (1979, p.

205) an underlying assumption that our real world can be exhaustively

analysed in such a way. Critical comments by innovators within formal

semantics, such as Barwise and Perry, and rebellious cognitive scientists

like Dreyfus and Winograd thus seem to converge. Their common target

seems to be the myth Wittgenstein so ardently attacked, i.e., "...nothing

less than the illusion...that language has foundations in simple concepts"

(Baker and Hacker, 1980, p. 163).

Scholars currently engaged in search of a novel paradigm are

seriously concerned with the perspectivity inherent in human cognition,

the dependency of linguistically mediated meaning on tacitly taken-for- granted background conditions, and its embeddedness in social interaction and goal-oriented collective activity. Human cognition and communication, it is argued, are situated (Barwise and Perry), concerned

(Dreyfus), and immersed in human projects and social commitments

(Winograd and Flores, 1986). Such presumedly uniquely human features are in part explicated by Dreyfus in terms of "what computers can't do".

Hermeneutic philosophers such a Gadamer (1975) and Ricoeur (1984)

insist that context-dependency is an inherent and essential feature of human - including linguistically mediated - meaning. It is interesting to notice that the same position is now explicitly stated and defended by philosophers from the analytic-rationalist camp as well. What is meant and made known by what is said is thus within the formal semantics developed by Barwise and Perry necessarily contingent upon the situation and upon certain background conditions. And , even though in his own contributions to speech act theory a follower of Frege, has to admit that there exists hardly such a thing as a completely context-free literal meaning of a word. What is conveyed by the English preposition

ON in the expression THE CAT IS ON THE MAT, he maintains, can be - 6 0 - unequivocally assessed only as long as "the situations we are in" allows for location of objects within a gravitational field (see Searle, 1978; and

Rommetveit, 1988).

Such a pervasive background condition is hardly acknowledged at all until it breaks down because we, for instance, suddenly find ourselves in a situation talking about cats in touch with mats floating around in various spatial constellations in outer space. But the same seems to hold true also for far less pervasive background conditions, and even for the constantly changing conditions of conversation in everyday life. They constitute part of a tacitly taken-for-granted "Lebenswelt" and "remain behind us" ("bleiben uns im Rücken"), as Habermas (1981, I, p. UUS) maintains, yet determine in a crucial way what is being meant by what is said. Our only possible scientific avenue to linguistically mediated meaning is accordingly participant observation. We have to view others as rational agents similar to ourselves and, if necessary, even "warp the evidence to fit this frame", as Donald Davidson (1980, p. 239) puts it.

On mental representations versus meaning potensials of words.

This is, in my view, the basic hermeneutic predicament of semantics according to Wittgenstein (1968), Gadamer and Ricoeur: We are as participants in language as a "form of life" ourselves imprisoned within human meaning, yet as researchers capable to reflect upon and systematically investigate our very embeddedness. And let us now explore potential implications of such a basic and try to indicate how a hermeneutically founded approach to human cognition and communication differs from approaches based upon a representational- computational view.

Consider, first, the issue of meaning, mental representations, and - 6 1 - attempts at exploring this issue is Mr. Smith, a fireman living in a suburb called Scarsdale and who early a Saturday morning is out in his garden mowing the lawn (Rommetveit, 1980). His wife, being inside the house, then receives a telehone call from a friend of hers. During their chat, her friend says: "That lazy husband of yours, is he still in bed?" And Mrs.

Smith answers: "No,..."

Mr Smith is WORKING this morning, he is mowing the lawn.

A short time afterwards Mrs. Smith receives another call, and this time from Mr. Jones, who often goes fishing with her husband. He asks:

"Is your husband working?" And Mrs. Smith answers: "No,..."

Mr. Smith is NOT WORKING this morning, he is mowing the lawn.

These episodes illustrate perfectly normal and appropriate uses of the word WORKING. This has indeed been firmly empirically corroborated as far as its closest Norwegian equivalent (ARBEIDER) is concerned: Norwegians consider Mrs. Smith's two answers entirely natural, appropriate, and true. But how can such spontaneous yet perfectly proper use of language be described and explained in representational-computational terms?

- Not at all, I shall claim, if we seek refuge in simple truth conditional semantics based upon Frege's theory of the relationship between reference and sense. A coherent sense or internal of the meaning of WORKING which makes it both unequivocally true and unequivocally false when referring to Mr. Smith's lawn-mowing is a logcial contradiction. The crux of the computational view is that contextually appropriate meaning is "computed" from a base of purely linguistically mediated ("direct" or "literal") meaning.

"Utterance meaning" is hence in Searle's computational version of speech act theory assumed to be arrived at "through literal sentence meaning"

(see Searle, 1979, p. 122). Inferential processes taking contextual factors -6 2 - into account are not supposed to have any impact upon such purely linguistically mediated meaning whatsoever, but to be initiated by and to operate upon it. - However, what kind of mental representation of the meaning of WORKING could possibly be mediated by purely verbal means and remain invariant across Mrs. Smith's two answers?

The most usual strategy adopted by defenders of the representational-computational view in order to evade such questions is simply to stipulate additional entries within our mental lexica. The word

WORKING, they may argue, has very likely multiple mental representations. It may mean "being on the job", "being physically active", etc. Multiplication of lexical entries, however, is an ad hoc strategy: It is always possible to add novel lexical entries once we have observed novel ways of using a familiar word. But it is only possible post facto and serves neither to eliminate nor to explain semantically productive features of polysemy.

A more promising approach is, in my view, the strategy adopted by

Philip Johnson-Laird in a very recent discussion of mental representation of word meaning. He argues (Johnson-Laird, 1987, p. 197) that open-class words such as FISH and EAT are more similar to pronouns than is commonly recognized. - The same is, in view of our Mrs. Smith episodes, also true of the word WORKING. Instead of stipulating multiple separate lexical entries for such words, Johnson-Laird argues, we may therefore conceive of "default values" as constituting a major component of their mentally represented meanings.

Default values are, in technical cognitive science terminology

(Johnson-Laird, 1987, p. 204), "specific values for variables that can be assumed in the absence of information to the contrary". In connections with mental representations of word meaning we may perhaps think of them as mental et ceteras or "other-thines-eauals" anchored in previous -6 3 -

use of the word and/or in pervasive background conditions. Their impact

upon processes of comprehension, moreover, should be inversely

proportional to the amount of available contextual clues to meaning. In

order to assess the default values of the word WORKING, we might hence

detach Mrs. Smith's two answers from the telephone conversation

contexts, tape-record them, and insert them into a psycholinguistic

experiment on recall of unrelated texts fragments. The subject is thus in

a Virgin Mary type of situation hearing from the tape:

"Mr. Miller is driving this car, he is enjoying the speed.

Mr. Smith is working this morninR, he is mowinR the lawn.

Mr. Clark is not coming this way, he is visiting the neighbour.

Mr. Smith is not working this morning, he is mowing the lawn”.

Let us assume that we can eliminate and disregard the impact of sequential order and focus on the two occurrences of the expression HE IS

MOWING THE LAWN. If the mental representation of the word

WORKING entails specific default values, we would expect comprehension of that expression to vary systematically and in significant ways, depending upon whether it is preceded by WORKING or by NOT

WORKING. My guess is that we will find no systematic difference between the two conditions at all. Our experimental Virgin Mary, I expect, will accept Mr. Smith's mowing of the lawn equally readily as an instance of, respectively, WORKING and NOT WORKING. I thus fully agree with Johnson-Laird that words such as FISH, EAT and WORK resemble pronouns. The unique feature of pronouns, however, resides in my opinion in default options rather than specific default values.

5. On linguistically mediated "propositional content” versus situated

truths in human discourse.

An examination of discourse situations from within differs sharply from an analyis of transmission of propositional information in accordance - 64- with a "conduit paradigm" of verbal communication (Reddy, 1979) and a representational theory of mind. As Lyons (1977, p. 724) has put it:

...the process of communicating propositional

information is readily describable...... in terms of the

localistic notion of a journey: If X communicates £ to Y,

this implies that £ travels, in some sense, from X to Y.

...... It may be suggested that |]£ is at X" (where X is a

person) is the underlying locative structure that is

common to "X knows £". "X believes £", "X has £ in

mind", etc.

What is being transmitted in any particular case, however, has to be set apart from that which is presupposed by both conversation partners prior to the process of communication. The "proportional content" £ of an utterance - what is "literally" being asserted within it, its "New information" (Clark, 1977, p. 412) - has hence in studies of semantics within generative grammar been conceived of as conveyed by its "focus".

The latter, Chomsky argued, "...is a phrase containing the intonation center...", and it "...must be composed of full lexical items - more generally, items that make a contribution to the meaning of anything outside the focus" (Chomsky, 1972, p. 100 and 102).

The "propositional information" about Mr. Smith "travelling" from his wife to his friend is according to such a conduit paradigm encoded in the focal expression NOT WORKING. The resultant belief that Mr. Smith is not on his job at the fire station, moreover, is according to Searle's theory of indirect speech acts and the maxims of conversation proposed by Grice the outcome of an "utterance meaning" inferred from "literal sentence meaning" (see Searle, 1978 and 1979; and Grice, 1975). It is hence "cognitively penetrable", i.e., in principle to be accounted for

"computationally" in terms of some contextually plausible chain of -6 5 - inferences from a purely linguistically mediated mental representation of the meaning of NOT WORKING. The latter contains - if we endorse

Johnson-Laird's proposal - specific and invariant "default values", whereas

Gricean maxims are invoked to account for (Chilton, 1987, p. 223) "...non­ literal elements of meaning (that is those elements beyond the scope of truth-conditional, logic-based semantic theory)..."

Notice how mentally represented propositional content according to such a paradigm is defined in terms of stipulated invariant, "direct" or

"literal" word meaning: Whatever Mrs. Smith "knows", "believes", or "has in mind" and transmits to her husband's friend by the expression NOT

WORKING is by definition the negation of whatever she "knew",

"believed", or "had in mind" and conveyed by the work WORKING to her own friend a few minutes ago. The two different and contextually plausible "utterance meanings" are hence "computed" or made

"inferentially characterizable" qua outcomes of reasoning based upon contradictory beliefs on the part of Mrs. Smith.

A representational-computational account of utterance meaning as a sequence of transformations triggered by a mental representation of

"direct" sentence meaning is in view of this example a hybrid of autonomous semantics and hermeneutics. The reconstruction of the agent's reasoning is based on an interpretation of her or his situation

"from within", but as the solution of a problem the agent does not identify as her or his problem within the situation. The stipulated initial internal representational state is simply not cognitively penetrable at all in the sense that it "can be described and identified by the agent". On the contrary: Mrs. Smith, for instance, will stubbornly deny having entertained contradictory beliefs, i.e., that she first meant £ and shortly afterwards not £, £ being explained to her in terms of some invariant,

"literal", and "direct" meaning of the word WORKING. Wittgenstein (1968) maintains that sharp categorial differences distinguish meaning and understanding from mental states and processes.

Cognitive psychologists committed to a representational-computational view disagree, and they use Fregean sense as a Troian horse to transport meaning into the individual mind. The default values stipulated by

Johnson-Laird, being defined in terms of sense, can thus easily be converted into mental representations. Default options, on the other hand, must be conceived of as merely possibilities or meaning potentials and can hence hardly be transformed into such Cartesian entities at all.

Instead of modelling linguistically mediated meaning in terms of individual mental representations we may hence conceive of ordinary language as dialogically constituted, i.e., as providing drafts of contracts concerning human categorization, by definition negotiable and constantly subject to contextual specification (Rommetveit and Blakar, 1979).

The term 'sense' (in German: 'Sinn') cannot easily be dispensed with in discourse about semantic issues. When hermeneutically oriented philosophers use that term, however, they deal with meaning as embedded in our human, only partially shared and very fragm entary known world.

An essential characteristic of sense as it is discussed by Wittgenstein in his Philosophical Investigations is thus according to Baker and Hacker

(1980) its inherent indeterminacy. And Gadamer (1975, p. 282) maintains that elaboration of true sense is actually an infinite process. (In German:

"Die Ausschopfung des wahren Sinnes...... ist in Wahrheit ein unendlicher

Process".) We make sense of the human world we are immersed in, he claims, by "bringing it into language".

Making sense of the world and bringing it into language, moreover, is a social, in certain respects circular and, as Bakhtin (1973) maintains, dialogical activity. Mr. Smith's mowing of the lawn, for instance, is spontaneously and fairly unequivocally made sense of and brought into -6 7 - language as WORKING by his wife when she is engaged in conversation with a lady accusing him of being lazy. It is equally spontaneously and unequivocally made sense of and brought into language as NOT WORKING when the very same Mrs. Smith is talking about the very same mowing of the lawn to a person who is interested in whether her husband is free to go fishing with him. What is being meant by the word WORKING is thus unequivocally true about one aspect of Mr. Smith's lawnmowing, yet unequivocally false about another.

The truth of Mrs. Smith's answers is not of the kind that can be assessed by matching a stipulated mental representation against a presumedly unequivocal external state of affairs. It is on each occasion situated, i.e., bound by an intersubjectively accepted perspective and a joint concern. And situated, concerned cognition implies necessarily perspectival relativity. We spontaneously "see" Mr. Smith working or engaged in leisure time activity depending upon whether we are concerned about his laziness or interested in his opportunity to go fishing with us.

And such - often: socially negotiated - concerns and interests do not distort what we "see". On the contrary, they serve to disambiguate our pluralistic world into intersubjectively attended-to aspects.

Truth conditional semantics is thus within a hermeneutic-dialogical approach something we may engage in only after a systematic investigation of background conditions, joint concerns, and intersubjectively endorsed perspectives. The truth tables of traditional truth conditional semantics must hence be replaced by "dialogical truth tables" (see Rommetveit, 1980): In order to find out whether what is being asserted about any particular state of affairs is true, we must first examine the discourse situation "from within" and identify the position from which that state of affairs is being viewed and "brought into language". - 68-

6. Epilogue: On situated and concerned human use of machine

representable knowledge.

Let me, finally, return to the Kantian problem and briefly explore some possible ramifications of it in view of pragmatically oriented attempts at machine representation of accumulated human knowledge within particular domains of scientific, technological and professional expertice. I have argued that current representational-computational models are doomed to fail qua explanatory accounts of situated, concerned human cognition and everyday verbal communication. This does by no means imply, however, that such models should be abandoned. They have already proved fruitful within the broad and vaguely defined context of

"anthropological thought" by provoking scholars such as Dreyfus and

Winograd to explore the peculiar situatedness of Man in novel and interesting ways. And such models may indeed prove highly instrumental and even essential in creative development of information technology as an extension of and supplement to mortal men's imperfect, situated and concerned knowledge of a mulitfaceted and pluralistic world.

Consider, for a moment, the ramification of ordinary language into scientific terminologies. This development may actually within a broad historical-pragmatic perspective be conceived of as a crucial step in a process of externalization of knowledge paving the road toward machine representation. Precision of scientific terms is gained at the cost of perspectival relativity within separate, restricted and well defined domains of enquiry, i.e., within "... departments which can be treated singly, in a less problematic,... less binding way", as Buber expressed it.

Emancipation of a term from its origin within everyday language may therefore in the absence of better indices actually be conceived of as a fairly reliable measure of its scientific status. Descendants of the verb

WORK within scientific ergometric and economic texts, for instance, are - 69 - obviously only remotely related to the word uttered by Mrs. Smith in her telephone conversations. An expression such as FATHER OF, moreover, is necessarily stripped of most of its meaning potential in everyday use when we encounter it as a scientific term within genetics. Aspects of fatherhood such as those dwelt with in legal texts about inheritance of property and of kinship roles, respectively, are thus excluded from the geneticist's proper domain of enquiry and scientific use of the expression.

What is "brought into language" in any given scientific text is thus not simply one particular fragment of our holistic, multifaceted and immediately meaningful "Lebenswelt", but a systematically disambiguated and transformed version of such a fragment. States of affairs studied within, e.g., genetics and provide truth conditions on scientific use of expressions such as FATHER OF and WORK because the state of affairs under investigation is viewed from one particular position, made sense of within a restricted domain of scientific enquiry, and brought into language by an expression embedded in a semantically nearly closed vocabulary.

Residual ambiguity of terms within a given scientific, technological or professional domain of knowledge is even further reduced when collectively accumulated expertice within a particular subregion of that domain is systematically exploited for the purpose of solving some specific and precisely formulated problem. What happens when the perspectival relativity of everyday human discourse is eliminated is that default options of word meanings are transformed into invariant default values of "technical terms" which, in turn, can be translated into computer language. The resultant machine representable knowledge is thus, when encoded in a semantically drastically impoverished and strictly propositional artifical language resembling Frege's idealized "Begriffschrift" (see Frege, 1969, p. 55), stripped of all ambiguity and polysemy due to perspectival relativity. My arguments contra representational-computational models qua (ingredients of) explanatory accounts of situated human cognition and everyday discourse may accordingly be rephrased as arguments £ro representational- computational designs for expert systems within certain restricted domains of scientific, technological and professional knowledge.

The development of modern information technology may from a certain hermeneutic-dialectic philosophical position be conceived of as institutionalized de-contextualization of knowledge, i.e., as a systematic detachment of enclaves of expertice from our understanding of a holistic and multifaceted "Lebenswelt" from within. But such enclaves of expertice are after all being developed in order to help us cope with problems of practical significance in our complex modern societies and are hence by no means detached from human purposes and concerns.

Intricate issues concerning perspectival relativity which may be bracketed by knowledge engineers absorbed in problem solving inside a given enclave are thus bound to reappear in rational, informed debate about use of information technology and expert systems.

The knowledgeability of human agents is, as Anthony Giddens (1982) so convincingly argues, always bounded by unacknowledged conditions and unintended consequences of action. Pervasive background conditions for making sense of what we observe tend indeed, as previously suggested, to remain unacknowledged as long as they stay fixed. And such bonds on human knowledgeability are not eliminated when it is extended by externalization and machine representation. It cannot be denied that public records of "data" collected for some particular purpose within a given institutional setting remain fairly unequivocal as long as they are interpreted and used within that particular setting. We may be tempted, - 71- therefore, to reach such records on the tacit assumption that they are uncontaminated by human interests and devoid of perspectival relativity.

Ronald Stamper (1984) has shown, however, how previously unacknowledged background conditions of publicly recorded "data" are brought into the open when individual computer systems covering different, though related, areas of human concern are converted so as to share the same data base.

Such cases serve to corroborate a claim about general boundary conditions on information technology which has perhaps been most cogently advocated by Winograd and Flores (1985): Even externalized and machine represented knowledge is necessarily bounded by and embedded in a subjectively meaningful and only partially shared context of human commitments and concerns. The investigation of meaning within the non- finite domain of human possibilities is hence in certain significant respects, as (1975) maintains, a "moral science". States of affairs are brought into everyday- and computer language by human beings, and the range of possible perspectives is therefore in part a reflection of the range of possible human interests and concerns. It is selfevident, yet by no means trivial, that legal responsibility for failure of expert systems is not attributed to the systems, but to their human designers, producers, owners or users (see Cannataci, this volume).

Hilary Putnam also maintained, nearly twenty years ago (quoted from Rorty, 1980, p. 189):

...the question: Are robots conscious? calls for a

decision, on our part, to treat robots as fellow members

of our linguistic society, or not so to treat them.

Qualifications for membership in our linguistic society have to do with capacity for participation and co-responsibility for what is meant in human dialogues. Putnam’s reflection on our conception of robots is thus -7 2 - actually "anthropological thought" about the nature of human agency.

And our options with respect to perspectives extend according to Anthony

Giddens even to such deeply philosophical issues. He maintains (Giddens,

1982, p. 16):

To regard social agents as 'knowledgable' and 'capable' is

not just a matter of the analysis of action; it is also an

implicitly political stance.

We may explore the world contextually, in a modular form, and as an aggregate of disconnected elements (Herbst, 1987). Viewing it contextually and hermeneutically enables us to establish a meaning structure within which, alas, the very identity of a given state of affairs is contingent upon the position from which it is viewed. In order to be able to exploit powerful analytic tools such as Boolean logic and classical set theory in externalization and machine representation of human knowledge, we have to view fragments of the world from fixed positions or engage in normative idealization of it. Such strategies are rational in a traditional analytic-rationalist sense. They are also thoroughly reasonable strategies when evaluated from the hermeneutic-dialogical position I have advocated, provided that we seriously examine the perspectival relativity and ethical fringe conditions of human knowledge and refuse to accept robots as fellow members of our linguistic society. - 7 3 -

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t e e E xteenalusathon o f K n o w l e d g e >

Henrik Sinding-Larsen Department of University of Oslo

The article's last pan sketches the outlines of a broad historical and comparative approach to a topic we could call "an anthropology of information technology". At the base is the idea of externalisation of knowledge as a historical process. Three main epochs of externalisation of knowledge are characterised by a) spoken language, b) writing, and c) computer programming. The article starts with a case study of expert systems in medicine as an introduction to the theme.

1. INTRODUCTION

When doctors make a diagnosis today they confront each of the observed symptoms with their own experience-based knowledge. Each symptom, and especially combinations of symptoms in syndromes, will awaken associations from their own previous observations as well as from descriptions of syndromes in medical literature. Hopefully, this process will result in a conviction of a diagnosis on which to base treatment. In this way the traditional diagnostic process will add to the doctor's experience-based knowledge with each new patient. An important part of our knowledge, not least within the professions, is practised and maintained in a similar way. A long term effect of using knowledge-based systems in medical diagnosis could be dramatic changes in this learning process. But before we look more closely at these consequences, we will look briefly at a system currently being developed at the Central Hospital of Akershus, Norway.

When the system comes into operation, doctors in the admissions department are supposed to rely upon the advice from a computer when they make decisions as to whether or not a patient should be admitted to the coronary unit. Caring for a patient in such specialised units is very expensive, and only patients who have suffered a heart

1 This is an extended version of my article " InformaUon Technology and the Management of Knowledge" A/ <4 SOCIETY: The Journal of Human and Machine Intelligence vol.l No.2. Oct- Nov.1987, ISSN: 0951-5666 - 7 8 -

attack require such intensive care. At present more than 50 per cent of admittances are "unnecessary" (assuming that it was known that the patient had not suffered a heart attack). In other words, a great deal of money can be saved by improving the initial diagnosis on admission not to mention the relief of stress and anxiety for the patient.

The system works by asking 38 multiple-choice questions which are displayed on the screen. When these have been answered the system compares the new patient's syndrome with information held on 2000 earlier patients. After a few seconds of statistical analysis of the historical material the system prints out a percentage indicating the probability that the newly admitted patient has had a heart attack. The system has proved to be 80-90 per cent accurate in tests, compared to the 50 per cent accuracy of today’s doctors. This may be due to the fact that no-one is able to remember all the details of 2000 patients, (or 20000 patients if the system is expanded further). If this use of computers is carried out to its extreme all a doctor would need to do to obtain a diagnosis would be to run through the computer program, answer all the questions asked by the system, press the print key and wait for the diagnosis and prescribed treatment to be printed out. So far the system cannot compete with an experienced doctor when used on a day-to-day basis. However, rapid developments are occurring in this area and we must expect that decisions made on advice given by a computer will become widespread in medicine, as indeed in a whole series of other fields. The research team at the above mentioned Norwegian hospital is only one small part of a multimillion pound international "race" in the fields of ’artificial intelligence' and 'knowledge engineering'.

2. COMPUTERS, DESICION-MAKING AND LEARNING

To the extent that decisions are made on the basis of an accumulation of 'facts' and the use of formalised rules, computers have a good chance of becoming superior to human beings. Obviously many decision-making processes are not formalisable in this way, but it is equally clear that many processes which today require human brain-power could actually be automated. Pocket calculators are a simple but illuminating example.

One reason for investment, which is rather widespread in many companies and organisations, relates to the possibilities for economising on the expensive expertise of specialists by allowing less expensive and less experienced personnel to take decisions which, without a knowledge-based system, would lie outside their area of competence. In this way the expens can be spared time-consuming questions from subordinates.

At Creighton University School of Nursing in Omaha, USA, a knowledge-based ci/clpm hac Hpv//>lr\npH u/hirh rrtllotpc nnrcinnr anH m pH iral Irnrvvi/lpHnp fr»r thp nc/> - 7 9 - of nurses 2. This private university (with support from the Kellogg Foundation) has spent 15 years and over $10M developing the system. The knowledge base is built on approximately three million medical terms and expressions which are all related to each other either directly or indirectly. After a period of use it became apparent that the nurses, in most cases, preferred to consult the system on medical matters rather than the doctors. One of the reasons for this was that the computer was more polite and less arrogant than the doctors, and otherwise just as accurate.

The fact that machines in more and more fields have proved to be "cognitively superior" to man has become a cause of concern for many scholars. Much of the debate surrounding "artificial intelligence" has been characterised by black and white thinking about a supposed battle which either man or the computer will win. Typically one of the most famous books criticising artificial intelligence is tided "What computers can't do" 3. It looks as though the critics, especially those with a philosophical orientation, wish to save what is "specifically human" by highlighting the fact that there are certain things man can do which machines can never learn. I do not believe that it is helpful to devote so much time and energy to a discussion of what machines ultimately can and cannot do. What is important is to understand the consequences for culture and society of what knowledge-based systems, both now and in the future, will in fact be used for, regardless of whether they do a good job or not in comparison to humans. An understanding of what future computers will be able to do and "think" is less relevant than an understanding of what future humans will do and think when they work with these machines.

Consequences for Human Learning One of the consequences which so far has been the subject of very little study is the relationship between knowledge-based systems, human learning and the long term development of knowledge. If, for example, doctors diagnose their patients by punching in findings and isolated results as a computer program asks for them, without needing to think of previous cases, it is doubtful whether this process will be as valuable in building experience as traditional diagnosis. The processual parts of diagnostic knowledge, which have been stored in the individual doctor, can now be stored externally in computer programs. Provided the computer system is available, the doctor can allow himself to ignore or even "forget" certain elements of his knowledge. This does not only affect fragmentary knowledge of an encyclopaedic nature but even

2 Ryan, S.A. (1985) "An Expert System for Nursing Practice - Clinical Decision Support", Computers in Nursing, March/April issue. Kaasb0ll, Jens (1987) "An Al-hascd System in the Development of Nursing Knowledge", in Buchbergcr, Ernst, Bo GOranzon. and Kristen Nygaard (ed.) 1987 Artificial Intelligence: Perspectives and Implications, CompLex 11/87, Oslo: The Norwegian University Press

3 Dreyfus, Hubert (1979)What Computers Can't Do, New York: Harper and Row. -8 0 - the most fundamental procedures used in the identification of illness. The use of pocket calculators has shown how surprisingly quickly simple arithmetics can be forgotten, especially by children, but also by adults, when machines free us from these mental efforts.

The consequences on the process of learning are particularly great when advanced knowledge-based systems are used by unqualified personnel. The less we understand about how a program reaches a decision, the more difficult it will be to generalise experiences gained from one instance to the next. If we do not already know how to multiply, using a pocket calculator will certainly not teach us 4. The new information technology has altered the conditions for the acquisition of experience and other learning processes. Currently this technology only affects limited areas of knowledge. However, with the developments we are witnessing in 'artificial intelligence', many activities which were previously considered intellectual challenges may soon become boring routine work. This may result in large quantities of knowledge being forgotten and disappearing. Knowledge has to some extent always become obsolete and disappeared. However this can take place much more rapidly and completely today than ever before, resulting in a vulnerability of considerable proportions. This vulnerability follows partly from the immediate dependence on machines which can go wrong and partly because increasing standardisation of knowledee leads to rigidity in relation to necessary changes 5.

The degree of vulnerability of a culture faced with changing environmental conditions is determined by its flexibility. The degree of flexibility is partly determined by variety, not least of knowledge. Modern satellites and other advanced information technology have created a situation where there are no technical barriers preventing any hospital in the world from consulting Creighton University Hospital's knowledge-base for medical advice. It is easy to see that such standardisation of available information, threatening the diversity of knowledge, does not just concern the learning process of the individual, but the very management o f knowledge in the western world.

Up until now I have concentrated mainly on the effects on learning following the professional use of knowledge-based systems. But the effects will be even greater when such systems are introduced in . At the University Hospital in Leiden, Holland, computer simulated patients have been introduced as an aid to learning the

4 Calculators can dearly be a help in learning "higher" forms of arithmetic but not for learning multiplication itself.

5 A serious example of the unwillingness to realize vulnerability in the face of advanced technology is the authorities in the Soviet Union who, after the accident at Chernobyl, declared that the responsibility rested entirely with human error, and that there was nothing wrong with nuclear technology as such. The American Star Wars programme is an example of complex and vulnerable information icchnolojjy - 81-

diagnostics itself. Professor Verbeek, of Leiden, aroused considerable interest for the introduction of the system in Norway, at a recent demonstration at the Faculty of Medicine of the University of Oslo. Slides were shown of lecture rooms where students, each with their own terminal, tested their diagnostic skills on computer simulated patients. This they do for the first few years before being allowed to try it out on real patients. The arguments for this include the fact that in the initial, pre-clinical stages of their studies, computerised teaching was more economical than having the students meet real patients under supervision. This development raises many questions: How are future students going to learn to look for relevant experiences in a crude and chaotic reality if they are used to getting knowledge served in an explicit, rule-based manner? How are future students going to leam about the connections between personal growth and growth in professional knowledge when their interacting computer never interrupts them with off-the-record questions about "how it feels"?

3. TOWARDS AN ANTHROPOLOGY OF INFORMATION TECHNOLOGY

We know little about the possible consequences of using modem technology to externalise (put into external storage) in computers large amounts of knowledge which today is stored in the minds of human beings (intracognitive storage). One aspect of the consequences will certainly be linked to the fact that computers do not possess the capacity to improvise and overstep certain strictly defined frames.

Anthropology and the social sciences in general lack concepts and theories for the description of this type of cultural change. What is happening? Is it a unique phenomenon or is it just a new form of something we have experienced before? I believe we have both existing theories and comparative empirical methods that are relevant, however, computer technology demands that these be gathered and synthesised in a new perspective. On a general level more fundamental theoretical work is required. In order to understand what information technology is doing to our society, I believe it is necessary to establish a broad historical and comparative perspective. On the global scale, we need an "anthropology of information technology", and for our own society we need a " of information technology".

With regard to developing countries, a serious lack of fundamental theories and concepts is particularly apparent in the debate. At a seminar on "Data and Development: Computer Technology as a future component in Norwegian Development Aid?" (at the Norwegian Computing Centre, Oslo, 21-22 May 1984), several delegates constantly returned to the argument that information technology could widen the "information gap" between industrialised and developing countries, calling for a "New Informatic World - 82-

Order". Developing countries were said to be in a state of "information poverty". This makes sense if, for example, one considers the elite’s need for buying and selling in an international market. However, from the point of view of the majority of Third World inhabitants, this can be seen as either meaningless or a rather extreme case of cultural imperialism. In the same way Western economists try to apply a single m odel o f economic development in all parts of the world, there are attempts to use a single model for the "management of information and knowledge". But the value of information and knowledge does not follow international standards. One aim of a comparative study should be to clarify the way in which various cultural ways of managing knowledge is related to various forms of information technology.

It is not the first time in our history that new information technology has altered the rules for the management of knowledge. However, it may be the first time that this has happened so abruptly that the process has become apparent to the extent that it has become the object of a comprehensive research program. Reflections on this theme are, however, nothing new, and once again we must resort to the old cliche : "the ancient Greeks — In Phaedrus, Plato lets Socrates discuss the relationship between knowledge and the new information technology of his time - writing:

If men learn this (writing), it will implant forgetfulness in their souls; they will cease to exercise memory because they rely on that which is written, calling things to remembrance no longer from within themselves, but by means of external marks. [...] and as men filled, not with wisdom, but the conceit of wisdom, they will be a burden to their fellows 6.

Those who know how history has progressed since Plato could not honestly say that writing has been a threat to all wisdom. However, on a most general level, Plato is correct. Our form of wisdom has given the human species a lower potential for survival than it had before we could write. Just look at the world's largest budget for development of advanced information technology, the American Strategic Defence Initiative or as it is popularly called the "Star Wars" programme. Serious critiques of SDI have shown that those who rely on computers as "external marks" in a space war may not only become, "a burden to their fellows", but a catastrophe to the entire humanity.

6 From Plain's Pheadrus cited in : Jack Goody and Ian Wall "The Consequences of Literacy" in: Jack n .rww4*. \ / i'm nmhridcH* I Tniv**rvifv Pit»«« 1Q7S n SO. -8 3 -

4. EXTERNALISATION OF KNOWLEDGE AS A HISTORICAL PROCESS

As pan of a general theory for understanding the cultural history of information technology, I have introduced the expression "externalisation of knowledge". In using the concept of "externalisation" I want to denote a historical process whereby knowledge previously stored in human beings, (i.e. intracognitively), is transferred to a storage medium outside the human mind (i.e. becomes extracognitively stored). The concept of externalisation, therefore, may characterise a process of change in society's management of knowledge.

Two historical examples that I am currently investigating in this perspective are the development of writing in ancient civilizations and the development of European musical notation in the Middle Ages. The relationship between the development of music and musical notation is especially interesting because it is a relatively limited, well-documented area which may serve as a good starting point for generating hypotheses on the contemporary computer situation. 7 For example, musical notation paved the way for the development of complex harmonic counterpoint and co­ ordination in large orchestras. Concurrent with growing dependence on notation, a decreasing emphasis on the ability to improvise can be discerned. Thus, notes, being externalised knowledge of music, brought about a change in the dimensions of the orchestra, as well as in the nature of the complexity of orchestral music. The peaks of complexity shifted from the improvisation of the individual musician to the composer’s ability to steer the orchestra through large scale written down harmonies.

The analogy is the following: The binary simplicity of computers is the foundation of software complexity as well as the complexity of human activity which these machines are able to organise, (cf. notes for the orchestra). A theme for further research would be to look more closely at different degrees and types of complexity in representational systems and social organisation.

We can employ the theory of externalisation of knowledge to divide cultural history into information technology epochs. The characteristics of three main epochs may in this way be discerned:

1 Externalisation of knowledge is made possible through spoken language and a social organisation of specialists.

7 The theme is developed further in the chapter Notation and Music: The History of a Tool of Description and its Domain to be Described in the present volume -8 4 -

2 Externalisation of knowledge is made possible through descriptions expressed in various forms of writing intended for human interpretation.

3 Externalisation of knowledge is made possible through descriptions expressed in various forms of programming languages intended for machine interpretation.

(NB.The new form of externalisation in one period does not replace the previous forms but supplements them.)

As a first elaboration of the theory of externalisation, I will present some examples from the three epochs.

Externalisation of knowledge and the social organisation of specialists. One of the characteristics of human culture is that we live in groups in which not all individuals are in possession of the same knowledge. Amongst the social animals, too, there is a certain division of knowledge based on age and sex, but on the whole individuals of an animal species are much more homogeneous than humans as regards the knowledge necessary for survival and reproduction. An interesting case representing an intermediate stage can be observed among hamadrya baboons in Ethiopia. The Baboon troops must keep alive at least one elder male who knows where water can be found during the periods of drought which occur every 10-15 years 8. In contrast to most other animals a young, mature baboon will not itself have sufficient knowledge to make optimal choices as regards such vital tasks as finding water during a period of drought. As a unit, however, the Baboon troop will possess enough knowledge even if it is stored outside the majority of the individual mature baboons. Because of their way of organising grazing the troop can only make use of this knowledge if they share some codes (languages and semiotic systems) which the elder baboon can use to communicate about the water holes. In fact, they appear to have a very primitive language for externalised representation of knowledge.

