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From: honavar@SPEEDY.WISC.EDU (A Buggy AI Program)
Newsgroups: comp.ai.digest
Subject: Re: Success of AI
Message-ID: <4571@spool.wisc.edu>
Date: Fri, 30-Oct-87 22:44:44 EST
Article-I.D.: spool.4571
Posted: Fri Oct 30 22:44:44 1987
Date-Received: Fri, 6-Nov-87 22:50:54 EST
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Reply-To: honavar@speedy.wisc.edu (A Buggy AI Program)
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In article <8710280748.AA21340@jade.berkeley.edu> eitan@wisdom.BITNET
(Eitan Shterenbaum) writes:
>
>Had it ever come into you mind that simulating/emulating the human brain is
>NP problem ? ( Why ? Think !!! ). Unless some smartass comes out with a proof
>for NP=P yar can forget de whole damn thing ...
>
>                Eitan Shterenbaum
>(*
>   As far as I know one can't solve NP problems even with a super-duper
>   hardware, so building such machine is pointless (Unless we are living on
>   such machine ...) !
>*)
	
Discovering that a problem is NP-complete is usually just the 
beginning of the work on the problem. The knowledge that a problem is
NP-complete provides valuable information on the lines of attack that 
have the greatest potential for success. We can concentrate on algorithms
that are not guaranteed to run in polynomial time but do so most
of the time or those that give approximate solutions in polynomial time.
After all, the human brain does come up with approximate (reasonably good)
solutions to a lot of the perceptual tasks although the solution may not
always be the best possible. Knowing that a problem is NP-complete only 
tells us that the chances of finding a polynomial time solution are minimal
(unless P=NP).

-- VGH