Xref: utzoo comp.ai.neural-nets:663 sci.philosophy.tech:1125
Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!ames!rex!uflorida!haven!uvaarpa!mcnc!thorin!coggins!coggins
From: coggins@coggins.cs.unc.edu (Dr. James Coggins)
Newsgroups: comp.ai.neural-nets,sci.philosophy.tech
Subject: Re: request for philosophic reactions to connectionism
Keywords: connectionism philosophy materialism representations
Message-ID: <8011@thorin.cs.unc.edu>
Date: 3 May 89 13:00:31 GMT
References: <370@eurtrx.UUCP> <935@syma.sussex.ac.uk>
Sender: news@thorin.cs.unc.edu
Reply-To: coggins@cs.unc.edu (Dr. James Coggins)
Organization: University Of North Carolina, Chapel Hill
Lines: 62


> Can anyone out there give me a hand?
> I am looking for philosophical papers, books or articles, with reactions
> to connectionism as a model for the mind.
> I would be interested in texts discussing representationalism, (sub)symbolic
> representations, materialism, and other philosophical subjects that could be
> influenced by a connectionist theory of the mind.
> .....etc.....

My assessment of the neural net area is as follows:
(consider these Six Theses nailed to the church door)

1. NNs are a parallel implementation technique that shows promise for
making perceptual processes run in real time.

2. There is nothing in the NN work that is fundamentally new except
as a fast implementation.  Their ability to learn incrementally from
a series of samples nice but not new.  The way they learn and make
decisions is decades old and first arose in communication theory,
then was further developed in statistical pattern recognition.

3. The claims that NNs are fundamentally new are founded on ignorance
of statistical pattern recognition or on simplistic views of the
nature of statistical pattern recognition.  I have heard supposedly
competent people working in NNs claim that statistical pattern
recognition is based on assumptions of Gaussian distributions which
are not required in NNs, therefore NNs are fundamentally different.
This is ridiculous.  Statistical pattern recognition is not bound to
Gaussians, and NNs do, most assuredly, incorporate distributional
assumptions in their decision criteria. 

4. A more cynical view that I do not fully embrace says that the main
function of "Neural Networks" is as a label for money.  It is a flag
you wave to attract money dispensed by people who are interested in
the engineering of real-time perceptual processing and who are
ignorant of statistical pattern recognition and therefore the lack of
substance of the neural net field.

5. Neural nets raise lots of engineering questions but little science.
Much of the excitement they have raised is based on uncritical
acceptance of "neat" demos and ignorance. As such, the area resembles
a religion more than a science.  

6. The "popularity" of neural net research is a consequence of the
miserable mathematical backgrounds of computer science students (and
some professors!).  You don't need to know any math to be a hacker, but
you have to know math and statistics to work in statistical pattern
recognition.  Thus, generations of computer science students are
susceptible to hoodwinking by neat demos based on simple mathematical
and statistical techniques that incorporate some engineering hacks
that can be tweaked forever.  They'll think they are accomplishing
something by their endless tweaking because they don't know enough
math and statistics to tell what's really going on.

---------------------------------------------------------------------
Dr. James M. Coggins          coggins@cs.unc.edu
Computer Science Department   A neuromorphic minimum distance classifier!
UNC-Chapel Hill               Big freaking hairy deal.
Chapel Hill, NC 27599-3175                -Garfield the Cat
and NASA Center of Excellence in Space Data and Information Science
---------------------------------------------------------------------