I'm an undergrad who's been lurking here for about a year. It seems to me that many people on this list take Solomonoff Induction to be the ideal learning technique (for unrestricted computational resources). I'm wondering what justification there is for the restriction to turing-machine models of the universe that Solomonoff Induction uses. Restricting an AI to computable models will obviously make it more realistically manageable. However, Solomonoff induction needs infinite computational resources, so this clearly isn't a justification.
My concern is that humans make models of the world that are not computable; in particular, I'm thinking of the way physicists use differential equations. Even if physics itself is computable, the fact that humans use incomputable models of it remains. Solomonoff Induction itself is an incomputable model of intelligence, so an AI that used Solomonoff Induction (even if we could get the infinite computational resources needed) could never understand its own learning algorithm. This is an odd position for a supposedly universal model of intelligence IMHO. My thinking is that a more-universal theoretical prior would be a prior over logically definable models, some of which will be incomputable. Any thoughts? ------------------------------------------- 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=95818715-a78a9b Powered by Listbox: http://www.listbox.com
