Int. d. Man-Machine Stadies (1978) 10, 295-299

Trends in Artificial Intelligencet

PATRICK HAYES

University of Essex, U.K.

(Received 4 January 1978)

This paper discusses the foundations of Artificial Intelligence as a science and the types of answer that may be given to the question, "What is intelligence ?" It goes on to compare the paradigms of Artificial Intelligence and general systems theory, and suggests that the links of general systems theory are closer to "brain science" than they are to Artificial Intelligence.

Introduction I will not attempt to survey the field of Artificial Intelligence, an impossible task in this restricted space. Rather I will try to indicate the underlying assumptions of the AI approach to understanding intelligence. The AI field has a methodology of its own, but which is usually unspoken. Many of AI's peculiarities make sense only when one under- stands these methodological assumptions.

A[ claims to be a (or sometimes the) science of intelligence. Now, one question which any such science must attempt to answer is, how does it happen that men are intelligent? What is it about people which makes them intelligent, when all known machines and (almost) all animals are not?

One can distinguish various kinds of answer which might be given to this question, according to different notions of what would count as an answer: what sort of an answer could possibly be given. These distinctions are not scientific but prescientific. They isolate the defining characteristics of different paradigm approaches to a science of intelligence.

I will distinguish four kinds of answer. Necessarily these brief accounts will have something of the nature of caricatures, but it is not my intention to satirize.

The first kind is the non-biologicaL under which term I include all answers based on some criterion other than the structure of the brain, thereby lumping together St Aquinas, Marx, Sartre, many sociologists and some linguists. I do this only to get them all out of the way.

The second kind of answer I will call the component or microstructure answer. This is one couched in terms of the microstructure of the brain, usually at the cellular level. Here one seeks to account for the uniqueness of the brain by assuming that it depends upon properties of the synaptic junction, or upon the neurone's ability to summarize multiple aspects of a situation, etc. Sometimes quantum-mechanical considerations are invoked in order to explain how brains can have free will. Such answers account well for differences between brains and machines, but less well for differences between mice and men.

tOriginal presented at International Conference on Applied Systems Research, Binghamton, New York, August 1977.

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The third kind of answer I will call macrostructure or system answer. This is one couched in terms of the overall structure of the brain--perhaps its anatomical structure, perhaps the pattern of its electrical activity. Typically, the microstructure is not regarded as especially significant: it is the organization of the millions of identical components which is the key. One might, for example, hypothesize that certain electrical patterns of activity could be identified with the subjective awareness of a certain concept, or a memory trace. One might seek evidence in the form of ablation or degeneration experi- ments and correlated idiosyncracies of behaviour. For some kinds of behaviour, ana- tomical correlations seem possible (especially linguistic behaviour, localized in the left temporal lobe) for other kinds, not (especially memory and general problem-solving ability). One would expect this "system" approach to brain science to seek such cor- relations between intelligent behaviour and systemproperties--anatomical, for example-- of the brain. Much of modern neurobiology and even clinical psychology rests on theor- etical foundations which follow from the acceptance of this paradigm. It is easy to explain the differences between men and animals, in principle: one has to find organizational differences between their brains. This is not very difficult, e.g. the human cortex is much larger in proportion to the rest of the CNS, and its grey matter (the intercellular con- nections) much greater in proportion to its white matter (the cortical cells). What is less obvious is just why these differences should result in differences in behaviour. It is also fairly easy to explain why no known machine is intelligent, since none of them is organized like a brain.

The fourth kind of answer I will call the computer answer. On this account, the brain is a computer, by which I mean a machine for performing computations. Intelligent behaviour is manifested by the computations performed by this computer. This is the answer which leads to the methodology of AI, which attempts to discover and model the computations which underlie intelligent behaviour.

Notice how this approach differs from the macrostructure approach. The same com- putation can be performed on computers with widely different structural organizations, so "AI" would not expect the fact that digital computers are structurally dissimilar from brains to be an a priori barrier to experimentation, whereas "macrostructure" would. Dually, a single computer can typically maintain a wide variety of kinds of computation, so "AI" would not expect a priori that anatomical or electrical properties of the brain could be directly correlated with behaviour, again like "macrostructure".

