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From: ssingh@watserv1.waterloo.edu (The Sanj - ISman (iceman))
Newsgroups: comp.ai
Subject: Re: What Has Traditional AI Accomplished?
Message-ID: <1990Oct21.044018.6973@watserv1.waterloo.edu>
Date: 21 Oct 90 04:40:18 GMT
References: <69609@lll-winken.LLNL.GOV> <1990Oct15.143325.26044@unislc.uucp> <1990Oct16.135631.6444@cbnewsj.att.com> <69929@lll-winken.LLNL.GOV>
Organization: University of Waterloo
Lines: 42

In article <69929@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren Petrich) writes:
>
>	Most Neural Nets now exist only in software form for the
>traditional brand of computer. And it is on such software Neural Nets
>that designs of hardware Neural Nets will ultimately depend -- it is
>much easier to rewrite a program than to design a new chip. And even a
>Neural Net chip would need to be controlled by such a computer.

If you are saying that even PDP systems implementing neural network
learning procedures in hardware would still need a Von Neumann
computer to control it, such as how a Connection Machine still
is connected to a front end computer, I beg to differ. It seems that
we are caught up in the "homunculus" (sp?) dilemma, where we see an
NN as just a bunch of elements exchanging information. But unlike
a traditional algorithm of the type on serial computers, an NN
algorithm in hardware would _change itself_ as it learns. There should
be no need for an external authority; ie, my brain is just a few
billion neurons, there is no "homunculus" implementing emotion, free
will etc. I AM the net, and the net is me. No assembly required :-)
Amendments and corrections welcomed.

>	The simplicity of the basic algorithms keep making me wonder
>why NN's did not take off earlier -- the basic code for one takes up
>only a couple pages of Fortran or C. Try writing one yourself. I guess
>that (in)famous book by Minsky and Papert, _Perceptrons_, with its
>seemingly airtight theoretical arguments, is what had squelched the
>field for so long.

It seems like you are miffed at Minsky. Minsky and Pappert showed the limit-
ations of the Perceptron for a three layered net, I believe. They did not
say anything about larger numbers of layers. The researchers in the field
decided to pack up.
>$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
>Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov



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