Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!rutgers!cs.utexas.edu!csd4.milw.wisc.edu!bionet!agate!shelby!lindy!news From: GA.CJJ@forsythe.stanford.edu (Clifford Johnson) Newsgroups: comp.ai Subject: Re: Is there a definition of AI? Message-ID: <4298@lindy.Stanford.EDU> Date: 11 Aug 89 17:18:04 GMT Sender: news@lindy.Stanford.EDU (News Service) Distribution: usa Lines: 51 Here's a footnote I wrote describing "AI" in a document re nuclear "launch on warning" that only mentioned the term in passing. I'd be interested in criticism. It does seem a rather arbitrary term to me. Coined by John McCarthy at Dartmouth in the 1950s, the phrase "Artificial Intelligence" is longhand for computers. Today's machines think. For centuries, classical logicians have pragmatically defined thought as the processing of raw perceptions, comprising the trinity of: categorization of perceptions (Apprehension); comparison of categories of perceptions (Judgment); and the drawing of inferences from connected comparisons (Reason). AI signifies the performance of these definite functions by computers. AI is also a buzz-term that salesmen have applied to virtually all 1980's software, but which to data processing professionals especially connotes software built from large lists of axiomatic "IF x THEN y" rules of inference. (Of course, all programs have some such rules, and, viewed at the machine level, are logically indistinguishable.) The idiom artificial intelligence is curiously convoluted, being applied more often where the coded rules are rough and heuristic (i.e. guesses) rather than precise and analytic (i.e. scientific). The silly innuendo is that AI codifies intuitive expertise. Contrariwise, most AI techniques amount to little more than brute trial-and-error facilitated by rule-of-thumb short-cuts. An analogy is jig-saw reconstruction, which proceeds by first separating pieces with corners and edges, and then crudely trying to find adjacent pairs by exhaustive color and shape matching trials. This analogy should be extended by adding distortion to all pieces of the jig-saw, so that no fit is perfect, and by repainting some, removing other, and adding a few irrelevant pieces. A most likely, or least unlikely, fit is sought. Neural nets are computers programmed with an algorithm for tailoring their rules of thumb, based on statistical inference from a large number of sample observations for which the correct solution is known. In effect, neural nets induce recurrent patterns from input observations. They are limited in the patterns that they recognize, and are stumped by change. Their programmed rules of thumb are not more profound, although they are more complicated, raw "IF... THEN" constructs. Neural nets derive their conditional branchings from underlying rules of statistical inference, and cannot extrapolate beyond the fixations of their induction algorithm. Like regular AI applications, they must select an optimal hypotheses from a simple, predefined set. Thus, all AI applications are largely probabilistic, as exemplified by medical diagnosis and missile attack warning. In medical diagnosis, failure to use and heed a computer can be grounds for malpractice, yet software bugs have gruesome consequences. Likewise, missile attack warning deters, yet puts us all at risk.