Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!tdatirv!sarima
From: sarima@tdatirv.UUCP (Stanley Friesen)
Newsgroups: comp.ai.philosophy
Subject: Re: Reasoning Paradigms
Message-ID: <22@tdatirv.UUCP>
Date: 11 Oct 90 21:06:47 GMT
References: <3586@media-lab.MEDIA.MIT.EDU> <69347@lll-winken.LLNL.GOV> <3593@media-lab.MEDIA.MIT.EDU> <69377@lll-winken.LLNL.GOV> <11@tdatirv.UUCP> <3642@media-lab.MEDIA.MIT.EDU>
Reply-To: sarima@tdatirv.UUCP (Stanley Friesen)
Organization: Teradata Corp., Irvine
Lines: 43

In article <3642@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes:
>In article <11@tdatirv.UUCP> I write:
>>..... And since the human brain is,
>>by definition, an NN this consititutes an existance proof for a way of
>>solving this problem in NN's. 
 
>This is a dangerous rhetorical trick.  Because in the usual context of
>discussion, a "neural network" or NN is considered to be a relatively
>homogeneous, uniform structure equipped with a relatively systematic
>learning procedure.  The brain is at least 400 different architectures
>interconnected in accord with genetic specifications that appear to
>involve the order of at least 30,000 genes.   

Okay, I will back off a little here. Not having followed the NN literature
I was unaware of how specialized the definition of NN had become.  I had
assumed the term was a general one for any parallel network of neuron-like
elements.  With the revised definition of NN, the human (=mammalian) brain
appears to be a three level compound NN (a network of networks of NN's).
In this model each functional column in the cerebral cortex would be an NN.
These are organized into architectural areas as a diffuse network.  Then
these areas are organized into a sparsely connected network using functional
principles.

>So the performance of brain-NNs does not constitute an existence proof
>for ways to solve similar problems by homogeneous NNs.

Quit true.  It does however consist of an existance proof for the use of
NN type technology to solve these problems. And if homogeneous NN's cannot
do the job a switch to a functional network of NN's very well might. [Indeed
a sufficiently general meta-network should be able to solve all of the
problems discussed previously]

My main point was that there is no reason to assume that complex networks
of neuron-like elements have any particular limitations, and that indeed we
have a counter-example to most proposed limitations.

By the way, given the rather specialized definition of NN, is there an accepted
term for a compound NN network?


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uunet!tdatirv!sarima				(Stanley Friesen)