On Thu, Oct 16, 2008 at 12:50 PM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote: > The reasons: > 1. The domain is well understood. > 2. The domain has regularities. Therefore a high intelligent algorithm has a > chance to outperform less intelligent algorithms > 3. The domain can be modeled easily by software. > 4. The domain is non-trivial. Current algorithms fail for hard problems in > this domain because of the exponential growing complexity. > 5. The domain allows a comparison with performance of human intelligence.
For all the same reasons I advocate program testing, writing and debugging as a good experimental domain for AGI. It also has the added reason: 6. You can make a big fat bucket load of money doing it. Which is not the case for mathematics. And: 7. The AGI can help you test, write and debug itself. And typically when I bring this up I get the blank stares as people figure out how much work making such an AGI would be. Except from some people who just think that programming is something only an advanced AGI could do and advocate that the way to can get from a proto-AGI to an advanced AGI is via embodied learning in a simulated environment. Because, apparently, knowing things about space and motion (aka playing fetch) is important when programming.. of which I'm not terribly convinced. I believe the same has been said about doing mathematical proofs. Oh yeah, and there's some others who think it wouldn't be so hard, so long as we're talking about a programming language that is "easy" for an AGI to "manipulate". *cough*LISP*cough* They'd have us believe that it would be significantly harder to develop an AGI that can do work that is actually marketable.. Trent ------------------------------------------- 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=117534816-b15a34 Powered by Listbox: http://www.listbox.com