On this account, the distinctions between men and machines and between men and mice are distinctions of degree rather than of kind. The human brain, and especially the human cortex, if it is a computer, is an extraordinarily powerful computer: it can perform a great many computations at very high speed. It is at least several orders of magnitude more powerful than the best man-made computer, especially as regards its memory capacity.

AI thus results from taking the brain-computer analogy absolutely seriously. It has one foot in psychology, one in computer science. Several aspects of AI work which give the field its characteristic flavour follow from this.

(i) Computing people are suspicious of claims for universal devices. When one knows that any computation can be performed in principle by a 3-state 5-symbol Turing machine, given enough time, one is led to be very cynical about the words "in principle", and to ask questions about how long a proposed computation can take. Hence, a hallmark of AI work is an emphasis on implementable systems and on working programs.

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(ii) Computing people are aware of the problems of interfacing components of large programs. One tends to be suspicious of proposals for mechanisms which do not take account of this issue. Hence an emphasis in AI work on building a whole integrated program. This is seen very clearly in work on natural language comprehension, where early work on grammatical parsing has been largely abandoned in favour of constructing integrated systems which exhibit the full range of linguistic behaviour---comprehension, inference, response. Necessarily, given the constraints on the power available in digital computers, such a system can operate only in a limited domain: nevertheless, AI tends to regard such work as more significant than building a single component--say, a parser--which operates in a wider domain, for it is not clear to what extent, if at all, a parser can be a well-defined component of a whole comprehension system. Similarly, there has been for over ten years a continuing emphasis on putting together integrated robotic systems.

(iii) Computer science is much concerned with techniques of representing information, and associated algorithms for manipulating such representations. This too has been a major concern of AI: indeed, many of the now classical computing techniques, such as list processing, were developed by AI researchers.

It is probably here, in its emphasis on representational languages, that AI is most clearly distinguished from the "macrostructure" paradigm. The software/hardware distinction frees AI from the need, imperative for the brain researcher, to give detailed correlations between cognitive representations and brain-system configurations.

(iv) Computer science is about algorithms (to put it crudely). Hence AI is much concerned with the design of algorithms to perform "intelligent tasks". How these algorithms are realized in a particular hard/software environment is of less scientific importance (although it may be a very important question of instrumentation, as in robotic and vision research). Thus for example one of the most interesting recent pieces of work in visual perception, by David Waltz at MIT, has been the application of a network-consistency algorithm to rapidly put together a globally consistent percept from a large number of locally ambiguous clues. Waltz implemented the algorithm using LISP on a large DEC system 10. It could be implemented directly in hardware, and would then run faster by a constant factor of about 100: but this does not seem to be a very important issue for AI.

Science and engineering Much AI work can be classed as "engineering" or applied AI, as opposed to basic or "scientific" AI. Thus for example one might contrast Waltz's "engineering" approach to natural language comprehension with Schank's "scientific" approach. Waltz's system is designed for use: it is supposed to be able to answer questions put to it in natural English by untrained users. To make this even remotely possible, the range of subject-matter is restricted very dramatically: it can answer a small range of queries concerning the servicing records of F-111 fighter aircraft, and nothing else. Even within this, its linguistic abilities are limited, but usable. In contrast, Schank and his students are concerned to investigate people's general ability to understand the full range of sentences which occur in English. Their work is consciously scientific: an approach to psycholinguistic theory.

One can make similar contrasts throughout AI. Industrial robotics vs . integrated problem-solving robotics research; visual pattern recognition for controlling machine

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tools vs. computational theories of the human visual system. And yet--and this is my point--both "applied" and "pure" AI involve constructing working programs. For applied AI, the program is the end product: for scientific AI, it is an experiment.

AI, as a science, is characterized by the way in which its hypotheses are tested. This distinguishes it from cognitive psychology, a closely related sister discipline. A theory in cognitive psychology ideally has to describe a computational mechanism which both works and is an accurate description of some human or animal mental or cognitive process. Very often, the former criterion is bent or weakened. One has very little idea how some proposed psycholinguistic processes could be implemented in detail, for example. But for AI, the working criterion is all-important. AI hypotheses always have the form : this piece of behaviour could be realized by this kind of computation.

