hi,
> > What I am interested in is if someone gives me a computer system that > changes its state is some fashion, can I state how powerful that > method of change is likely to be? That is what the exact difference > between a traditional learning algorithm and the way I envisage AGIs > changing their state. > I'm sure this question is unsolvable in general ... so the interesting question may be: Is there a subset of the class of possible AGI's, which includes systems of an extremely (and hopefully unlimitedly) high level of intelligence, and for which it *is* tractable to usefully probabilistically predict the consequences of the system's self-modifications... > > Also can you formalise the difference between a humans method of > learning how to learn, and boot strapping language off language (both > examples of a strange loop), and a program inspecting and changing its > source code. > Suppose one has a program of size N that has some self-reprogramming capability. There's a question of: for a certain probability p, how large is the subset of program space that the program has probability > p of entering (where the probability is calculated across possible worlds, e.g. according to an occam distribution). > > I'm also interested in recursive self changing systems and whether you > can be sure they will stay recursive self changing systems, as they > change. I'm almost certain there is no certainty in this world, regarding empirical predictions like that ;-) 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=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
