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From: bph@buengc.BU.EDU (Blair P. Houghton)
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: Re: Connectionism, a paradigm shift?
Message-ID: <3750@buengc.BU.EDU>
Date: 14 Aug 89 01:02:33 GMT
References: <9143@thorin.cs.unc.edu> <4559@uhccux.uhcc.hawaii.edu>
Reply-To: bph@buengc.bu.edu (Blair P. Houghton)
Followup-To: comp.ai
Organization: Boston Univ. Col. of Eng.
Lines: 36

In article <4559@uhccux.uhcc.hawaii.edu> lee@uhccux.uhcc.hawaii.edu (Greg Lee) writes:
>From article <9143@thorin.cs.unc.edu>, by coggins@coggins.cs.unc.edu (Dr. James Coggins):
>
>>5. Neural nets raise lots of engineering questions but little science.

Eh?  Science has been the forming of models and the fitting of them to
observed phenomena.  In the case of artificial neural systems, the
models are physical entities (neuromimes, simulations of neuromimes,
simulations of behavioral models of neuromimes and of elements composed
of neuromimes, etc.) rather than tautologies (laws, theorems, etc.),
and the fit is a behavioral one, as is every theory, until a new,
deeper observation is made of the behavior, or until we are prepared
to discard degenerative assumptions that limit our study of currently
observed behavior.

>The idea seems to be that one can escape the
>necessity to achieve an understanding of human perception and leave that
>to a machine (or algorithm, rather).  Since scientific understanding
>(new and old) is so difficult to come by, it's a very seductive idea.
>But not a reasonable one.

I seem to remember having this same conversation before...anyway:

Doing neural nets this way is akin to allowing probability to be
a mathematical field, and to statistical mechanics
and quantum theory.

The understanding has, and consciously so, been behind the techniques in
those areas since the techniques were first found to be superior to the
understanding in predictive power.

				--Blair
				  "It's quite reasonable.
				   It's quite reasonable to assume
				   that my thesis won't be half this
				   erudite."