Path: utzoo!utgpu!news-server.csri.toronto.edu!rutgers!sun-barr!lll-winken!uunet!tdatirv!sarima From: sarima@tdatirv.UUCP (Stanley Friesen) Newsgroups: comp.ai.philosophy Subject: Re: Reasoning Paradigms Message-ID: <11@tdatirv.UUCP> Date: 8 Oct 90 22:55:48 GMT References: <3586@media-lab.MEDIA.MIT.EDU> <69347@lll-winken.LLNL.GOV> <3593@media-lab.MEDIA.MIT.EDU> <69377@lll-winken.LLNL.GOV> Reply-To: sarima@tdatirv.UUCP (Stanley Friesen) Organization: Teradata Corp., Irvine Lines: 75 In article <69377@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren Petrich) writes: >In article <3593@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes: > There are other difficulties with NN's, at least at the >present time. For instance, NN's are generally constructed around data >structures that are linear and whose lengths are fixed. This is OK for >a wide range of problems, but there are difficulties for representing >data structures whose length may vary, and even which are nonlinear, >an example being a treelike one. I suspect that this is more an indication of the relative immaturity of NN technology, since human brains seem to be able to deal with non-linear, variable-sized data structures reasonably well. [e.g. human language]. I have no more idea than you what the solution is in NN technology, but I suspect it will be found. >> 2. No: the NN-like structures cannot replace the "reasoning >>systems" of "traditional AI", unless we supply architectures that >>embody those goal-oriented processes. For example, "annealing" does >>not replace all other kinds of intelligent heuristic search. > I agree. Except that, again, I suspect many of the current limitations in NN's will disappear with time. Certainly any useful heuristic technique which humans are capable of can be implemented in NN's. So unless current AI software is using non-human heuristics, there is no long-term barrier to NN's replacing traditional AI in this area also. [And even if non-human heuristics are being used it may be that the human ones are in some sense better any way]. The main reason I see for continuing to use traditional AI techniques is the question of efficiency. For certain classes of heuristics and decision processes traditional programming may produce a faster and/or cheaper implementation. > I understand your point. However, my colleagues and I have >occasionally been able to interpret the weight values produced by >NN's. One project we did was to evaluate spectra produced in etching >chips. By examining them, we hoped to train a NN to determine how much >hydrogen was in the etching chamber. We discovered that the weights >were largest in some small regions of the spectrum. These corresponded >to lines of H and one of CO, a reaction product. It was surprising to >us that the NN might have been using a CO line as a diagnostic for the >amount of hydrogen. Good example. I suspect this type of result may prove to be common. One avenue towards more advanced AI systems might be to try to automate this process of meaning extraction from the NN weight values. If we xould do this we would have gone a long way towards developing a self-learning system. It would also provide a solid basis for conceptual analysis. >>Here is a simple, if abstract, example of what I mean. Consider one >>of the most powerful ideas in traditional AI -- the concept of >>acheiving a goal by detecting differences between the present >>situation ("what you have") and a target situation ("what you want"). >>The Newell and Simon 'GPS' system did such things (and worked in many >>cases, but not all) by trying various experiments and comparing the >>results, and then applying strategies designed (or learned) for >>'reducing' those differences. > > I see the point. It seems to me very difficult to imagine how >to get a NN to do something like that. Perhaps so. But again this seems to be fairly close to how humans approach difficult goals without an obvious solution. And since the human brain is, by definition, an NN this consititutes an existance proof for a way of solving this problem in NN's. [The only way to counter this is to provide evidence from psychology that humans do not, in fact, ever solve problems this way] -- --------------- uunet!tdatirv!sarima (Stanley Friesen)