Path: utzoo!utgpu!jarvis.csri.toronto.edu!rutgers!sun-barr!cs.utexas.edu!uunet!mcvax!ukc!icdoc!syma!aarons From: aarons@syma.sussex.ac.uk (Aaron Sloman) Newsgroups: comp.ai Subject: Re: Is there a definition of AI? Keywords: defining AI Message-ID: <1213@syma.sussex.ac.uk> Date: 6 Aug 89 17:02:11 GMT References: Organization: School of Cognitive & Computing Sciences, Sussex Univ. UK Lines: 199 kim@watsup.waterloo.edu (T. Kim Nguyen) writes: > Date: 5 Aug 89 02:17:40 GMT > Organization: PAMI Group, U. of Waterloo, Ontario > > Anyone seen any mind-blowing (I mean, *GOOD*) definitions of AI? All > the books seem to gloss over it... > -- > Kim Nguyen kim@watsup.waterloo.edu > Systems Design Engineering -- University of Waterloo, Ontario, Canada Most people who attempt to define AI give limited definitions based on ignorance of the breadth of the field. E.g. people who know nothing about work on computer vision, speech, or robotics often define AI as if it were all about expert systems. (I even once saw an attempt to define it in terms of the use of LISP!). What follows is a discussion of the problem that I previously posted in 1985 (I've made a few minor changes this time)! -- Some inadequate definitions of AI ------------------------------ Marvin Minsky once defined Artificial Intelligence as '... the science of making machines do things that would require intelligence if done by men'. I don't know if he still likes this definition, but it is often quoted with approval. A slightly different definition, similar in spirit but allowing for shifting standards, is given in the textbook on AI by Elaine Rich (McGraw-Hill 1983): '.. the study of how to make computers do things at which, at the moment, people are better.' There are several problems with these definitions. (a) They suggest that AI is primarily a branch of engineering concerned with making machines do things (though Minsky's use of the word 'science' hints at a study of general principles). (b) Perhaps the main objection is their concern with WHAT is done rather than HOW it is done. There are lots of things computers do that would require intelligence if done by people but which have nothing to do with AI, because there are unintelligent ways of getting them done if you have enough speed. E.g. calculators can do complex sums which would require intelligence if done by people. Even simple sums done by a very young child would be regarded as an indication of high intelligence, though not if done by a simple mechanical calculator. Was building calculators to go faster or be more accurate than people once AI? For Rich, does it matter in what way people are currently better? (c) Much AI (e.g. work reported at IJCAI) is concerned with studying general principles in a way that is neutral as to whether it is used for making new machines or explaining how existing systems (e.g. people or squirrels) work. For instance, John McCarthy is said to have coined the term 'Artificial Intelligence' but it is clear that his work is of this more general kind, as is much of the work by Minsky and many others in the field. Many of those who use computers in AI do so merely in order to test, refine, or demonstrate their theories about how people do something, or, more profoundly, because only with the aid of computational concepts can we hope to express theories with rich enough explanatory power. (Which does not mean that present-day computational concepts are sufficient.) For these reasons, the 'Artificial' part of the name is a misnomer, and 'Cognitive Science' or 'Computational Cognitive Science' or 'Epistemics' might have been better names. But it is too late to change the name now, despite the British Alvey Programme's silly use of "IKBS" (Intelligent Knowledge Based Systems) instead of "AI" -- Towards a better definition of AI ------------------------------ Winston, in the second edition of his book on AI (Addison Wesley, 1984) defines AI as 'the study of ideas that enable computers to be intelligent', but quickly moves on to identify two different goals: 'to make computers more useful' 'to understand the principles that make intelligence possible'. His second goal captures the spirit of my complaint about the other definitions. (I made similar points in my book 'The Computer Revolution in Philosophy' (Harvester Press and Humanities Press, 1978; now out of print)). All this assumes that we know what intelligence is: and indeed we can recognise instances even when we cannot define it, as with many other general concepts, like 'cause' 'mind' 'beauty' 'funniness'. Can we hope to have a study of general principles concerning X without a reasonably clear definition of X? Since almost any behaviour can be the product of either an intelligent system (e.g. using false or incomplete beliefs or bizarre motives), or an unintelligent system (e.g. an enormously fast computer using an enormously large look-up table) it is important to define intelligence in terms of HOW the behaviour is produced. -- Towards a definition of Intelligence --------------------------- Intelligent systems are those which: (A) are capable of using structured symbols (e.g. sentences or states of a network; i.e. not just quantitative measures, like temperature or concentration of blood sugar) in a variety of roles including the representation of facts (beliefs), instructions (motives, desires, intentions, goals), plans, strategies, selection principles, etc. NOTE.1. - The set of structures should not be pre-defined: the system should have the "generative" capability to produce new structures as required. The set of uses to which they can be put should also be open ended. (B) are capable of being productively lazy (i.e. able to use the information expressed in the symbols in order to achieve goals with minimal effort). Although it may not be obvious, various kinds of learning capabilities can be derived from (B) which is why I have not included learning as an explicit part of the definition, as some people would. There are many aspects of (A) and (B) which need to be enlarged and clarified, including the notion of 'effort' and how different sorts can be minimised, relative to the system's current capabilities. For instance, there are situations in which the intelligent (productively lazy) thing to do is develop an unintelligent but fast and reliable way to do something which has to be done often. (E.g. learning multiplication tables.) NOTE.2 on above "NOTE.1". I think it is important for intelligence as we conceive it that the mechanisms used should not have any theoretical upper bound to the complexity of the structures with which they can cope, though they may have practical (contingent) limits such as memory limits, and addressing limits..... (The notion of "generative power", i.e. which of a mechanism's limits are theoretically inherent in its design and and which are practical or contingent on the implementation requires further discussion. One test is whether the mechanism could easily make use of more memory if it were provided. A table-lookup mechanism would not be able to extend the table if given more space.) NOTE.3. No definition of intelligence should be regarded as final. As in all science it is to be expected that further investigation will lead to revision of the basic concepts used to define the field. Starting from a suitable (provisional) notion of what an intelligent system is, I would then define AI as the study of principles relevant to explaining or designing actual and possible intelligent systems, including the investigation of both general design requirements and particular implementation tradeoffs. The reference to 'actual' systems includes the study of human and animal intelligence and its underlying principles, and the reference to 'possible' systems covers principles of engineering design for new intelligent systems, as well as possible organisms that might develop one day. NOTE.4: this definition subsumes connectionist (PDP) approaches to the study of intelligence. There is no real conflict between connectionism and AI as conceived of by their broad minded practitioners. The study of ranges of design possibilities (what the limits and tradeoffs are, how different possibilities are related, how they can be generated, etc.) is a part of any theoretical understanding, and good AI MUST be theoretically based. There is lots of bad AI -- what John McCarthy once referred to as the 'look Ma, no hands' variety. The definition of intelligence could be tied more closely to human and animal intelligence by requiring the ability to cope with multiple motives in real time, with resource constraints, in an environment which is partly friendly partly unfriendly. But probably (B) can be interpreted as including all this as a special case! More generally, it is necessary to say something about the nature of the goals and the structure of the environment in which they are to be achieved. But I have gone on long enough. Conclusion: any short and simple definition of AI is likely to be shallow, one-sided, or just wrong as an description of the range of existing AI work. Aaron Sloman, School of Cognitive and Computing Sciences, Univ of Sussex, Brighton, BN1 9QN, England INTERNET: aarons%uk.ac.sussex.cogs@nsfnet-relay.ac.uk aarons%uk.ac.sussex.cogs%nsfnet-relay.ac.uk@relay.cs.net JANET aarons@cogs.sussex.ac.uk BITNET: aarons%uk.ac.sussex.cogs@uk.ac or aarons%uk.ac.sussex.cogs%ukacrl.bitnet@cunyvm.cuny.edu UUCP: ...mcvax!ukc!cogs!aarons or aarons@cogs.uucp