> Could you give me a little more detail about your thoughts on this? > Do you think the problem of increasing uncomputableness of complicated > complexity is the common thread found in all of the interesting, > useful but unscalable methods of AI? > Jim Bromer
Well, I think that dealing with combinatorial explosions is, in general, the great unsolved problem of AI. I think the opencog prime design can solve it, but this isn't proved yet... Even relatively unambitious AI methods tend to get dumbed down further when you try to scale them up, due to combinatorial explosion issues. For instance, Bayes nets aren't that clever to begin with ... they don't do that much ... but to make them scalable, one has to make them even more limited and basically ignore combinational causes and just look at causes between one isolated event-class and another... And of course, all theorem provers are unscalable due to having no scalable methods of inference tree pruning... Evolutionary methods can't handle complex fitness functions because they'd require overly large population sizes... In general, the standard AI methods can't handle pattern recognition problems requiring finding complex interdependencies among multiple variables that are obscured among scads of other variables.... The human mind seems to do this via building up intuition via drawing analogies among multiple problems it confronts during its history. Also of course the human mind builds internal simulations of the world, and probes these simulations and draws analogies from problems it solved in its inner sim world, to problems it encounters in the outer world... etc. etc. etc. ben ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=120640061-aded06 Powered by Listbox: http://www.listbox.com
