Path: utzoo!utgpu!jarvis.csri.toronto.edu!cs.utexas.edu!usc!apple!well!nagle
From: nagle@well.UUCP (John Nagle)
Newsgroups: comp.ai
Subject: Re: Building a brain, revisited
Message-ID: <15460@well.UUCP>
Date: 11 Jan 90 05:38:09 GMT
References: <15439@well.UUCP> <11673@csli.Stanford.EDU>
Reply-To: nagle@well.UUCP (John Nagle)
Distribution: comp
Lines: 32


      First, note that my total RAM size computation was off; 8192 4MB SIMMs
are only 32 gigabytes, not some number of terabytes.  I apologise for the
error.

      Another way to look at this computation is that eight of these
processors offer the computational power of a mouse or squirrel, again
using Moravec's figures.  This is an interesting result, because those
animals have good eye-hand coordination, almost as good as humans, which
implies that the computational power for well-coordinated robots is
almost in hand.  That problem is a bit better understood, and there are
techniques known that are presently too slow to use.  I'm thinking here
of Kass's optimization techniques, Girard's legged animations, and
Craig's adaptive control algorithms.  It's noteworthy that all of these
are based on heavy number-crunching.

      Personally, I am coming around to the position that a basis for
the portion of AI that deals with relationships with the physical world
will be found in computational geometry and nonlinear optimization.
Over the next few years, this hypothesis will be tested, by myself and
others.

      Systems that do brain-like processing may not need a high volume
of long-distance vs local data transmission internally.  The brain is
severely limited architecturally by the speed of neural impulse
propagation, which is on the order of thousands of feet per second.
So whatever is going on can't involve extensive, repeated transmission
between physically distant parts of the brain; it would take too long.
Against this, of course, the brain has very many connections.  It's
worth thinking about the architectural implications of this. 

					John Nagle