Thus, all of AI--applied and scientific--is intimately concerned with implementing large programs which actually work. If they don't work, then there is taken to be some- thing wrong with the theory.

AI and general system theory This emphasis on working programs forces AI to pay much attention to detail, especially to computational detail: exactly how to implement some proposed idea or mechanism. General systems theory, by contrast, emphasizes generality. It attempts to elucidate structures which are common to a wide range of examples. Applied to intelligence, one would expect the general systems approach to seek general properties of intelligent systems.

In an ideal world, one would hope that these two approaches would complement and enrich one another. The detailed AI implementations might suggest or refute general system properties: the broad approach of general systems could give direction and perspective to the unified science of intelligence. In this imperfect world, however, the two approaches seem to have virtually no influence on one another, and even to regard one another with some suspicion.

This situation, I will suggest, is due to more than natural academic conservatism and paranoia. The A[ and general systems approaches to intelligence are bound to diverge.

The reason is related to the distinction between the "macrostructure" and "AI" approaches to intelligence mentioned earlier. Let us ask whether there are any general properties of intelligent systems--properties which distinguish intelligent from un- intelligent systems, that is. On the "macrostructure" approach, one would certainly think so. This approach is characterized by the search for system properties of large physical assemblies such as brains. On the "A[" approach, however, things are more difficult. If we agree with "AI" that a brain is a computer, whose intelligence is exhibited by the programs running on it, then any general system property of intelligence cannot be a property of the physical organization of the computer. For, typically, the same programs could be made to run on a different computer; and moreover the same com- puter organization could be used for quite unintelligent purposes ("number-crunching" as it is elegantly called. If we knew how to reprogram them (which God forbid we ever should) we might be able to use brains to do number crunching). So--if we take the "AI" approach--any general system property of intelligence must be a property of the software. The only relevant kind of general property would be part of a theory of program structure. But this is not the kind of "general system" which general system theory seems to concern itself with.

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In fact, the situation is even worse than this. For, in investigating the structure of AI programs, one finds no special kinds of software structure which are somehow special to "intelligent" (as opposed to "unintelligent") programs. While it is true that, historically speaking, many techniques of software engineering have been pioneered by AI workers-- a process which is continuing today--these techniques have immediate application in "ordinary" programming. Indeed, the very boundary between intelligent and ordinary programs is constantly shifting. In 1959, FORTRAN compilers were considered examples of applied AI: now they are almost undergraduate exercises in computer science. So, even if general systems theory descended into computer science, it would find no dis- tinguishing structural properties of AI software.

From the AI perspective, intelligence is not a system property. That is why general systems seem so remote to AI workers.

What of the other way? How does AI work appear from the general system point of view ? Here I must be careful, as I stand on the AI side of the fence, but I think the same divergence is visible from there also. To general systems theory, the AI thesis about intelligence could not be correct. There must be some way of characterizing what it is which makes some systems intelligent. There are only two directions such a search can go (apart from into mysticism); into behaviourism; or into brain science, into what I have called the "macrostructure" approach to describing intelligence.

I think no-one will be offended if I say that the academic links between general systems theory and brain science are far closer than those between general systems theory and AI. This is, I suggest, no accident.

If I am right, AI and general systems theory are on divergent paths. It is too early to say which paradigm will yield the most fruitful approach to our common goals. No doubt, none of us will be wholly successful. But there is nothing to be gained by each trying to persuade the other of the error of his ways. Let us all work within the methodology which suits us best, and get on with science.

References SCHANK, R. (1975). Conceptual Information Processing. Amsterdam: North-Holland. WALTZ, D. (1975a). Understanding line drawings of scenes with shadows. In WINSTON, Ed.,

The Psychology of Computer Vision. McGraw-Hill. WALTZ, D. (1975b). Natural language access to a large data base: an engineering approach.

Proceedings of the Fourth International Conference on Artificial Intelligence, Tbilisi, Georgia.