The baboon troops live on cliffs which function as a kind of map of the terrain the animals graze. Each morning, when the day's movements in their territory are decided, the leading male (normally the strongest but during a drought, the eldest), performs some special movements on that part of the cliff, which corresponds to the best watering hole. This is an early example of social management of knowledge. The example encompasses 1) an internalised language, (a code for common understanding of the correspondence between the cliff and the water holes), 2) a semiotic system for the externalisation of the language (i.e. the various points on the cliff), and 3) a social

8 From a lecture given by the ethologist Robin Dunbar held at the Nordic research seminar Cultural Evolution and Transmission, Oppdal, Norway, 11-18 April 1985. Sec also Robin Dunbar, !

-8 5 -

organisation of knowledge (ihe older male with knowledge of previous periods of drought).

In human cultures based on hunting and gathering, age and sex are the predominating organising principles for the division of knowledge. But the great majority of other cultures manage their collective knowledge through a number of specialists who pass on their knowledge from generation to generation. Early examples of specialists are medicine men and political leaders. Nowadays we have all the 'professions'. Developments in many areas have gone so far in the direction of specialisation that we are seeing the beginning lack of 'generalists'.

If we take an individual ape as a starting point and look at the transition to human society we can see that steadily increasing portions of the knowledge needed for survival is stored externally to the individual through a social management of knowledge.

The signs the baboons used in communicating about water holes were not produced explicitly to mean anything. They were unprocessed parts of the cliff where the troop lived, and they constituted, as signs, only a portion of the animals' natural environment to which they had chosen to assign a certain meaning. Nevertheless, we can regard this as a primitive form of externalisation because the cliff as a carrier of information had an "external continuity" with significance for the internalisation of knowledge of new baboons. The cliff as a semiotic system together with the baboon's linguistic capacity constitute the possibilities and the limits of what can be externalised. In many pre- literate cultures, too, the natural environment (landscape, animals and plants) is important as a source of semiotic externalisations needed for the structuring and processing of meaning 9.

Externalisation of knowledge through writing made for human reading. Among humans knowledge is never just stored mentally (cognitively) in relation to an unprocessed natural environment. From the earliest times, houses, tools, clothes and parts of the cultural landscape have been material manifestations of knowledge, and have always been essential for learning (internalisation). But in pre-literate societies man-made physical objects which "mean something" will be considerably fewer than in a society where writing is used as storage medium for knowledge. Precisely because this distinction is so crucial, it is meaningful to use "pre-literate societies" as a category. The process whereby a society becomes literate is an interesting comparative field in order to understand what occurs during a profound information technology revolution.

9 Fredrik Barth. Ritual and Knowledge among the Bakiaman of New Guinea. Oslo: UniversitclsforlagW 1977 - 86-

This is the ease for modem literacy processes in the Third World, but it is especially interesting to look at the endogenous literacy processes that occurred in Mesopotamia and Egypt.

The ancient civilizations which became literate not only developed writing to store "the spoken word" but a whole range of semiotic systems, such as various types of maps of the sky and the earth, counting systems, monetary systems, architectural diagrams, maps for town planning and a series of other drawing systems for description and design. In early Sumerian as well as early Egyptian cultures these representational systems seemed to develop more or less simultaneously ,0. This was the second great stage in the externalisation of knowledge; this time externalised both in relation to individuals and groups. This process of externalisation was based upon the description o f structure. Common to the majority of semiotic systems in the age of writing, is that the descriptions can only store structures statically. A recipe, for example, is static and contains no "motion" until someone uses kitchen utensils and lets the structure of the recipe guide the process of preparing food. Similarly, a novel is the static structure of a story which is set in motion and brought to life through the reading process. The law consists of structures for court procedures, and medical books can impose structure on the practice of diagnosis and treatment. But in their externally stored form, all texts are static.

A semiotic system which in the middle of the age of writing, constituted an important exception was the mechanical clock. In this case not only is the structure for the measurement of time externalised (or represented) but also the process itself. A man- made process, the movement of the arms of the clock, is a dynamic representation (simulation) of another process, (the movement of the sun, the beating of the heart or other "natural" manifestations of time). If we attempt to live without a clock in a "clockless society", we would soon notice that our externalised management of knowledge of time has to become re-intemalised.

Externalisation of knowledge through computer programming It is however with the advent of computers that the third great process of externalisation began, that is, the externalisation of knowledge processes. In contrast to writing, which could only be read by human beings, computer programs store descriptions of structures which can guide physical processes directly by means of computers. Robots are the most obvious and comprehensible evidence that computers store knowledge of processes. But even a pocket calculator is an example of how structures (algorithms for reckoning) operate on numbers without requiring us to be conscious of it.

10 Naissance de l'icriiure. Cuneiformes et Hiéroglyphes. Editions de la Reunion des Musées Nationaux, Paris 1982. -8 7 -

What wc regard as knowledge is typically the structures which guide us when we are carrying out specialised tasks. Since structures can be stored in written form, we do not need to remember all of them, we can "look them up" in a book and just follow the procedure given by the text. But we cannot escape the fact that we must understand the structures in order to be able to carry out the operation. We cannot use a recipe as structure without having some idea about the process of cooking. We must possess knowledge about both the structure and the process and, generally, if we perform an action often enough following a written description, we will get to know it by heart. This is not the case with knowledge stored in the form of computer programs. In this case both the structure and the process can be stored and we can have operations carried out without us human beings having to understand or be aware of what is happening. The process of externalisation is more complete at this stage. Today children can perform mathematical operations which Einstein would have found hard to solve.

Developers of expert systems are engaged in a world-wide race where the aim is to automate knowledge in ways that are attractive to private and public users. Great expectations exist as to the supposed increase in efficiency.

In the Norwegian central office for social security a legal expert system for handling housing benefit claims is being implemented. The relevant laws and regulations operate as structure in the computer program. The structure is hidden and just produces a sequence of questions about sex, age, income and expenditure of the members of the household, and eventually, the amount of financial support to be awarded in accordance with the law. This is an externalisation of processes and must as such be considered in a completely different light from written law texts. Legal expert systems in public offices can easily lead to personnel handling claims without actually understanding the rules applicable to a particular type of claim 11. As yet we have only, to a small extent, seen the consequences of this development.

5. SOME REMARKS ON THE EXTERNALISATION OF LANGUAGE AND THE EVOLUTION OF CULTURE

The history of language may be regarded as an aspect of the general history of externalisation. If we look at the three main periods in this perspective the second step, writing, externalised the ephemeral phonetic structure of the first step, speech. That

*1 Dag Wicsc Schartum "Offentlig forvaltning i en ny teknologisk virkelighet", Projektbeskrivelsc, Institutt for rettsinformatikk. Univ. i Oslo. 1986 (in Norwegian). -8 8 - prcpared the ground for an unprecedented accumulation of knowledge. But it also had an important influence on spoken language itself.

Languages of illiterate people have in general no concepts for the characterisation of linguistic entities like verb, noun, grammar etc. Many tribes don't even have a concept corresponding to our word language'. Language is something they use, not something they talk about. The externalised quality of writing made it possible to inspect and analyse sentences in ways inaccessible to oral cultures. 12 In a most important sense, it was writing that made language, as a conceptual entity, emerge from speech. But traditional writing was just able to externalise the end-product of our linguistic activity, namely the phonetic structure of speech. By means of computers we are able to make an externalisation of several of the linguistic processes producing speech e.g. simple syntax and semantics. This externalisation creates new possibilities for reflection upon the phenomenon of language as well as providing new conditions for the linguistic practice. To program a computer is to be able to think and express ideas in programming languages. Thus it is reasonable to say that computer programming (and to some extent every use of computers) is kind of linguistic activity. To a greater extent than ever before language has become a question of conscious choice, we can evaluate the properties of one language against another. Through programming, and in particular through the construction of programming languages, language has become an object of construction and invention.

To make or change a natural language one is dependent upon the agreement and adjustments of the members of a linguistic community. In fact to construct a natural language is identical to the construction of a linguistic community. With computers, it is possible to settle a kind of linguistic convention be means of some key-strokes. Language engineering has become a possibility. The conventions made up as an agreement between the programmer and his^er computer can quickly be spread through software distribution and indirectly have consequences for human language communities.

Language has always been more than a tool for conversation or inter-human messages. Language is a way of organizing complexity through comprehension and design. Language has never been a purely inner activity, neither of an individual nor of several individuals communicating. Language is linked to our material projections onto the world. It is a way of living in the world. We try to make our world intelligible through making it readable. In fact we transform our environment more and more according to our linguistic vision of the world, so most of our living becomes a reading of our own

12 Jack Goody (1977) The Domestication o f the Savage Mind Cambridge University Press -8 9 - texts. Computers and telematics are pushing the evolution of culture a great step forward in just this direction.

Current research in social science in the sub-field of information technology and society is largely lacking theories and concepts for understanding what is happening at this general level. The majority of social scientists working with computers receive their research grants from industry or technically oriented research councils. Often this "social science research" takes the form of assistance to engineers engaged in designing more "human" computer systems. It is high time that large, international research efforts are dedicated to the basic understanding of what is happening to human culture in face of new information technologies.

It has become commonplace to talk and write about the information society. Tacitly it is assumed that to a understand the information society is to understand the role of advanced information technology in society. But all societies through all times have been information societies. Computers and other information technologies have just forced us to realize it in a new way. To understand our present information society we need to re-write our cultural history from an information technology point of view. The present article can of course do nothing more than to scratch the surface of such an endeavour.

NOTATION AND MUSIC: THE HISTORY OF A TOOL OF DESCRIPTION AND ITS DOMAIN TO BE DESCRIBED >

Henrik Sinding-Larsen Department of Social Anthropology, University of Oslo, Norway

Introduction

Computer technology is predicted to have great impact on the evolution of culture. One way of getting reasonable ideas about the long-term future changes is to look at historically documented changes following similar inventions. But which inventions of the past are actually comparable to the computer?

We sometimes encounter attempts to compare the present situation with the great Industrial Revolution of the last century (Bolterl984). However this revolution was mainly a product of new energy technologies like the steam engine, electricity and the combustion engine. Not even the telephone or the printing press are really comparable to the computer because they did not imply new ways of representing knowledge. They only made the already written and spoken word more accessible in space and time.

What makes the comparative study of the computer difficult is that it must be regarded not just as one but a whole range of technologies. In one sense it is a communication medium and may, in this respect, be compared with other mass media. In another sense, it is the steering mechanism of advanced machinery and could be compared with other mechanical devices. Perhaps its most unique quality is its ability to simulate and thereby represent natural and mental processes that formerly could not be "expressed". Common to semiotic systems before the computer is that they can only describe the

1 To be published in CYBERNETIC (1988) ISSN: 0883-4202. An earlier version (1985) has been informally distributed as part of a memo titled The Writing of Language, the Notation of Music and the Programming of Computers: Towards a Historical and Comparative Study of Information Technologies, The research program SYDPOL, Institute of informaucs. University of Oslo. -9 2 - static structure of a process. A recipe, for example, is static and contains no "motion" until someone uses kitchen utensils and lets the structure of the recipe guide the process of preparing food. Similarly, a novel is the static structure of a story which is set in motion and brought to life through the reading process. On the other hand, computer programs store descriptions of structures which can guide physical processes directly by means of computers. Running programs are processes that represent processes. This makes the computer an entirely new tool of description. Therefore, to make historical comparisons, we must look for other periods when new systems for representing knowledge appeared.

The history of musical notation is an interesting and relatively unexplored case of knowledge representation within this perspective. Musical notation does not have the cultural importance of writing for example, but it is exactly this limited and specialized position which makes its history well suited as a "laboratory case". In addition its early development is relatively well documented as musical notation has been a topic of controversy from its inception. On the other hand, the early development of writing occurred at a time lacking other recording systems which could be used to discuss and comment on this new invention.

The aim of this article is to present some glimpses from the history of music that may be valuable as a background for an understanding of the present computerization of knowledge.

My interest in notation started several years ago when I was doing anthropological fieldwork among Norwegian country fiddlers. I was struck by their generally strong and articulated resistance to any kind of musical notation. Later I discovered some interesting parallels between the fiddlers' scepticism towards notation and the fear many professionals express toward artificial intelligence and knowledge engineering. Before we look more closely at the contemporary dilemmas, we are going to trace the story of notation back to where it all began, in the Middle Ages. 2

2 My knowledge of early notauon is based partly on reading and partly on my experience as a musician in Kalenda Maya, an ensemble performing medieval and renaissance music. Endless debates about the interpretation of old manuscripts have been particularly helpful. - 9 3 -

From mnemonic device to "programming language" Music and musical notation as a c?se of coevolution

Pope Gregory I (ca. 540-604) dispatched monks to most regions of Western Europe to preach Christianity and expand the monastic orders. The use of liturgical chants, later known as Gregorian chants, was an integral part of their missionary work. In the scriptoria of different monasteries, monks were copying these religious texts on parchment, the most advanced information technology at the time. The question of standardization was important as these hymns were part of the papal plan to establish a unified Roman Church in Western Europe. The unification project was greatly enhanced by the marriage of clerical and profane power on Christmas Day 800, when the Pope himself crowned Charlemagne as the first Christian emperor since antiquity.

The cultural impact of the monastic parchments was not limited to the expansion of a new religious faith. The content of the texts was perhaps less important than the written language itself. Literacy and Latin were, in many respects, the basis for the new Christian civilization. Both language and religion were subject to standardization with the spread of the psalms and scriptures.

The monks used writing to coordinate the chants, so everyone would sing the same words regardless of the monastery they belonged to. The golden age of chant composition is considered to be the period from the fifth to the eighth century. Contemporary texts from this period have been found, but they contain no indications for the melodies as these were composed and maintained within a purely oral tradition (Parrish 1959:8). The desire to standardize texts by means of an alphabet for the notation of words was, from the ninth century, extended to a desire for an "alphabet" for the notation of music. The aim was to facilitate the learning and secure the standardization of melodies 3

3 Rudimentary and archaic forms of musical notation existed both in ancient Egypt, Greece and Rome. This notation had, as far as we know, only very limited practical significance. Within these civilizations, music remained an orally transmitted knowledge. -9 4 -

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Fig. 1 Musical notation from the end of 9th century (ibid.:Plate II)

Figure 1 is a section of one of the oldest preserved sheets of written music. We may discern the possible precursor of musical notation, namely accents indicating when the voice should move up and down. These notes, or neumes as they were called, gave rather inaccurate information about the pitch or duration of each tone. This is a problem for present-day research and interpretation. It was not a problem for the ninth century monks however, as the early neumes were never used as an independent source for learning music. They were only a mnemonic device within an otherwise oral tradition.

The earliest systems of notation developed simultaneously in several European regions. The results were different "dialects" of notation some persisting for several centuries. The monasteries that "invented" notational systems, distributed treatises ("manuals") explaining the code as well as giving the rationale behind their systems. The situation was clearly competitive, not unlike the competition we see in the electronics field today. It was generally agreed that the situation would eventually lead to a standardization, but no one knew when this would happen, or which system would be victorious. The "war" was not settled until the printing houses' mass productions replaced the scribes of the monasteries.

In the beginning, notation was intimately linked to the tradition of religious plainchant. Notation of instrumental music did not appear until several centuries later. Primarily, - 9 5 - the monks wanted a sign system for the description and promotion of an existing vocal tradition. They were largely unaware of the fact that the tool of description they developed with the aim of preserving a tradition, would become a major agent of change, transforming the very music they wanted to preserve.

However, some of the inventors were sharply aware of the fact that they lived in a decisive period in the history of music learning. In the late tenth century Guido d'Arezzo refined a system of notation to make it more suited for learning new and unknown songs directly from parchment. One of his inventions was a better system for referring to the notes. He proposed both the alphabetical C,D,E,F,G and the well known Do-Re-Mi-Fa-Sol. In a letter (ca.1032) to a fellow monk in his old monastery, he evaluates his invention which he, in accordance with medieval humbleness, attributes to God. Not so humble are his visions on how he and his helpers will be rewarded for this invention:

Therefore I, inspired with charity by God, have, with the greatest haste and care, conferred not only on you but on as many others as I could the favour given by God to me, all unworthy as I am; so that the ecclesiastical songs which I and all those before me learnt with the greatest difficulty may he learnt with the greatest ease by men to come, and that they may wish eternal health to me and you and my other helpers and may ask God's mercy to grant a remission of our sins, or at least that a small prayer may comc from the gratitude of so many. For if those men, who have up to now been barely able in ten years to gain an imperfect knowledge of singing from their teachers, intercede for those teacher most faithfully with God, what do you think will be done for us and our helpers, who produce a perfect singer in the space of a year, or two at most? (cited in Cattin 1984:176, my emphasis)

This same optimism on entering a new era where learning will be easy and accurate can be found among many researchers within artificial intelligence and computerized teaching. And in the same way, as we shall see later, the inventors see mainly the gains in efficiency and, less clearly, how the traditional domain is transformed (or lost).

Guido d'Arezzo only systemized a few conventions concerning the representation of musical knowledge. This was enough to make him famous throughout most of the Latin world. In 1030 Pope John XIX called him to Rome to give a demonstration of his Antiphonary (book with liturgical chants). Guido tells this about the encounter: -9 6 -

John of the highest apostolic seat, who now governs the Roman church, hearing the reputation of our school, and how by our Antiphonary boys were learning songs they had not heard, was much astonished, and invited me to him by three messengers. [...] And so the Pontiff [PopeJ rejoiced greatly at my arrival, making much conversation and asking various questions; and, often turning over our Antiphonary as if it were some prodigy, and pondering the rules forming the preface, he did not cease or move from the place where he was sitting until he had satisfied his desire by learning one versicle [chant] that he had not heard, so that he suddenly recognised in himself what he scarcely believed in others. What need of more words? (ibid: 176-7)

This is the account of a demonstration of new information technology. It was invented by a previously unknown monk in a relatively small and unknown monastery. It was eagerly adopted by the clerical authorities in Rome and became a powerful tool in their centralizing work.

The use of notation developed slowly and for several centuries the description and perpetuation of oral traditions dominated. But the power of this new semiotic system was greater than necessary for efficient teaching of an existing repertoire. Gradually, it became a tool for composers exploring new possibilities for design and organization of polyphonic choir music. The tool was made to describe an existing domain of knowledge, but its power to create and prescribe new kinds of knowledge changed its original domain of description.

Detailed control of musical activities requires a high degree of correspondence between the tools for, and the object of, description. This correspondence developed gradually in a way that we could think of as coevolution 4. The mutual development of musical notation as a tool of description and music as the domain to be described continues for a period of about seven centuries; from the 9th to the 17th century (Parrish 1959:xiii). This picture of only two evolving units is a simplification. The actual coevolution was

4 The typical case of cocvolulion in biology is a development where each of two or more species continually adapts itself to the possibilities and limitations created by the others. They constitute mutual environments for evoluUon, and the result is often some kind of symbiosis (Bateson 1979). It is of course a question whether semiotic systems like music and notation can be regarded as evolving units in this more biological sense. Without investigating this, in my opinion, interesting quesuon further, we should take the idea of coevoluuon as a metaphor. - 97- certainly more complex, involving at least several other aspects of music-making like instruments, esthetic theory and the organization of music performances.

A possible chain of evolutionary steps may have been the following: - the originally simple notation creates the first possibility for experimenting with three and four part choir singing - these experiments stimulate the refinements of the notational system - experiments are extended to instruments, which gradually become standardized to facilitate harmonic coordination - complex social organization of large orchestras (e.g. the performances of symphonies) becomes the frame for an esthetic ideal.5

At the end of the 17th century there was a fixation in the development of both notation and instruments. Since Bach, there has been no major change in the traditional orchestral string instruments or in the system of notation, or in the scale. 6 Music, however, continues its development, inside the frames set by the instruments, the notation and the tempered scale. It is difficult to give reasons why the standardization appeared at that time, and why these particular systems proved viable. Semiotic systems seem to be relatively open to change in certain periods, but can be remarkably stable in other periods. 7

In the examples that follow, we shall see small glimpses from a several centuries long evolution, in which, scores gradually evolved to contain more and more musical information. Apparently as a paradox, this happened as each single note or sign contained less and less information. More emphasis was placed on the semiotic system and its syntax, and less on each element8. From a more or less approximate and analog description of melodic movements, notes became exact digitalized in a well defined system.

5 The "actors" of this coevoluUon may have a possible parallel in the current development of computerization: formal languages ("tools of description and prescription”), computers ("instruments for realizations of the formal descriptions"), computerized work settings ("complex social organization") all of which contributes to the "esthetics" of formalized knowledge.

6 Contemporary electronic music is not considered in this context.

7 The Egyptian hieroglyphs remained stable for more than 3000 years after a short period of evolution.

8 This is a typical tendency in the refinement of semiotic systems and it is closely linked to the process of abstraction. - 98-

Fig. 2 Musical notation, 11th century (ibid.: Plate XXa)

In figure 2, lyrics and notes are connected so the different syllables of the words are placed along a line moving analogously to the melody. This way of intervening in the writing system did not last long, but it is a good illustration of the way early notation was inseparably tied to the words of the chants. The notation was only a means to render the melodic aspect of the lyrics. Much of the musical information was still hidden in the words and the oral tradition. As a consequence, the notation aid not need to be explicit and unequivocal.

/ w Ai V'W ^cctrrnc diif fmnntS Q ^ u m n i f *d cfr tfee

» v • * fic no{ ‘ cm ir x FÏ k. ..<*«• ■nof~cmtr

Y I • S tfpctma>

jP ^ S e c u n d u f. A ^ c a p i

Fig. 3 Musical notation, end of 11th century (ibid.: Plate V) - 99-

The pitchcs in figure 3 are symbolized both by a "melodic line" moving up and down in an analogous manner, and by the "Guido d'Arezzo letters" a,b,c,d,e,f and g 9.

As we may see from figure 3, the same note letters are placed approximately on the same horizontal line. Thus, the pitch is indicated by double information; its vertical placement and the note designation in the form of a letter. This was simplified by using only a vertically placed dot (the note head) indicating the pitch (see figure 4). The letters still continue to be used as an oral means of referring to the graphically depicted notes.

Sr.yrcrtt b tjiu m .Inu&f cbjifchcur.ntti ______•

Fig. 4 Musical notation, 12th century (ibid.: Plate XXII)

We will now take a closer look at how the evolution of systems of notation, polyphonic music and instruments influenced each other. Both figure 2 and figure 4 show notations of early polyphonic music (i.e. music with two or more harmonizing parts). The description of the music is still quite imprecise as far as the dimension of time is concerned. There is no systematic use of barlines to indicate the basic metrum (rhythm punctuation), and the note symbols give only a very limited selection of different duration values. Moreover, some of the early notes had context dependent duration values; the same note could signify different durations depending on the value of the surrounding notes. (ibid.:73)

9 . . . A little exercise in : Note that the alphabet here is used to represent something quite different from the phonemes of spoken language. The alphabet as a semiotic system for the description of phonemes is not the set of contrasting elements (the characters) in itself, but a set of elementary contrasts in one domain (signs on paper) coupled with a set of contrasts in some other domain (spoken sounds, musical sounds etc.). -1 00-

Polyphonic music requires a synchronization of several different melodic lines. The musicians would then need a system of notadon continually informing them about their exact location in the music in progress. This function is attended by barlines and notes giving exact duration values. Such a degree of precision was not crucial in the early notation, because the melodies were monodic (one-part, plainsong) and learned mainly through oral tradition.

The vertical placement becomes more accurate with the introduction of staves made up of horizontal lines. The degree of precision was measured against efficiency and finally staves with five lines each were decided as the most suitable.

Fig. 5 Musical notation, 15th century (ibid.: Plate LX)

Figure 5 is the notation of a song by Landini arranged polyphonically for a keyboard instrument. There are six lines in each staff. Although it may look somewhat compact, the notation is unambiguous, clear and much closer to modern notation than the previous examples.

Most of the evolution of medieval systems of notation can be seen as a struggle for the creation of independent (context free) sets of written semiotic contrasts for each of the musical dimensions duration (rhythm) and pitch (melody). In the first centuries of this evolution, a certain text could impose a certain scale and melodic mode which in tum imposed a certain rhythm which in tum imposed a certain performance style. In fact, many of the musical elements that we today recognize as the basis of any scholarly -1 0 1 - music education, did not exist as independent conceptual entities; they were performed, but they could not be referred to because they were integrated aspects of different "esthetic packages".

In this situation it would be a useless redundancy to symbolize every dimension separately. However, when the composers wanted to coordinate the singing of several parts with different melodic and rhythmic patterns, the temporal precision of the notational system was crucial. In 1320 Philippe de Vitry invented the measured music and the use of barlines. He wrote a treatise titled Ars Nova on the new rhythmic and harmonic complexity made possible through his notation. This was a decisive step in the coevolution of music and notation. Equipped with this tool of description, there was virtually no limit to the number of voices that could be coordinated.

Figure 6 is taken from an orchestral score by Stravinsky. In this example, the composition is not described by one or two five line staves, but by 32 staves of 5 lines each, making a 160 line system. -1 0 2 -

Fig. 6 (page 60 from The Rite of Spring by Stravinsky) - 1 0 3 -

The musical complexity of "The Rite of Spring" would be inconceivable in an oral tradition. Each part is not necessarily technically more difficult for the individual musician to master than a medieval dance tune, but the medieval composers did not have at their disposal a semiotic system capable of coordinating so many musicians simultaneously. In "the Rite of Spring", all 160 lines are connected by barlines, making the score a 137 page long prescription for a complex polyphonic musical performance.

That such a musical complexity could not be achieved without the aid of musical notation, implies that the work could not have existed as a musical reality prior to its description. The dominant function of the notation has changed from description and mnemonics to composition and design ("programming of the orchestra”). However, the composing of "orally inconceivable" music did not happen for the first time with Stravinsky. The 12th century composer Perotin developed four-part polyphony in Paris while the Notre Dame cathedral was under construction. His music was of a complexity that would have been very difficult to conceive as well as to preserve in a purely oral tradition. We may acknowledge this as the moment when music, as a domain to be described, was definitively influenced by its tool of description.

The complexity of polyphony also had consequences for the social organization of musical performance. An orchestral work with the dimensions of "The Rite of Spring" would be equally inconceivable without a strictly hierarchical orchestra headed by a conductor.

Music, within oral traditions, exhibits, in general, a complexity based on modularity in such a way that each "module" is easy, or at least possible, to rehearse and remember. We could call this an organic hierarchy. The music can only survive if it can be internalized in the memory of musicians. This constraint serves, at the same time, as a check against making incomprehensible music. On the other hand, modem composers sometimes use geometrical or other extra-musical patterns in the score as the basis for their melodies. The notation is then neither a tool for the description of an existing musical reality, nor a tool for the realization of the composer's musical ideas since the vision of the music as sound did not exist prior to its notation. Complexity can be designed independently of the sounding result, and the orchestra can by means of the externalized score collaborate in what we could call a synthetic hierarchy. One result of this liberation from the constraints of memory, has been the composition of a huge amount of modem music that many people consider totally incomprehensible. Although he plays a part, it would be unfair to blame Guido d'Arezzo for this. -1 04-

Fields of complexity

Many textbooks present the history of music as a development from simplicity to complexity. In certain respects this is quite correct, but as a general conclusion it is mistaken. Rather, it is a question of replacing one kind of complexity with another, or replacing complexity in one dimension (or domain) with complexity in another. For example, improvisation was a kind of musical complexity that disappeared from orchestral performances as the orchestras increased to ever larger dimensions. Presented with a score with the complexity of "The Rite of Spring" it would only lead to disorder if each musician should add or disregard notes in accordance with his/her own mood. The performance as a totality becomes more complex even though the task of each musician becomes simpler.

There seems to be some general trends in the development of semiotic systems for description and prescription of human activity. Each elementary symbol in early musical notation was more composite and complex, i.e. contained more information than each note in modem systems. As a semiotic system develops, the elementary contrasts tend to be simplified in ways that make them more suitable for combination into more complex statements. If the symbols are simple and context independent on an elementary level, then they can more easily be combined to produce complexity on a composite level. The syntactic (grammatical or combinatorial) aspects of the semiotic system will accordingly increase in importance.

The same tendency can be observed in a whole range of fields: the letters of the phonemic alphabet can be combined to more numerous and more complex words than the signs in a pictographic system. Each industrial worker building a moon rocket can learn and accomplish his job more easily than a traditional carpenter building a house. The ultimate simplicity of the electronic binary code has produced an unforeseen complexity of computer applications. The modem complexity seems to appear at m ore composite levels than the "primitive" complexity, but this does not mean that its total complexity is greater. -105-

Musical notation as the creator of "false notes"

Scales with a great variety (and varying!) intervals are found in many orally transmitted musical traditions. A scale with twelve identical steps is only found in the Western classical tradition. The traditional way of tuning fixed pitched instruments became inadequate when both instruments and compositions, to an increasing degree, were made for polyphony. The scale had to be standardized to exploit the full range of harmonies made possible by notation. This was especially important for keyboard instruments. A major work of Bach ("Das wohltemperierte Klaver") is dedicated explicitly to the exploration of harmonic transformations made possible by the new tempered scale of his keyboard instruments. A simplification (standardization), at the level of the elementary pitch intervals, was the basis for an increased complexity at the more composite harmonic level. This standardization of scale was unnecessary in earlier periods because the music was concentrated on a few closely related harmonies. It was only when one attempted to use non-standard scales within the "new" polyphonic music that the result would sound dissonant or "false" 10.

Through the prestige power of bourgeois music, the tempered scale was gradually adopted as the standard for all music, making some of the tones regarded as correctly pitched in traditional scale systems, "false notes". The tempered scale and harmonies became a hallmark of bourgeois music appreciation. The system of notation was mentally coupled to this particular scale. All notes that could not be placed within this system were considered false. This will be illustrated by an example from an encounter between classical and folk music. The story, as well as the other references to Norwegian folk music, are based on my thesis on the cultural history of Norwegian folk music (Sinding-Larsen 1983).

A "millimeter" from the tradition

Some time in the 1880's, the country musician Olav Brenno played the folk instrument langeleik for tourists at a hotel in the Norwegian mountains. The old Norwegian langeleik is a string instrument in the dulcimer family. The pitch of each tone in the langeleik's scale is determined by the position of its frets. The principle is basically the

10 It is important to realize the difference between the tempered scale as an acoustic phenomenon and as a phenomenon of human esthetics. There are no "false notes" in the physical realm of acoustics. But through culturally specific socialization, we may learn to perceive and judge a certain set of tone intervals as correct- This is forcefully done in the case of Western music because the tempered scale is physically embedded in many sound producing instruments (e.g. the piano). -1 0 6 - same as for the guitar with one important difference: the intervals of the scale on a langeleik are not necessarily standardized as they are on the guitar.

After the performance, a member of the audience walked up to the musician and praised the music. However, the tourist, referring to his position as an organist, insisted that two of the tones of the langeleik were "false". He offered to correct this by moving the relevant frets a few millimeters. Olav Brenno agreed to this, without knowing how miserable the outcome would be. The classically tempered scale imposed by the organist was incompatible with Brenno's conception of, and way of, remembering his tunes. When he tried to play on his "correct" instrument, he did not recognize the tunes and "lost track". In the end, he gave up playing the langeleik.

Several decades later after the turn of the century, when Brenno had become an old man, he was contacted by young scholars in folk music who realized the cultural value of the old scales. They helped him move the frets back again to their original positions. They also gave him back some of his self-confidence by redefining the "false" notes as a valuable cultural trait. Brenno resumed his langeleik playing and many of the tunes came back to his memory after almost a generation in oblivion. The dominance of the tempered scale was, in this case, absolute because it was imbedded in the sound producing instrument.

The "imperialism" of the tempered scale is more subtle but obvious enough in a text written in 1850 by the Norwegian musicologist L.M.Lindemann. The citation is taken from an introduction to a collection of folk songs that Lindemann himself had collected and transcribed. Observe to what extent the notation of the music is intimately linked to the standardized scale:

"The problem of transcribing the melodies does not only consist in the lack of distinction and clarity in the old people's way of singing. Far worse is the fact that one repeatedly is presented with notes that are a quarter of a step higher or lower pitched than the appropriate ones; i.e.. notes that are placed exacdy in between our half-note steps. It is the task of the collector to determine to which note, the higher or the lower they belong. (...) By frankly confronting the old people with the two alternatives the singer can be guided to a choice of his own as to which tone to be considered as the right one." (cited in Dal 1956:182, my emphasis) -1 0 7 -

In this case it is obvious how the musical notation, as a tool of description, strongly influences its object of description. Lindemann did not recognize this as an inadequacy of the tool, but rather as a "deficiency" of the reality to be described.

Knowledge engineers and elusive answers

It is possible to draw some parallels between the collecting of folk music and knowledge engineering. The important task in both cases is the preservation of oral and unformalized knowledge by means of formal tools of description. While the ethnomusicologist uses musical notation as a tool for the description of music, the knowledge engineer uses various programming languages as tools for the description of professional experts' skill and knowledge n .

Knowledge engineers commonly complain of vague and elusive answers from experts whose knowledge does not easily lend itself to precise description. Because their job is to make running programs, they will have to use tools of formalization to confront the experts with the possible programming alternatives. The experts will be guided to a choice o f their own as to which knowledge to consider as the right one.

Once the knowledge is embedded in an expert system we may encounter the same kind of problems as the old langeleik player. The computerized form of the knowledge may be incompatible with a pre-computer way of thinking. The knowledge that "sounds false" will slowly be forgotten or actively suppressed.

As in the case with the notation and the piano, the expert systems and the knowledge acquisition tools may set the standard for all knowledge, not only what is subject to computerization.

11 In fact, the knowledge engineer will usually not use a programming language directly, but raiher a set of description tools callcd "systems description languages" or "knowledge acquisition tools". As the end product in any case shall be a program in some programming language, all these other languages will have to approach the same overall logic as the final programming language. -1 0 8 -

The tape-recorder - a perfect tool of description?

With the above mentioned problems in mind, it should be obvious that tape recorders have some great advantages for the preservation of music when compared with written musical notation. For instance, taped music renders all the subtleties of traditional scales that are eliminated in the abstractions of written music. But the problem of preserving the knowledge of music-making is by no means eliminated by this technique.

Norwegian folk musicians have always placed great value on a correct transmission of the tunes of their tradition. The registration of folk music by means of music textbooks and, to an even greater extent, by means of cassette tape recorders has contributed to the idea that a correct transmission from one generation to the next is equal to a detailed copying of the old forms. But music as a living esthetic expression is not something that it is possible to copy in every detail. To some extent it must always be re-created at each single performance, and its success as music will never solely depend on an accurate transmission of every tone. The detailed and truthful rendering of a single musical performance does not show how intonation and improvisation are related to changing public and performance situations.

In a certain sense, learning orally transmitted music from tape will preserve the tradition in a better way than learning from simplified and formalized written music. But the descriptive (and prescriptive) power of a tape recording is so great that an exact copying will leave even less room than written notes for giving the music a personal flavour. What has happened in some extreme cases among Norwegian folk musicians is that fanatic guardians of the tradition detect and arrest any deviation from well-known taped forms. Their corrections are particularly effective when these guardians act as judges in the annual contest of traditional fiddlers.

