On Wed, Apr 30, 2008 at 4:11 PM, Matt Mahoney <[EMAIL PROTECTED]> wrote: > I think the more traditional classification is D = symbolic, S = > pattern recognition/motor, or D = high level, S = low level. The > D-then-S approach has been popular not because it is biologically > plausible, but because D by itself lends itself to optimizations that > enable it to work on available hardware. Unfortunately these > optimizations make it incompatible with S, for example, Cyc's (D) > failure to interface with natural language (S). The most successful > language models are statistical, a pattern recognition problem.
*nods* All of the above is true. I have some ideas about how to make D-then-S work (essentially by getting D to the point where it can reason about short programs, then encoding some S algorithms for it to play with). Why hasn't this been done hitherto? Primarily because the hardware wasn't up to it (S algorithms are computationally intensive, and encoding them in accessible script incurs a hefty slowdown), and we tend to flinch away from solutions that won't run on the hardware we have in front of us. Even these days, I've had to train myself into compensating for this limit by rejecting solutions that _will_ run on available hardware. I gather you're a proponent of S-then-D though. How do you propose going from one to the other? ------------------------------------------- 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=101455710-f059c4 Powered by Listbox: http://www.listbox.com
