Xref: utzoo comp.ai:4635 comp.ai.neural-nets:847
Path: utzoo!utgpu!watmath!att!tut.cis.ohio-state.edu!cica!ctrsol!IDA.ORG!potomac!jtn
From: jtn@potomac.ads.com (John T. Nelson)
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: Re: Connectionism, a paradigm shift?
Summary: neural nets as a charade
Message-ID: <7977@potomac.ads.com>
Date: 15 Aug 89 14:33:05 GMT
References: <24241@iuvax.cs.indiana.edu> <568@berlioz.nsc.com> <9143@thorin.cs.unc.edu>
Organization: Advanced Decision Systems, Arlington VA
Lines: 39

> 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.

A sweeping generalization.  Computer scientists aren't the only ones
working on neural networks and not all computer scientists are
"student hackers."  I wish people would stop confusing "programming"
activities with thinking and research activities.  They are distinctly
different.  One is engineering and the other is not.  There are
computer scientists who approach problems as theoreticians and there
are computer scientists who approach problems with ad hoc solutions in
mind.

However...... (time to get up on my soapbox oh boy!)....

In my opinion we don't have a deep macroscopic understanding of what
neural nets are capable of doing or are doing even in the simplest
networks.  Researchers are spending a lot of time and effort focusing
on the optimization of small techniques (e.g. backpropigation) and too
little time on developing formalisms for describing and understanding
NNs as a whole.

A deep understanding of any complex paradigm will be reached only
through the efforts of many researchers, tackling the problem from
different viewpoints (like multiple sculptors chipping away at a
block of marble to reveal the statue hidden inside).  It's fairly
useless for all of these metaphorical artists to chip away at a big
toe all at once, yet they must also posses the same overall goal and
understanding of the problem, otherwise the final piece will
not be consistant and balanced.

Well you get the idea.