Lately this kind of traditionalism has been criticized. A growing number of young Norwegian folk musicians argue that the very idea of a detailed registration and copying of music as sound structure is incompatible with the intention of preserving music as a living tradition of music making. Improvisation, change and spontaneous response to each unique audience have always been important in folk music. The mastering of these aspects are the result of second order learning, above the morphology (first order) of a single tune (Bateson 1973:250-80). These aspects cannot be fully described with the actual tools of musical notation or registration. Perhaps they never will. This is knowledge that is rooted in a very complex interpersonal level. -1 0 9 -

Improvisation, intervals outside the tempered scale, and other characteristic traits of Norwegian folk music not captured by musical notation have been gradually disappearing since the beginning of this century. There have been two basically opposite strategies to counteract this development. The scientific folkloristic strategy is to refine and improve the tools of description to avoid loosing information in the act of description. The other more paradoxical strategy, followed by some young folk musicians, has been to delete information from written tunes that are considered too detailed. In this way, the written music once more increases its dependence on an oral learning context, the score as description becomes so coarse-grained that it is worthless as a prescription unless the musician considers the performance situation and the oral tradition. Learning the tunes directly from old performers with a minimal use of any descriptions or recordings is, by some fiddlers, considered the only real way of perpetuating the tradition. In their opinion, the concept of tradition should not be attached directly to the music at a describable morphological level but to the principle traditional conditions for learning and performance.

This story has relevance for the current situation where previously human dependent knowledge becomes externally available through knowledge based systems. Many expert systems are made and marketed with the idea of making expen knowledge more cheaply available to students. This is achieved as computers decrease the dependence on human expens as an expensive oral learning context. Explicitly or implicitly, it is assumed that this is a way of preserving knowledge traditions currently maintained by living expens. But it is an open question what kind of maintenance the computers enhance since computer based description can never capture more than some aspects of the total knowledge. And it is an equally open question whether more refined tools of description will make the situation better.

The Norwegian folk musicians realized that it was not the "bad" descriptions that were threatening the living tradition but the "good" ones. A good description generally means a context free description which means the folk musicians as a social group, (the context) loose control over their music tradition.

Gregorian chants and the reconstruction of authenticity

At the Benedictine Abbey of St. Pierre at Solesmes in France, monks have been working for more than a hundred years on the restoration of the old Gregorian chants. Their situation is, in many respects, similar to that of the young Norwegian fiddlers. In -1 1 0 - thc opinion of the Solesmes monks, the old chants have been "killed" by a too sophisticated and precise system of notation. Their strategy is twofold; on the one hand they re-edit facsimiles of the oldest Gregorian manuscripts, and on the other hand they develop an oral practice around these rudimentary manuscripts. A thousand years after these chants were written down with the first systems of musical notation, we see a systematic work of reconstruction of lost qualities of pre-notational plainsong. The Medician edition of the chants (1614) was considered to be the most oppressive and standardizing one, and the monks of Solesmes labored to recover from this papal act of description several centuries after it was imposed. The music had been trapped in its own system of preservation.

In an earlier version of the present paper, the story of the Solesmes monks ended at about this point. However, empirical descriptions of "reality" do not tend to be eternal, and some months ago I met a french musicologist, Marcel Perez, who made me reconsider my view of the project of Solesmes where he himself had been a pupil. Perez was doing research on the role of improvisation within Gregorian chant, when he found new evidence that it was formerly in widespread use. Primarily, it was an oral praxis, but early manuscripts contained many symbols indicating when the singer should elaborate or improvise and when he should follow the written music. The system of notation implied some kind of alternation where the initiative and control moved back and forth between the singer’s imagination and the prescriptions of the score. This was eliminated in later and more precise systems. But the skills of improvisation survived as an oral tradition within certain schools until the 19th century.

The monks of Solesmes dismissed this practice as unauthentic and insisted on the study of the earliest notation as the sole way to the "true" Gregorian chant. Because the early notes contained less information, we could say that their strategy increased the music’s dependence on an oral tradition. However, all their studies of the oldest and most "correct" manuscripts made them more obsessed with notation than ever before. The result of this quest for authenticity was that the little that remained of the still living traditions of improvisation disappeared completely. Instead they founded and promoted an entirely "new oral tradition" based on the "oldest written tradition".

As the justification for authenticity is based entirely on notation, there should be no surprise that the resulting music is rather rigid and without improvisation. - 1 11-

Some conclusions

When humans make tools for describing human activities, they will always involve themselves in a coevolutionary process. An improvement of the tools for description of a certain domain will, in general, also be the starting point for new design and prescription which will change the domain originally to be described.

Tools of description (languages, semiotic systems) are the basis for extemalization of knowledge; the storage and processing of knowledge independent of the individual human mind l2. Since the dawn of civilization, human knowledge has always been perpetuated and developed by an interplay between internalized and externalized representations of knowledge. Oral tradition is the typical case of internalized knowledge while writing is the most widespread tool for extemalization. The balance between the two kinds seems to be important for creativity and flexibility.

Looking at the history of culture, we see a general trend where tools of description become increasingly more precise and comprehensive. This is generally considered as a positive development. However, as demonstrated by the examples in this article, we also find cases where people consider precise descriptions negatively. The above mentioned Norwegian fiddlers, and to a certain extent the monks of Solesmes, seem to perceive the externalized versus internalized representation (or dependence vs. independence of an oral tradition) as a choice.

In my opinion, the tendency to think of precise descriptions as something inherently good or, at least, as an inevitable result of science expanding in accordance with some kind of destiny or natural law, is too strong. With the advent of the computer, and in particular, with the development of artificial intelligence and knowledge engineering, we should become increasingly conscious of our possibilities to choosc in this domain. Many essentially oral knowledge traditions stand at the threshold of an era of description comparable to that of music in the Middle Ages, but the current tools of description are far more refined and powerful.

Computer technology has made possible dynamic descriptions of processes through simulation. The range of possible tools of descriptions will widen as science makes progress in the understanding and description of fundamental properties of communication, perception and reasoning. The descriptive power of the computer has

*2 The concept" extemalization of knowledge” is developed further in my article Information technology and the management of knowledge (Sinding-Larsen 1987) -1 1 2 - reduced the role of human language itself from the principal tool of description to the principal object of description. The principles of advanced computer functioning become meta-knowledge and meta-language in many domains.

Most critiques of artificial intelligence have concentrated on the limitations of computers. Typically, one of the most famous books criticizing artificial intelligence is titled "What computers can’t do” (Dreyfus 1979). I think it is time to realize that we are caught in a situation with a twofold threat (and twofold possibilities). If the computer's power of description is too poor then we loose subtleties and details. If the tool of description is "too good", we may loose in orally based improvisation and flexibility, and that will, in the long run, be more serious.

Flexibility and improvisation are not only esthetic concerns. Every knowledge tradition needs living sources for its renewal. History can teach us how the long-term processes shaping culture are the expression of a balance between two regimes; one characterized by notation, explicit knowledge, fixation and standardization, the other by oral tradition, tacit knowledge, openness and improvisation. Our choice is of course not one side or the other but achieving a viable balance between the two. - 1 13-

References:

Bateson, Gregory 1973 "The Logical Categories of Learning and Communication" in: Bateson 1973 pgs. 250-80

Bateson, Gregory 1973 Steps to an Ecology of Mind, Paladin publ.

Bateson, Gregory 1979 Mind and Nature, E.P. Dutton publ.

Bolter, J. David 1984 Turing's Man Chapel Hill: The Univ. of North Carolina Press ISBN: 0-8078-4108-0

Canin, Giulio 1984, Music of the Middle Ages I, Cambridge: Cambridge Univ. Press.

Dal, Erik 1956, Nordisk folkeviseforskning siden 1800, Copenhagen (in Danish).

Dreyfus, Hubert 1979 What Computers Can’t Do New York: Harper and Row.

Geertz. Clifford 1973, The Interpretation of Cultures, Basic Books, N.Y.

Hindley, G. (ed.) 1971 The Larousse Encyclopedia of Music, Hamlyn ed., London.

Holbæk-Hansen, Håndlykken and Nygaard 1975 Systems Description and the Delta Language, Norwegian Computing Centre publ. no 523. Oslo.

Kvifte, Tellef 1981 On Variability, Ambiguity and Formal Structure in the Harding Fiddle Music In: Studia Instrumentorum musicae Popularis VII, Stockholm

Kvifte, Tellef 1985 Hva forteller notene? Om noteoppskrifter av folkemusikk. ("W hat's in the written music? On notation of folk music.") In: Ame Bjørndals hundreårs minne. Bergen: Forlaget Folkekultur, (in Norwegian)

Kvifte, Tellef 1988 Musical Instruments and man-machine-interaction, or Why Play the Tin Whistle when You Got a Synthesizer? In: Cybernetics winter issue 1988

Naissance de l'écriture. Cuneiformes et Hiéroglyphes. 1982, Catalogue d'exposition, Paris: Editions de la Reunion des musées nationaux ISBN: 2-7118-0201-9 -1 1 4 -

Parrish, Carl 1959 The Notation of Medieval Music New York: Norton & Co. (Lib. of Congress Cat.No. 59-10939)

Sinding-Larsen, Henrik 1983, Fra fest til forestilling, Magister thesis, Dept, of social anthropology, University of Oslo, (in Norwegian)

Sinding-Larsen, Henrik 1985 Le rite et le jeu - deux modes d'expérience dans la fête in: Le Carnaval, la fête et la communication, Actes des premières rencontres internationals Nice 1984, Nice: Editions Serre/UNESCO, ISBN: 2-86410-063-0

Sinding-Larsen, Henrik 1987, Information technology and the management of knowledge AI & SOCIETY: The Journal of Human and Machine Intelligence vol.l No.2, Oct-Nov.1987, ISSN: 0951-5666

Stravinsky, Igor 1947, The Rite of Spring, Boosey & Hawkes: London.

Winograd, Terry and Fernando Flores 1985 Understanding Computers and Cognition Norwood, New Jersey: Ablex Publ. Corp. ISBN: 0-89391-050-3 Im plicit Knowledge and. Expert Systems

Dianne C. Berry

Department of Experimental Psychology,

University of Oxford,

South Parks Road,

Oxford. 0X1 3UD.

England.

Running Title: Implicit Knowledge -116-

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Abstract

Implicit knowledge Is a major problem for those working In the area of knowledge elicitation. Despite this, the topic has attracted little discussion or research. Moreover, In the few Instances where the problem has been referred to there seems to be some confusion over what implicit knowledge is or might be.

The present paper looks at how the term expert systems has been used in the expert systems and artificial Intelligence literature and attempts to settle some of this confusion. It also looks at possible ways of assessing Implicit knowledge, as well as the constraints that implicit knowledge Imposes on the likely role of expert systems. I

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Implicit Knowledge

Knowledge elicitation has long been cited as being a, if not the, major

bottleneck in the development of expert systems (Barr and Feigenbaum, 1982;

Hayes-Roth, Waterman and Lenat, 1983.) Although this view is endorsed by many

researchers and practitioners in the field there Is less agreement about why

this is so, or how the problem could be overcome. One major difficulty in the

area is that people have tended to think of knowledge as being a 'unitary

thing'. They see experts' heads as being full of bits of knowledge and the

problem of knowledge elicitation is to "mine those Jewels of knowledge out of

their heads one by one" (Feigenbaum and McCorduck, 1983). As Young (1984)

emphasises, "knowledge is seen as a 'substance', portions of which can be

emitted and / or hewn off by the knowledge engineer".

Such views of knowledge have led to the suggestion that what is needed is

a large scale study to find good knowledge elicitation techniques or a

comparative evaluation of the strengths and weaknesses of several of them. A

more realistic and positive way to approach the problem, however, is to

recognise that even in a single domain expert knowledge is of several different

kinds. Moreover, the different kinds of knowledge require different knowledge

elicitation techniques to capture them most effectively. As Gammack and Young

(1985) point out, "the problem becomes transformed from that of finding one (or

several) ’good' techniques to that of amassing a suitable battery of techniques

and knowing how best to match them to the different kinds of knowledge".

Although a few researchers in the field have attempted to identify broad

categories of knowledge within limited domains and match these up with suitable

elicitation techniques, they have tended to focus on the more explicit, - 1 1 8 -

Im plicit Knowledge

reportable aspects of an Individual's knowledge. They have not paid sufficient attention to the crucial problem of Implicit knowledge.

Implicit knowledge Is a major problem for those working in the area of knowledge elicitation however. If people learn to perform tasks in such a way that important aspects of their knowledge are implicit In nature, then knowledge engineers will not be able to extract this knowledge and represent it in a meaningful way in a computer system. It is likely that even with greatly

Improved techniques a substantial amount of expertise will remain unelicited.

The difficulty in this area, however, is not Just that there has been relatively little acknowledgement of the problem of implicit knowledge, but that

In the small number of cases where the problem has been referred to there seems to be some confusion over what implicit knowledge Is or might be. People have used the term In different ways to refer, in some cases, to fundamentally different things. The present paper therefore looks at how the term implicit knowledge ha6 been used in the artificial intelligence and expert systems literature. It also looks at suggested ways of eliciting Implicit knowledge, and attempts to evaluate these. Finally, it considers the constraints that implicit knowledge Imposes on the likely role of expert systems.

Implicit Knnwledge and the Expert Systems Literature

One distinction that can be made with regards to implicit knowledge is between knowledge that has at one time been represented explicitly or declaratlvely, but no longer Is, and knowledge that arises as a result of an

Implicit learning process and has never previously been explicitly represented. -119-

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DiscuBsion in the expert systems literature has tended to focus on the former.

This type of Implicit knowledge is associated with models of learning such as

those put forward by Fitts (1964) and Anderson (1982). According to such

models expertise arises as a result of a three-stage learning process. In stage

1 (cognitive stage) an individual learns from instruction or observation what

actions are appropriate in which circumstances. In stage 2 (associative stage)

the relationships learned in phase 1 are practised until they become smooth and

accurate. In stage 3 (autonomous stage) relationships are compiled through

practice to the point where they can be done 'without thinking'. Declarative

knowledge thus gets transformed into procedural form. As the same knowledge is

used over and over again in a procedure we lose our access to it and thus lose

the ability to report it verbally.

As far as the implications for expert systems are concerned, Johnson

(1983) refers to the 'paradox of expertise'. This is where as Individuals

master more and more knowledge in order to carry out a task efficiently they

also lose awareness of what they know. Those who have acquired substantial

skill In a task, whether cognitive or motor, generally lose awareness of the

basis for their expertise. As Johnson states, "the very knowledge we wish to

represent in a computer program, as well as the knowledge we wish to teach to

others, often turns out to be knowledge that individuals are least able to talk about",

It is not necessarily the case, however, that expertise is simply associated with a declarative to procedural shift. In the medical domain, for example, medical students are taught certain explicit rules and strategies.

Experiments have shown that newly qualified doctors outperform experienced -1 20-

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physicians on paper and pencil tests that assess this type of knowledge (Jacoby

and Dallas, 1981). One possible explanation for these findings Is that the

knowledge is no longer available in the expert doctors because It has been

compiled Into a procedural form and is thereafter called up automatically when

needed. They no longer have verbal access to it. Alternatively, however, it

could be that the knowledge that the expert doctors use Is not a derivative of

the originally taught rules and strategies but Is new knowledge that has been

built up while gaining their expertise. This knowledge has been acquired as the

result of an implicit, as opposed to explicit, learning process. In practice It

Is probably a combination of these two possibilities that determines

performance.

A growing number of experiments in the psychological literature support

the notion of implicit knowledge gained as the result of an implicit learning

process (Reber, 1967, 1976; Berry and Broadbent, 1984, 1987, in press; Broadbent,

FitzGerald and Broadbent, 1986; Hayes and Broadbent, in press). In contrast to

the above type of implicit knowledge, this latter type has never been

represented explicitly or declaratlvely. Reber, for example, has suggested that

"complex structures such as those underlying language, socialisation, perception and sophisticated games are acquired implicitly and unconsciously," (Reber,

Kassln, Lewis and Cantor, 1980). As far as human experts are concerned they not only have difficulty describing what they do because their knowledge is no

longer in declarative form, but because some aspects of their knowledge never

have been represented explicitly. They have been learned through experience,

rather than being picked up from one or more textbooks. -121-

Im plicit Knowledge

Broadbent and colleagues have carried out a series of experiments

demonstrating the existence of implicit learning and knowledge in relation to

computer implemented control tasks. Berry and Broadbent (1984), for example,

showed that practice significantly improved ability to control complex computer

implemented systems, but had no effect on ability to answer related questions.

In contrast, verbal instruction significantly improved ability to answer

questions about the relationships within the system but had no significant

effect on control performance. Moreover, across a series of experiments there

was no evidence for a positive association between task performance and

question answering. Individuals who were better at controlling the task were

actually significantly worse at answering the questions. The tasks required subjects to reach and maintain target values of an output variable by varying

one or more input variables. For example, in one version subjects took on the

role of manager of a small sugar production factory and were told to reach and

maintain specified levels of sugar output by varying the number of workers

employed. Various different types of questionnaire were used but in each case

the pattern of results remained the same. Berry and Broadbent concluded that

the tasks were performed In some Implicit manner with individuals not being

verbally aware of the basis on which they were responding.

Although these two types of implicit knowledge have different origins and hence may possibly differ in terms of their potential retrievability, they can nevertheless both be classed as being implicit. There are other examples in the literature, however, where the term is used less accurately. In particular the term implicit or tacit knowledge Is mistakenly used to describe knowledge that could actually be made explicit given the right conditions. Collins, Green -122-

In p liclt Knowledge

and Draper (1985), for example, cite a project that Involved building

Transversely Excited Atmospheric Pressure lasers (TEA-lasers). They found that scientists were unsuccessful at building TEA lasers even though they consulted detailed written sources of information (which the authors believed contained every relevant fact and heuristic) and engaged in long discuosions with middlemen who knew a great deal about such devices. Even when scientists had prolonged contact with a successful laser builder this would not necessarily guarantee success.

Collins et al. report that It eventually became clear that one of the reasons why the unskilled laser builders were unable to make their models of the TEA-lasers work was because the leads running from their capacitors to the electrodes were too long. These leads had to be less than eight Inches In length, yet at the time nobody knew this, or rather, knew that they knew this.

Novice builders working from circuit diagrams had no idea of the importance of lead length whereas novice builders who learned their craft from an accomplished master would make similarly short leads without knowing or realising why. Collins et al. state, "neither expert nor successful apprentice need consciously know anything about the importance of lead length in order to pass on the skill and neither would, or Indeed could, report this feature of the device to even the most persistent knowledge engineer. One might say that this feature was known tacitly to the laser builder; it comprised part of their non- artlculateable laser building skill."

But Is this really so? If a successful laser builder had been asked, "how long do you make your leads", then surely he could have said something like, "I tend to make them this length", possibly gesturing with his hands. In other -123-

Im plicit Knowledge

words he could have cone up with a reasonable estimate If the right question

had been asked. This Is different to true Implicit knowledge, where the expert

would be unable to provide an answer or would provide an Inaccurate answer to

such a question. In the former case, although the laser builders might not have

realised the importance of lead length, it is likely that they would have had

some verbal access to their knowledge about it.

There is a distinction, therefore, between knowledge that is not usually

explicit but that can be made explicit, and knowledge that Is truly implicit.

The explicit - implicit distinction should not be thought of as being

synonymous with a verbal - non-verbal distinction however. It is also possible

to have knowledge that Is explicit, that Is, that can be demonstrated on demand,

but that cannot be verbalised. In some situations it is easier to show how to

do something than to explain how to do It. This can be contrasted with

implicit knowledge that is not callable on demand, but nevertheless plays a

vital role in task performance.

Assessing Implicit Knowledge

Given that in many domains a significant portion of an expert's knowledge

Is likely to be implicit in nature the obvious question is how can knowledge engineers possibly hope to elicit such knowledge? Even if they knew what questions to ask they would be given either uninformative or misleading answers. The problem Is not simply that experts would not be able to answer the questions, but that they might well give answers ‘that were fundamentally

Incorrect (lisbett and Wilson, 1977). Human experts are very good at what -124-

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Johnson calls "reconstructing“. According to Johnson, reconstructed methods of reasoning are rules for how a task might be done that seem plausible to a practitioner or group of practitioners in some domain of reasoning. These rules are often fairly general and are usually intended to apply to a variety of forms of a given task. Johnson distinguishes reconstructed reasoning from authentic reasoning, the latter of which are methods of reasoning inferred from actual observation of performance. He points out that much of professional education, particularly medical education, consists of experts teaching students reconstructed methods for doing tasks. The fact that many students have difficulty solving problems successfully on leaving medical school suggests that reconstructed methods are not adequate. “The novice physician is usually faced with a variety of specific situations in which he must apply what he is taught. Reconstructed methods must either become authentic through a process of relearning or, what is more likely, entirely new methods for doing the task successfully must be developed" (Johnson, 1983).

Collins et al. also point out that many experts actually believe in a formal text book model of knowledge. They say that even where an expert knows that the skills he has are essentially practical he will tend to think of this as a deficit rather than an accomplishment. Where a source of what is taken to be equivalent book knowledge is available it may be presented by the expert as a more 'perfect' representation of the area in question. This can lead experts quite unwittingly to offer formal book knowledge in the same way as, or instead of, their practical accomplishments, thus misleading the knowledge engineer. It is also the case that because experts cannot come up with answers to what seem like reasonable questions they can end up feeling threatened by the knowledge -125-

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elicitation process. This will result in their seeing themselves as being

inconsistent or illogical thinkers,

Protocol analysis

One approach to the problem of experts not being able to provide reliable answers to questions has been to systematically observe experts performing real world tasks (either natural or simulated). Generally, experts are asked to provide running commentaries while they carry out a task. The commentary is recorded, transcribed and analysed for Information about the expert's knowledge and problem solving strategies (protocol analysis). Gammack and Young (1985) suggest that the merit of protocol analysis is that "it goes beyond what experts can explicitly tell you in a problem solving situation to permit inference of what knowledge they must be using but either cannot verbalise or are unaware of. By reconstructing the solution using inferred production system rules, the expert's knowledge can be modelled. Such a method is particularly useful for eliciting procedures that experts use in problem solving, which they may not be able to articulate".

In order for protocol analysis to be effective, however, the knowledge engineer must be sufficiently acquainted with the domain to understand the expert's task (note that this Is different to being able to perform the task).

If using a simulation technique the selection of problems is also crucial In order to get a representative sample. If studying the expert in his or her natural setting behaviour must be recorded for a sufficiently long period of time to cover a representative sample of activities. This is obviously very -126-

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time consuming. Burton, Shadbolt, Hedgecock and Rugg (1987) carried out a formal evaluation of various knowledge elicitation techniques including protocol analysis. They found that protocol analysis not only took longer to perform and analyse than comparable techniques, but also yielded a substantially smaller amount of the necessary information.

There are other problems with this type of assessment method. Protocols are often incomplete. They may contain evidence about how knowledge is used but not about its full range. Such techniques cannot be used to establish the limits of an expert's knowledge. If something is not mentioned it does not mean that the expert does not know it. Moreover, experts generally cannot verbalise as fast as they reason. They may well leave out steps in the reasoning process. They may also omit things that seem obvious to them.

It is also the case that experts need experience at 'thinking aloud'. Not all experts are able to produce running commentaries. As Kidd and Velbank

(1984) point out, non-professional experts, such as technicians, have particular difficulty with such techniques. It can be an even more alien process to them.

In many domains providing a running commentary is a demanding secondary task.

This is a particular problem where the task in question requires a great deal of mental capacity. In this case protocols are likely to be particularly sketchy.

A more serious problem is that producing a running commentary can affect the way a task is actually done. Laboratory studies have shown that concurrent verbalisation can affect the way in which a task is carried out by forcing concentration on critical task aspects. Berry and Broadbent (1984) used concurrent verbalisation in a study with their computer implemented control -127-

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tasks. They found that although verbalisation alone had no significant effect on control performance or question answering , overt verbalisation following detailed verbal Instruction did lead to a significant Improvement In task performance over that produced by the Instruction alone. If experts have difficulty describing the way in which they carry out a task, because of the nature of the knowledge Involved, forcing them to produce a running commentary is likely to make them approach the task in a different, possibly more systematic, way. The line of reasoning chosen by an expert when asked to comment on his or her behaviour while solving a problem will therefore be different to when that same expert solves the same problem under more natural oondltlons.

It is clear that verbal protocols can provide a further source of information about an expert's knowledge, from that which can be obtained by interview techniques. It is also clear, however, that protocol analysis will still leave many aspects of an expert's knowledge untapped. Although it is possible that knowledge engineers will be able to infer some of the more

Implicit aspects of an expert's knowledge from observing and recording their behaviour and commentary, this will only be the 'tip of the iceberg'. Vhere behaviour is particularly complex knowledge engineers will stand little chance of isolating the subcomponents and assessing exactly what the expert knows about each of these. They will only be able to observe the expert's behaviour as a whole. -128-

Im plicit Knowledge

Machine Induction

Some researchers and practitioners believe that it is possible to sidestep

the problem of Implicit knowledge by using machine induction techniques. The

principle of machine induction is that the human expert provides a number of

examples of different types of decision from the task domain (called the

training set). The expert also provides the relevant factors (attributes)

influencing each decision. These are then fed into the system as raw data and

the system applies an inductive algorithm to discover the simplest set of rules

that will generate those examples. This results in formulation of the decision

process and enables prediction of decisions for examples not contained in the

training set. Advocates of the technique claim that it is very easy for human

experts to provide sets of preclassified examples. Moreover, an induction

system will always account for all of the example cases. Nichalskl and

Chllausky (1980), for example, compared machine induction with expert derived

rules for diagnosing soybean diseases. They found that the experts first set

of rules covered some 80 percent of cases and although he tried to produce

further rules he found it impossible and therefore elected to adopt the machine

Induced rules in preference to his own.

Automatic induction is obviously a powerful method for producing rule

bases very rapidly. It is also one way of getting at the problem of tacit

knowledge because it is not necessary for the expert to be armed with a clear

mental picture of the rule that he uses when performing a particular task. As

Michle (1984) points out, "the expert Is enabled to transfer to the machine a

Judgemental rule which he already had in his head but had not explicitly -129-

Im plicit Knowledge

formulated." There are some problems with Induction techniques, however. In

some domains it Is not possible to supply a database of documented cases.

Moreover, what comes out of an Induction system can only be as good as the

examples that go into it. The algorithm requires different cases of decision

types Including both the common or mundane and rare or special. A random

sample from the problem space will not give this. A further problem is that

the rules which are Induced from a set of examples will not necessarily be the

same as the ones a human expert uses. They tend to be far more complex and

difficult to understand and hence lose the transparency which is believed to be

critical In many expert system domains.

The Role of Expert Systems

Collins et al. put forward two solutions to the problem of Implicit

knowledge. First, they suggest that the knowledge engineer should spend a

period of apprenticeship on the task In question. They recommend that, "the

only solution to the problem demands that the knowledge engineer must do more

than tap the knowledge of the expert at one remove, but must undertake at least

a short apprenticeship - a period of participant observation - as part of the elicitation process". This is not really a suitable solution, however. Even If knowledge engineers were to serve a sufficiently long apprenticeship to gain some 'tacit* knowledge about a particular area of expertise, they would simply have transferred the problem of elicitation. They would still be faced with the problem of representing the knowledge in some explicit form without distorting it in any way. -130-

Im plicit Knowledge

Secondly, Collins et al. suggest that because of the problem of tacit knowledge we need to review the likely role of expert systems. They argue that with the current state of the art systems should not be built with the aim of replacing skilled persons. Their only role at present is as aids to increase the productivity of skilled persons, or to replace the relatively unskilled. In both cases the relevant tacit knowledge can be supplied by the user.

Expert systems were originally conceived of as replacements for human experts, or as a way of making expertise more widely available. It has become clear, however, that in many domains it is not possible to extract all of the necessary aspects of an expert's knowledge and represent these in a computer system. This does not mean to say that expert systems cannot fufill a useful function. The existence of implicit knowledge might restrict the scope of expert systems but it should not lead to their total abandonment.

One possible guiding principle is that human resources should be complemented in the most optimal way. Humans have various talents that should not be ignored. It is necessary to get the best mix of man and machine. Some researchers are now striving in this direction and are attempting to develop

'cooperative assistants' (for example, Rector, Newton and Harsden, 1985). In a similar vein, Woods (1986) has recently suggested that rather than building systems to simulate those aspects of human reasoning at which human experts are highly competent, a more effective way to employ AI technology would be to support those aspects of reasoning at which most humans (experts or not) are weak as a result of their built in cognitive limitations. Even relatively simple systems can be of benefit to human experts. Dreyfus and Dreyfus (1986) describe a diagnostic prompting system called Reconsider, developed by Blois -131-

Im plicit Knowledge

and colleagues. The purpose of the system is to address the real world problem

that the 'most frequent diagnostic mistake is oversight'. Reconsider is an

interactive encyclopedia designed to help doctors determine a patient's disease.

The doctor merely types in the symptoms and the systems presents a

comprehensive list of associated diseases. Plausible diagnoses are ranked in

order of their probability and adding more symptoms reorders the list. The

doctor uses Reconsider to avoid Jumping to conclusions and to remind him or

herself of other possibilities. But in the end it remains the task of the

doctor to make the diagnosis.

Conclusions

In order for knowledge engineering to advance as a science it is necessary

to recognise the existence of implicit knowledge as well as the constraints

that it imposes on system development. At present there is no satisfactory way

of eliciting much of what we know as implicit or tacit knowledge. Certain

techniques (protocol analysis and machine induction) allow us to infer more about an expert's knowledge than we would be able to on the basis of direct questionning alone, but there are also significant problems associated with the use of such techniques. Research needs to be aimed at identifying those aspects of Implicit knowledge that are potentially elicitable and developing new techniques to elicit these. A major difficulty here is that it is often necessary for knowledge engineers to have some preconceived ideas about the knowledge that they are trying to assess in order to design a test to assess it. They need a certain degree of a priori knowledge in order to design a -132-

Im plicit Knowledge

satisfactory test and In many cases this Is not possible. Even with improved techniques, however, It is likely that a substantial amount of expertise will remain unellcited. It is also the case that we need to review the likely role of expert systems. Rather than developing systems to replace human expertise, a more profitable approach night be to develop systems to complement human expertise. People and computers could then be viewed as Joint cognitive systems. -133-

Im plicit Knowledge

BeXereaces

Anderson, J.R. (1982). Acquisition of a Cognitive Skill. Psychological Review.

89. 4, 369-406.

Barr, A. and Feigenbaum, B.A. (1982). The Handbook of Artificial intelligence.

Pitman Books. London.

Berry, D.C. and Broadbent, D.B. (1984). On the Relationship Between Task

Performance and Associated Verbalisable Knowledge. Quarterly Journal of

Experimental Psychology. 36A. 209-231.

Berry, D.C. and Broadbent, D.E. (1986). Bxpert Systems and the Man Machine

Interface. Bxpert Systems:__The International Journal ot Knowledge

Engineering. 3., 4, 228-231.

Berry, D.C. and Broadbent, D.E. (1987). The Combination of Explicit and

Implicit Learning Processes. Psychological Research. 49. 7-15.

Berry, D.C. and Broadbent, D.B. (In Press). Interactive Tasks and the Implicit -

Bxplicit Distinction. British Journal of Psychology.

Broadbent, D.B., FitzGerald, P. and Broadbent, M.H.P. (1986). Implicit and

Bxplicit Knowledge in the Control of Complex Systems. British Journal of

Psychology. 77. 33-50.

Burton, A.M., Shadbolt, H.R., Hedgecock, A.P. and Rugg, G. (1987), A Formal

Evaluation of Knowledge Blicitation Techniques for Expert Systems. Report

on Alvey Project IKBS 134, July 1987.

Collins, H.M., Green, R.H. and Draper, R.C. (1985). Where’s the Expertise?:

Bxpert Systems as a Medium of Knowledge Transfer. In M. Merry (ed).

Expert Systems ‘85. Cambridge University Press. -1 3 4 -

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Dreyfus, H.L. and Dreyfus, S.B. (1986). Mind Over Machine. Basil Blackwell.

Oxford.

Feigenbaum, B.A. and McCorduck, P. <1983). The Fifth Generation. Addison

Wesley. Hew York.

Pitts, P.M. (1964). Perceptual-Motor Skill Learning. In A.V. Melton (ed.).

Categories of Human Learning. Academic Press. New York.

Gammack, J. and Young, R. (1985). Psychological Techniques for Eliciting Expert

Knowledge. In M. Bramer (ed.). Research and Development In Expert Systems.

Cambridge University Press.

Hayes, K.A. and Broadbent, D.E. (In Press). Two Modes of Learning for

Interactive Tasks. Cognition.

Hayes-Roth, F., Waterman, D.A. and Lenat, D.B. (1983). Building Expert Systems.

Addison Vesley. New York.

Jacoby, L.L. and Dallas, M. (1981). On the Relationship between Autobiographical

Memory and Perceptual Learning. Journal of Experimental Psychology:

General. 110. 306-40.

Johnson, P.E. <1983). What Kind of Bxpert should a System Be? Journal of

Medicine and Philosophy, 77-97.

Kidd, A. and Welbank, M. <1984). Knowledge Acquisition. In J. Fox .

Info tech State of the Art Report on Expert Systems. Infotech. Pergamon.

Michalski, R.S. and Chllausky, R.C. <1980). Knowledge Acquisition by Encoding

Bxpert Rules Versus Computer Induction From Examples: A Case Study

Involving Soybean Pathology. International Journal..□! Man Madilng-Studles,

12* 63-87. -7 3 5 -

Kichie, D. (1984). Automating the Synthesis of Expert Knowledge. ASLIB 17th

Annual Lecture. June 1984.

Nisbett, R.B. and Vilson, R.D. (1977). Telling More Than Ve Can Know: Verbal

Reports on Mental Processes. Psychological Review. 84. 231-259.

Reber, A.S. (1967). Implicit Learning of Artificial Grammars. Journal of Verbal

Learning and Verbal Behaviour. 5* 855-863.

Reber, A.S. (1976). Implicit Learning of Synthetic Languages: The Role of

Instructional Set. Journal of Experimental Psychology:__ Human Learning and

Memory. 2* 88-94.

Reber, A.S., Kassin, S.M., Lewis, S. and Cantor, G. (1980). On the Relationship

Between Implicit and Explicit Modes of Learning a Complex Rule Structure.

Journal ai Experimental fsychalagy;__Human Learning and Memory.

492-502.

Rector, A.L., Newton, P.D. and Marsden, P. (1985). What Kind of System does an

Expert Need? In M. Merry (ed.). Expert Systems '85. Cambridge University

Press.

Woods, D.D. (1986). Cognitive Technologies: The Design of Joint Human-

Machine Cognitive Systems. Al Magazine. Vinter (6) 86-92.

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Infotech State of the Art Report on Expert Systems. Infotech. Pergamon.

THEORIES OF KNOWLEDGE AND MISCONCEPTIONS IN A.I.

Anna Hart

Faculty of Science

Lancashire Polytechnic

PRESTON PR1 2TQ

UNITED KINGDOM

Introduction.

This paper is intended to be provocative, and to point out some dichotomies in A.I. work. It makes no claim of originality, nor does it purport to describe the views of 'all' workers in artificial intelligence. (Indeed the views of the A.I. conmunity vary considerably). However, it does raise issues which are still somewhat controversial amongst practitioners of A.I. (more specifically, developers of expert systems) and some researchers and teachers of the subject. These issues are seen to be particularly important in the context of the use of expert systems in the professions, and the future role of human competence.

