Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!ames!elroy!gryphon!sarima
From: sarima@gryphon.COM (Stan Friesen)
Newsgroups: comp.ai
Subject: Re: Symbolic Connectionism? (was Re: What IS a symbol?)
Message-ID: <15323@gryphon.COM>
Date: 30 Apr 89 15:50:55 GMT
References: <11313@bcsaic.UUCP> <17467@cup.portal.com> <5418@cs.Buffalo.EDU> <17548@cup.portal.com>
Reply-To: sarima@gryphon.COM (Stan Friesen)
Organization: Trailing Edge Technology, Redondo Beach, CA
Lines: 43

In article <17548@cup.portal.com> dan-hankins@cup.portal.com (Daniel B Hankins) writes:
>
>     I'll grant that *most* ANN systems in use are fixed-topology, where
>the topology is designed in advance.  I personally don't find them very
>interesting - I'm into generality.
>
	Yes, generality is nice, but I know of no natural neural-nets that
have generality at a low level.  The human brain only achieves generality by
consisting of numerous complex subnets that are individually quite specialized. 
>
>     The number of units, connections, and types of functions really are
>not part of the topology... but they are often chosen by the programmer in
>response to a particular problem setup.  Again, I consider this cheating,
>and am interested in more general-purpose systems.

	Again, how is this cheating, most natural neural nets are pre-
programmed, by evolution, for special puproses.  It is just that a complex
collection of specialized neural nets tends to approximate generality.

>
>     You're writing here of supervised learning, which is again the most
>common form used (because it's the easiest to work with, both
>computationally and mathematically).  I still think it's cheating, because
>it isn't the way biological systems do it.  Nobody supervises directly the
>learning of bio networks.  I think that many do this because it makes the
>network converge faster on 'the solution'.
>
	Now here you have what I consider a serious limitation of current
neural nets.

>     When I write of connectionist systems that can achieve intelligence
>(or combinations of connectionist and symbolic), I am thinking of more
>biologically accurate approaches - non-back-propagation, self-organizing
>topology, and unsupervised learning.

	Hold on! What's wrong with back propagation!  Every bioogical
neural system more complex than a reflex loop has extensive back-propagation!
It is a key element of feed-back based control in such systems.  I do not
beieve that any adaptive system can be achieved without it.
-- 
Sarima Cardolandion			sarima@gryphon.CTS.COM
aka Stanley Friesen			rutgers!marque!gryphon!sarima
					Sherman Oaks, CA