🚀 go-pugleaf

In article <35@tygra.UUCP> jpp@tygra.UUCP (John Palmer) writes:
>
>Artificial neural nets will still be unable to solve hard problems 
>(patttern recognition, REAL language processing, etc) because they
>are implemented in silicon (usually as a virtual machine on top of
>a standard digital computer) and are therefore inherently inefficient.
>In theory (Church-Turing Thesis) it is possible for such problems to
>be solved by digital computers, but most of the hard problems are 
>intractable. We are very quickly reaching the limits of 
>speed of silicon devices.

The inherent inefficiency you attribute to digital computers may be due
in part to the Von Neumann bottleneck (See Hillis, W. Daniel, The Connection
Machine, MIT Press 1985). The strong version of the Church-Turing Thesis,
described in Hofstadter (Metamagical Themas, Bantam Books, 1985) implies
that a digital computer can, given enough time, solve ANY and ALL problems.
This was the intention of having such a general architecture for computers;
ie, if everything is done in the memory, nothing need be physically changed.
But when you try to program something as intensive as image or language
processing on such a general architecture, things quickly bog down because
the serial architecture of the computer, while capable of carrying out the
computations necessary, gets stuck in the bottleneck between processor and
memory. Image or language processing are problems that lend themselves
well to a parallel architecture because they can be broken down and solved
over many processors, providing a far greater information throughput than
is possible with a purely serial design. Serial machines are good for
simulation, because they are so open-ended, but as actual implementations
of intelligence, they are somewhat lean.

>The only hope of solving these hard problems is by developing devices 
>which take advantage of the laws of physics and that have a very
>strong structure/function relationship. 
>
>My point: We are not going to solve the hard problems of AI by 
>simply developing programs for our digital computers. We have to
>develope hardware that has a strong structure/function relationship.

This is why neural nets are the preferred mode of exploration today in
large parts of AI research. There does indeed exists a strong structure/
function relation between the NN's parallel design, and the parallel
nature of the problems they are being built to solve.


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$anjay "lock-on" $ingh
ssingh@watserv1.waterloo.edu

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