Assumptions of Artificial Intelligence

Although A.I. practitioners may not consciously engage in philosophical debate, their work often indicates that they are making important philosophical assumptions. This is to be expected, as the thrust of their work is to model intelligence and knowledge on computers. Interestingly, the Handbook of A.I., which is still used as a reference text, actually warns against asking too many detailed questions about the nature of knowledge.( Feigenbaum and Barr, 1981, p. 144 ) - 138-

An examination of papers and books by eminent people, and discussion with many, many practitioners yielded the following as a sunmary of an ’EXTREME' view:

Human Intellectual activity will be best described 1n the

terms we Invent to describe A.I. programs. I.e. 'true'

knowledge is that which can be represented on a computer.

WHAT needs to be represented is known a priori. The main

problem is HOW it is to be represented.

Knowledge is data structures and interpretive procedures

(or sometimes facts plus rules). This knowledge is

explicit and objective.

Logical consistency is an ideal; all the knowledge for a

particular domain can be described verbally and modelled

explicitly. Knowledge can be structured, and expert systems

which are closed and consistent can be built, provided that

the builder can find a suitable representation.

This underlying theory has its roots in various Schools of philosophy, particularly Descartes, Leibniz, Kant , Boole, and

Hume. In contrast to this positivist view others emphasise the importance of judgement,intuition, human values, and other non-mathematical aspects of human knowledge.

(see Pratt,1987; Haugeland, 1986)

In such extreme views the following terms are inadequately defined, and some of the concepts ignored. -139-

TACIT KNOWLEDGE

CONFIDENCE

COMPETENCE

CONTEXT

BELIEF

RISK

MOTIVE; HUMAN VALUES

EMOTION; INVOLVEMENT

INSTINCTIVE BEHAVIOUR.

(Appendix 1 gives contrasting definitions for these terms)

Consequences of the Assumptions.

Such beliefs are manifested during knowledge engineering,

principally in the knowledge elicitation process. For example,

here are three different views of a situation where the 'expert'

is having difficulty verbalising knowledge in a form which is

suitable for a computer model.

The expert is inadequate because he/she is incapable

of verbalising the knowledge in an unambionous format.

The knowledge engineer is using an inadequate method of

knowledge elicitation.

The problem is inherent in the nature of knowledge, and

varies in magnitude from domain to domain.

These three 'lines' are most certainly taken by researchers and

practitioners. Their consequences in terms of the role of -140-

knowledge and the Importance of different types of knowledge are very Important.

It 1s manifestly true that many experts describe their knowledge

1n fuzzy, Imprecise and emotive terms. It is by no means self-evident that this 1s due to some Inadequacy on their part.

Many experts have learnt to 'do'rather than to explain and justify. They may have learnt to 'cope with',rather than to fully understand, the uncertainty. Their behaviour may be based on a a mix of feeling and logic, rather than wholly on the latter. In some domains this is surely how they talk.

(Josefson, 1987; see also Appendix 2).

Such behaviour tends to support the models proposed by the

Dreyfi(1986). Current work by the author suggests that similar principles apply in one of the most logical domains i.e. mathematical reasoning.

For example, it seems that very often experts stress the following:

the importance of being in the situation with a real

problem

their feelings, especially in the context of previous

related experiences

their motives, beliefs, fears and risks

what they DO NOT know, and their uncertainty about what - 141-

There 1s evidence that emotion plays a part 1n the coding and retrieval of knowledge (Mandler, 1975). While the part played by feelings may be seen as 'Irrational' or 'unscientific' it may be the very strength of people. Clearly judgements need to be 'fair' within the context of knowledge and values, otherwise they become prejudices or biases. 'Involvement' in decisions can be more fair than mere cold logic.

Professional Competence.

It 1s useful to describe competence 1n terms of 'confidence' and

'performance'. My thesis 1s that competence 1s a mix of these two aspects, and that the two 'co-evolve' together. It is important that they are compatible, I.e. that a person's level of confidence In his/her ability matches the performance level.

Performance 1s that which can be measured in terms of adequate solutions to problems and explanations of behaviour. (This 1s the criterion which is applied to computer systems, and increasingly demanded of professions such as teaching and nursing) Confidence is the willingness to apply, modify, break 'rules', to try new solutions or abandon an approach,to tackle new problems, and to guess, question and argue. It 1s difficult or impossible to quantify, but can be observed.

In terms of the Dreyfus model confidence is the factor which enables the professional to progress from being competent to being expert by developing an instinctive behaviour which 1s not

'rule-bound'. It is that which allows the person to become involved in situations and problems as described by

W1nograd(1986).

The role of tacit , or implicit, knowledge Is extremely important. -142-

The existence of Implicit knowledge (sometimes referred to as

'knowledge of experience1 in contrast to 'proposltlonal knowledge') 1s still questioned by some. It Is seen as a convenient excuse for an Inability to extract or verbalise knowledge. However, the transcripts already referred to provide suggestive evidence for the prevalence of tacit knowledge, and the work by Berry (1987) gives empirical verification. In fact, if

Berry's experiments, which are necessarily small scale and restricted, demonstrate the development of implicit knowledge, it is likely that in less well-defined professional situations the phenomenon may be more pronounced. If further evidence is needed then the connectionist work in neural nets and parallel distributed processing shows that systems storing knowledge implicitly can remember, learn, and mimic human cognitive behaviour. This is not to say that they function in the same way as human beings, nor is it clear how such systems should be used in practice; it is, nonetheless a powerful demonstration that knowledge does not have to be stored explicitly in order to be used effectively. (McClelland et a1.1986)

It seems clear that there are at least two types of implicit knowledge; that which has once been explicit, and which given time and effort can be made explicit again and verbalised, and that which BY ITS VERY NATURE is implicit. The connectionist work shows the feasibility of the latter and it is, of course, this knowledge which presents problems for knowledge engineering.

While ingenious methods of knowledge elicitation may tease out the former type of knowledge, the latter seems to be inaccessible.

In practice, it may not be known a priori (or at all) which types of knowledge are important in any domain or situation.

The feasibility of distributed models also challenges the assumption -- ^ ' **ADplr\c» ( d . i t o W )

t ir\Jf \\j

Fc^JkCO- 1

\/ »S i(\tAi'pn.hv«- proc.s.sv> * (\.I ^^Vçjyns o jy _ ^ \ j . r \ W irXSLf p rtr^Ofvi. -144-

that knowledge Is clumped or modular, and that knowledge for a set of tasks

can be Isolated from the rest of the cognitive system. Cognitive systems

may operate as Interconnected entitles (Varela, 1979). The conscious

verbalisation «ay be a sunmary of the most Important aspects of the

cognitive state, 1n a particular context. It has been known for

some time that the conscious is capable of handling a relatively

small number of concepts at once, but this does not mean that

the subconscious has the same structure or similar restrictions.

(See Fischler et al, 1987;McClell and et al, 1986 vol 2 chapter 22

for conments on related matters. )

The Externalisation of Knowledge.

Building an expert system is a process of externalisation of

knowledge as described by Sinding-Larsen ( *).

What is being represented is 'machine representable knowledge', as

proposed by Nygaard{*). Figure 1 indicates the stages

that take place in this process. There are two mappings; from

the cognitive system of an 'expert' onto the verbal system, and

thence onto some rule-based representation. Distortion is likely

and almost inevitable at each of these two stages. This raises

the importance of language in the context of thought. The figure,

and this argument, suggests that verbal language cannot and does

not fully describe the associated cognitive process. It is,

however, a means of communicating from person to person, and also a way of consciously influencing the cognitive process. Verbal

comnunication works reasonably well from person to person when

the two people have compatible understandings of the situation or

context of discussion. Much of this is tacitly understood, and

is brought to notice more when the understandings are

incompatible. The work of Rommetveit(*) describes this very -145- well. On a computer a possible problem 1s that knowledge elicited 1n one context can be produced 1n an entirely different and sometimes unsuitable context. Making every single context explicitly clear 1s Impossible , and for human beings unnecessary.

This 1s related to the Issue of 'throwness' described by

Wlnograd.(1986)

Language Is therefore an Interpretation of some cognitive process

or state, and , like behaviour, is highly context dependent.

Objective meaning 1s therefore not something which can be achieved

in all situations. The two paradigms proposed by Wittgenstein

(Le1nfeller(*)) illustrate this dichotomy; 'meaning

Is usage' fits our current thesis, but not the positivist view of

intelligence and language.

This puts a different perspective on knowledge based systems.

They are problem-solving tools. They cannot reason in the same way as people , but they can be used to complement human

knowledge, and thereby to provide a better environment for

decision-making.

Important issues.

Any model is, by definition, incomplete. Human cognitive models

are Incomplete but in many ways versatile and effective.

Machine-representable knowledge is powerful in some respects and not in others. Computer systems should be designed to make people better at what they are good at; this does not mean replicating all of human reasoning. It may mean complementing

1t. This will also have the by-product of changing some aspects of human knowledge (Sinding-Larsen, (*)) Mary Douglas(1987) proposes the Idea of an Institution thinking; we do not know what would happen to knowledge or thought if machines became members of those institutions. -146-

fvgiA/e 2

Ê^pw hse. lAvolvt» OUJQ/ftiMlV) o f>

buJt »vcfc djLptwtlA%UL On,

We need to be careful that we are not de-skill Ing people or

undervaluing their true skills or strengths. Confidence ,

Intuition and Implicit knowledge need to be developed, and there

1s a danger that the more naive views of A.I. may undervalue these

aspects because they cannot be modelled in rules. Over-reliance on a

model produces irrationality. A knowledge of logic, mathematics

probability theory etc. can increase rationality, but an over

reliance has a detrimental effect,as shown in Figure 2. This must

be true of A.I. systems as well.

Conclusion

Extreme positivist views of knowledge, as perpetrated by some A.I.

enthusiasts, ignore very important aspects of human knowledge.

Responsibility and motive are intrinsic to many professional

activities, where people develop expertise and knowledge. This

knowledge is often intuitive and difficult to express

'scientifically1. Some A.I. systems, designed with a true

appreciation of the strengths of users in mind together with a

respect for their abilities, may help people to develop

professional competence . Others may de-skill by encouraging an

over-reliance on computer models and a corresponding reduction in

human confidence and ability. Lyotard (1984) warns against views of knowledge which allow merely 'truth' and disallow critical reflexive knowledge associated with values, desires and goodness. Roszak(1986) adds warnings about the 'information cult'.

Note

Many of the issues raised in this paper were discussed later in the meeting in Paris. Typically there were attempts to provide unambiguous definitions of important concepts, contexts and terms. -148-

To a certain extent we seem to be able to manage with uncertainty, and this may be the essence of knowledge. (Even modern physics shows that precision decreases with certainty). This does not

Invalidate attempts to make things clear and explicit, but 1t does mean that we should not Insist that everything has to be made so before it can gain acceptibil1ty. -1 49-

References.

(note that references marked (*) refer to papers given at the

Paris meeting)

Berry, D. (1987) The Problem of Implicit Knowledge

Expert Systems, the Journal of Knowledge Engineering

Vol 4 no. 3

Douglas, M. (1987) How Institutions Think. Routledge and Kegan Paul: London

Dreyfus, H; Dreyfus S. (1986) Mind over Machine. Free Press

New York

Feigenbaum, E.A. ;Barr, A. (1981) The Handbook of Artificial

Intelligence Vol 1. London Pitman Books.

Fischler, M.A.;Firschein, 0. (1987) Intelligence. The eye, the brain and the computer. Addison-Wesley. ( see especially table 2.3)

Haugeland.J (1986) Artificial Intelligence; the very idea

London: MIT

Josefson, I. (1987)Knowledge and Experience. Applied Artificial

Intelligence. 1 : 173 - 180 -150-

Lyotard, J.F. (1984) The Postmodern Condition : a Report on

Knowledge. Manchester University Press

Mandler, G. (1975) Mind and Emotion. Wiley: New York.

McClelland, J.L.; Rumelhart, D.E. (1986) Parallel Distributed

Processing: Explorations in the Microstructure of Cognition.

Vols 1 and 2. MIT Press.

Pratt, V. (1987) Thinking Machines: the evolution of

artificial intelligence. Blackwell: Oxford.

Roszak, T. (1986) The Cult of Information.Lutterworth Press:Cambridge

Varela,F.J. (1979) Principles of Biological Autonomy. North Holland

New York.

Winograd.T; Flores,F. (1986) Understanding Computers and

Cognition: a New Foundation for Design. Ablex Publishing Co.

Norwood, New Jersey. -1 51-

Appendlx 1

SOME IMPORTANT CONCEPTS FOR KNOWLEDGE - CONTRASTING DEFINITIONS.

Hard line A.I. definitions Alternative definitions

TACIT KNOWLEDGE

Often equated with heuristics i.e. Knowledge which 1s not explicit rules which are usually unspoken but or verbal either because It was once which are capable of being explicit but has now been coded in verbalized. It is often thought another form, or which has never been to have been explicit but become explicit. It can be used but not tacit or implicit through repeated necessarily consciously known, and Is an use and experience. intrinsic part of holistic knowledge

which codes context and experience

as well as the more explicit facts

and rules. Some of that which was

once explicit can be teased out by

elici tation.

CONFIDENCE

Confidence factors are degrees of A necessary part of competence ;

belief in models of uncertainty ; occurring in symbiosis with performance

these are perpetrated in shells. The willingness to apply/break/change rules/behaviour and to perform /explain

Knowing what you dare try and what you

dare not -152-

COMPETENCE

Success rate In terms of % correct Confidence and performance. Ability to answers or adequate explanations; perform well on familiar and unfamiliar, recent work sees 'competent' having the confidence to try or refuse, systems as those with EXPLICIT then to reflect on performance and to knowledge and reasoning (sometimes justify outcomes. Ability to apply but not always causal models) which theoretical and explicit knowledge and to can provide useful and meaningful develop tacit knowledge. explanations.

Also related to flexibility 1n being able to respond to a variety of problems and to graceful degradation.

(In the context of natural language understanding e.g. Chomsky there is a distinction between competence and performance, but this is rather di fferent) I

-153-

CONTEXT

The set of conditions/assumptions The entire situation Including history,

under which a rule/fact can be true environment, knowledge, beliefs, fears,

or applied. This 1s related to feelings. A description of the world at

the frame problem and the 'current the time. It 1s impossible to be sure

g o a l 1. of what Is present, or what is relevant

In natural language understanding to the situation. Experience can be

the context for semantic encoded in a holistic way.

understanding 1s more pertinent

(e.g.plans, scripts)

IF A AND B THEN C

A and B are the context for C

BELIEF

Knowledge!

Held 1n contrast to facts which are A statement which has some degree of

deemed to be TRUE. truth; not easily distinguished from a

- not consensual fact, except that facts are more

- deal with conceptual entites generally accepted. Most of knowledge

- represent alternative worlds is beliefs, and the features on the left

- have an evaluative/affective apply to knowledge as well as to beliefs

component

- based on subjective experiences

- do not know a priori what

knowledge is relevant to a belief

- credibility and emotion Interact

in evaluation leading to valuers

- not measurable nor objective. -154-

RISK

Expected loss for a particular Possible loss, Influencing belief, values decision; can influence the control and reasoning; an intrinsic part of strategy reasoning which can affect probability

estimates.

We sometimes (often) reason in terms

of risk rather than likelihood.

MOTIVE

guiding principles, drive; affected by feelings amd emotion; in natural language processing we behave 1n a way which brings about a purposes and plans associated with feeling of satisfaction or comfort- the judgement of good/bad rationalization of this 1s called motive

Seldom questioned excpet in the case of

confllet.

EMOTION

feelings influencing belief, feelings associated with a situation; it acknowledged as not totally affects motive and drive, and therefore irrational, but often ignored. influences behaviour and reasoning

Some claim(e,g, Minsky) that

important emotions are capable of being modelled on a computer [

-155-

INNATE KNOWLEDGE/INSTINCTIVE

BEHAVIOUR

Assoclated with heuristics Implicit knowledge which is genetically

encoded e.g. how to learn; / behaviour

which is governed by implicit and

subconscious knowledge

RESPONSIBILITY

Usually in the context of legal the 'onus' for a decision and its

discussions; can the computer implications; usually in the context of a

be responsible for its own decisions mistake. Wider than a legal issue- it

relates to ethical issues and competence. -156-

Appendlx 2

QUOTES FROM INTERVIEWS WITH THE LEADER OF A MOUNTAIN RESCUE TEAM

NOTE This man's team Is acknowledged to be one of the very best In the UK; they have an excellent record for getting to casualties quickly, and for a low rate of fatalities.

He was asking about the possibility of using a computer to assist In searches and rescues because he felt that the team was not efficient enough to be able to cope with what was turning Into a 'big non-profit-making business'.

In addition to using the computer for accounts and word-processing he was considering using the computer to assist with handling information and giving assistance

'operationally' i.e. during a call-out, operating at base.

In effect he was asking for an expert system, although he did not realise it at the time.

I am the leader so I make all the decisions . The others do as I say. You have to have someone in charge or else there

Is chaos. 1 have more experience than the others. When

I'm away then somebody else takes over. They aren't very happy about this, and after a call-out they phone me up to tell me what happened. They always tell me about their mistakes. I think you have to be vey careful in criticizing them for what they have done. They don't always I

-157-

do what I would have done, but that does not mean that they

are wrong. But sometimes I think that they may have made a

mistake. I don't tell them on the phone; I just reassure

them. After all, the person 1s almost always alive, so It

doesn't matter too much, and I want them to have the guts to

go out there again 1f they have to. But afterwards 1n the

pub I bring it up 1n conversation. I say what I would have

done, and they are happy to talk about it. They need

encouragement. It isn't that they aren't keen; they are all

volunteers. Nobody gets paid. We all risk our lives. We

do it because we want to save the people. We care. We

all want to get it right and it's very important.Because of

that I can't TELL people what to do. But they do look to me

as their leader. I don't think I know it all. In fact

they should know as much as I do, but they all look to me.

It scares me a bit. I don't have a clue what I'm doing

sometimes.

I've tried to talk about rescues as part of training. We

used to have sessions when we discussed what had happened on

a call-out, and analysed what we had done. But people never

talk about it as it really was. Sam talks and talks and

talks. You can't shut him up, and it's all irrelevant.

But get him on a real call-out, and he goes real quiet. His

face sets, and you know it's the real thing. Then he knows

what to do. You wouldn't think it was the same person. So

we have practices, but we tend not to talk too much about

history.

I keep a diary of call-outs. Often I look through it to try

to recall them. I think I need to do this. We all ought to -158- do 1t. Sometimes when I get a call-out I know it's similar to one before. There's something similar about 1t. I can't say what It Is. Somehow 1t just feels the same. I can't describe it to you. I rack n\y brains to think what It Is.

I just can't get it together. Could a computer help here?

Could It store all the call-outs and look for similarities for me? All the time I'm thinking 'There's something I should know about this'. Often when I get in the van and the adrenalin is flowing I remember. It feels the same. I radio back to base and ask them to look up a rescue we had there. Where was the body? What have I written down about it? Sometimes I have to get out on the fell and I see the moon coming up from behind the rock and I know I've been there before. Then I remember what it is about a previous case. Why can’t I remember it earlier? I wish I could get these historical cases organised.

There are some things they should remember but they don't.

See this gulley here. If someone has fallen down here then you should approach it this way round. Never go this way at night. You get trapped by this waterfall here. It's just too wet. You can’t get over. This looks longer, but it's faster. You have to get there quickly.

Every time I have to tell them. I don't know why they don't remember.

Every call-out is a series of mistakes. It's awful when I think about it. Looking back it's so obvious what I should have done. I try to get them to criticise me, but they don't. I make so many mistakes. One day somebody will sue -1 59- us.I don't know what I will say. The solicitor would say why didn't I get the helicopter 1f 1t was a heart case. I didn't know it was a heart case. There's so little you do know . The Information Is changing all the time. You can't even be sure of the grid reference. The relative or friend comes down, and you try to get as much Information out of them as possible. But after that It keeps changing.

Never take anything for granted. Anything could be wrong.

They usually know how many people are lost, but they seldom know where. We have to use our knowledge of the fells.

That's where the history comes in. Once you have all the facts it's clear what you should have done; but you don't have them. Well, sometimes that Isn't true. Should I get the helicopter? Which way should I take them down? I have to guess what the injury is. I never know. So it's all guess work. If people knew how bad we were they wouldn't go on the fells. Every time it's a complete cock-up.

We make our best decisions and our worst decisions on the real call-outs. The practices aren't the same as the real thing. They're all keen, but it Isn't the same if there's a real body lying out there. You can feel the atmosphere.

A practice feels different. On a practice we make safe decisions. On a call-out we make daring decisions.

Perhaps we aren't so bad then? I just had this vision of the computer handling all the information. There's so much to keep track of, and we forget so many things. I suppose we get there in the end. Maybe 1 worry too much about it all.

Perhaps I'm better than I think. The lads think I know what

I'm doing. They trust me. It's me who is unsure. -160-

I think you night be right that there 1s a lot 1n my head, and I just can't explain It. You seem to know what I mean.

I couldn't really write ft. all down. You're right about the diaries too. It Isn't what Is written down that matters. That somehow reminds me of the Incident, and then

I know what to do. I can remember more of the Incident than what I write down. It Isn't easy to see what is Important and what 1s not.

We'll press on. I'll have to get the lads to try to take control more often. Perhaps that 1s the only way. Maybe you have to feel 1t to know what to do. Kristen Nygaard Institute of informatics University of Oslo P.O. Box 1080 Blindem N-0316 OSLO 3

The over-head foils from his talk, containing propositions and conceptual clarifications about Al, language and the professions. - 162- r A1 and the Future...------

We are developing system s th a t are networks of people, computers and other kinds

of machines, linked together with human and an increasing proportion of electronic com* m unication links

------PARIS nov. 87 ---- O Kristen Nygaard, University of Oslo.' DU«: 87.06.26 ’nils: We are developing ...

- A I and the Future... — — — — — — — — —

Cognitive Phenomena (in minds of people)

Manifest Phenomena (e.g. computer program executions)

■ ■ - — PARIS nov. 87 O Kristen Nygurd. University of Oalo. Date 67.06.26 TlUe: Co^nltlve/Manlfeet Phenomena -163-

i- A l and the Future...

Informatics

Informatics is the science that has as its domain information processes

and rela ted phenomena in artifacts, society and nature

- ■■ ■ - — PARIS nov. 87 O Krlatan NyCaord, Unlverally of Oalo. Du«: 87.00.BB Title: InJormalica

r Al and the Future...

Education in informatics (including AI) must emphasize comprehension, not only memorization

To program is to understand

PARIS nov. 87 O Krlatan Nygaaro, Univomt/ a< Oalo. Dal* TlUa: -1 64- Al and the Future... ------

A cocnsistent conceptual platform for informatics is necessary fo r a proper discussion of w h at a machine representation consists of.

L — - PARIS nov. 87 © Kristen Nyg&ard, University of Oslo Dolo: Title:

• Al ninl Ilw I'ulm r...------

• Application oriented (computer) languages - Jo b o rien ted (co m p u ter) languages Better:

Designed extensions of a profession's (natural) language to provide a conceptual and operational integration of information technology in the profession. - Computer oriented (natural) languages

providing the platform for many

- Profession oriented (computer) languages. PARIS nov. 87 O K risten NygnArd, University of Oslo Date: Title -165-

r Al ami the Future...

Experts, Knowledge and Machines Machine representable knowledge:

quantifiable facts, f ormalizable rules. Knowledge based systems = (Machine representable) knowledge based systems. Expert systems = Knowledge based systems that are intended to :

Contain a large knowledge base constituting the machine representable parts of the knowledge of an expert in some domain. Operationalize this knowledge through an inferencing algorithm.

1 PARIS new. 87 — O Kristen Nygmrtl, University of Onto Dai«- 87 08 88 Title: Kxperts/Knowledge/Machlnes

r Al and the Future...

The uselful introduction of Al tools and techniques would be easier if it was accepted that Al deals with machine representable knowledge Then one could establish a new battle line: "What is machine represen table?" accepting that this is an open issue.

PARIS nov. 87 O K.iflUm NygAArd, Unlvem lty of Oslo D&la Tltl«: -166-

Al and the Future... ------

Will the AI community accept the statem ent "in AI we shall by knowledge understand machine representable knowledge ?

Probably the community will be divided, since many of the earlier claim s of AI has been built upon the assumption that everything that should be labelled knowledge is machine representable.

------PARIS nov. 87 © Kristen Nyga&rt), University of Oslo. Date: Title:

r A I and the Future... ■

Environment functions 1. Quality Control 2. Risk Handling 3. Domain Policies 4. Labor M arket Policies 5. Technology Policy 6. Financing 7. Education and Access to Information

I'ARIS nov. 87 O Krlaun Ny«urd, University of Oslo Dtl* T1Ü«: -167- Al and the Future...

In social situation notions as value, interest, power are unavoidable elements, and knowledge based systems will be manipulated by opposing actors as tools to achieve contested goals, regardless of the intended purpose of these systems

PARIS nov. 87

Al and the Future...

Notions like conflicting values, interests, power cannot be properly treated by quantitative methods

PARIS nov. 87 -168-

r Al and the Future...

Social in th e m eaning of societal: pertaining to society

PARIS nov. 87

r A l and the Future...

Turing's Criterion for Machine Intelligence (rephrased):

An electronic information system is intelligent if a hum an being in remote communication with the system is not capable of deciding whether the communication partner is human or machine.

------PARIS nov. 87 O Krleten H ygurd. University of Oalo DtU: Title: Turing'* Criterion -169-

r A l and the Future...

Alternative Criterion:

An electronic information system has expert capability in a domain according to a reference g ro u p if members of that group consider th e system to perform eqally well as a recognized human expert in the domain.

— ^ ^ P A R I S nov. 87 O Krlatoa Nyga&rd. OnlvsrolLy of Olio. D*le; 87 00 £8 TUI* A lU m aU n Criterion

PAUL HENRY C.N.R.S,Paris LAHGUAGE. SPEECH AMD A 1-BASED SYSTEMS

Our research program "Al-based systems and the future of

language, knowledge and responsabi11ty in professions" Is focused

on the Impacts of the development and of the Implementing of

Al-based systems In various fields of social and professional life.

In previous meetings, we have mainly worked on the

implementing of Al-based systems in law, medecine, education and

professional training. These are among the fields in which that

implementing seems to be going the most quickly and may have the

most direct and immediate impacts. But these are also domains in

which language and speech play a decisive part. Thus we could not

but encounter that question, language and AI, which is the title of

this seminar, even if it is from a specific point of view. Ve are concerned with those aspects of the question language and Al which have a direct or undirect link with the manifest or possible

impacts of Al-based systems. But we know also that language -may be we should specify n a t u r a l language even if that specification is not so clear as it may seen- is a key problem for Al, from the theoretical as well as from the technical and pratical points of -172-

vlew: the mastering of language has always been considered as a

touchstone for Intelligence and it is so for artificial

Intelligence too. Of course we don't intend to cover such a big and

complex question but we are all aware of the fact that we cannot

avoid Its most general Implications.

Ve need at least:

1) an analysis of the part played by language and speech in

the social prat Ices concerned by the development and implementing

of Al-based systems, and

2) an analysis of what the développement and use of Al-based

systems may , in that respect, bring and change In them.

In previous meetings, we have had contributions concerning

the general problems raised by the development of AI but we have often also adopted a strategy starting on the consideration of case

studies. Applied to the topic of this seminar this strategy would

lead us:

- to examine and analyse what did the Implementing of an

Al-based system in a definite setting has concretly brought and what it has concretely changed in the part played by language and speech in that particular case, as well as in the language itself,

- to discuss the consequences of these transformations,

- and, eventually, to suggest measures and methods which may correct the defective consequences If it appears that there are -1 73-

some, or measures and methods which may improve and reinforce the

positive contributions of the implementing of such systems.

Such a method is valuable if and only if, at least, the

following conditions are fulfilled:

1) Ve have a clear view of what is the part played by

language and speech in the social prat ices we are dealing with,

considered independently of the implementing of Al-based systems

2) Ve have a clear view of the situation of AI in front of

our own language in order to be able to decide whether or not the

observed transformations are really the consequences of the

lmplenting of Al-based systems, or to grasp what in Al-based

systems is responsible for these changes.

None of these conditions can be considered as effectively

fulfilled, or, more precisely, what we can say concerning both of

these Issues is subject to controversies because they raise a whole array of fondamental questions concerning the nature of language,

the part played by language in social life and culture, what the

fact of being speaking beings makes of us, the links between our

language and our knowledge, between language and thought, and many

other related questions. Those questions have been, since the beginning of philosophy, under the scrutiny of philosophers and remain so. The whole of what has been brought by anthropology, -174-

psychology, 1ln g u itic s . . . has not put an end to the con tro ve rsies.

The development of AI, as well as of what is now called the

"cognitive sciences", may appear as opening new perspectives. They may appear as opening new possibilities in the direction of a mastering of language and, thus,of what depends on language. But what kind of mastering? Is language something whichcan be mastered and in what sense? That, may be, is the question.

Ve may considered that, in the history of humanity, the inventio n of w ritin g has been one step in the way of a kind of mastering of language. Ve may consider AI as another step in the same way, leading to another kind of mastering. Ve can even try to compare the two and we w ill have c o n trib u tio n s in th is seminar which go somehow in that lin e . But the question is what does th is mastering, if mastering there is, leaves apart, and what will be the fu tu re of what w ill be so le ft apart in what is a lrea dy c a lle d the "information society”? This is how we are concerned with the famous and c o n tro ve rs ia l question of the " lim it s of A I". The economic as well as commercial stakes of AI seem so big, the already expending market of expert systems seems so promising, the technical potentialities of AI are so fascinating and seem so new that we may fear a kind of desequilibrium which consequences may be, in the more or less long run, disastrous if nothing is done to restore some equilibrium. Some would say that the invention of the tool, that of writing or even of language, have had obviously good as well as evil consequences, that we cannot avoid that for AI too -1 75-

and cannot help. "Wait and see: we will cross that bridge when we

will arrive to it". Such a policy is, from my point of view, just a dismiss of intelligence and thought. It is not true that we cannot help. It is not true that we cannot at least make some

realistic propositions, if we hazve a somehow clear perception of

what is at stake.

So we have that question: what kind of mastering of iangage and of what depends on language AI can bring, and, conve rsely, what does this mastering leaves apart? Some claim that what may be left apart is Just temporarily left apart. They claim that no principle a p r i o r i forbid that Al-based systems w ill some day reach the level of any human capacity. But what are those capacities? What do we consider as a capacity? Others, on the contrary, claim that the kind of mastering of language and of what depends on language which

may bring cannot but miss fondamental aspects of language and of

at depends on language and speech in Individual as well as social l i f e . But what can i t be? Is language (and speech) such an unwell defined abject that some could not see that? To deal with those questions we are brought back to the two types of questions 1 previously raised.

We have of course to look at AI. We have to c le a r up our conception of AI. It isclear that people put under these words quite different things. But we have also to look outside of AI. We -1 7 6 -

have to look at language and speech In In d iv id u a l and s o c ia l lif e ,

to what in then depends on language and in what. T h is is where our

concern with medecine, law, education and training may be the most

profitable if we look at the conrespondln^ social pratices in the

rig h t way. 1 mean by right way in that case the way which consist

to look not so much at what we can do w ith language in them < s t l ll

a mastering p o s itio n ), than at what language does in them, or even

at what it makes of us In them.

Just after this introductory adress, we will have three contributions concerning medical pratlce. The case of this practice

is worth being considered from the point of view I have in mind. It

would be interesting to have a look at the history of medical

pra ctice s and at the part played by language and speech In i t . The

part played by language In the e laboration and use of th at part of medical knowledge which is based on biology Is not s p e c ific . But is

the knowledge involved in medical pratice limited to that type of knowledge? Vhat does a physician do when he prescribes a placebo?

Why does he do such a thing? Why such a prescription may have effects, concrete effects? What type of knowledge is Involved in

that case? Of course, we can Imagine an expert system which, when a patient complains of such and such sufferings and when there is no physiological evidence of any kind of objective sufferings, would tell:"Prescribe a placebo". We may even rightly consider that the expert system may be of some help to check wether or not all the -1 7 7 -

ava lble in v e s tig a tio n s have been made in order to id e n tify the existence of an abjective physiological suffering. But this is still a too simple case because we are s till too close to problems of diagnotic and prescription. I consider that medical prat Ice should have to do notonly with how is our body but also with how we are with our body, that this is also an essential part of medical pratice. Vhat we are with our own body is something which

Im plies language, with what we say and with what Is said about our own body. If something has to be done or may be done concerning that, what type of knowledge does that needs? Isn't it a kind of knowledge which depends on a kind of listening more than on observation Cas the biologically based medical knowledge)? Can such a knowledge be transmitted and acquired as the so-called objective medical knowledge can be? I s n 't i t a kind of knowledge based more on listening than on observation of the organism? Isn’t it something closer to what Freud called Inconscious knowledge than to anything else? I Just raise those questions which, I think, we will re-encounter in the discussion of the Just coming contributions.

I think that similar questions could be raised concerning law, education, training, and any other kind of social pratices involving language and speech. And the question is: what is the future of such knowledges. I think that we may have some fear for them. It is worth noting that Bichat, while giving to clinical medecine its foundation, dismissed the pratice of taking notes at the bead-head of the patients as if their endless discourses on -178- their sufferings could not but be, for the physician, Just a spurious noise. Of course these remarks outpass largely the problem of Al and of the possible impacts of Al-based systems.

Nevertheless, I th ink that an overemphasis on what can be done with

A I-based systems, on the type of knowledge they can grasp, at the level of training as well as at the level of research, and of pratices, cannot but stress a tendency to disconcider these noneless fondamental forms of knowledge 1 have Just evocated. Tha_PJace of Interactive Video in Intelligent Teaching Systems (D-emonstration of a medical video-disc on the prevention of back injuries)

presented to meeting in Paris. 2 - 4th November. 1987 on

Al-Based Systems and lhe Future of Language. Knowledge and Responsibility in Professions.

Dr Gordon Jameson. University College. London.

Interaction techniques with video-discs; The interactive video-disc system "Back to Basics’ was demonstrated to the meeting as an example of the potential of this type of learning approach for intelligent systems. This interactive video training programme was designed 3 years ago almost before interest in artificial intelligent and interactive video had begun to develop. The purpose of this programme is to train nurses or those in industry in the principles of lifting patients and objects safely. The video-disc cannot give a total solution to training in lifting and hence the approach was to provide a store of visual information in the form of video and still pictures which could be accessed rapidly under the control of a computer, and which could be used to show how basic principles of mechanics, anatomy and occupational health could be applied when teaching and training in a variety of situations. The division between industrial and nursing' problems showed the difference between lifting animate objects which have some muscular function such as patients and inanimate objects, which can give no assistance to the lifting process.

This interactive video demonstration showed lhe implementation of some ideas about man-machine interaction in particular (he functions which allowed the user to access rapidly any information on the disc. The general catalogue of the contents of the disc was contained in menu pages, and selecting a box with the mouse could move the user to any section of tne disc, i.e. give geographical control of the information. In some cases it was necessary to provide sub menus to facilitate more accurate selection of the information.

A further level of control was provided by an original approach to give the student direct control of the disc player itself. A series of options comparable with a drop down menu could be caused to appear across the screen as a menu-bar by pressing a button on the mouse at any time, allowing the user to stop, replay, play fast or slow etc. thus giving the student direct control of the player to display images in a manner best suited to the individual's learning style. - 1 8 0 -

In the design of an interactive video training system attention must be directed at two main aspects: 1. The educational content, i.e. the information which the designer or teacher wishes to transfer to the student, comprising a knowledge base of textual and visual information. 2. The learning environment, which involves student models, interaction modes etc., and is to some extent constrained by the hardware available. The educational content will bear the stamp of the expert who provides the knowledge and the style of presentation will also be influenced to some degree by that person's style of teaching. This video-disc was directed at nurses where the prime objective in training is to transfer principles of lifting with specific factual information to prevent damage to the spine and back, rather than discuss attitudes to particular problems. This has resulted in a style which can be criticised for being too pedantic.

