2008/11/30 Ben Goertzel <[EMAIL PROTECTED]>: >> 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...
Good luck with that! > 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. Yes, so that people learn one problem, then it helps them to learn other similar ones. Is there any AI software that does this? I'm not aware of any. I have proposed a problem domain called "function predictor" whose purpose is to allow an AI to learn across problem sub-domains, carrying its learning from one domain to another. (See http://www.includipedia.com/wiki/User:Cabalamat/Function_predictor ) I also think it would be useful if there was a regular (maybe annual) competition in the function predictor domain (or some similar domain). A bit like the Loebner Prize, except that it would be more useful to the advancement of AI, since the Loebner prize is silly. -- Philip Hunt, <[EMAIL PROTECTED]> Please avoid sending me Word or PowerPoint attachments. See http://www.gnu.org/philosophy/no-word-attachments.html ------------------------------------------- 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
