To be honest, I am not completely satisfied with my conclusion on the post you refer to. I'm not so sure now that the fundamental split between logical/messy methods should occur at the line between perfect & approximate methods. This is one type of messiness, but one only. I think you are referring to a related but different messiness: not knowing what kind of environment your AI is dealing with. Since we don't know which kinds of models will fit best with the world, we should (1) trust our intuitions to some extent, and (2) try things and see how well they work. This is as Loosemore suggests.
On the other hand, I do not want to agree with Loosemore too strongly. Mathematics and mathematical proof is a very important tool, and I feel like he wants to reject it. His image of an AGI seems to be a system built up out of totally dumb pieces, with intelligence emerging unexpectedly. Mine is a system built out of somewhat smart pieces, cooperating to build somewhat smarter pieces, and so on. Each piece has provable smarts. On Sat, Jun 21, 2008 at 6:54 AM, Jim Bromer <[EMAIL PROTECTED]> wrote: > I just read Abram Demski's comments about Loosemore's, "Complex Systems, > Artificial Intelligence and Theoretical Psychology," at > http://dragonlogic-ai.blogspot.com/2008/03/i-recently-read-article-called-complex.html > > I thought Abram's comments were interesting. I just wanted to make a few > criticisms. One is that a logical or rational approach to AI does not > necessarily mean that it would be a fully constrained logical - mathematical > method. My point of view is that if you use a logical or a rational method > with an unconstrained inductive system (open and not monotonic) then the > logical system will, for any likely use, act like a rational-non-rational > system no matter what you do. So when, I for example, start thinking about > whether or not I will be able to use my SAT system (logical satisfiability) > for an AGI program, I am not thinking of an implementation of a pure > Aristotelian-Boolean system of knowledge. The system I am currently > considering would use logic to study theories and theory-like relations that > refer to concepts about the natural universe and the universe of thought, > but without the expectation that those theories could ever constitute a > sound strictly logical or rational model of everything. Such ideas are so > beyond the pale that I do not even consider the possibility to be worthy of > effort. No one in his right mind would seriously think that he could write > a computer program that could explain everything perfectly without error. > If anyone seriously talked like that I would take it as a indication of some > significant psychological problem. > > > > I also take it as a given that AI would suffer from the problem of > computational irreducibility if it's design goals were to completely > comprehend all complexity using only logical methods in the strictest sense. > However, many complex ideas may be simplified and these simplifications can > be used wisely in specific circumstances. My belief is that many > interrelated layers of simplification, if they are used insightfully, can > effectively represent complex ideas that may not be completely understood, > just as we use insightful simplifications while trying to discuss something > that is completely understood, like intelligence. My problem with > developing an AI program is not that I cannot figure out how to create > complex systems of insightful simplifications, but that I do not know how > to develop a computer program capable of sufficient complexity to handle the > load that the system would produce. So while I agree with Demski's > conclusion that, "there is a way to salvage Loosemore's position, > ...[through] shortcutting an irreducible computation by compromising, > allowing the system to produce less-than-perfect results," and, "...as we > tackle harder problems, the methods must become increasingly approximate," I > do not agree that the contemporary problem is with logic or with the > complexity of human knowledge. I feel that the major problem I have is that > writing a really really complicated computer program is really really > difficult. > > > > The problem I have with people who talk about ANNs or probability nets as if > their paradigm of choice were the inevitable solution to complexity is that > they never discuss how their approach might actually handle complexity. Most > advocates of ANNs or probability deal with the problem of complexity as if > it were a problem that either does not exist or has already been solved by > whatever tired paradigm they are advocating. I don't get that. > > > > The major problem I have is that writing a really really complicated > computer program is really really difficult. But perhaps Abram's idea could > be useful here. As the program has to deal with more complicated > collections of simple insights that concern some hard subject matter, it > could tend to rely more on approximations to manage those complexes of > insight. > > Jim Bromer > > ________________________________ > agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