Muit'-meciia techniques: One of the most powerful features of interactive video is that it combine several different media as a means of presenting knowledge and information, and as a consequence is a flexible technique. It usas both visual and aural senses, allowing still, moving and graphic pictures to be combined with music, commentary and conversation. It does not provide for the other senses such as taste, smell and touch and we find in surgery this is a serious limitation. For example the examination of a patient with a lump involves touch and smell to a significant degree and great care and ingenuity must be taken to overcome these difficulties by visual means. The responsibility of the designer of a video-disc is to bring together the various media and use these to the best advantage.

The present day situation is complicated by the technical confusion which exists because there is no industrial standard of hardware or software in this field. Beside present day analog video discs, we have to chose between, CD-Rom, CD-Interactive, two forms of CD-Video. We must wait to see which format becomes the most successful commercially over the next few years. Also we can expect to see significant developments in the presentation of text, graphics and video, in multi-window systems, operating in multi-tasking environments, and digital graphic systems which will reduce much of the effort of post production.

Physical features of the learning environment; We must consider the users of the system when designing the learning environment for interactive video disc systems. They may be small groups or individuals and will effect the learning style. It is hoped that Al methods will lead to learning styles which -181- can be automatically adjusted to the learning needs of the user. Educational psychologists suggest that working in small groups is beneficial because the social intercourse stimulates a sense of inquiry. It is important that the computer software can provide flexibility, speed and that the depth of information in the knowledge base meets these demands.

There is a need to consider the interaction techniques which are closely related to cognitive intelligence methods; should the mouse, the tracker ball or the touch screen be used. The screen format should be carefully considered, ideally making use of windows and icons etc. The procedures should be inductive and self-evident. There are severe problems in this area because some subjects lend themselves readily to representation by icons and others lead to ambiguous interpretation. There is obviously an important need for help systems and these should be self-adjusting to be relevant to the particular stage of the program when help is needed. Colour associations can be very valuable in relating the student to particular ideas, but again these should be selected carefully so that no ambiguous associations are unwittingly introduce into the environment. Finally the obvious must be stated that the text on the screen should be easily read and of a size and font that it is easy for ,a small group as well as the individual user seated immediately in front of the screen. The framing of text whether in questions or stating information is a surprisingly difficult aspect of design; to arrange for the correct level of approach, and not to insult or overwhelm the audience.

Control of Knowledge available to the student: If a system is to be intelligent there must be a measure of control in the hands of the user which may be overridden if necessary by the system. There must be built into the system primary control of the material which can be accessed in the visual data-base, there must be control of the hardware functions, and there must be control of the different media employed in the system. This latter control may be part of the general hardware facilities. The objective must be to develop a system where the balance of the control between that directed by the user and that taking place automatically through the system intelligence, must be such that the user is not aware that the functions are being taken over and that he does not consider it an intrusion on his actions. The system of control must provide for learning styles ranging from highly structured (as in Computer Based Training) to sophisticated methods of browsing. These might emulate the cognitive processes we use when browsing through a book.

Balance between video, photographs and graphics in the visual medium: Careful attention must be given in the learning style to the use of the different media. When video is used the emphasis must be on the recording of moving sequences in -182- real life. When photographic stills are used then the emphasis must be on the recording of still information in real life. Both these methods record large amount of detail and the viewer must be able to distinguish readily between the relevant and irrelevant information. Since the task of extracting information must be more difficult in these real-life situation, there must be a valid purpose for displaying the full detail. Further great care must be taken to decide when the use of video or still photographs is the best use of resources. Video uses up the available space in a disc very rapidly and since photographs are capable of storing vast amounts of detail there are many situations where these should be favoured, provided the techniques of transferring still pictures to video are adequately controlled. If however the movement is a vital part of the information to be transferred to the student than video is justified. If the prime objective is to display the principle behind some process, then it is more effective to present the information in graphical form where all extraneous information has been removed.

Student performance assessment:

Another feature of an intelligent system will be the ability to assess the performance of the student, and this information may be necessary to allow the system to adapt to the students' learning requirements. Considerable attention will be given to what and how remedial action can be taken. I believe caution must be taken not to be too enthusiastic concerning developing diagnostic techniques for remedial action in a teaching system, since the remedial options which are available may well be limited, no matter how precise and detailed the diagnostic data. The performance assessment can take many forms, e.g. recording the questions answered, the number of attempts at a question, the time spent reading and answering a question, and reaction times, which can all be built into the computer system and can take place without the student knowing that this information is being collected about him/her. This information can become a very important database which can be used to feed-back information to the student, the tutor and the designer and which could lead to the rules of the system being changed if they are demonstrated to be inadequate or inaccurate. The rapid feedback of information from such a database could be a very valuable development tool, and has its parallels with the direction some systems are developing in the medical and clinical fields, in the use of clinical databases. It must be recognised that the design of the questions asked of the student will be crucial to building up the knowledge domain of the student's performance.

PfQtsasional liability; The remit of this conference includes professional responsibility for the intelligent system we develop. In the case of teaching systems one has to consider whether they are - 1 8 3 - being developed for a particular institution or for commercial advantage. In fact I believe the real situation will be some mixture of the two. One also has to consider the balance between the information contained within the computer system and that on the video-disc, because the latter cannot be changed without re-mastering the disc. Since this is educational material, and provided it has been assessed and approved by ones colleagues, the presentation is to some degree the personal view of the teachers concerned. If that is stated in either the accompanying literature or in the banner headlines of the visual presentation this would seem to me to be satisfactory. However the responsibility for the knowledge and information embedded within the system designed for educational use is different from a normal expert system, because several levels of domain are required, and ideally an open ended situation should be provided. In practice there must be constraints and assumptions of previous knowledge and experience; e.g. in clinical medicine the student can be expected to refer back to pre- clinical teaching and natural science learning.

Ike Impact a t Artificial Intelligence on the Future a t

Professions: Same Reflections

A Report to the COST 13 Meeting, Paris, November 2nd - 4th, 1987,

Dr. Julian Hilton, University of East Anglla/TAI.

If you are uncertain, or not in full agreement, as to what a phenomenon Is, It Is hard to assess its impact on something else.

Perhaps for this reason, the meeting in Paris spent longer hearing reports on developments in subject domains where AI is having, or could have an impact than in assessing the likely overall economic, social and cultural impact it will have.

This observation is neither a criticism of the meeting itself as a whole, nor of the individual contributions to it. Some of the papers, notably those from French colleagues, struck me as quite outstanding In both clarity and coherence. But the strength and weakness of the occasion both stemmed from the same source. The weakness was that the great range of disciplines represented have no recent tradition of intellectual exchange amongst each other, which meant th a t our competence to comment on d o m a in -s p e c ific problems was ve ry r e s t r ic t e d . The s tre n g th , however, was in the recognition that AI, by its very nature, demands that a common interdisciplinary discourse be found. The mere fact of such a meeting was a success: but it will require a huge effort, by groups such as that assembled to tackle the task of finding a common discourse for knowledge.

One way to approach the search for such a discourse lies In the problem of responsibility. Whatever disciplines do not have in - 1 8 6 - common , they share the responsibility for ensuring the highest possible standards, both Intellectual and ethical, In the pursuit of machine-represented, or machine-representable, knowledge. In

The Advancement of Learning. Lord Bacon sees all knowledge transfer as built on a "contract of error"; that is, the act of transferring knowledge is in Itself one of generating error in the receiver. If we compound such error by the compute!— enhanced factors of speed and quantity, what will be the consequences? The recent events of world stock markets which led to machines being turned off to allow humans the chance of catching up suggest how grave m atters could become.

At the end of the meeting therefore, I was Invited to compile a list of questions about responsibility which had emerged during the meeting, cast in the form of rhetorical propositions. I have attempted to schematise those propositions below. If we accept that Al will involve a quantum leap in at least the capacity for error in any system, we must make an equivalent quantum leap in our professional responsibility and capacity for coping with error, because responsibility Is the natural correlative of and corrective to error. In accepting such responsibility we need, if only for our own protection, to identify an agenda in which the consequences of our research are prefigured.

The roots of such an agenda are twofold. On the one hand, we now have a great deal of empirical knowledge about the Impact of automation on Jobs and on "w orking cla s s " l i f e . T h is must p o in t us towards thoughts on "middle class" professional life. On the other hand, it is perhaps time to think about Al as in itself a profession, or perhaps better a meta-professlon. In which case we I - 1 8 7 -

need to think about how to generate and sustain a professional

ethic. Central to this latter Issue will be our reasons for

pu rsu ing A1 and on whose be ha lf we are p u rsu ing i t .

A1 and Responsibl11tv

Prof. Andler, who is not alone In his view, suggested that AI

might currently be a solution without a problem. In Britain I

have frequently been asked by those concerned with marketing AI

products what applications are suitable for their (exciting...

new. .. expensive...) products. This sets us a hard question. Is

AI profoundly irresponsible, wasting vast human and financial

resources on insoluble problems to satisfy the whims of research

scientists, who have fetishised powerful machines and now wish to

delude the rest of us with the prospect of discoveries so complex

none of us will be able to understand them anyway? Is it that AI

is really no more than another version of the story of Emperor's

new clothes and that no-one has yet had the courage to say that

the discipline Is, in effect, naked? Or is it that there is a

necessary and long lead time between the development of such a

powerful new tool and the discovery of applications for it? It

might be argued that the has always been

demand le d. Ve o n ly in v e n t to o ls when we have a demand fo r them.

So how can AI defend i t s e l f the wrong way round? Does t h is

therefore, mean that AI is the late twentieth century equivalent

of alchemy?

On the other hand, it might be argued that the explosion of costs

and waiting time in such professional domains as medicine and law

demand th a t ways be found fo r autom ating many d ia g n o s tic and

decision making processes, a task Ideally suited to Intelligent

machines. - 1 8 8 -

It may be, however, that the real motor of AI is mythical, even irrational, and that its benefits may, ironically, be "natural" not "artificial". The very formulation, artificial intelligence, locates the Intellectual effort of AI in a tradition of Interest in intelligent machinery as old as European culture. In which case, we may defend the discipline for circumstantial reasons: while it may not in itself generate much in the way of useable intelligent machinery, what it forces us recognise about human intelligence may be of such substance as to recharge the spiritual batteries of western man with a new sense of wonder at

"machine" man.

This returns us to the epistemological problem: what may AI do to human (belief) systems? Such a question clearly impacts directly on any notional professional ethic AI may produce. In his celebrated book on narrative, Mimesis, Eric Auerbach, defines two dialectically opposed epistemological types in narrative, paratactic and syntactic. The former is legitimated from outside, by God or religion, the latter from inside, by man, or even by itself. If we locate AI within this framework we are faced with two different definitions of intelligent machine, the one that in effect imitates human Intelligence and is validated by human use, the other that validates itself.

If our definition of AI is premissed on*the former possibility, then the complexities it faces are not materially different from those faced by human intelligence. If, however, the machine is to validate itself, then we are faced with a new type of problem, though not necessarily a problem of machine intelligence. In response to this problem of the self-valldating machine, I put to I -189-

the meeting the question: "Can we make a machine that we can

trust which validates Itself?” The answer, universally, seemed,

to be "no", a "no" motivated by lack of trust. But whether in

future the negative will relate to the practical question of

making the machine or to the epistemological question of trusting

It is unsure. Turing, for example, is in no doubt that we will

make intelligent machines seems to suggest that we cannot avoid

some form of trust in them: "An important feature of a learning

machine is that its teacher will often be very largely Ignorant

of quite what is going on inside, although he may still be able

to some extent to predict his pupil's behaviour". (Computing

Machinery and Intelligence) Is this so terrible? Ve know already

that in many situations, humans prefer talking to machines than

talking to people. Ve know also from history that it is a

dangerous misconception of human nature to argue that humans are

more trustworthy than machines. So the epistemological doubt is

surely less about the Intelligent machine than what unscrupulous

people will do with such intelligent but amoral machines. The

question of responsibility is squarely a human one.

Responsibility: An Essal at an Agenda

There is as yet no consensus what the A1 agenda is, nor even any

clarity about the range of disagreement within the discipline, if

indeed it turns out to be a discipline at all. I do not have

anything useful to add to the current debate about what AI is;

but whatever its outcome, the debate must be conducted within the

framework of social and personal responsibility. I do not mean

responsibility in the legal sense of liability, important though

that question Is to the future impact of AI (or expert systems)

on our lives. Rather, I mean that if we are concerned about

contemporary forms of engineering such as genetic engineering, we -1 90- should be equally concerned with a concept such as knowledge e n g in e e rin g , f o r the p o te n tia l fo r i n f l i c t i n g damage on humanity is as great with the knowledge engineer as with the genetic engineer.

In practical terms this means that no project undertaken with public funding should be permitted to avoid addressing the issue of responsibility. Indeed, there is a strong case for expecting any application for funding to include specific reference to this

Issue and its part within the research and development programme intended, as for example, medical ethics should be at the heart of all medical research programmes. But clearly, responsibility can also become an issue so contentious in itself that it precludes all research on the grounds that inactivity is better than risking disaster. This is an impracticable position since now that the possibility of AI has been released Into our consciousness, like the spirits emerging from Pandora’s box, it cannot be unthought, and we cannot return to state of unknowing.

We have, therefore, to grow up responsibly with AI.

The propositions listed below are largely concerned with the role of experts and their responsibilities, since we are so far from knowing what the broader consequences of AI might be that even my penchant for idle speculation loses confidence. Experts in this field are however, of two kinds, subject experts and experts at generating expert systems. Vhat may be at stake is the passage of power from subject experts to those who can represent the knowledge of subject experts in machines and so become surrogate experts, with all the implications such a power transfer would

Inevitably entail. In another paper for an earlier conference in ! -1 9 1 -

the COST 13 series I described this as the process of

substituting virtual (one might even say polItical) for real

knowledge, a phenomenon fraught with practical as well as

epistemological problems. I will not however, rehearse the

arguments. Rather, I will simply foreground the problem as,

u lt im a te ly , one of power.

While presented in pseudo logical format, the logic of the

propositions is not internally robust - it is polemical. "OR”

statements are not intended as mutually exclusive

complementary; some are contradictory. I see no reason to

apologise for this since a) there is no consensus as to what AI

is or might be and b) it is In the nature of experts to disagree

with one another, a feature one might expect to be transferred

into expert systems. I have deliberately not attempted to engage

here with the methodological problems of AI since Individual

papers address such questions as the future of natural language

programming, tacit knowledge, reasoning etc. with a degree of

expertise that far exceeds my own.

AI poses fundamental challenges both to concrete and to abstract

relationships between:

EXPERTS and NON-EXPERTS

KNOWLEDGE and REASON ING

POWER and DEMOCRATIC CONTROL

THE WILL TO KNOW and THE ETHICS OF KNOWING.

These challenges determine the subsequent set of propositions. -192-

I f Aj. Then What?

1. IF it is a function of the modern world that experts are

expected

- to know more

AND

- to be more skilled in the application of their

knowledge

AND

- to be more accountable in their Judgements

than n o n-exp erts THEN do we

- attempt to reduce public expectations of human

experts (eg Gods in white coats syndrome) 1.1.

OK

- automate expert reasoning as an aid to human experts 1 . 2 .

OR

- automate expert reasoning as an alternative to

consulting human experts 1.3.

1.1. IF we attempt to reduce public expectations of human experts

THEN do we

- Induce realism in public attitudes by consciously

destabilising the status of the expert

OR

- destabilise the confidence of experts by removing

their access to government

OR

- destabilise public legitimation processes (eg legal

Judgements, quality control)

OR

- destabilise the epistemology of all knowledge - 1 9 3 -

1.2. IP we seek in principle to automate human reasoning as an

aid to experts

THEN do we assume th a t a l l human reason in g can be

- regarded as rational

AND

- captured

AND

- described

AND

- automated

AND

- machine enhanced

AND

- represented (re-presented) in a way transparent to

• non-experts

OR

- do we seek to define the limits to the extent to

which reasoning can be automated as determined by

domain

AHD

- tacit knowledge

AND

- mediation by experts (ie only experts use expert

systems)

1.3. IF we Beek in principle to automate human reasoning as an

alternative to consulting human experts

THEN do we destabilise the position of experts

AND

- provoke a backlash

OR -1 94-

- make exp e rts more accountable

OR

- free experts to spend time on more complex tasks

OR

- democratise expertise

2. IF we succeed in automating human reasoning

THEN do we d e -s k i 11 expe rts 2.1.

OR

- enable e xpe rts to work more q u ic k ly 2. 2 .

OR

- enable better Judgements by experts 2.3.

OR

- democratise expertise 2.4.

OR

- redefine expertise as "expert use of expert systems" 2.5.

OR

- alienate executive action from the reasoning process 2 . 6 .

OR

- reduce the necessity of acquiring general reasoning

skills (de-skilling of non-experts) 2.7.

OR

- reduce the need for low-level repetitive reasoning

(eg office work) freeing the worker to perform

higher— level (more satisfying) reasoning 2. 8. OR

- greatly enhance the potential for error in Judgement 2.9.

2.1. IF we de-skill experts

THEN do we create -195-

- intellectual alienation equivalent to the alienation

of labour

OR

- displacement of subject expertise into generic

expertise and so induce progress in the pursuit of the

limits of thought per se

OR

- enforced démocratisation of complex ^reasoning,

enabling those hitherto inexpert to act in expert ways

and to enact expert Judgements

OR

- dangerous concentration of power in the hands of the

knowledge engineers

OR

- profound epistemological uncertainty and social crisis

2.2. IF we enable experts to work more quickly

THEN do we need fewer expe rts

OR

- give more people access to experts

OR

- ovei— administer knowledge

2.3. IF we enable better Judgements by experts

THEN do we undermine the present basis of all professional

training and codes of behaviour

AND

im ply th a t human e x p e rts no lo n g e r deserve the

appellation expert in comparison with their machines

2.4. IF we democratise knowledge

THEN will dispossessed groups of domain experts react in

o p p o s iti on -196-

OR

- w ill genuine democracy take a quantum leap forward,

equivalent to the (historically) recent mass franchise 2. 4. 1.

OR

- will society fragment still f u r th e r 2.4.2.

2.4.1. IF démocratisation is agreed

THEN will cultural (educational, political, economic)

systems have to be radically changed

2.4.2. IF society fragments still further

THEN w ill i t break down under change

2.5. IF we redefine expertise as "expert use of expert systems"

THEN do we hand power to knowledge engineers

2.6. IF we alienate executive action from the reasoning process

THEN do we create the preconditions for a resurgence of

totalitarianism since those who enact decisions are

denied the means of auditing the reasoning of those who

have made them

2.7. IF we reduce the necessity of acquiring general reasoning

ski 11s

THEN do we compound the a lie n a tio n of e xecutive a c tio n

from reasoning by further weakening the auditing skills

of those not in power

2.8. IF we reduce the need for low-level repetitive reasoning

THEN will the leap to high-level reasoning become Impossible

OR

— will the effect be for low-level reasoning Jobs the

equivalent to the effect of automation in manufacture,

ie great loss of Jobs 2.8.1.

2.8.1. IF there is great unemployment in low-level reasoning

THEN will the nature and infra-structure of business - 1 9 7 -

centres radically change with consequent effects on

urban decline

2.9. IF we enhance the potential for error in Judgement

THEN have we created the preconditions for a disaster in the

accurate maintenance of our cultural knowledge base

3. IF we accept that knowledge is power

THEN w ill machine represented knowledge démocratisé knowledge

and hence power 3. 1.

OR

- will powerful interest groups centralise even more

knowledge and hence power

3.1. IF myth mediates between knowledge and power

THEN does AI need a myth, eg as p s y c h o -a n a ly s is has a myth 3.1.1.

OR

- is it beyond mythology

OR

- is it its own mythology

3.1.1. IF AI needs a myth

THEN is the Pygmalion myth the best candidate

4. IF AI is a leap forward in automation

THEN is this leap quantitative, enabling more of the same to

be done more q u ic k ly (4.1.)

OR

- q u a lit a t iv e , e n a b lin g new th in g s to be done, new

thoughts to be conceived (4.2.)

4.1. IF AI is quantitative leap in the automation of human

processes

THEN AI is best described as an engineering discipline,

enabling the faster and more economic production of -198-

raore, and more complex, a rte fa c ts 4.1.1

4.2. IF the leap is qualitative

THEN A1 i s not merely an enhancement of an e n g in e e rin g

discipline but a totally new science

4.1.1. IF AI enables the faster and more economic production of

more goods

THEN will the further extension of consumer power mean an

enhancement in the quality of life

5. IF we automate human processes

THEN we a ffe c t a l l Jobs

5.1. IF we automate

THEN do we generate more work 5.1.1.

OR

- generate new kinds of work 5.1.2.

OR

- create unemployment on an unprecedented scale 5.1.3.

5.1.1. IF we generate more work

THEN have we lost the opportunity of the leisuresociety

5.1.2. IF we generate new kinds of work

THEN does t h is demand a new value system (economic change)

5.1.3. IF we generate more unemployment

THEN w ill society experience massive upheaval

5.2. IF we automate expert knowledge

THEN do we promote the speed and efficiency of diagnosis and

J udgement

OF

- increase the numbers of those going to experts and

overload the system

OR

- deskill experts and hence weaken their control over -199-

knowledge

OR

- create massive unemployment In expert groups

OR

- provide experts with expert helpas the only way of

ensuring that they remain expert in the face of ever

more, and ever more complex, domain knowledge

5.3. IF we represent "common sense" in machines

THEN do we deskill society as a whole (aggravating mass

illiteracy and innumeracy) 5.3.1.

OR

- create the ultimate leisure society 5.3.2.

5.3.1. IF we deskill society as a whole

THEN do we experience a decline ofthe kind at the end of

the Roman empire

5.3.2. IF we create the leisure society through automation

THEN do we automate le is u r e

AND

- reinforce social fragmentation (eg through extending

the personalisation of stereos, games, computers)

OR

- provoke compensatory revivals of old skills and crafts

6 . IF AI is a new d is c ip lin e

THEN are current theories of Intelligence, knowledge (etc)

capable of constituting its theoretical base

OR

- is a major new conceptual effort required

7. IF AI is a new p ro fe s s io n

THEN is the profession of AI knowledge engineering (the

manipulation of knowledge) 7.1. - 2 0 0 -

OR

- a ”meta-profession"

knowledge) 7.2.

7.1. IF AI is knowledge engineering

THEN is this a real discipline or a political device dreamed

up by applied scientists to vex pure scientists

7.2. IF AI is a meta-profession

THEN should there be a p ro fe ssio n a l e th ic fo r i t 7.2.1

7.2.1. IF there should be a professional ethic

THEN should there be a research moratorium while the ethic

is drafted 7.2.2.

7.2.2. IF a moratorium cannot be enforced

THEN should ethical evaluation be Integral to all research

in AI and represented as such in budgets and personnel,

monitored by a standing conference

C oncluslon

If this agenda has any validity, then it leaves us with two conclusions:

1 . that there is an urgent need for an integrated programme of research into the generic question of the impact of AI on professions based on long-term case-study of a carefully chosen spectrum of p ro fe ssio n a l a p p lic a tio n s , managed by a group whose first and principal task is the development of professional standards within AI and knowledge engineering in particular.

2 . that there is an urgent need for introducing criteria of quality enhancement into our Judgements of the likely benefits of

AI. Appendix 1

European Science Foundation Interdisciplinary workshop on Control-structure-meaning

Zürich, 20-23 September 1986

Studying Cognition Today

★

Daniel And1er

U.E.R. de mathématiques et informatique Université Paris VII & Centre de recherche Epistêmologie et Autonomie (C.R.E.A. ) Ecole Polytechnique

Paris

The main part of this paper has

formerly been published in

EIDOS, vol.5 no.9, 1986 - 2 0 2 -

SUMMARY

Introduction 203

I. ORTHODOXY

1. Cognition as computation on representations 203 2. Artificial intelligence: the literal construal 2u6 3. Cognitive science: idealizations and foundational concerns 209

II. HETERODOXY

4. Dissent 213 5. Alternatives 217 a. Two kinds of reaction b. Connectionism c. The third approach 6. The search for natural boundaries 221

Conclusion 224

APPENDIX: TWO AREAS OF COGNITIVE RESEARCH

1. Learning 225 a. The centrality of learning b. A tentative definition c. Kinds of learning; inductive inference d. Formal learning theory e. Applications of formal learning theory at the 'micro-level' f. Learning-theoretic constraints at the 'macro-level' g. Learning and cognitive studies 2. Relevance and communication 2 33 a. Critique of the code model b. Ostensive-inferential communication c. Manifestness and cognitive environment; mutual manifestness d. The communicative and the informative intentions e. Relevance f. A tentative assessment

Notes 238

Bibliography 241 -203-

Human mind and computers, human intelligence and potential computer intelligence may be very much alike in crucial respects, considered from the proper angle. This is the intuition shared by artificial intelligence and the main currents of cognitive psychology and linguistics, a number of trends in the neurosciences, and generally all the disciplines whose eventual aim is to contribute to a unified science of cognition. This intuition has given rise to ambitious theoretical elaborations as well as considerable amounts of empirical and engineering research. Cognitivism is a convenient label for the new way of thinking about the mind which subtends all of these scientific and philosophical endeavors. But the form taken by cognitivism in the course of the first twenty years or so of research has raised many objections and encountered both practical and conceptual difficulties. In the last few years, it has become clear that cognitivism is entering a new phase. How deep are the changes it is undergoing? How successful are the new proposals being made, whether aimed at foiling previous criticism, at avoiding new conceptual pitfalls or at providing opportunities for genuine advances on one or another front? These are some of the current questions which this paper attempts to clarify in the light of the discipline's short history, without presuming to offer answers.

I. ORTHODOXY

1. Cognition as computation on representations

The story of cognitivism in its modern avatar <1> begins with Hobbes's famous dictum:

Reason is nothing but reckoning. ( Leviathan , 1651) where ’to reckon' means 'to conceive a sum total from the addition of parcels'. The depth of this insight, as we appraise it retrospectively, is only matched by that of the questions it raises: 1. How does one 'conceive a sum total'? 2. What stuff are the 'parcels' made of? 3. And anyway, how can a mental calculation have anything to do with the many-colored , many-flavored ways we apprehend the world's complex situations, as we do when we think? Here are, in rough outline, the answers which were beginning to be spelled out at the time; in reverse order: - The mind contains, according to Descartes, symbolic representations, so mental calculations operate on mental symbols which stand for the many-colored, many-flavored objects in the world. - For Leibniz, the business of reason, regardless of subject matter, is, or should be, conducted in a universal language: once we have found this 'universal characteristic', arguments of any kind will be settled by pure computation; thus the parcels from which the sum total is to be conceived are the words of the universal language. - Hume holds that 'mental powers and economy' should be analysed in the same fashion as any other part of nature; so what Newton did for the physical world, one ought to be able to do for the mind. The aim is to -204-

discover 'the 9ecreC springs and principles by which Che human mind is actuated in its operation'; thus how the mind 'conceives a sum total' should be explained in terms comparable to those which account for the movements of the planets. La Mettrie is even more specific: mental computations result from actual mechanical interactions; man is a machine, and conversely a machine can be, in the mental realm, like a man.

A century separates La Mettrie's L 'Homme-machine (1748) from Leviathan , and two centuries will elapse before Turing's celebrated paper in Mind (1950), which endeavors to establish artificial intelligence as a philosophically respectable, if daring, scientific venture.<2> It took that long for the conceptual tools to emerge: what was needed was a general account of representation, of language and of mechanical computation which would be amenable to mathematical treatment. Such an account was provided by a set of three basic notions which, not fortuitously perhaps, reached maturity almost simultaneously (between the mid 1930's and the mid 1940's). Rather than give definitions (textbook material familiar to many), I shall draw a vastly simplified and somewhat allusive sketch of the conceptual role these notions play here:

- A formal language or system, as defined in mathematical logic, provides both the notion of an abstract, (relatively) universal language and an account of its 'formal-semantic' dimension — in other words, sentences of a formal language may be regarded as expressing facts about the world, albeit so to speak, platonically. The question of how a language, as used by a 'thinking system', actually gets to refer to aspects of the world, is left unasked. - Information, as used if not defined in the mathematical theory of information, is precisely meant to fill that gap: it is an abstract, neutral, domain-independent medium for semantic representation; information, when it flows, is what accounts for something such as a sentence in a formal language being about something else, such as a state of affairs. - Turing machines are abstract models for mechanical computers: they are (in principle) mechanical; although they process (binary or natural) numbers, a preliminary coding allows them to process combinations of symbols, which may in turn stand for any predefined aspect of the world; moreover, they are automatic: once given data and set in motion, they carry out a sequence of operations and (in the favorable cases) come to a halt, all without outside intervention. Finally, some Turing machines are universal, in the following precise sense: they can imitate any other Turing machine, thus computing the exact same function. A universal Turing machine can therefore compute any function which is computable by some Turing machine, hence (granting 'Turing's thesis') any function which is computable by any mechanical device whatsoever, as well as (granting 'Church's thesis') any function which is computable by man. In terms familiar today, a universal Turing machine is thus equivalent to a (programmable, digital) computer.

Contemporary cognitivist doctrine can now be summed up as follows:

- Thinking, whether performed by man or machine — henceforth referred to as 'the (thinking) system' — is computing, and computing is manipulating symbols according to the rules of a formal language whose vocabulary is made up of these symbols. - The symbols are material objects, manipulated by physical devices which operate causally, hence automatically, according to rules -205-

which are built into the system and are sensitive to the form (the relevant physical features) of the symbols, and nothing else. - Symbols carry a meaning; when a thinking system manipulates symbols, it constructs composite objects whose meaning is obtained by compounding the meanings of their elements according to the meaning of the bonds between them: the syntactic structure entirely determines the semantic one, given, of course, the interpretation of the elementary parts. - The meaning carried by the symbols is information about the world (in the widest sense one wishes: things, states of affairs, thoughts, utterances...); the system thus carries representations of the world, and in thinking modifies them.

It hardly takes a sharp eye to detect, in this formulation of the cognitivi9t doctrine, several equivocations or downright fudges. Some are artefacts of the compact, non-technical presentation. But others go deeper, and require at this stage at least a passing mention:

- Thoughts come in many varieties, and it certainly seems rash to lump them all together. One may, in particular, insist on distinguishing between thinking that (seven squared equals forty-nine, the moon is made of green cheese, the reader is growing impatient...) and thinking about (the square of seven, that ball of green cheese called the moon, the reader with that impatient look upon his face...). However the formal-computational framework can accommodate both kinds. To think about object 0 Is to hold or behold a designator o whose meaning is 0 — if o Is atomic, the system need merely bring it to its focus of attention (a privileged location, perhaps), while if it is composite, the system must first assemble it (a computation) from its parts. To think that state of affairs S obtains is for the system to hold or behold a sentence s of its formal language whose truth value is True just in case S obtains — s may again be primitive, in the sense of being an axiom, or derivative in which case the system will have to infer it (a computation again) from previously ascertained sentences. It could perhaps also be argued that thinking that S is something like thinking about s in, say, a trusting way, or conversely that thinking about 0 is thinking that x - o , where x is the syntactic variable interpreted as the system's focus of attention. In any event, this kind of explication may well raise more problems than it settles — the reader should be prepared for some obscurity (independently of what may be due to the exposition): cognitivism is a research program, not a logical corpus. - Meaning and Information usually appear to be, by and large, categorially distinct, although the meanings of both terms are notoriously hard to circumscribe. Roughly, meaning is subject- (system-) oriented, while information is objective — such is, at least, the mainstream cognitivist construal. Having thus separated the two notions, cognitivism proceeds to posit information as primitive, and to»conjecture that meaning is, somehow, derivative. <3> - Rules are called in twice: a formal system comes with formation rules (both morphological, specifying well-formed formulas, and syntactic, specifying correct inferences), and a Turing machine (or any equivalent computing device) processes data according to a set of rules. Now the rules of a formal system are (under appropriate coding) computable, so a Turing machine can be built to compute the formulas and Inferences of a given formal system. <4> On the other hand, the program of a Turing machine can be construed as a sequence of conditional rules or instructions ('IF such-and-such DO so-and-so ELSE DO this-and-that'), -206- which determines the behavior of the machine whatever the input. Therefore the formation rules can be regarded as second-order with respect to the instructions of the machine, taken as first-order. There are also, as we shall see, a third category of rules, called heuristic: they constrain the application of the second-order rules, and can accordingly be considered as third-order. Where the rules which are alleged to operate in human cognition stand in this hierarchy is essentially an open question; and though the cognitivists take them to be composites of formal or conditional or heuristic rules, it is not obvious that it makes sense to do so.

2. Artificial intelligence: the literal construal

Artificial intelligence — AI for short — is, historically, the first attempt to turn the cognitivist insight into a full-blown scientific program. Herbert Simon's and Allen Newell's famous 'physical symbol system hypothesis' <5> brings out the full flavor of AI's interpretation:

A physical symbol system has the necessary and sufficient means for general intelligent action.

Artificial intelligence sees itself as a science whose domain of investigation is a natural kind (a species) called information-processing systems, which come in two varieties (as far as we know), biological and artificial; a science based on a philosophy or doctrine and which in turn becomes the basis of a technology. The doctrine bears on human Intelligence and the technology's task is to manufacture intelligent information-processing systems. In principle, these three levels dynamically support each other: the technology is bound to succeed because it is based on a real, well-founded and -articulated science, whose development is guaranteed, in the long run at least, by the doctrine; conversely, the doctrine is strengthened by the scientific results obtained, and the latter are confirmed by their technological consequences. This is of course no more than a hope — as such, respectable: how else did physics get started? Needless to say, the proponents of AI refrain from claiming that the history of physics from its humble beginnings guarantees a similar future to their pioneering efforts — but they sometimes appear to be tempted to believe something of the sort. While it deals with information- processing , AI takes information for granted: the systems it studies or builds are regarded as provided from the start with an access to flesh-and-blood, semantic information; only then does AI's real business begin. This stance is perhaps natural for what is, after all, historically and to this day institutionally, a branch of computer science (also known in various areas as informatics — an antlphrasis?). Some justification seems in order, though, for such a significant methodological choice. For this two strategies can be used. The first relies on the eliminative argument alluded to above: any essential difference between information-cum-meaning (significance) and information as characterized by objective correlation between the extremities of a physical communication channel is bound to vanish in a mature science of cognition. The second is based on a factoring argument: the general problem of cognition, whether natural or artificial, factors out into the problem of original meaning or intentionality of information and a set of information-processing problems, the latter being solvable independently of the former. Either argument seems acceptable on a -207- conjectural basis. Actually, for the purposes of AI, both arguments, although different, boil down to a single essential disclaimer — a fact which may explain why they have tended to be run together, causing needless confusion in the many heated debates to which AI gave rise in its early years. Another basic tenet of AI transpires in the Simon-Newell statement: intelligence is equated with intelligent action. AI is thus behaviorism pursued by other means, which is hardly surprising in view of the overall technological orientation of the field. However, seen from another perspective, AI, siding with cognicivism as a whole, is directly opposed to behaviorism, since it postulates the existence, and requires the engineering concept, of inner states and representations. Finally, the reference to general intelligent action Is indicative of a tendency, in AI, to view intelligence as a basically homogeneous, domain-independent faculty or property. Specifically, AI's global research strategy consists in defining and building 'horizontal' resources or procedures, then combining them in ways appropriate to the various tasks and domains of intelligent behavior. On this point AI is in tune with most contemporary views on human intelligence, ranging from Piaget to educational doctrine and practice, and presumably common sense as well.

