On artificial intelligence

Artificial intelligence (AI) is a fundamentally interdisciplinary subject, combining ideas from psychology, computer science, linguistics, mathematics and even philosophy (see, for example, Sloman, 1978). It is also a very young field. One consequence of its youth and interdisciplinary nature is that until fairly recently one of its most distinctive characteristics was its state of confusion. In the last few years, however, AI has begun to take on a much more coherent form. Reasons for this include the identification of key issues and fundamental areas and the development of approaches possessing considerable generality. I shall, very briefly, outline some of these below, but before doing so I shall explain why I believe AI to have a very important role to play in psychiatric research (indeed, also in psychological and sociological research and in the behavioural sciences generally). The study of AI has two primary motivations. The first is to build computers (or, more correctly, to write programs) which behave in an intelligent way; and the second is to investigate the nature of human intelligence. Clearly, although these aims are related, they are not identical. In particular, we may note that while being an adequately close model of human brain function is a sufficient attribute for a program to demonstrate intelligent behaviour, it is not a necessary one. One can make an analogy with flying: while the study of flying animals (birds) can lead to ideas about flying (the use of wings), this is not a necessary approach (use rockets instead). Nevertheless, it is obvious that a mutual enrichment must follow from the interaction between AI and the human based mental sciences. Certainly, AI and cognitive psychology exhibit parallel theoretical developments and the hard formalism imposed by the rigorous theory formulation necessitated by the AI methodology has aided the testing of psychiatric (Colby, 1975) and sociological (Abelson, 1973) hypotheses. At present, both human-based models (for example, in natural language processing see below) and abstract mathematical approaches (for example, in pattern recognition, Duda & Hart, 1973) have been successful. Some, however (for example, Hayes-Roth, 1978), believe that ultimately the best approach to AI may not be via the non-human information processing paradigms but via closer modelling of human memory and cognition. And, conversely, it may also be the case that the best approach to the study of human mentation is via the new field of AI. The point is that it is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognize when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognized to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using 'classical' mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach a different mathematics is needed, and that AI provides just this approach (see also Boden, 1977). When formulating a theory as a program, which is exactly what AI does, the oversights are spotted, the gaps made apparent, and the oversimplifications made explicit. And a theory so formulated can be rigorously tested and appropriately refined. Weizenbaum (1976) puts it thus:

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