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From: andrew@berlioz (Lord Snooty @ The Giant Poisoned Electric Head )
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: Re: Connectionism, a paradigm shift?
Message-ID: <568@berlioz.nsc.com>
Date: 4 Aug 89 02:00:38 GMT
References: <24241@iuvax.cs.indiana.edu>
Organization: National Semiconductor, Santa Clara
Lines: 57

In article <24241@iuvax.cs.indiana.edu>, dave@cogsci.indiana.edu (David Chalmers) writes:
[A discussion on fads and the rapid growth of connectionism, and a prediction
of its demise through hype]

I think you should crosspost this to comp.ai.neural-nets, whose members seem
to exhibit the usual healthy cynicism of a comp.. group; not a pack of
zealots by any means! 

I agree that there exists a danger from over-rapid over-exposure and
the concomitant media hype. This is a constant warning cry made at the
conferences, and by people who popularise the field. You have to bear in
mind that we're only human, and become naturally excited even as researchers
and informed observers when new results appear. It is not necessary to
*immediately* understand the nature of the underlying mechanism when
a new and successful application is created (in this sense, your analogy
to cold fusion is spot-on).

I think that what is required to save the field from the "hype seesaw"
is a healthy rate of generation of solid new theoretical results.
Two fairly recent results, for example, which could be seen to qualify:

1) A preprocessing paradigm using a simple one-layer net and an easily-
   implementable learning algorithm, which extracts the eigenvalues of
   the input autocorrelation - useful for image compression, etc.
   In particular, information-theoretic approaches are producing new
   results.  [Sanger, Linsker, Foldiak]
2) A formal proof of an algorithm for a restricted class of nets, which
   predicts detailed network dynamics given the training pattern set.
   [Lemmon, Kumar]

There is a tremendous amount of high-quality work going on, bolstered by
the application of formal mathematical techniques.
It seems to me that this truly sets NN research apart from the much
more "hand-waving" stuff that I encountered when looking at conventional
AI, when expert systems were on the rise in the early- and mid-80s.
Here one found tree traversal stuff and Bayesian statistical variations,
definitons of "frames" and the like; the ad hoc component was significant.
(although fuzzy set theory has to some extent set some of this on a more 
formal footing, I have to agree).

The analogy I have in mind equates NN research to the microstructure of
cognition, and as such is akin to "physics". When dealing with the atoms
of behaviour, it's possible to produce significant and fundamental results.
Symbolic AI smacks to me much more like "inorganic chemistry".

The consensus view seems to be that these two paradigms will eventually
cooperate in future artificial cognitive systems. Work is already
ongoing to combine expert systems with NN coprocessors. However, taking
the brain as an existence proof, it's clear that NN technology can
implement all levels of cognition, whereas it is unclear whether symbolic
methods are capable of this [see e.g. Steve Harnath: subsymbolic and 
symbolic processing].
-- 
...........................................................................
Andrew Palfreyman	There's a good time coming, be it ever so far away,
andrew@berlioz.nsc.com	That's what I says to myself, says I, 
time sucks					   jolly good luck, hooray!