At this point, the typical AI problem can be characterized as follows: given a more or less clearly defined cognitive task which seems at least to require some amount of intelligence, in whatever form, from the human agent accomplishing it, how to devise a (computer) program which (under the intended interpretation) allows the computer to perform that task tolerably well, within certain limits. Whatever the solution of the problem, it will invariably ^ involve basic elements or features and 1_ use heuristic rules. The AI scientist thus first posits elements and/or features which combine into the objects and states of affairs in terms of which the given task is defined. Properly analysed in terms of basic operations on the latter entities, the problem then reduces to a search for efficient ways of finding successful paths in a decision tree. That tree is usually much too large for systematic exploration to be feasible <6>; one must then Incorporate into the program a number of so-called 'heuristic' rules, which, by dictating certain choices along the way, select some paths while excluding (most) others from consideration. These rules are not guaranteed to yield a solution, let alone an optimal one, in all cases: just like we fallible humans following, more or less consciously, rules of thumb and neglect whole arrays of possibilities, the program will occasionally fail to produce an appropriate solution in a appropriate amount of time. Now where does the AI programmer find an appropriate set of heuristic rules? Some are based on programming constraints and know-how, some on previous experience in AI problems, and finally some on the programmer's sense or analysis of procedures humans use in the task at hand (for the cognitivist, such procedures must exist, and necessarily amount to nothing but articulated sets of rules). What this admittedly vague description of an AI problem, and particularly the reference to heuristic rules, suggest is that AI tends to focus on processes of a certain scale, which one may call the 'personal' scale in contradistinction to the 'subpersonal' one. It is not easy to say precisely what this distinction amounts to. Roughly speaking, personal processes are those which concern, and have significance for, the system as a whole: though they need not be consciously performed, they can be described and identified by the agent (as such, not qua scientist), and in many cases actually brought to consciousness while they are being performed. Utterances, gestures, deliberate decisions are typical -208-

examples: we may not always be focally — or even at all — aware of shouting 'Hi Peter!', of waving our hand, nor of deciding to cross the street for a quick chat with Peter, but we can certainly individuate such pieces of behavior, and if absolutely necessary perforin them deliberately and in full awareness of what we are doing. Subpersonal processes cannot be given straightforward characterizations in terms of behavior or states of mind; they elude our attempt (again, as simple agents) at recognition, monitoring or instruction. We cannot, for example, instruct ourselves to perform on the two-dimensional retinal array now activated by the presence of our mother's face in our field of vision whatever complex transformation leads to a three-dimensional cortical representation of our mother's face, nor to access in our long-term memory a representation of the word 'Mother', nor to increase differentially the tensions of our facial and lingual muscles in order to produce the utterance 'Mother!'. Such processes are, it seems, as radically inaccessible to inspection and volition as the release of hormones or the constriction of capillaries. One might think of putting the matter more crisply by saying that subpersonal processes are the elementary processes which make up the fabric of personal processes. But though this vague idea, appropriately spec ifed, would presumably be regarded by most cognitivists as a quasi-tautology, in point of fact it has not been taken up so far in AI research. Indeed, as Douglas Hofstadter, who puts much emphasis on the distinction, points out, an orthodox AI thinker such as Newell does not consider subpersonal processes at all interesting, i.e. part of his business (he might assign them to such fields as neurophysiology or psychophysics), because they do not belong to a realm fit for a representational-computational explanation. This is precisely the way a maverick like Hofstadter feels about personal processes: they are not the kind which can be explained thus. <7> So, _if_ the purpose of AI is to seek representational/computational accounts and simulations of (some) cognitive processes, what for Newell properly belongs to AI is exactly what for Hofstadter does not. At least they agree on the borderline, which is symbolized by the '100 milliseconds threshold': very roughly, one tenth of a second is what it takes to recognize one's mother's face, while a typical personal process requires several times that lenghth of time. Alternatively, instead of holding on to some fixed definition of AI which excludes certain processes, one may seek, as will be seen in section II, principled grounds for broadening the scope of AI, so as to Include the 'wrong' kind of process as well.

These are just some of the many 'intellectual issues' — as Newell calls them — which are raised by AI. The proper perspective from which to assess them, along with the proclaimed or implicit tenets of the field, Is pragmatic. The question of how AI has fared since its beginnings some thirty years ago is essential.<8> But this is itself an extremely complex and controversial subject.<9> So, despairing for lack of space of producing anything like a balanced and truly informative account, the best I can offer is an Interpretation of what I see as a major trend in the history of AI. When AI set out to tackle the 'problem' of intelligence — with an enthusiasm which can hardly be imagined today: Marvin Minsky, one of the field's founding fathers, is reported to have assigned 'the problem of vision' to a graduate student as a summer project! <10> — , It looked for some general strategy, In the form of generative principles and bold conjectures. Workers like Simon postulated that the basic ingredient of intelligence is a general problem-solving capacity, or again that there is in principle no qualitative difference between (potential) computer -209- intelllgence and human intelligence, consequently AI stands to cognitive psychology as synthesis to analysis. Other early AI practioners had similar postulates to propose. Now to exclude — as secondary or simply irrelevant — many or most of the distinctions, aspects and dimensions which tradition regards as essential is the hallmark of a true scientific program, rather than conceptual rigor or clearly stated predictions. Early AI was Indeed such a program. But the subsequent history of classical AI can be read as the gradual readmission into the core of basic concepts, into the very center of its field of investigation, of many of the 'pretheoretlcal' notions which it had at first discarded. Not only has AI given up or put off, one after the other, such crucial goals as 'strong equivalence' (functional isomorphism) between human and artificial intelligence, or the modeling of a versatile, general intelligence, but again proceeding step by step, it has reached the point where it now requires 'meaning' (in some idiosyncratic sense), and quantities of 'knowledge', especially of the open-ended informal sort, appropriately 'represented'; AI now needs notions of context and of relevance; it longs for a natural, general learning capacity; it wishes to dwell in a multidimensional world, etc. So on this uncharitable reading AI finds itself in some sense back on square one. Some researchers in the field, in fact, feel pretty much that way: for example Roger Schank, who heads the AI program at Yale University and remains one of the staunchest advocates of the enterprise as a whole, writes in 1985 that 'the most significant advance in the last decade has been the appreciation of just how complex the nature of thinking is'. <11> Still, they regard intelligence, or cognition, as a problem, however tough, to be solved — which is of course the normal attitude of a scientist; the question which they do not raise Is precisely whether what Is pretheoretically circumscribed as 'intelligence' is a natural scientific domain. It would of course be preposterous to close this brief account of AI's development without referring to the Impressive results It has obtained on one front: many specific, comparatively well-defined tasks which do require Intelligence on the part of a human agent are now performed by computers, and the intellectual tools developed and applied in this sort of research (sometimes referred to as 'knowledge engineering') — the bread-and-butter of AI — do make up a highly promising conceptual network, well worth a detailed examination. Strong enough to support significant technical achievements, this network however cannot be assumed, in my opinion, to prefigure a new science, much as domain- and task-specific, semantically pre-structured heuristic programs cannot be assumed to throw light on the 'secret springs and principles' of the human mind.

3. Cognitive science: idealizations and foundational concerns

'Cognitive science' Is construed, in the wide sense, as the cluster of approaches, research programs and so on which deal with natural and/or artificial cognition, at any level of description and generality. In the most common, narrower, construal, its domain is restricted to natural, or even to human cognition. There exists a yet narrower meaning: taken In the 'M.I.T. sense', cognitive science is the science based on the conjecture that computationally characterizable mental representations are the key explanatory construct in the study of human cognition. In that sense, cognitive science, still essentially little more than a promise, consists in those experimental results and theoretical constructs which psychologists and philosophers of cognitivlst persuasion see as pleading - 2 1 0 -

in favor of their central conjecture. This leads us directly to the question whether there is room for a different sort of cognitive science , or whether outside of 'High Church computationalism', as Daniel Dennett puts it <12>, there can be no properly scientific salvation. Section II deals with some of the positive answers that are being put forward — most of them tend to challenge the computational side of orthodox cognitivism more sharply than its representational side. It does indeed take a considerable effort to free oneself entirely from a representational account of the mind. Not only because it has been with us for so long, but also because, as Fodor has recently recalled , there is so much latitude in the concept of a mental representation. Let us begin with a sketch of the orthodox view. A mental representation is (something like) a description, couched in an inner language which Is, obscurely enough, a kind of formal language. The mind thus contains (in an appropriate sense) sets of formulas, and causally interacts with them in virtue of their form, I.e. their physical properties (apprehended at the relevant level of description). The kind of operations which the mind performs on its representations are describable as compounded Turing machine elementary operations. Now mental representations are in themselves inert. In order to account for the mind's actlvity , one is led to think of mental states as 'propositional attitudes': a mental state consists, typically, in the system's or organism's belief, desire, denial, etc., of the content of its mental representation at the time considered.<14> That is how a representation gets to be about states of affairs for the system: unlike AI, cognitive science is_ concerned with intentionality. Or perhaps one should say theoretical cognitive science, or speculative psychology. For what does the picture that has just been sketched have to do with science? This question leads to substantial epistemological and methodological issues. The most pressing may well concern the relation between cognitive psychology as a whole and biology, especially neurobiology. Isn't all this loose talk about computationally individuated mental states bound to be superseded by a mature neurobiological theory of cognition? However that may be, wouldn't a sufficiently developed cognitive science find its proper, natural place within neuroscience? Most neuroscientists, not surprisingly, tend to believe so. There are also philosophers, linguists, psychologists and even computer scientists who hold that the bond linking the psychological and neurological levels is at the very least far deeper and more complex than the orthodox cognitivist view allows for. One defense of the cognitivist view rests on the now well-publicized type/token distinction: although any given token of a mental state is identical to, or instantiated by, a neurological state, the counterfactual-supporting generalizations of psychology can only be captured at the descriptive level of functionally individuated types of mental states. Neurobiology, the argument goes,*does not involve the predicates and nomological regularities necessary for psychological explanation: not only have these not yet been exhibited by neurobiology, but there is no logical necessity that they should be and in fact it is implausible that they ever will be, considering that at least some mental functions are instantiated as computer programs; the vocabulary adequate to describe both computer and mental operations is unlikely to properly belong to biology.<15> This is not the place for a discussion of this thesis, but at least one might point out that to believe in the (relative) autonomy of psychology as a science does not require espousing the extreme mentalistic stance of orthodox cognitivism.<16> -211-

Lec us now take Che possibility of an autonomous science of cognicive psychology for granted and ask about the relation becween Che cheorecical views under scrudny and empirical psychological scudies. The scriking thing is ChaC Chey largely underdeCermine one another. The theoretical views express no more than fairly general constraints on a yet unfathomable theory of cognition, nor can they be expected to do more, considering how sparse the evidence is. Conversely, much empirical work seems compatible with theories which violate, to a greater or lesser extent, the constraints of pure cognitivism. This considerable looseness (which cognitivism shares, to be sure, with all psychological theories to date, but is perhaps alone in recognizing as clearly) occasionally causes misgivings. Dennett, for one, calls for a truce in the search for abstract constraints on theories <17>; it can indeed lead to orgies of philosophical subtlety which not only carry the risk of hangovers, but more seriously completely leave out the 'ordinary', empirically-minded cognitive scientist. However, independently of occasional excesses, the epistemological situation in the field seems healthy, since quite a number of researchers, whether their main concern be theory or empirical evidence, are aware of the radical underdetermination of one by the other. Some, like Chomsky or Fodor, while stressing the methodological necessity of drafting rich, fairly complete conjectures and defending their own with gusto, nevertheless regard them as probably wide off the mark — as they expect the next few centuries of cognitive research to show. When matched by an equally militant consideration for dissenting views, such an attitude is unexceptionable. Dissenters, at any rate, can only be comforted by the possibility of holding on to whatever empirical results they consider robust and fruitful, while remaining free to set them in a theoretical framework they find less implausible than 'High Church computationalism'.

But the relation between abstract cognitive science on the one hand and empirical psychology and neurobiology on the other also raises a more specific question. In the latter two sciences, what is the place of the rules and representations postulated in the first? The answer involves two somewhat distinct issues. On the one hand cognitive science, regardless of how realistic it means to be about its theoretical constructs, quite legitimately demands the right to proceed at some appropriate level of Idealization. As Chomsky has repeatedly stressed, the physical sciences do not proceed otherwise and are hardly refuted by the wealth of irregularities whose fine grain keep them from getting caught in the teeth of the theory; to put heavier constraints on the still embryonic cognitive science(s) is simply unreasonable. Of course there are risks inherent in any idealization: the chosen level may be unamenable to scientific description, it may leave out some essential features of the pretheoretically Identified phenomena, and so on — but again these are the professional hazards of any sciencific (and for ChaC maccer, more generally, any cheorecical) encerprise. On Che oCher hand, Chere is Che difficulC question of the psychological 'reality', under idealization, of rules and representations. At first blush, it factors out into two partly independent questions: 1. Are rules and representations actually present (or...'represented') in psychological processes , or are they merely convenient descripCive devices? 2. Are rules and represencacions presenc in consciousness (or can Chey conceivably be broughc Co consciousness under suicable condidons)? A comparison mighc help: Che (escablished) face ChaC Che movemenc of planées conforms Co Kepler's laws is consCrued by some as demonscracing Che acCual exisCence of rules followed by planées, quiCe -212- independently of the observer's eye. This naturally does not commit them to hold that in some anthropomorphic sense planets compute their trajectories by applying the appropriate mathematical formalism. Still, this stance is considered by others as unintelligible: planets are not in the business of following rules in however abstract a sense; only mathematical representations devised by the human mind do that sort of thing. That is all standard material, and there is nothing more to it, because question 2 does not apply in the case of planets. It does in the case of minds. Here one may be tempted to accept a negative answer to question 2 and still consider question I as meaningful, and indeed calling for a realistic, rather than merely Instrumental, answer. Let us take each point in turn. £ . As opposed to the rules considered in the early stages of AI, when Newell and Simon, for example, were doing 'cognitive simulation' and studying human problem-solving protocols, many of the rules entering contemporary accounts of, say, speech perception, syntactic parsing, depth analysis and generally the various 'subpersonal' processes mentioned earlier cannot be regarded (as we have already noted) as accessible to consciousness under normal conditions. Moreover, since at any rate there is no guaranteed way of proving or disproving the accessibility to consciousness of a given rule, one had better be prepared, for methodological reasons, to take non-conscious rules at least provisionally under consideration. Thus, for example, it is hard to predict what kind of evidence would discriminate between some Rule A and a Rule B saying 'Conform to Rule A' or a Rule C reading 'If 0“1 do X else do A'. There are In fact Infinitely many rules which are functionally equivalent to any given rule. It is hard to believe they could all be present in consciousness: Nature is not that logically fastidious. Supposing for instance that just one of the lot is present, a well-intentioned scientist may not be lucky enough to hit upon that particular one; yet one would presumably not want to reject his explanation wholesale simply because he has called on an inconsistent notion. So much for the reasons for answering question 2 in the negative. b_ . The fact that this negative answer does not in itself rule out question 1 as meaningless is clear to anyone who thinks of mental reality as mnifested by processes . Those who are tempted to believe in something like the cognitivist account will moreover adhere to the realistic position: in the last analysis a rule is implemented in the 'wetware'; qua rule it may not have biological reality, yet any Instance of It is presumably accounted for by a biological process. The realist does have to worry about the status of idealizations: what is a rule which is not actually, but only ideally followed? Is it a rule with built-in exceptions? A rule which gets broken some of the time? A fuzzy or approximate or in whatever sense quasi-rule? Let us simply note in passing how much harder the epistemological problem is in the case of rules (be they non-conscious) than in the case of physical laws. The problem vanishes, of course, If one is willing to take an instrumental stance: psychological processes might be merely describable in terms of rules which are in no way intrinsic, let alone present in consciousness, and then idealization occurs, as in the standard situation, at the purely descriptive level. There is however one perspective from which both the realistic and instrumental interpretations of non-conscious rules become untenable. Take someone who has succeeded In following Wittgenstein's advice: 'Try not to think of understanding as a "mental process" at all.— For that is the expression which confuses you.' ( Philosophical Investigations , I, §154) -213-

For such a person, the only kind of rule which could possibly enter a psychological explanation would be conscious (or capable at least in principle of being brought to consciousness); the very idea of a subpersonal process subtending the act of understanding, and taken to be phenomenologically primitive, undecomposable, thus becomes unintelligible, and is accordingly rejected; with it goes the possibility of unconscious rules. Such a position is forcefully argued (in a wider context) by Hubert Dreyfus.<18> A propos conscious rules and representations, one final remark on consciousness. This is an entity which belongs to what is known in the trade as 'folk psychology'. Cognitivists are divided on the future of folk psychology. Some (such as Stephen Stich, P. and M. Churchland, D. Dennett, K. Wilkes) think it doomed — it will simply fall out of use, at least as a genuine explanatory framework, as cognitive science develops. Others, like Fodor, insist on saving it; they take on the burden of an additional major constraint, but leave open the possibility of getting help along the way from an old, and perhaps wise if unsophisticated ally.

II. HETERODOXY

All along the preceding pages, the reader has been getting whiffs of deviancy and foretastes of outright dissent. For the sake of clarification and classification of the different 'heterodox' approaches, this section provides a summary of ths major lines of criticism. This is followed by a brief review of alternative proposals for cognitive modeling and a tentative account of the present search for new structural principles.

4. Dissent

One may distinguish, among the various expressions of radical departure from orthodox cognitivism, four main lines of argument, not mutually exclusive.<19> In decreasing order of strength, as estimated (I surmise) by the cognitivists themselves <20>, they challenge the doctrine on conceptual, biological, phenomenological and formal grounds respectively.

The conceptual challenge takes many forms which are evidently connected, though in ways which are not always completely clear. Without attempting to do anything like justice to the arduous debate which has been going on for many years, I propose to describe what I think are the three principal ways of launching the conceptual attack. They are centered respectively on meaning, symbolic level and information. The 'problem of original meaning', as it is sometimes referred to, Is posed in equally sharp terms both by critics, radical or moderate, of the doctrine, such as John Searle or John Haugeland, and by supporters, radical or moderate, like Fodor or Robert Cummins.<21> One way of stating the problem is: how do mental representations represent? Or equivalently: how does a cognitive system act on the meaning of symbols while by definition it stands In causal relation solely to their form ? Yet another way of putting the same question, but without bringing in the meaning/form dichotomy is simply: where does our brain, qua mind or -214- organ of understanding, get its causal powers? Finally, there is a particularly explicit formulation, proposed by Cummins, whose argument runs as follows: Some — but (N.B.) far from all — input/output systems, whether natural or artificial, are 'inferentially characterizable', which is to say that an observer can make sense of their behavior (produce explanations, counterfactual-supporting generalizations) by attributing meaning to the tokens which they manipulate in such a way that one can go from input to output through a sequence of inferences starting with the sentence which is the attributed meaning of the input token, proceeding through the sequence of inner states of the system and ending with the sentence which is the attributed meaning of the output token. Such systems exhibit what Cummins calls 1*-cognition'. The property minds have 'in addition' to *-cognition is that they are inferentially characterizable under the attribution of meaning which they themselves (as opposed to the observer) stipulate. So our problem becomes: what is the ’added' factor that makes ^-cognition the real thing? On this point the borderline between critics and supporters is predictable: the latter regard the problem as a puzzle, a challenge, a temporary difficulty to be overcome by a new idea, while for the former, it points to a fundamental weakness, or outright inconsistency of the approach, hereby shown powerless to deal with the most important aspect of thought. I venture to suggest that the solution to the philosophical riddle of intelligence allegedly provided, at least potentially, by the computer metaphor, may be something of a Pyrrhic victory: the problem one is left with may prove as untractable as the initial 'problem of intelligence', the difference (and loss) being perhaps that the new problem, clear as It appears, may be but the result of a misguided attempt at solving the old one which, though ill-defined, at least points to some genuine questions. To use a rudimentary and admittedly lopsided comparison: inventing photography and accounting for the structure of photographic images in terms of properties of the objects photographed is one thing; attempting then to account for the structure of objects in terms of their photographic images, and even to (re)create the objects from the images is quite another thing. More seriously perhaps: the meaning/form distinction, that stroke of genius which gave birth to the computer (and formal systems hence modern logic) makes perfect sense insofar as it coincides exactly with the observer/object distinction. Remove the latter (as one must when taking the computer metaphor literally), and the former becomes mysterious — meaning turns either into an aspect of form, which thus burdened is no less of an enigma as meaning itself, or into a correlate of form, in which case the enigma lies in the correlation. An enigma which Fodor and Searle see in a different light: Fodor keeps asking how the correlation is established (and, one might add, maintained), while Searle's question is: what could the nature of the correlation be, what stuff, so to apeak, would it be made of? Symbolic level is a topic for critics only. They argue that the notion of a symbolic, or information-processing level distinct from both the physical level of energy-carrying particles and the phenomenological level of meaningful entities is simply unintelligible: there are chairs, and complex physical processes which cause my perception of chairs, but no level in-between, where energy quanta (which remain of necessity material) would somehow get transmuted into chair-impressions (which never were). For Dreyfus at least the conclusion Is obvious: mental representations simply don't make sense. Information itself, with its mandatory input/output scheme, is the third primitive notion that some critics focus on. Specifically, they do not -215-

believe in the possibility or fruitfulness of a straightforward transfer of the infonnation-theoretic framework from the domain of inert physical systems to that of organisms. Living systems are, of course, physical systems, and as such they exchange information with their environment in the usual sense; but living systems cannot be adequately described in terms of informational exchanges, because, as opposed to non-living systems, they are in the business of living, i.e. of perduring through inner and outer transformations, and it is that purpose — to perdure — which determines, from moment to moment in the unfolding of the life-process, what counts as information for the system concerned. Granted, the description of the evolution of a physical system by an outside observer also involves selection of the type(s) of information that are relevant in the chosen descriptive context. But there is, so goes the argument, a vast difference. First of all, an organism cannot be regarded as a (mere) self-observer, nor can its purpose be thought of as (merely) a variety of self-description. This observation has prompted a number of workers to sketch radically new epistemological frameworks for the study of organisms and biological (sub)systems such as the central nçrvous or the immune systems; the Palo Alto constructivist school or the Chilean school of autopoiesis thus attempt to redefine the relations between subject and reality, between observing system and observed system. Secondly, information in the physical sense consists in certain combinations of energy exchanges which can in principle be described in the vocabulary of mathematical physics: information is a natural kind belonging to the realm of physics. Why should we assume that the kind of information which the organism chooses to regard as being exchanged between itself and the environment be likewise composed of mathematically-physically natural combinations of elementary energetical events? The only compelling reason to do so is the belief that the world in which organisms — humans, say — dwell is actually made up of predefined discrete elements, assembled according to a fixed set of rules. This belief is what Dreyfus calls the 'ontological assumption', which by his judgment has remained part and parcel of traditional philosophy since Plato and which, in line with thinkers like Merleau-Ponty and Wittgenstein, he urges us to give up. Here the 'conceptual' challenge links up with the phenomenological challenge, of which a little more is said below.

But the biological challenge should be taken up first, since cognitivists probably tend to take it more seriously. For a cognitivist — though committed, as we have seen, to developing an explanatory framework Independent of biological description — is of course also bound, as an uncompromising materialist or physicalist, by the constraints which emerge on the biological level: any proposed explanation on the psychological level must be compatible with biological laws, i.e. (in the present case), any proposed information-processing mechanism must be implementable in the nervous system. Here arguments are even less decisive than in other aspects of cognitive studies, for cognitive neuroscience is barely coming into existence, and among non-neuroscientists only a precious few are capable of appraising whatever is at present known or surmised. But it does appear that many, if not most biologists who have given some thought to the problem tend to be skeptical about the chances that classical cognitivist schemes will ever turn out to be biologically plausible. Global, non-technical objections on biological grounds have been offered by (computer sclentist-cum-Renaissance man) Hofstadter and (philosopher) Dennett.<22> They argue that cognitivist schemes are, in 'unbiological' -216-

ways, austere, thrifty, hierarchic, deliberate, perfect in design — while 'Nature's ways' depend partly tm waste, tinkering, scattered decision-making, improvisation and the like. The arguments from biology will clearly play an increasing part in the debate, and much is to be expected, perhaps in terms of novel facts but surely in terms of conceptual clarification, from investigations now being conducted by scientists and a handful of philosophers who have submitted themselves to substantial doses of neurobiology.

The phenomenological challenge rests on arguments developed by 'existential' (as opposed to 'transcendental') phenomenologists — Heidegger, Merleau-Ponty — and the later Wittgenstein; a number of these arguments are supported by the contributions of various psychologists, mostly of the Gestaltist persuasion, who have also proposed alternative methodologies.<23> Since summarize we must, these arguments may be roughly and somewhat misleadingly grouped under two headings: the nature of human experience, the nature of human cognition. So it is argued that human experience — which includes, though not as a separable part, those facets usually regarded as making up its cognitive capacity — is characterized by its multidimensionality, with each dimension inseparable from the others and equally essential. There is man's corporality : we have, are bodies — our Intelligence is embodied through and through, so that factoring out the 'bodily dimension' is intrinsically impossible. There is our Involvement in the process of living : as living beings, we are ab initio and remain steeped in a flux of goals, purposes, fears and desires, in such a way as to make any factoring out of an 'intentional modulation' of some 'neutral' cognitive state impossible. There is man's affectivlty — the pervasive, permanent influence of moods and emotions, which again cannot be understood as a modulation of a neutral mode. And there is our Immersion in a social process which from the very beginning permeates our every thought and action, whose meaning can therefore be apprehended only in an indefinite horizon of social practices and implications. Turning now to cognitive experience, or intelligent behavior, it follows that meaning, rather than atomic-compositional, is holistic and emergent; that intelligence is always exercised in context, and (this is perhaps the single most important point) that context cannot be captured formally by a set of rules, meta-rules, etc.: ultimately the highest-order rules can only be stated and followed under ceteris paribus clauses on which the whole attempt at formalization founders; that problem-solving, in the strict, inferential, generate-and-test sense of the word, may be rather untypical of cognitive activity, which on the contrary, even when applied to the (limiting) case of a clear-cut search for a solution to a well-defined problem, may actually consist in the global, quasi-instantaneous recognition of the essential similarity ( i.e. in all relevant respects) of the situation at hand to one or more of an enormous collection of situations which we remember and use as prototypes; that rules perhaps play no part whatsoever at the highest stage of ability in any skill, be it piano-playing, car-driving, domestic or professional decision-making, social Intercourse, etc., even If they have been in fact taught and/or applied in the early stages of learning.<24>

Finally, for the sake of completeness, the attempts at formal refutation of cognitivism should be mentioned — no more than mentioned, however, for on the one hand the arguments involved are quite difficult and can hardly be summarized In a few lines, and on the other they no longer seem to loom large in the debate. This Is because it now seems -217- generally accepted that purely formal arguments — whatever their intrinsic philosophical interest, for example in the wider context of a discussion of materialism or determinism — have no direct bearing on the foundations of cognitivism. Two related strategies of formal refutation have been proposed. One consists in showing that the completion of a science of cognition is contradictory because it implies a perfect self-knower/ self-predicter. The other is to establish a contradiction between Godel's first and/or second incompleteness theorems and the assumption that the mind (conceived, under idealization, as an immortal theorem-prover) is a (Turing) machine.<25>

5. Alternatives a. Two kinds of reaction There are two ways someone dissatisfied with orthodox cognitivism can go: he can try to gain an alternative perspective on what cognitivism is actually about, actually achieves or proposes to achieve; or he can try to build alternative frameworks for the study of cognition. The first move is essentially epistemological, the second scientific; so they can be combined, although they are usually carried out by different individuals. Thus Dennett condones both strategies: on the one hand he declines, as we have seen, to take 'High Church computationalism' literally, and suggests we consider the whole program as a grand 'thought experiment' <26>; on the other, he lays high hopes in (neo)connectionism, a major alternative proposal developed by psychologists, computer scientists and physicists ,to which we shall shortly return. For Hofstadter, the cognitivist account applies only to subcognition — the domain of subcognitive processes. Seeking to extend it to cognition proper is the 'Boolean dream'. In cognition a key role is played by analogy, so that is what Hofstadter's scientific work focuses on. A theory of analogical reasoning seems to him a crucial first step.<27> (How far this may take him from standard, if elaborate, cognitivist theory is not yet clear.) In a somewhat parallel fashion, psychologist Benny Shanon's first step is to restrict cognitivist explanations to a special, 'representational' kind of cognitive process (without endorsing them wholesale even so restricted); he then argues for the existence of 'a-representational' modes of cognition, which he attempts to characterize along lines roughly consonant with J.J. Gibson's theory of 'direct perception' A milder form of deviancy consists in breaking the monopoly held by formal languages over mental transactions. Mental images as such (and not In the guise of symbolic descriptions ) should be (re)admitted, it is argued, among mental representations. Generally, information should not be thought of as necessarily or exclusively digital (or as language-like), but also as analogical (or as image-like). <28> b. Connectionism However, the most conspicuous alternative to orthodox cognitivism is an approach called (neo)connectionism. Taking up McCulloch and Pitt's seminal work, in the 1940's, on networks of 'formal neurons', pursued by von Neumann in the 1950's, present-day connectionists stand squarely in the cybernetic tradition, without necessarily endorsing all of its tenets nor, in fact, caring particularly.<29> It is fairly easy to describe, in rough outline, what certain connectionist devices look like (they come in a wide variety); a little harder to explain how they work (partly because they can be made to -218- function in a number of different ways); harder still to explain how they achieve what they are being used for (especially since there are several ways of using them); positively difficult to account for the statistics, physics and mathematics at work; very risky to conjecture how far their domain of applicability extends; and impossible to give an uncontroversial account of the overall conceptual role they play — or might be called to play— in the foundations of cognitive science. The few particulars which space permits here must therefore be taken with due caution.<30> Functionally, a connectionist model is described as a network of interconnected elements, or nodes; between any two nodes, i and j, In that order, there may or may not exist a connection, which Induces in node j a (positive or negative) fraction of the activity at node i; and the activity at any node is determined in a simple fashion ( e.g. as a weighted sum) by the activities induced through its various afferent channels. Certain nodes are accessible to the environment, which can initially activate some of them; this initial pattern of activation constitutes the input. Then every node 'computes' its level of activity, and the pattern of activity resulting from these simultaneous, independent parallel computations constitutes the state of the system at stage 1, and the basis for a new series of simultaneous computations leading the system into stage 2 and so on. The process may either continue indefinitely, perhaps leading after a while to a global oscillating movement — or it may stabilize. In the latter case, the output can be taken to be the stable state of either the whole system or some preselected set of nodes accessible to the environment. In most cases, the local computation rules and the connectivity of the network are taken to be simple and homogeneous, so that the system's characteristic dynamic properties (to be Interpreted as its 'cognitive capacities') lie In the strength of the connections. Moreover.whereas the architecture of the system usually remains fixed, the connection strengths may be taken as varying from one process, or sequence of processes, to the next, so that under favorable conditions learning can take place as a gradual tuning of the connections such that the system eventually conforms to a number of prescribed input-output pairings. The 'cognitive function' of the 'adult' system can thus be made to obey certain constraints, while retaining a degree of 'autonomy': it is left free to react as it 'sees fit' to patterns at variance with, or totally unrelated to, those commanding a prescribed response. It turns out that networks can exhibit striking properties of stability: an input related to a given input with a prescribed response will often elicit an output standing in roughly the same relation to the prescribed output. As for unrelated patterns, the system can subsequently be taught to respond to them in prescribed ways without forgetting all it has learnt previously: continuing education works! Now an essential feature of the use of such networks for the modeling of cognitive functions is that no one-one mapping between nodes and elementary psychological features (atoms of meaning for example) need be postulated. Often such systems of 'local' representation are abandoned in favor of a system of 'distributed representation', in which single nodes do not (in general) represent any psychologically significant element, while some sets of nodes, or patterns of activity, do.<31> Moreover, the most recent systems have 'hidden' units which are run by the system Itself. In other words, in the learning phase, it is the system instead of the experimenter which determines the level of activity of these units. The system can thus be said to create its own internal representation for the task at hand. From a mathematical point of view, the hidden units are the most salient difference between these modern systems and F. Rosenblatt's famous perceptron. A perceptron is a so-called linear classifier and is intrinsically limited to a small number of recognition tasks, as M. Minsky and S. Papert showed in 1969, thus virtually putting an end to this line of research. These limitations can be overcome by precisely introducing intermediate layers of hidden units. So, although in a weak sense computationally reducible to a Turing machine (in fact, the networks just described are instantiated as networks of finite-state automata whose behavior is simulated on regular computers), connectionist systems differ substantially from classical AI systems in the interpretation of the phrase 'information processing' which they call for: meaning appears gradually and globally in the course of the process, while in AI meaning is already there, only waiting to be appropriately combined. In a connectionist system, there is no formal, strict parallelism between the functional and the semantic level. In fact, these systems have sometimes been claimed to perform non-symbolic computations, or to operate somehow without 'representing1 in the usual sense. However appropriate or fruitful such descriptions may be, they do point to a genuine difficulty in applying standard information-processing notions to such systems.

The increasing popularity enjoyed by connectionist models has several reasons:

- From a pragmatic standpoint, they succeed in modeling, with surprising grace, a whole range of phenomena — in such areas as memory, recognition, learning, lexical access, automatic motor control — which traditional AI has so far found it difficult, and in some cases even impossible, to deal with. They are able to handle in a direct, 'natural' way — without having recourse to suspect, ad hoc notions of fuzziness or probability — such psychologically Important phenomena as the treatment of incomplete information, the generation of prototypes, generalization, memory as evocation or as reconstruction, etc. - They compute in a highly parallel fashion. Now parallelism is generally (though not unanimously) seen as the key to breakthroughs in computer science and classical AI, as well as an essential factor in the performance of the human brain, a performance all the more remarkable as neurons are very slow compared to electronic components. - They operate in a non-hierarchic, somewhat haphazard, 'organic' way, with no executive control; components are interchangeable and exceedingly simple; no complex semantics are (directly) involved in their processes. This all meshes well with neurobiology, and also points to a possible 'dissolution' of the puzzle of *-cognition: perhaps no meaning-endowing ghost is needed at any stage in the machine! - Although no longer thought of as literally models, however simplified, of structures made of neurons, they do resemble these to some extent, so that their implementation in the 'wetware' does not appear entirely implausible. - They do not follow an all-or-nothing, on-or-off logic: they can be active without doing anything much, without undergoing a goal-oriented process; they do not seek solutions, but eventually stumble on them, in much the same way as a mechanical system finds a position of equilibrium; they can operate (with impaired accuracy) when damaged. All this is pleasing from a phenomenological standpoint. - They lend themselves, in some cases at least, to elegant, powerful mathematical-physical methods, which is satisfying in itself but also appears to some workers (usually physicists) as, unmistakably, an auspicious sign. -220-

On the other hand, the connectionist approach meets with serious obstacles. There are many tasks which it cannot handle in a natural way, yet are the bread-and-butter of classical AI. Probably the most basic reason for this (besides the sheer recency of the endeavor) is that network models are ill-suited for composition : there are no standard procedures for combining smaller networks into larger ones instantiating the logical combinations or operations which a Turing machine can perform. This cannot be regarded as a failing: it is consonant with the whole approach; nor does it necessarily betray a fatal weakness with respect to the ultimate goal of accounting for human cognition: indeed it may turn out as an advantage, should human cognition be found to operate very differently, on whatever level of description, than a programmed computer. Meanwhile however we are so used to modelling complex processes by complex computer programs that we feel at a loss when deprived of the usual programming commands and concepts. Another problem with connectionist models, one which worries investigators more, is that they tend to be very slow as soon as the task at hand involves many data: stabilization takes a long time, and many cycles are needed before learning is achieved. But none of these difficulties constitutes an immediate threat for the connectionist research programme, whose assets are numerous and which is obviously worth pursuing. c. The third approach There is yet a third approach to cognition, distinct from both orthodox cognitivism and connectionism, though somewhat more akin to the latter. This approach, however, has until now operated on a rather different level than its competitors: it commands a certain view of the study of biological systems — the brain and some of its subsystems, mostly the perceptive systems, the immunological system, the evolutionary ecosystem; it is relatively clear on the philosophical foundations it aims to build on; it has produced a highly abstract, partly mathematicized theory of the systems it means to model — the theory of autopoietic machines created by H. Maturana and F. Varela <32>. But it has not yet reached a level of applicability where it can be said to actually produce concrete models of cognitive functions comparable to those of orthodox AI and cognitive psychology or to the connectionist networks. It does not even have a name, although one of its leading proponents, Varela <33>, has just recently proposed to call it the 'hermeneutical approach', but is having second thoughts after the rather mixed reactions to that label. It is illustrated by the works of F. Flores and T. Winograd in AI <34>, Varela and W. Freeman in neuroscience, by Maturana in eplstemology, and to some extent (exclusive of affiliation, be it informal) by the thoughts of philosophers such as Dreyfus, Searle and R. Rorty, and of psychologists such as Piaget and E. Rosch. No doubt this list is even more precarious than such lists are bound to be: either the 'hermeneutical' alternative matures, presumably under a different label, and many names, retrospectively, will need to be added, or it will fail to grow and the present names will migrate to other areas on the conceptual map of approaches to cognition. Orthodox cognitivism and connectionism both appeal to two fundamental notions: representations (which remain very much at the center of connectionism — as investigators of that persuasion have recently stressed — even though connectionism favors rather different systems of representation than those used by orthodox cognitivism) and information processing. The third approach challenges the relevance of both. First, there is no such thing, it is argued, as a pre-given external world which cognitive systems are in the business of representing faithfully. Thought does not mirror Nature, nor does it attempt to, as traditional Western philosophy has by and large assumed. Second, viewing cognitive systems as information processing, input-output machines completely misses their fundamental function. These systems should rather be seen as self-organizing, autonomous or 'autopoietic' machines, operating within the bounds of a self-defined closure, informationally closed to the external environment, though open of course to a flux of energy crossing its boundaries in either direction. With time, certain perturbations originating in the environment lead to a change of stable regime, in other words to a displacement of the equilibrium position of the dynamics of the system from one attractor to another. However, on one hand not every outside event leads to such a displacement; on the other, an efficient perturbation does not determine the new state of the system. Although plastic in its local structure, the system exhibits a resilience to alteration which is the mark of an autonomous organism. Thus no information is processed, properly speaking, and the world with which the autopoietic system interacts is, in all relevant aspects, brought forth in the course of time. Cognition should therefore be understood as resulting from the continued history of coupling between an autonomous system and its milieu. And instead of a pre-existing world to be represented by the system, there is an emergent realm of meaning inseparable from the history of the system. The constraint operating all along is not to be construed in terms of a series of tasks to be accomplished, of problems to be solved <35>, but of the necessity for the system to survive, i.e. to maintain its integrity through change: the constraint is system-centered rather than milieu-centered. Once the notion of a pre-given external world is disposed of, human cognition can be seen as operating within a social as well as phylogenetic and ontogenetic history. So that the social — in particular the language — dimension is no longer seen as an added factor, as some extra level of constraints to be considered at some later stage of development of cognitive science; but rather as one of the defining conditions of human cognition. No doubt this programme holds much appeal, especially for social scientists for whom the last point is particularly obvious and important. It remains to be seen, however, how the 'hermeneutical' proposal evolves. In the immediate future, as Vare .a stresses, research should be geared not towards task-oriented design, as in AI and connectionism, but simulation of prolonged of system-milieu coupling and classification of evolutionary strategies. This is a search for basic concepts and laws, not immediately applicable engineering devices. A step back, as many in cognitive science and especially AI would think; but then perhaps a necessary one.

6. The search for natural boundaries

It might be thought that cognitive science today is essentially a battlefield where two or perhaps three camps contend for 'paradigmatic* preeminence: classical AI with orthodox cognitivism on one side, and facing them, connectionism and related nonstandard computational models allied to — or perhaps actually in partial opposition with — some form of neo-Gestaltist, neo-cybernetic self-organization theory of mind, yet to be articulated in full. This appraisal, though not entirely wrong, actually ignores crucial aspects of the situation. First of all, modem AI (much like modem advertising, capitalism and most things modem) enjoys a tremendous appetite for dissenting views; it -222- assimilates, with surprising ease, many suggestions which only some years ago would have appeared quite subversive. Old AI was perhaps wrong on many counts, but it had strong principles. Young AI is relaxed and borders on opportunism; it would perhaps be fairer to say that AI can now afford to be pluralistic — there are room and resources for many schools, many intellectual temperaments, many ideas. So AI, for example, is definitely interested in connectionism, which it regards as a highly promising and legitimate offspring; it is willing to look into iconic representations; it is turning so holistic and context-oriented that Fodor has to remind it that, after all, some such utterance as 'You have a live giraffe in your breast pocket', though devoid of any (reasonable) context, is instantaneously and perfectly understood under normal conditions of communication — not every process is 'top-down', as the expression goes <36>; AI nowadays so much believes in flexibility that it proposes prototype-based systems of knowledge representation which fail to filter out, among exceptional cases, the really preposterous ones from those which it is trying to make room for — something of an inconvenience for use in the real world <37>; and so on. Work in AI has certainly become more varied, and wild ideas, as well as abstract ventures (for example in systems of non-monotonic, epistemic and other nonstandard logics) can be pursued, more easily than before, along with more traditional, task-driven and program-oriented research and design. Cooperation between AI and other disciplines has become smoother, more open-minded. But it is still too early to say what will come out of it all. On the other hand, several investigators in AI and in cognitive science — Fodor, Zenon Pylyshyn, (the late) David Marr and others — have undertaken a search for sound foundations. Their aim is twofold: to specify methodological criteria for deciding what a problem is, and what a solution to a given problem should look like; and to identify promising areas of investigation. The second, and more fundamental goal, to which I shall confine myself here, involves the search for natural boundaries. The hope is to discover a robust taxonomy of task domains and/or mental faculties. This would allow first to draw a conjectural line between what might properly belong to the cognitive science of tomorrow and what might not; second to organize cognitive research into natural divisions, and to choose, for every subdomain, the appropriate level of description. In other words: there is, presumably, something like an architecture of the mind; should we be able to get at least some rough idea of what it is, based on evidence provided by a map of cognitive performance or behavior, then we would determine constraints on the proper structure of our knowledge (and ignorance) in the cognitive realm and direct our efforts accordingly. While the 'analytic', scientific branch of cognitivism attempts to draw the contours of the mind's architecture, the 'synthetic', engineering branch is devoting much effort to the Invention of new computer architectures. Architecture, as applied to computers, is what distinguishes on the functional level such computationally equivalent machines as a universal Turing machine and a standard von Neumann digital computer (with unbounded resources). How related the meanings of the word (as applied to the mind and to the computer) are depends of course on how appropriate the computer is as a metaphor for the mind. It is worth noting, in that respect, that within AI, what connectionism is providing is typically regarded as proposals for a new architecture, not a fundamentally new approach to cognition. The case of connectionism also illustrates the kind of classificatory effort deployed by thinkers like Fodor and Pylyshyn. The latter suspects connectionist models to be limited in their application to processes which - 223-

are part of the architecture of the mind, as opposed to what he regards as properly cognitive processes. Pylyshyn holds that the connectionist's processes are 'cognitively impenetrable', by which he means that they are unaffected by the subject's desires and beliefs. So connectionism might be right insofar as it yields a true picture of a certain collection of processes which subtend cognition (much in the same way, say, that a particular electronic system allows a given computer to respond to a certain command in machine language), but wrong if it claims (which in actual fact it is increasingly reluctant to do) to account for cognition itself. A case could also be made, I suppose, for the idea that appearances notwithstanding, connectionist and classical AI information-processing, are simply instantiations of the same underlying process under two distinct descriptions. Fodor disagrees with Pylyshyn on this particular point: he doubts that networks process information at all, at least in the sense Information must necessarily have to meet the requirements of cognitive science.<38> So for him connectionism, at whatever level and applied to whatever kind of task or process, simply does not provide any explanation at all. On the other hand, he does have a sweeping proposal to make about the architecture of the mind, and the corresponding structure of cognitive science.<39> Roughly, he argues in favor of a sharp distinction between two kinds of functional cognitive units: modules , which are, among other things, fast, automatic, narrowly specialized, 'informationally encapsulated' ( i.e. with highly restricted access to information held in other units, hence in particular insensitive to context, beliefs, purposes, etc.); and central processes , which are by and large everything modules are not. Modules accomplish perceptual and linguistic tasks, while central processes are involved in 'belief fixation'. This simplifies Fodor's construction to the point of caricature, but what should be stressed here is the aim pursued: Fodor spares no effort to muster theoretical and empirical support (however Indirect — it always Is to some extent, in such matters) in favor of a bold general conjecture concerning the overall structure of cognition, not so much in order to account for any specific category of phenomena, but to provide principled reasons for deciding upon a specific organization of research, prefiguring the possible structure of mature cognitive science. Indeed Fodor argues further that modules stand a good chance of eventually receiving a satisfactory cognitivist account, while central processes emphatically do not. This, if true, implies on the one hand that the subject-matter of cognitive science splits into natural subdomains corresponding to independent faculties with certain typical properties, which in turn affects future research strategy; on the other hand, that cognitive science cannot, should not attempt to deal with much of what was initially believed to be its domain! It Is interesting to note that Pylyshyn and Fodor, who share many views, take opposite stands on the issue of the real scope of cognitive science. Yet even Pylyshyn concedes that many of those central, higher functions which are pretheoretically included in the science's field of investigation may after all escape it, due to what he calls 'possible noncognitive effects', such as learning, development and moods. His hope is that not every higher cognitive process will turn out, as a Dreyfus would hold, to Include non-factorable noncognitive effects. At any rate, the tactical purpose served by these attempts to carve out, within the mental realm, a subdomain fit for science, is to refute those expressions of skepticism about cognitivism as a whole that are based on evidence showing that some mental phenomena cannot possibly enter such an explanatory framework. Actually Dreyfus, the arch-critic, was the first to attempt to draw precisely the sort of map -22-1- assigning principled boundaries to cognitivist explanation. His principles, admittedly, were not couched in a vocabulary acceptable by cognitivists, and he was also confining his attention to early Al; yet to my mind, none of the essential features of his 1972 map seem outdated today.

There is still another way of dealing with the problems of cognitivism. It is to launch a frontal attack on one that is deemed decisive, without worrying about the others, and in particular without burdening oneself with unnecessary commitments to dubious assumptions. This requires inspiration, and inspiration seems Indeed to be guiding two recent endeavors addressing very different problems in very different styles: formal learning theory and relevance theory. These two approaches are described in some detail in the Appendix. Both show, I believe, that it is possible to develop, on the hypothetico-deductive mode, within a broadly cognitivist framework, theories which are in themselves sufficiently rigorous and fruitful to withstand, without major damage, fairly drastic changes in the theory of mind in which they are embedded. Thus while cognitivism, if eventually successful, will owe a great deal to these theories and similar enterprises, conversely the auspicious beginnings or even outright success of some of them hold no binding promise for cognitivism as a whole. Such is the price of the loss-cutting strategy described above.

* * *

The fundamental reason for the continued optimism of cognitivists — of those who have maintained the high ideals of the pioneers — rests, I think, on a deep faith in the power of mathematics to induce spectacular 'Gestalt shifts' which turn conceptually chaotic situations into orderly structures. Classical cognitivism has bet on logic, freshly mathematicized <40>, to bring about that shift, while the move to connectionism can be interpreted as a bet on physics, intermediate positions corresponding to a hedging of bets. Running under the logical or the physical banner requires positing some basic tenets. Work can then proceed. After a while however, the questions arise, of how valid the tenets are and how basic to the work being done. If the answer turns out, for both questions, to be: not very much, then one is left with the reverse problem — to find an appropriate framework for those good ideas and robust fragments of theory which have been forthcoming. The solution may not necessarily take the form of a pure, fully articulated mathematical science. It may look more like biology, a fragmented, fairly atheoretical science. It may even resemble one of those sciences of man 'constantly said to be in their "infancy"' (Charles Taylor), though in their constant endeavors to present illuminating and reasonably rigorous descriptions of a number of aspects of human reality, surely they have not always failed. I -225- APPENDIX: TWO AREAS OF COGNITIVE RESEARCH

1. Learning

a. The centrality of learning Among cognitive phenomena, learning is perhaps the most characteristic; it appears as the most central to any notion of intelligence: it is indeed hard to imagine one without the other. No full-fledged intelligence, as far as we know, can evolve without intensive learning; and learning requires at least some modicum of intelligence. However, far from occupying an equally central place in the various fields which make up the cognitive domain, learning has actually often been pushed to the side, put off for later examination. It has been considered from such diverging perspectives as to provide a criterion of demarcation between various approaches. Artificial intelligence parted ways with cybernetics largely due to its (AI's) refusal to regard learning as the key initial problem.<41> In fact, through most of Its short history, AI has held that deep insights into the question of learning would not be forthcoming until significant progress had been made in the understanding of general intelligence. Only recently has learning again become a respectable subfield within AI.<42> But before that, AI had also moved away from cognitive psychology, to which it was since its inception closely connected, again in large part because of differing interests in learning as a research topic. Today one striking way of contrasting connectionism and AI is through their respective approaches to learning. Chomsky and Piaget, as is well known <43>, disagreed profoundly on the nature of learning, while formal learning theory, which is consonant with Chomsky's views, remains quite separated from both classical AI and connection!sm. But regardless of how and when they propose to tackle learning, all programmes in cognitive research must recognize it as a decisive area for both application and testing. A sound theory of cognition should have fruitful applications in learning methodology whether for child, man, animal or machine, while conversely an inability to analyse and direct the learning process effectively would eventually signify failure for a proposed approach of cognition.

b. A tentative definition Interestingly enough, there is at present no universally agreed upon definition of learning. For present purposes, I believe the following will do: learning is any transformation undergone by a cognitive system whereby that system acquires a new disposition. This characterization, abstract as it may appear, captures a number of important ^spects of the phenomenon:

i. Insofar as 'cognitive' remains a somewhat mysterious quality, there is something circular in the statement. This is as it should be, reflecting as it does our intuition of a deep link between learning and cognition — the two mysteries go hand in hand, and it would be illusory to propose an elucidation of one which does not refer back to the other. ii. The notion of transformation of a system, imported from physics of course, implies the existence of a well-defined initial state of the system. This is important in view of the ultra-empiricist tendency to -226- regard the environment as shaping an absolutely plastic system, a system without any initial structure, a tabula rasa devoid of any properties whatsoever. ill. Acquiring something new implies changing — perhaps the single most important feature of learning: no learning takes place through a mere happening; stones do not learn how to fall when th«y fall; and people have not learnt from an experience when they behave, feel, think the way they did previously. Acquiring also implies doing something, so that an absolutely passive process is ruled out as learning: being told a fact is not learning it; that demands somehow making it one's own. iv. It is qua cognitive that the system is transformed. So that a piece of metal does not learn a shape when it is hammered into it; a grasshopper does not learn how to sit still when one cuts its legs off; and a human being does not learn anything properly speaking by swallowing pills or undergoing neurosurgery or simply maturing (these being all processes by which the brain undergoes physical changes, which result in a different cognitive system rather than a change in the capacities of a fixed system). v. A system Is defined in terms of its environment (and conversely). In the present Instance, our definition implies that the system undergoes a change in the presence of a cognitive environment. Here it is the ultra-innatist fallacy which is being circumvented: the contribution of something distinct from the system in its initial state is essential to the learning process. vi. What is acquired is variational and dispositional (I shall simply say 'dispositional' for the sake of brevity): it is an ability to perform a variety of tasks. Learning a name is becoming able to use it under different circumstances. Learning a language is acquiring the faculty to understand and utter a variety of sentences.

This last property is to be contrasted with another disposition, not specified in our definition because it occurs at a higher level. The ability to learn is Itself dispositional in character: a system which learns one particular disposition on one particular occasion, and never again anything else, cannot be regarded as processing a learning ability. But this is not a logical impossibility, nor does it run against our definition. It is simply an empirical fact, It belongs to the phenomenology of learning (or of intelligence). We cannot imagine, and would be hardly interested in building, 'single-shot' learners. This, however often overlooked, is an essential fact, and any theory of learning will have to take it into account; just as anyone wanting to find out something interesting about the value of sin y had better remember that sin is a function and study the properties of that function first. c. Kinds of learning; inductive inference Now one can start distinguishing between kinds of learning. There are of course a great many ways of going about such a classification. First of all one can focus either on the kind of thing learnt, disposition acquired, or on the process of acquisition Itself. Among acquired dispositions, one may distinguish knowledge of the knowing—that variety, and knowledge of the knowing-how variety. So, as we are dealing with dispositions, we may find pure propositional-inferential capacities on one side, skills on the other, which may in turn be thought of as perceptive or motor and inferential, or as strictly non-inferential ( i.e. non-representable). (I take no stand here on whether all of these kinds of learning actually exist; this depends of course on the true nature of cognition.) Among acquisition processes, we may first establish differences according to their intentional character; for example some learning is voluntary or deliberate, some is not; some learning is methodical (learning a language in school, for example), some is not (learning one's mother tongue); some is conscious, some subconscious; and perhaps some is 'personal', some 'subpersonal' in the peculiar sense explained in Part II. We may also discriminate according to the kind of information provided by the environment: rules, examples, presentations... Finally, and most Importantly, we should distinguish between ampliative and non-ampliative learning. In the latter kind, a full characterization of the Item to be learnt is provided by the environment, and the learning process consists in decoding the input, then recoding and storing it in the format, and under the label appropriate for retrieval in relevant circumstances. Ampliative learning consists in transforming incomplete evidence for, or presentations of, the thing to be learnt into a full description of it (in the appropriate format as before). In this case, learning is seen, essentially, as inductive inference (accompanied by a process of recognition, or representation, or decoding/recoding, which may, in the first analysis, be considered separately). d. Formal learning theory Formal learning theory attempts to characterize that dimension of learning which amounts to a process of inductive inference.<44> Born from the ideas of H. Putnam, R.J. Solomonoff and mostly E.M. Gold, this theory is in fact now also called 'machine inductive inference' because it aims at giving an account of inductive inference precise enough to be implemented on a computer. II does not however start out with a postulate of computability: the abstract learner which is being considered need not, at first, be thought of as following rules of computation. What is needed at the outset is a fully formalizable, explicit representational framework, e.g. the Chomskyan notion of a language, generated by a formal grammar. The intention, though, is to concentrate on computable learners. Cold founded the field by providing it with a paradigmatic situation, which constitutes by itself a major conceptual clarification of some deep issues surrounding the phenomenon of learning. The field however owes much of its interest to the possibility of countless variations on Gold's paradigm. I shall now briefly describe the paradigm, then mention some dimensions along which fruitful variations have been proposed, and finally describe some applications. Learning consists here in identifying, in view of partial evidence, a correct representation of the thing to be learnt among a set of possibilities. Correct Identification need not occur right away: as the learner gets provided with more evidence, he may change his mind. But he must eventually settle on one conjecture and stick to it regardless of further evidence; and that conjecture nust be correct if he is to be regarded as successful. Learning Is thus seen, in the present context, as identification in the limit . For the sake of precision, let?7 be the family of possibilities (objects among which the learner will pick its conjectures). Suppose learner JL is attempting to learn, i.e. identify some object 0 in 7 . JL operates In an environment whose relevant part Is a presentation, a gradual unveiling of 0: the environment provides .£ with a sequence of partial presentations of 0, say p, , p, , p} .... At each stage n, after having seen p„ (and perhaps remembering some or all of p, ,..., p,., ), JL conjectures some representation C„ . If at some stage N,j£ ceases to change his mind (so that C„= C*« “ ...) and CN is a representation of 0, •£ has identified 0 in this particular environment. It is reasonable, at least as a first - 228-

approximation, to demand that «£ Identify 0 in all (reasonable) environments presenting 0; if that is the case, <£ is said to identify 0 (period). Finally, if «£ identifies each 0 in & , then £ is said to Identify f . The crucial notion is the last: much as we have little use for a watch which has stopped at noon, even though it shows the correct time once a day, there is no purpose, as I have stressed earlier, for a learner capable of identifying but a single object. Typically, young children have the potential for learning any one among the 5000-plus languages spoken at present on earth (and perhaps others as well). Thus Gold’s paradigm fully takes into account the dispositional character of learning. However it leaves out the dispositional character of the thing learnt, assumed to be simply a representation in the cognitivist sense of the word. Such is the price to pay for, as well as the advantage of, separating competence from performance; or in other words, assuming tKfct our ability to perform cognitive acts is based on formalized knowledge. Now the above array of definitions has a double purpose. Conceptual clarification is one — as one reflects, one gains an ever clearer insight into more and more phenomena; some examples are given further on. The other is to prepare the ground for a fully mathematical treatment of the inductive process. By the now familiar device of coding, objects to be identified as well as conjectures become natural numbers (0,1 ,2 ,3... ), families of such objects become sets of numbers, and learners become functions taking numbers (coding finite sequences of codes of partial presentations) into numbers (coding conjectures). So that the whole theory is couched in number-theoretic, or more precisely recursion-theoretic terms. It then becomes possible to produce theorems — fully rigorous statements to the effect that certain collections are identifiable, while others are not; or that some collections which are identifiable by certain learners cannot be identified by learners who conform to certain policies or suffer from certain limitations. (To appreciate the significance of such results, it is important to note tha-t any single object, taken by itself, is identifiable by practically any kind of learner, regardless how 'dumb' — again the stopped watch provides a useful analogy.) These results, In turn, may be transposed in the empirical realm, and used to transmit, in a rigorous way, specific constraints from part of a theory (e.g. generative linguistics) to another ( e.g. acquisitional studies of language): suppose for example that empirical studies show that child-leamers apply a learning strategy which, according to formal learning theory, prevents them from learning the set of all languages as modelled by a given generative theory; then that theory is under severe strain, and probably calls for revision. But the theorems of formal learning theory can also be construed in a less littéral way as suggesting distinctions which may be useful In empirical research, or in the evaluation of philosophical theses. For example, the mere existence of unidentifiable collections considerably weakens the case for that version of empiricism which postulates the existence of a universal learning ability. To illustrate, it will be useful now to give examples of possible restrictions on the learner and of variations on the initial Gold paradigm.

A. restriction on the learner deserves to be called a strategy : by conforming to certain rules, by not being allowed to produce certain sequences of conjectures In a given environment, the learner narrows down his set of possible moves to those which can be said to be compatible -233-

with, or to make up, some strategy. What is accessible to a learner who follows strategy J — i.e. those collections which he can identify — is in general strictly less than what is accessible to a learner held to no strategy at all. Formally speaking, 3 is just a subset of the set of functions from and into the set of natural numbers. The most conspicuous such subset is the set of (partial) recursive functions: it is indeed natural, within a cognitivist framework, to focus on computable learners, i.e. on learners who compute, in the classical Turing sense, their conjectures from the evidence provided. An easy mathematical argument shows that the recursive strategy is indeed restrictive: some collections are unidentifiable by recursive learners yet are identifiable by some general learners. Besides computability, there are several interesting strategies which, combined with computability, produce additional constraints. For example, consistency , defined as the strategy which prohibits a learner from conjecturing an object which does not encompass all the evidence produced thus far, effectively limits what a recursive learner can identify. Complexity is another important criterion, given the unsuperable limitations of both artificial and natural computing devices. There are various ways of taking complexity into account: one can limit the complexity of the conjectures which are open to the learner, relative to the complexity of the evidence; or one can limit the amount of computation performed to reach a conjecture. The first strategy effectively restricts the power of a recursive learner; the second does not. Another kind of limitation follows from bounds imposed on the memory of the learner, who may be unable to take old evidence into account. This restricts the power of a recursive learner, but also those of a general learner. Many other strategies have been or are being investigated, as the theory moves back and forth between fields of prospective applications (which suggest formalizing some natural empirical constraint as a new strategy) and its internal logic (strategies easily formulated within the theory suggesting empirical hypotheses to be tested in one field or the other). Another major dimension along which to deviate from the Gold paradigm is the environment. The paradigmatic environment presents the evidence in any order, with or without repetitions, but without omitting any piece of evidence (which may nonetheless show up at some very late stage). Other, wider notions of environment cry out for consideration: imperfect environments allowing a small ( i.e. finite) number of foreign elements to intrude, and/or conversely omitting a small number of pieces of evidence; environments presenting both positive evidence (examples) and negative evidence (counter-examples), identified as such; environments presenting the evidence in a given order, or in an order conforming to certain constraints; environments repeating every item many times, etc. Finally, one can depart from the Gold paradigm with respect to the criterion of success. Learning some object 0, in the paradigm, is stabilizing after a finite number of trials on some fixed label or representation of 0. Alternatively, one may weaken this demand to stabilization on a label for some close approximation of 0; one may allow indefinitely many changes of label, as long as they designate 0 (or an approximation of 0); one may on the contrary demand stabilization on a relatively simple, fixed label, etc. Probabilistic learning can also be envisaged, and success demanded for a large (measure-one) subset of the collection to be learned, or stabilization in a large subset of environments, etc. -230- e. Applications of formal learning theory at the 'micro-level' Space permits but a very brief glimpse on actual and potential applications of learning theory in the cognitive sciences.

•< . Linguistics.<45> By far the most elaborate application is found in linguistics.<46> Language acquisition is seen, in this context, as the process by which a child, exposed to a sequence of sentences of a given language L, correctly identifies some grammar for L, in the sense of formal learning theory: after hearing a (finite) number of sentences, he holds on to some correct conjecture of a grammar for L. Now in this framework, any theory of universal or comparative grammar is bound by the following constraint: the collection of languages which it generates must be learnable, i.e. identifiable. It has been shown, for example, under suitable hypotheses, that Chomsky's theory of 1957 does not meet this requirement. But there are many other constraints that formal learning theory may bring to bear on a theory of comparative grammar by transmitting results of empirical studies of language acquisition. Examples of such results are: children as language-learners are, or appear to be, memory-bound — they take no account of sentences heard long ago; they get little negative information ( i.e non-grammatical sentences designated as such); they abandon rules conjectured at an early stage, rather than simply enriching their set of rules; they postulate rules which generate infinitely many sentences, etc. What learning theory does is formulate rigorous counterparts of these properties and infer some of their effects on the collections of languages which may be learnt, of which the set of natural languages is one. As a final example, formal learning theory has brought some support to the so-called 'strong natlvist' hypothesis, according to which the class of natural languages (defined in a reasonable way) is finite. This support assumes the form of a theorem stating that, under certain hypotheses bearing on the language-learning function used by children, any identifiable collection of languages Is finite. Naturally the validity of the conclusion rests on the strength of the hypotheses (granted the general framework). Though questionable, these are not regarded by workers in the field as preposterous, and they are subject to empirical support or counter-support by studies yet to be conducted; they also appear to some investigators as less questionable, in any case, than strong nativism itself: a measure of the ground which has been gained. Above all, for present purposes, this result Illustrates the kind of job which formal learning theory can perform, thus illuminating its epistemological status. ft . Scientific knowledge. The arch-example of inductive inference is of course provided by the (empirical) scientific process. The learner in this case is the scientist (suitably idealized) who is investigating some domain or natural law, e.g. some functional dependency between variables, and is seeking to uncover a correct description of that domain, law, or function. As empirical data accumulate, the scientist's conjectures change, and in certain favorable cases, stabilizes on some correct hypothesis. Scientific knowledge Is thus seen as the result of successful learning. Popper has taught us, of course, that scientific knowledge is conjectural, i.e. that we have (in general) no way of knowing that our conjecture is correct. This fact — or rather conjecture — of Popper's is fully borne out by formal learning theory: It can easily be shown that a learner who (always) knows — i.e. is able to signal — when he has reached a final (hence presumably correct) conjecture can only identify very simple collections. Work in this area, as might be expected, is far less developed than in the field of language. Two directions, at this early stage, seem promising :

i. To recast the problems and distinctions of traditional epistemology in learning-theoretic terms. Formal learning theory could then establish rigorous inferential links between 'acquisitional* studies, viz. case studies in the or empirical studies of actual scientific practices, on one hand, and theories of scientific knowledge and Its structure on the other. For example, it has been shown that a computable learner which applies a bayesian strategy in a certain probabilistic setting is unable to perform certain inferential tasks which are achieved by some non-bayesian computable learners.<48> But again, one may hope for conceptual clarification and enlightenment as well as, Indeed — given the difficulty and unclarity of many issues in the field — perhaps more than, clear-cut and significant results of the above kind. II. To develop a theory of machine learning, which would be able to predict what sort of Inferential discovery a given program can make, or has a good chance of making in a given amount of time. Work in this area is actually underway.

It seems to me, however, that a distinction should be drawn here. Like any theory, formal learning theory has a chance of succeeding only in areas which exhibit regularities, and where moreover some local, domain-specific theory allows the observer to capture these regularities in formal, or theoretical terras. Language acquisition is clearly such an area, and so is machine learning, as well perhaps as a yet to be delineated subset of scientific tasks. The situation is very different when one seeks to embrace scientific practice in general. There the learning-theoretic approach may shed little light — but then it would not be worse off, In this respect, than a good many other theories. p . Learning-theoretic constraints on the 'macro-level' Social and cultural phenomena, albeit collective by definition, depend in part on the individuals' cognitive mechanisms. How seriously this obvious fact should be taken, what its consequences may be for the methodology of the sciences of man and of society, these issues have of course been hotly debated for a long time, and will not be discussed here. It seems possible however to bring the learning-theoretic viewpoint to bear on such issues. Social and cultural representations which need to be transmitted in order to acquire more than ephaemeral existence must perforce be learnt by individuals (learning just being transmission from the receiving party's point of view).

»< . Dan Sperber has proposed an epidemiological approach to culture.<49> He argues that 'cultural things are distributions of representations in a human population, ecological patterns of psychological things', and further that 'just as an epidemiology of diseases has to be rooted in individual pathology, an epidemiology of representations has to be rooted in cognitive psychology.' And while anthropologists have by and large assumed that 'intra- and inter-subjective processes (...) ensure (...) the simple and easy circulation of just any conceivable representation', Sperber suggests that 'human cognitive and communicative abilities might work better on some representations than on others.' Indeed, If anything like formal learning theory applies here, no inter-subjective process can ensure transmission of all representations. At any rate, some form of learning occurs, and it would be interesting to characterize processes of social and cultural learning. Concept learning is an example, and an -232- interesting one Insofar as it Involves a form of inductive inference: usually no amount of finite evidence, whether ostensive or explanatory, can provide the child with a full characterization of a given concept; he must therefore identify it on the basis of incomplete evidence, and this process is obviously successful for all, or most concepts of a given culture, and all, or most children raised in that culture. Some concepts moreover seem to be transcultural, which would seem to point to the existence of an inborn, universal concept-learning function in humans.

R . The learning-theoretic approach has been applied to social preferences in an attempt to overcome Arrow's 'paradox' concerning the non-existence of a collective choice function which is compatible with the preference orderings of each individual.<50> The idea is to restrict one's attention to communities in which preference orderings are transmissible ( e.g. from parents to children), i.e. learnable. A child, on the basis of a sequence of pairwise preferences expressed by his parents, will converge on the correct ordering in a finite amount of time. It has been shown that, under suitable hypotheses, if individuals adhere to orderings chosen among an identifiable set of orderings, then there exists an aggregate choice function. g. Learning and cognitive studies The case of learning illustrates several aspects of the present stage of development of cognitive studies. First, learning has emerged over the last few years as a major aspect of cognition, and a central concern for all the fields concerned with cognition. This has not always been the case, and this new emphasis can be expected to bring some perhaps dramatic changes In the overall structure of cognitive studies in the near future. Second, one sees what the cognitive approach can bring to an old problem which traditional disciplines (philosophy, psychology, education) have been studying for centuries. Surely no one will claim that 'the problem of learning' has been, or is about to be 'solved' by cognitive science, even in a limited sense or in a special case. It is nonetheless clear that the very concept of learning is undergoing such a deep reappraisal that one no longer thinks of it in quite the same way as thinkers in the past, from Plato to Piaget. Last, learning raises in an acute way the issue of where science stands with respect to engineering, or in other words how pure cognitive science (or cognitive science proper) can be brought to bear on the problems of applied cognitive science (or technology). D. Angluin and C. Smith, in concluding their noted 1983 review of formal learning theory, state it rather bluntly: 'The most significant open problem in the field is perhaps not any specific technical question, but the gap between abstract and concrete results. It would be unfortunate if the abstract results proliferated fruitlessly, while the concrete results produced little or nothing of significance beyond their very narrow domain.' It is indeed striking that intimate contact has been achieved by formal learning theory only with already highly theoretical enterprises such as generative linguistics and branches of economics, whose relevance to the empirical field they aim to capture is not so clear as to escape challenge from various quarters. Meanwhile workers in cognitive science, in AI, in connectionism, in neuroscience are (at present) hard-set to find any relevance at all of formal learning theory to their immediate or even not so immediate concerns. One response is to stress that this situation is quite general: there is an immense distance between any branch of pure mathematics and any engineering problem, which only centuries of heroic intellectual effort, and today years of arduous formal training, allow investigators to bridge. And there simply has not been enough time for cognitive science to erect the long and presumbly complex bridges between its inner shores. This brings us to the second response: it is simply too early to try and evaluate the chances of success of any one particular research programme. All that can be said is that the overall success of the cognitive enterprise rests on the eventual convergence of its pure and applied branches.

2. Relevance and communication

Relevance theory is a major attempt by French anthropologist Dan Sperber and English generative linguist Deirdre Wilson to develop a new model of human communication, building on 's fundamental proposal to link communication to the recognition of intentions.<51> a. Critique of the code model It has been assumed, in the linguistic tradition and to this day by a majority of investigators, that there is a general model of the communicative process, viz. the code model. In oral communication, for example, the speaker is seen as encoding his thought according to some set of rules into an acoustic stimulus, which Is then decoded by the hearer who uses the same rules In reverse, and thus retrieves the speaker's thought. This model is shown to be descriptively quite inadequate. Examples can easily be found which show beyond doubt that the acoustic stimulus simply does not contain all the evidence needed to retrieve the thought conveyed. Referential indeterminacy, semantic ambiguity, ambiguity about the speaker's intention, Implicit import are some of the commom phenomena which the basic code model cannot account for. Having become aware of this, some linguists have attempted to enrich this model by adding an extra layer of decoding on top of the traditional phonetic/ syntactic/ semantic decoding processes. They have postulated, and sought, a set of rules which could be applied to semantic representations to yield complete conceptual representations. This attempt to construct a formal, rule-governed pragmatic level is still underway, in linguistics as well as artificial intelligence, where the goal of formalizing so-called 'situated language' is of paramount importance. The problem is quite simply that no such set of rules has been forthcoming: although in some cases imcomplete semantic representations can be filled in using uniform rules such as '"I" designates the speaker' or '"it" designates the inanimate object last encountered and leading to an Interpretation compatible with the overall meaning of the sentence', in most cases it Is obvious that simple uniform rules just will not do, and that a great amount of complexity is to be expected of the required rules, should anyone come up with them, which has not been the case. The whole attempt rests on assumptions that one need not accept, among which the notion that linguistic communication is a completely isolated cognitive phenomenon. It might be more fruitful to regard it instead as one cognitive phenomenon among others, whose grammatical dimension does not constitute the only source for explanations. b. Ostensive-inferentlal communication Paul Grice (and other philosophers such as D. Lewis and S. Schiffer) have proposed an alternative model, based on the idea that communicating -234-

is producing and interpreting evidence of a certain kind, in a certain way, which they have tried to spell out. Sperber and Wilson attempt to improve on their model, which they find lacking in some respects. Here I shall content myself with a brief and incomplete sketch of the Sperber-Wilson theory, bypassing the earlier 'inferential' theories. Communication, in their view, is a process by which a communicator modifies, through physical means, the cognitive environment of an audience, while the audience, by recognizing the communicator's intention, infers some assumptions which are, at least in part and if the communication is successful, those which the communicator meant to convey. So instead of encoding we now have ostension; instead of decoding we have inference; instead of direct modification of an audience's thoughts we have modification of its cognitive environment. This is not to say that the code model applies to n£ aspect of communication, that it should simply be cast away. Clearly language is a code, it 1s a mapping taking e.g. the phonetic representation of a sentence onto its semantic representation (or onto a set of candidates for semantic representations). There is however a gap between the semantic representation and the thought communicated by an utterance. This gap is filled by an inferential process whereby the audience recognizes the communicator’s intention. Coded communication is thus seen as a subprocess, within linguistic communication, of a wider ostensive-inferential process. It remains to see more precisely what is being communicated, and how it is done. c. Manifestness and cognitive environment; mutual manifescness We must pause here and introduce a number of somewhat technical, though quite intuitive notions. Sperber and Wilson define manifestness so as to make it, in the cognitive realm, an equivalent of accessibility in the physical world. So an assumption — i.e. a representation which is regarded by he who holds it as true or probably true — is manifest to an agent just in case it is perceptible or inferrible to him (at a given time, in a given situation). It is therefore not necessarily entertained by the agent, just as an object within his field of vision is not necessarily seen by him, should he be paying no attention to it. The agent's cognitive environment is then defined as the collection of all assumptions which are manifest to him at a given moment. Manifestness is a matter of degree, just like accessibility or visibility: assumptions are more or less easily retrieved by inference or perception or a combination of both, and are more or less close to the focus of attention. So communication, like other cognitive processes, can also result in a strengthening or a weakening of the manifestness of an assumption already manifest to an agent. When two agents interact, their respective cognitive environments are brought into contact: their shared cognitive environment is the intersection of the two environments, i.e. it is the set of assumptions which are manifest to both of them. Within this shared cognitive environment one may find assumptions enjoying the peculiar property of being mutually manifest : P Is mutually manifest to A and B If:

- P is manifest to A and to B; - it is manifest to A and to B that P is manifest to A and to B; - it is manifest to A and to B that it is manifest to A and to B that P is manifest to A and to B; - etc. Examples are: if À and B find themselves in a room, it is mutually manifest to them in normal circumstances that they find themselves in that room; if A and B play chess, It is (again under normal circumstances) mutually manifest to them that they both know the rules — in fact the rules themselves are mutually manifest to them. The notion of mutual manifestness is a weakening of, and to my mind a considerable improvement upon, the notion of mutual knowledge put forward by Schiffer and Lewis. The appeal to mutual knowledge was motivated by the need to insure communication against possible misunderstandings and deceptions: if one wants inference to play a role in communication and one still holds on to the code model, then the context in which Inferences are made must be mutual knowledge. Suppose for example that A and B both know P but that B does not know that A knows that B knows P. Then when A tells B 'P implies Q', B will not be able to figure if A means to (implicitly) convey Q to B, or simply (perhaps for future reference) P implies Q. (Less abstract, but longer examples have been provided in large quantities.) In the Sperber-Wilson approach however, there is no attempt to uncover a failsafe mechanism for communication; engaging in communication is seen as a risk involving possible misunderstandings. Mutual manifestness, while not strong enough to guard communication against misunderstandings (B may be too lazy or dumb or preoccupied to access the assumption 'It is manifest to A that it is manifest to me that P', or B may not trust A to have actually entertained the assumption 'It is manifest to B that P', etc.) ensures the possibility of correct understanding. And it has, over mutual knowledge, the great advantage of being psychologically more plausible, avoiding as it does an appeal to infinit representations, or infinitely nested assumptions. As a final preliminary, inference is to be understood in the present context as non-demonstrative . It proceeds by conjectures and operates on not necessarily certain propositions. Moreover, it operates on open bodies of data or assumptions rather than in closed contexts. The human mind is therefore thought to proceed according to various heuristics which cut down its possible conjectures to one or a few. The overall direction of the process is guided, as we shall see, by a very general principle, optimizing 'relevance'. d. The communicative and the informative intentions As Grice saw, and P.F. Strawson explicated, communication involves several layers of intention. Sperber and Wilson simplify the account and make do with Just two layers. According to them, in ostensive-inferential communication the communicator produces an ostensive stimulus with two intentions in mind:

-an informative intention -a communicative intention.

The informative intention is to render manifest, or more manifest, to a given audience, some set of assumptions S. The communicative intention is to render mutually manifest to communicator and audience that the communicator, by producing the stimulus, has the above-mentioned informative intention. Thus communication does not cover all cases of Information transmission. Communication alters the audience's environment in two ways, the second of which, though secondary with respect to the transmission of S, is of great potential import for the cognitive Interaction between communicator and audience, in particular for further communication (it is hard to write again to someone without knowing whether one's previous letter has been read !). -236- e. Relevance It remains to see how communication is achieved, i.e. how the two intentions which it carries are actually fulfilled. First, the communicator produces a certain stimulus, i.e. a modification of the physical environment of the audience, but not any modification: one which is designed to achieve cognitive effects. Such a stimulus is generally one that will draw the audience's attention and yet be devoid of any relevance to the audience except as indicating the communicator's intention. If the audience indeed comes to assume that the communicator has an informative intention, then the (second-order) communicative intention Is fulfilled, and it remains for the audience to retrieve by inferential means, from its cognitive environment and the stimulus, the set of assumptions S which the communicator intends to make manifest to the audience. This Is where relevance comes in. Relevance is meant by Sperber and Wilson to be a technical notion for which they claim to provide an adequate characterization, and which is approximated to varying degrees by the various meanings of the word as used in a loose, non-technical sense. An assumption T Is said to be relevant in a context C if there are (logically) non-trivial consequences of C and T which cannot be inferred from C alone. (These consequences are called contextual effects of T In C.) This classlficatory definition is complemented by a comparative definition: T Is relevant in C to the extent that it has large contextual effects, and also to the extent that the effort required to process T in C in order to obtain those effects is small. We have noted however that in communication agents do not proceed in a fixed, closed context. In their inferential search, they might extend the context by drawing, from the stock of assumptions which are manifest to them, some increasingly distant items. Therefore Sperber and Wilson need to define relevance to an individual : this is done in terms of relevance in a context, by quantifying over contexts and retaining an optimal context, one in which the effects are relatively large and the processing effort relatively small. These definitions easily extend to cognitive phenomena other than assumptions. A stimulus can thus be regarded as (more or less) relevant in a context, or to an individual, in the technical sense. Let us now return to the point where the audience is faced with the cask of inferring the set of assumptions S which the communicator wants to convey. Sperber and Wilson claim — this is their 'principle of relevance' — that any ostenslve stimulus carries an AUTOMATIC presumption of relevance . Simply by recognizing the communicative intention, the audience recovers one member of S which, regardless of the yet to be determined remainder of S, is about S, and comprises two parts:

R1. S is sufficiently relevant to the audience to make it worth the audience's while to process the stimulus.

R2. The stimulus is the most relevant among those available to the communicator to convey S.

The audience can now build an interpretative strategy for recovering S, based on R1-R2 and on a general assumption of rationality:

R3. By producing the stimulus, the communicator has reasons to believe that it will have the desired effects.

The audience seeks a set of assumptions which confirms R1 and does not disconfirm R2. As soon as it has found such a set, It stops. If the communication has succeeded, what the audience has inferred is precisely S. And Sperber and Wilson argue that in the many cases where the process leads to successful communication, it does by following this course. f. A tentative assessment A theoretical enterprise of such scope is not easily summed up, much less assessed. It Is certainly too early to evaluate the impact of relevance theory in pragmatics and more widely in cognitive science, or to foresee extensions and applications. 1 should however like to offer, on a very tentative basis, a few remarks.

i. The overall framework of the theory in its present formulation, I.e. the cognitive psychology in which it is embedded, conforms to the strictest cognitivist standards. As the present summary has not shown, but is abundantly clear in the book (Sperber & Wilson 1986), the authors conceive of the mind as operating sequentially, by discrete, computer-like or logical steps, on representations couched in an inner language (although they seem to accept the notion of non-linguistic representations in some cases). There is no room in their account for parallelism or interactive activation. ii. There is no clear indication of how the audience is supposed to assess its prospective processing effort without actually performing the actual computation (which would of course completely defeat the whole scheme). The authors are aware of the problem, but offer (to my mind) no convincing suggestion. iii. The size or largeness of contextual effects, on which the definition of relevance entirely rests, is never Independently defined. As number or complexity of consequences seem (to me) ruled out as plausible criteria, the claim to have characterized relevance in the technical sense independently of some pre-theoretical notion seems ill-founded. On the other hand, there is hardly a mention, in the theory, of the agents' concerns (although in the numerous and fascinating examples they provide, the authors prove themselves to be not Insensitive to this dimension). My suggestion would be to define the magnitude of contextual effects In terms of their importance for the agent's concerns, which naturally vary through time and may well be influenced by the ostensive stimulus itself (we often care about what someone is trying to convey). This move might perhaps be seen as trivializing the theory, but I believe it could strengthen it. It could also lead to difficulties of its own: it all depends on how one proposes to characterize the notion of concern. iv. Whatever Its possible shortcomings, relevance theory as it now stands provides a viable alternative to the code model of communication, an operational, richly explanatory inferential account of this central cognitive process which does justice, to a large extent, to Its complex phenomenology. And although I believe that one should attempt to recast it In a different psychological framework, so as to make it compatible with a different view of cognition, perhaps closer to connectionism or autopoiesis, I also think the theory is robust enough to retain its essential features, a large part of its conceptual apparatus, and most of its explanatory power. And if it turns out indeed to be a better approximation to the truth than other available accounts of communication, this should In the long run have a considerable effect on the development of theories in the social sciences. -238-

NOTES

1. For a careful tracing of cognitivism in Western thought, see Dreyfus 1972/79 and Haugeland 1985. The fir3t cognitlvist is Plato, according to Dreyfus, who sees artificial intelligence as the direct extension, and if successful the crowning achievement, of the central tradition in Western philosophy. 2. The article is reprinted in Anderson 1964 and In Dennett & Hofstadter 1981. On Turing's life and work, the reference is Hodges 1983. 3. Dretske 1981 presents the most thorough attempt to explicate and defend this view, which Fodor rejects (see in particular his 1984), for reasons which appear in §4. 4. In fact this is where the existence of a universal Turing machine takes on significance. There are many, many formal games an intelligent system may be expected to play; it must therefore be at least somewhat universal, that Is to say, akin to a (programmable) computer with its physical limitations, on pain of being merely a tremendous conglomerate of special-purpose subsystems. The modularity hypothesis mentioned in §6 Is an attempt to strike a principled compromise between this extreme and that of a perfectly homogeneous universal system. 5. Stated in their 1975 Turing Award acceptance speech, published in Communications of the A.C.M. , 19 (March 1976) and reprinted in Haugeland 1981. 6. Sometimes it actually is manageable: a strictly algorithmic, fail-safe solution is then possible. The problem is one for (classical, pre- or non-AI) computer science, and might seem to present no conceptual challenge (however awesome the technical obstacles may be). After all, a task which can be accomplished, regardless of circumstances, by plain systematic search surely has little to do with Intelligence. Would one want to attribute intelligence to a pocket calculator? Well, the answer is not as straightforward as one might wish. To begin with, it may depend on whether the process undergone by the calculator is in some sense identical, or at least related, to the process followed by a human calculator. If it is not, that very fact is of interest by itself, and also suggests a non-vacuous research program, aimed at characterizing those cognitive tasks which can be executed through non-human, possibly 'non-intelllgent' processes. On the other hand, a similarity between machine and human processing leads one to ask what it is about the machine which makes it behave — in certain circumstances — as if it were possessed with intelligence. One answer to this second question may be: the programmer's intelligence. Then one may want to determine those dimensions or aspects of Intelligence that can thus be 'copied' or 'canned', and those which cannot. And so forth: quite a number of the theoretical Issues raised by the cognitlvist endeavor — some of which are discussed In this paper — can be asked, It turns out, about the kind of minimal 'intelligence' involved in the execution of algorithmic processes. 7. See Hofstadter 1985, and Dennett 1978, especially the chapter 'A Cure for the Common Code1, a review of Fodor 1975. 8. The birth of AI Is usually taken to have officially occurred at the 1956 Dartmouth conference, where It received its present name, suggested by John McCarthy. Research had been carried out before, in particular by Simon, Newell and Shaw. Detailed accounts of the history of the field are found in McCorduck 1979, anecdotal and unabashedly apologetic, Dreyfus 1972/79, philosophical and critical, and Newell 1983, an insider's view, technical, optimistic yet open-eyed. 9. As evidenced, for example, by the wide spectrum of responses made to an in-depth questionnaire drafted by Artificial Intelligence , an authoritative Journal in the field; see Bobrow & Hayes 1985. 10. This is but one example of the early hopes of AI. Dozens of predictions were made in the 1950's and 1960's which no worker today would commit himself to. 11. In Bobrow & Hayes 1985. Expressions of self-doubt and calls for new ideas are frequent in recent literature; see especially AI Magazine . 12. See Dennett 1984. A terminological review might be useful at this point. I use cognltlvlsm to refer to a broadly-defined doctrine concerning minds and computers which subtends a cluster of research programs conducted within AI, psychology, philosophy, linguistics, anthropology, neurobiology etc. Cognitive science , in daily practice and, when not qualified, in this paper as well, refers to the branches of psychology, linguistics, anthropology and other sciences of man (but excluding, In general, the neurosciences) which operate under the broad constraints of cognitivism; so cognitive science comprises cognitive psychology , generative linguistics etc. The above-mentioned cluster of approaches based on cognitivism therefore splits into three main components: AI, cognitive science and (cognitive) neuroscience. The confusing thing is that cognitive science is occasionally used as a blanket term for the whole cluster, with the purpose of stressing the potential unity of the field; this usage is rare and will not occur in the remainder of the paper. 13. See Fodor 1985. 14. This analysis is not supposed to apply to so-called qualitative states of mind, such as pain, pure color perception and so on. But then such states do not In themselves enter, it would seem, in properly cognitive processes — the state of headache-impression does not, on the present view, directly trigger an aspirin-taking strategy: only the belief that I have a headache, together with others, such as the belief that aspirin relieves headaches without harming the patient, can play the required causal role. See Fodor 1981. 15. See Fodor 1981. 16. See for example Shanon 1985. 17. In his address to the 1984 Sloane conference at M.I.T. See Dennett 1984. 18. In Dreyfus 1972/79. See Pylyshyn 1974 and 1984 for a counterattack. 19. The salubrious task of sorting out, among the results actually claimed by empirical research or cognitive computer modeling, the wheat from the chaff, is of course in principle of a different nature than the theoretical questioning reviewed here. Dreyfus has sometimes been taken to task for having allegedly confused, or run together, both kinds of critical strategy. This, It seems to me, is like blaming the fireman for the fire: the strategy of early AI, to a large extent, was to marshall support for a hazardous theoretical framework from prospective general results in cognitive modeling, which in turn could not fail to come about, considering both partial results already obtained and the very theoretical framework in need of support. Dreyfus took pains to sort out pragmatic assessments from theoretical , thereby contributing a clarification which AI, for its own sake, badly needed — as conceded by some of his critics ( e.g. Pylyshyn 1974). 20. It should again be stressed that cognitivism is far from monolithic (see for example note 26). A 'cognitivist', as I have been using the word. -240- is somewhat of an 'ideal type1. 21. The problem of 'original meaning' is discussed, among others, by Haugeland (see his own two papers in the anthology Haugeland 1981, and his 1985), Searle, Fodor (in particular In his 1981 and 1985), Cummins. 22. See Dennett 1985, 1984, and Hofstadter 1985. 23. Dreyfus was the first to submit AI to a phenomenological critique. The contrast between existential and transcendental phenomenologists in their attitude towards cognitive science is stressed in the introduction to Dreyfus 1982, where Husserl's anticipation of cognitivism is discussed. 24. In the detailed five-step theory of skill-acquisition developed in Dreyfus 1986, specific conjectures are made concerning the vanishing role of rule-following. 25. The most detailed attempt to base a refutation of mechanism on Godel's theorems is Lucas 1961 (reprinted in Anderson 1964). The main contributions to the ensuing debate are Benacerraf 1967, Lucas 1968, Lewis 1969 and 1979. One gets a hint of GSdel's own views on the matter in Wang 1974. Dennett 1978 (chapter 13) approaches the problem in the context of cognitivism. Webb 1980 is an essential work, which puts the issue in proper perspective, and argues that the incompleteness brought to light by Godel actually acts as a 'guardian angel* to mechanism. 26. In Dennett 1984. There are other philosophers (S. Stich, P. and M. Churchland...) who, though in basic sympathy with cognitivism, part from it on important theoretical issues. 27. See Hofstadter 1985, especially chapters 13, 24, 26. 28. On the 'imagery debate', see Block 1981 and 1983, Kosslyn 1980, Pylyshyn 1984 and the references there. On the subtle distinction between analog and digital, see Goodman 1968, Lewis 1971, Haugeland in Biro & Shahan 1982, Pylyshyn 1984. 29. The birth, development, decline, initial loss to AI and present renaissance of cybernetics, within the wider context of theories of self-organizating systems make a fascinating story, not yet fully understood. See Dumouchel & Dupuy 1983 and the forthcoming Cahiers du CREA vol.7, especially the contributions by Dupuy, Livet, Heims, Stengers. The Chilean school (Maturana, Varela) is also a member of the self-organization 'galaxy', and has sketched a theory of 'autopoietic' machines which may develop into a full-blown, workable alternative to the cognitivlst approach to both human and machine intelligence — 3ee Varela 1979 and 1984, and the references there). It is still, however, the work of a small kernel of pioneers, while connectionism, although or perhaps because it carries less theoretical, especially epistemological ambition, has over the last few years attracted a wide audience of workers, supporters and critics. 30. The classic references are Pitts & McCulloch 1943, von Neumann 1966, Minsky & Papert 1969. Modern connectionism has many roots; see Atlan 1972 and 1979, Dumouchel & Dupuy 1983 (the papers by Atlan, Weisbuch, Fogelman-Souli§), Hinton & Anderson 1981, Hopfield 1982, Kohonen 1984, Hinton 1984, McClelland & Rumelhart (to appear); the Issue of Byte on AI (April 1985) contains excellent non-technical expositions of some of the major aspects of connectionism. A special issue of Cognitive Science (September 1985) offers a more technical survey of the field. 31. See Hinton 1984. 32. See Varela 1979, 1984 and Maturana & Varela 1985. 33. What follows mostly reflects Varela's views on the matter. 34. See their 1986. 35. For a critique of problem-solving as a universal explanatory concept, see Andler (to appear). 36. In Fodor 1983. -24 1 -

37. See Brachman 1985. 38. See Fodor 1984. Hubert Dreyfus agrees (personal communication). 39. The whole of Fodor 1983 is devoted to this proposal. See the discussion in Behavioral and Brain Sciences 8 n* 1 (March 1985). 40. This appears quite clearly, for example, in Herbert Simon's reminiscences in Lemoigne & Demailly (to appear), and in the debates within the cybernetic movement ( e.g. at the Macy conferences organized by McCulloch in the late 1940's and early 1950's) — see the references given in note 29. 41. See Newell 1983. 42. See Michalski et al. 1984 and 1986. 43. See Piatelli-Palmarini ed. 1980. 44. See Gold 1967. A rather full presentation of the theory is found in Osherson et al. 1986; see also the survey Angluin & Smith 1983. 45. See Osherson et al. 1984a, 1986. 46. In fact, Chomsky's notion of a learning theory for an organism 0 in a domain D, LT(0,D), which he defines in his 1975, is exactly what formal learning theory calls a learner or a learning function. More generally, this theory has developed in a largely Chomskian climate. 47. See Osherson et al. 1984a. The conjecture Is found in Chomsky 1981. 48. See Osherson et al. 1986 *. 49. See Sperber 1984 and the references there. 50. See Osherson et al . 1984b. 51. See Grice 1957, 1975 and Sperber & Wilson 1986.

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AP PEND IX 2

List of participants at the seminar Artificial Intelligence and Language. Paris 2.-4. Nov 1967. Cardin, Jean-Claude Andler, Daniel Centre de Recherches Archéologiques CREA (Centre de Recherche Epistemotogi CNRS 23, rue de Maroc et Aulonomi) 75019 PARIS Ecole Polytechnique France 1. rue Descartes 75005 PARIS Hart, Anna France School of Computing Lancashire Polytechnic Berry, Diane Preston PR1 2TD Deptartment of experlmental psychology United Kingdom Unlverslty of Oxford Souths Banks Road Henry, Paul OXFORD 1 3 VD CNRS United Klngdom 20. Avenue des Gobelins 75005 PARIS Blng, Jon France Norwelglan Research Instltute for Computers and Law Hilton, Julian Unlverslty of Oslo Nlels Juelsgt. 16 The Audio-Visual Centre N-0272 OSLO 2 University of East Anglia NORWICH NR4 7TJ Norway United Kingdom Bourcler, Danlelle Jameson, Gordon CNRS-CONSEIL D ETAT Audio Visual Department Palais Royal University College London 75001 PARIS Gower Street France LONDON WCIE 6BT United Kingdom Buchberger, Emst Austrlan Research Instltute for Artlllcial Joaefeon, Ingela Intelligence Swedish Center for Working Life Schottengasse 3 Box 5606 A-1010 W1EN S-114 86 STOCKHOLM A ustrla Sweden Canna tact, Joseph A. KaasbelL Jens Mailing adress: 77. St Trophlmus Street. Institute of Informatics Sllema, Malta University of Oslo Work affiliation: P.O. Box 1080 Blindem Norwelglan Research Instltute for N-0316 OSLO 3 Computers and Law Norway Unlverslty of Oslo N-0272 OSLO 2 Lagrange, Marte-Salomé Norway Centre de Recherches Archéologiques CNRS Francfort, Henri-Paul 23, rue de Maroc Centre de Recherches Archéologiques 75019 PARIS CNRS France 23. rue de Maroc 75019 PARIS Lanzara, Giovan Francesco F rance Dipartimento di organizzazionc c sistcnia politico Université dcgli studi di Bologna Via Giuseppe Pctroni, 33 40126 Bologna Italia -243-

Lcinfellner, Elisabeth Department of social anthropology Department of llngulstics Unlverslty of Oslo Unlverstty of Vlcnna P.O. Box 1091 Blindera Quartngasse 22 N-0317 OSLO 3 A-1100 WIEN Norway A ustrla Szczecinlarz. Jean-Jacques Léon, Jacqueline Département d'éplstémologle Laboratoire d'informatique Sciences Université de Paris X H um aines 19. rue Fabre d Eglantine Malson des Sciences d l'Homme 75012 PARIS 54, boulevard Raspall France 75006 PARIS F rance

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CompLex 9/83 Frede Cappelen Edb-basert informasjonssystem for forvaltningens praksis NOK 36,- CompLex 10/83 NORDIPRO Legal Acceptance of international Trade Data NOK 142,-

CompLex 11/83 Jørgen Hafstad og Thomas Prebensen Steen Teleks rett og merverdiavgift på programvare NOK 42,-

CompLex 12/83 Kristin Kjelland-Mørdre Om forenkling av regler out o f print CompLex 13/83 Jon Bing Edb: Mulighet og problem ved forenkling av regelverk NOK 92,-

CompLex 14/83 Tarjei Stensaasen Utvalgte emner i jus og edb (3. utgave) NOK 190,-

CompLex 15/83 Anette KJafstad og Ulf Alex Samer Plan for et forsikringsrettslig informasjonssystem NOK 60,-

CompLex 1/84 Jon Erling Skjørshammer Kabelnett: Bygnings- og ekspropriasjons lov NOK 40,-

CompLex 2/84 Tore Andreas Hauglie og Dag Wiese Schartum Forslag til et helserettslig informasjonssystem NOK 120,-

CompLex 3/84 J ustisdepartementet Den elektroniske grunnbok out o f print

CompLex 4/84 Datatilsynet Årsmelding 1983 out o f print

CompLex 5/84 Tove Fjeldvig Tekstsøking: Teori, metoder og systemer out o f print

CompLex 6/84 Jon Bing Offentlighetsloven og edb NOK 94,-

CompLex 7/84 Gunnar Bach, Beate Jacobsen and Vidar Stensland The National Social Insurance System of Norway NOK 51,- CompLex 8/84 Dag Frøystad Data Protection in Practice I: Identifying and Matching Elements NOK 80,- CompLex 9/84 Tove Fjeldvig og Anne Golden Automatisk rotlemmatisering - et lingvistisk hjelpemiddel for tekstsøking NOK 90,-

CompLex 10/84 Elling Øyehaug Ose Retstidene som informasjonssystem NOK 120,-

CompLex 1/85 Jon Bing Data Protection in Practise II: International Service Bueraux and Transnational Data Flow NOK 47,-

CompLex 2/85 Jon Bing ODphavsrett og edb NOK 176,-

CompLex 3/85 Dag Wiese Schartum Codex, Calculation and Computers out of print

CompLex 4/85 Olav Torvund To informasjonsrettslige arbeider NOK 52,-

CompLex 5/85 Datatilsynet Årsmelding 1984 out o f print

CompLex 6/85 Hans Chr Aakre (red) Utvalgte artikler i rettsinformatikk out o f print

CompLex 7/85 Johannes Hansen SARA: Brukerveiledning og programdokumentasjon NOK 150,- CompLex 8/85 Johannes Hansen Modelling Knowledge, Action, Logic and Norms NOK 90,-

CompLex 9/85 Thomas Prebensen Steen (red) Kompendium i informasjonsrett out o f print

CompLex 10/85 Tarjei Stensaasen Opphavsrettslovens § 43 («katalogregelen») NOK 97,-

CompLex 11/85 Jo n B in g Straffelovens definisjon av «trykt skrift» anvendt på datamaskinbaserte informasjonssystemer NOK 65,-

CompLex 12/85 Magnus Stray Vyrje Vanhjemmel, opphavsrett og datamaskinprogrammer NOK 161,-

CompLex 1/86 Anne Kirsti Brække Formidlingsplikt for kabeleier NOK 80,-

CompLex 2/86 Dag Wiese Schartum Stans av edb-tjenester i krigs- og krisesituasjoner NOK 80,-

CompLex 3/86 Ingvild Mestad «Elektroniske spor» - nye perspektiv på personvernet NOK 100,-

CompLex 4/86 Jon Skjørshammer Opphavsrett, databaser og datamaskinprogrammer: Kontraktrettslige aspekter NOK 205,-

CompLex 5/86 Datatilsynet Årsmelding 1985 NOK 73,- CompLex 6/86 Johannes Hansen Simulation and automation of legal decisions NOK 132,-

CompLex 7/86 Stein Schjølberg Datakriminalitet og forsikring NOK 54,-

CompLex 8/86 Thomas Prebensen Steen (red) Kompendium i informasjonsrett (2.utg) NOK 70,-

CompLex 9/86 Peter Blume Edb-retlige foredrag NOK 77,-

CompLex 1/87 Joseph A Cannataci Privacy and data protection law: International Development and Maltese Perspectives NOK 170,-

CompLex 2/87 Jon Bing og Jon Bonnevie Høyer Publisering av rettsavgjørelser NOK 90,- CompLex 3/87 Hagen Kuehn The Social Security System in the Federal Republic of Germany NOK 77,-

CompLex 4/87 Hanne Plathe Maartmann Personvern i sykepengerutinen NOK 70,- CompLex 6/87 Jon Bing Electronic Publishing: Data Bases and Computer Programs NOK 55,-

CompLex 7/87 Å n d e S o m b y Selektiv gjenfinning av bestemmelser i bygningslovgi vningen NOK 119,- CompLex 8/87 Jon Bing FOKUS: Knowledge based systems for NOK 125,-

CompLex 9/87 Dag Wiese Schartum The introduction of computers in the Norwegian local insurance offices NOK 230,-

CompLex 10/87 Knut Kaspersen Kredittopplysn i ng NOK 75,-

CompLex 11/87 Ernst Buchberger, Bo Göranzon and Kristen Nygaard (ed) Artificial Intelligence: Perspectives and Implications NOK 120,-

CompLex 12/87 Jon Bing, Kristine M Madsen og Kjell Myrland Strafferettslig vern av materielle goder NOK 118,-

CompLex 13/87 Tove Fjeldvig Effektivisering av tekstsøkesystemer: Utvikling av språkbaserte metoder NOK 175,-

CompLex 14/87 Jo n B in g Journalister, aviser og databaser NOK 66,-

CompLex 15/87 Andreas Galtung Skatterettslig ekspertsystem: Et eksempel basert på skattelovens §77 NOK 82,-

CompLex 1/88 Robin Tharp-Meyer Utvalgte emner i rettsinformatikk (4. utgave) NOK 228,- CompLex 2/88 Ernst Buch berger, Bo Göranzon and Kristen Nygaard (ed) Artificial Intelligence: Perspectives of AI as a social Technology NOK 178,-

CompLex 3/88 Jon Bing og Anne Kirsti Brække Satellittfjernsyn NOK 128,-

CompLex 4/88 Jon Bing Journalists, Databases and Newspapers NOK 68,-

CompLex 5/88 Joseph Cannataci Liability and Responsibility for Expert Systems

CompLex 6/88 Datatilsynet Årsmelding 1987 NOK 58,-

Fremtidige utgivelser i skriftserien CompLex

Nedenfor følger omtale av CompLex-utgivelser som vil foreligge i nærmeste fremtid. De kjøpes/bestilles på samme måte som for tidligere utgitte nummer i serien - se foran og kupongen bakerst.

CompLex 8/88

Jon Bing

DATABASE FOR OFFENTLIGE PUBLIKASJONER: FREMTIDIG ORGANISERING

I samarbeid mellom Stortinget og Forbruker- og administrasjonsdepartementet er det etablert et system for dokumentasjon av offentlige publikasjoner. Systemet innholder dels bibliografiske opplysninger om publikasjoner fra Storting og Regjering, dels andre dokumenter - bl.a. utredninger som ikke tidligere har vært trykt i autentisk tekst. Systemet - som kalles DOP for «database for offentlige publikasjoner» - har i en tid vært i prøvedrift under Lovdatas paraply.

Denne utredningen er utført på oppdrag av styringsgruppen for DOP, og diskuterer prinsipper for fremtidig organisering. Bl.a. gjennomgås systemet for distribusjon av offentlige publikasjoner i Norge, hvilke bibliografier og andre hjelpemidler som finnes, hvilken rolle Statens informasjonstjeneste, biblioteker mv bør spille, og hvilke driftsmodeller som er aktuelle for en slik tjeneste.

Foruten å diskutere den aktuelle problemstillingen knyttet til DOP, gir altså rapporten bred informasjon om spredning av offentlige publikasjoner i Norge, og synspunkter på hvordan det offentlige bør eller kan ivareta sin informasjonsplikt overfor almenhcten.

Ca. 150 sider Pris ca. kr 170,-

CompLex 9/88

Jon Bing

CONCEPTUAL RETRIEVAL

By «conceptual» retrieval is indicated the use of knowledge based methods to retrieve documents in natural language. The area is seen as part of the emerging field «Artificial intelligence and law».

In this report, several attempts of marrying text retrieval systems and knowledge based technology are discussed, for instance Carole Hafner’s LIR, the attempt by the Swedish KVAL and Stanford Research Institute known as POLYTEXT, the rather successful US RUBRIC system, and a system based on novel connectivist, AIR.

In addition, the report discusses the design proposed by the Norwegian Research Center for Computers and Law under their F*KUS program, a rule-structured interface to a text retrieval system often discussed as a «norm-based thesaurus». This part also enclose a structured presentation of the basic old age pension provisions of the social welfare act («Folketrygdloven»). CompLex 10/88

Henning Herrestad

ALDERSTRYGDEN I ET NØTTESKALL - SKAL, SKAL IKKE

Denne rapporten er utviklet i forbindelse med Institutt for rettsinformatikks prosjekter knyttet til forvaltningsorienterte, kunnskapsbaserte systemer (font«4>F*KUS). På markedet finnes det i dag flere såkalte «skall» som tillater konstruksjon av ekspertsystemer, dvs systemer hvor brukeren beskriver sitt problem gjennom en dialog med systemet, og hvor systemet til slutt gir råd om hvilken avgjørelse som bør eller kan trefTcs.

Rapporten beskriver et forsøk på å beskrive reglene i folketrygdlovens §§ 7-1 og 7-2 og grunnpensjon ved hjelp av et slikt program, Crystal. Dette sammenlignes med Thome McCartys arbeid innenfor rammen av det kjente TAXMAN-prosjektet. Sammenligningen gir grunnlag for en kritikk av hvor egnet slike skall er til å lage ekspertsystemer eller kunnskapsbaserte systemer generelt innenfor det juridiske domenet. Man tar opp spørsmål omkring utforming og vedlikehold av slike programmer, brukergrensesnitt og hjelpefunksjoner, og om hvilket formål slike systemer kan tjene i juridisk arbeid. COMPLEX

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dkaderruka■ lNTI»NASiONAl »*G»0«HANDll

Karl Johansgt. 47 0162 Oslo 1

I “The keyboard I am currently touching while writing this text has 80 keys labeled with different characters. It would make no difference for the computer if all the keys were relabeled with numbers from 1 to 80. But that would make a great dif­ ference for me. The number of buttons and especially the number of acceptable sequences of buttons make it impos­ sible for human beings to manipulate computing machinery without some kind of “linguistic organisation” of the interaction” (from the introductory chapter). This is one of the many reasons why language is a key issue in informatics in general, and in artificial intelligence in parti­ cular. The present report is based on papers, presentations and ideas from the research seminar Artificial Intelligence and Language that took place Paris, November 1987. The seminar was organised by the research project Al-based Systems and the Future of Language, Knowledge and Responsibility in Professions, a project under the research programme COST- 13 (the Commission of the European Communities). The seminar in Paris was an ambitious multi-disciplinary experiment. The participating researchers covered a wide range of disciplines: informatics, cognitive science, linguistics, philosophy, law, medicine, anthropology, archeology, psycho­ logy, and literature; all of them concerned with the impact of artificial intelligence on society and culture. Our intended audience is not restricted to practitioners of Al, but includes all researchers concerned with the social, ethical, epistemological, and philosophical implications of this fasci­ nating, new and powerful technology.

ISBN 82-518-2550-4

9 788251 825504