Path: utzoo!attcan!uunet!husc6!bbn!rochester!cornell!uw-beaver!tektronix!sequent!mntgfx!msellers
From: msellers@mntgfx.mentor.com (Mike Sellers)
Newsgroups: comp.ai
Subject: Biological relevance and AI (was Re: Who else isn't a science?)
Summary: where are you getting your info?  It appears out of date...
Message-ID: <1988Jun14.135709.307@mntgfx.mentor.com>
Date: 14 Jun 88 20:57:06 GMT
References: <3c671fbe.44e6@apollo.uucp> <10510@agate.BERKELEY.EDU> <13100@shemp.CS.UCLA.EDU>
Organization: Mentor Graphics Corporation, Beaverton Oregon
Lines: 106

[This is a (slightly edited) re-post of a message that I don't think made it 
out into the net.  Apologies if you've already seen this. ]

In article <13100@shemp.CS.UCLA.EDU>, bjpt@maui.cs.ucla.edu (Benjamin Thompson) writes:
>In article <10510@agate.BERKELEY.EDU> weemba@garnet.berkeley.edu writes:
>> Gerald Edelman, for example, has compared AI with Aristotelian
>> dentistry: lots of theorizing, but no attempt to actually compare
>> models with the real world.  AI grabs onto the neural net paradigm,
>> say, and then never bothers to check if what is done with neural
>> nets has anything to do with actual brains.

Where are you getting your information regarding AI & the neural net paradigm?
I agree that there is a lot of hype right now about connectionist/neural nets,
but this alone does not invalidate them (they may not be a panacea, but they 
probably aren't worthless either).  There are an increasing number of people 
interested in (and to some degree knowledgeable of) both the artificial and 
biological sides of sensation, perception, cognition, and (some day) 
intelligence.  See for example the PDP books or Carver Mead's upcoming book 
on analog VLSI and neural systems (I just finished a class in this -- whew!).
There have been recent murmurings from some of the more classical AI types 
(e.g. Seymour Papert in last winter's Daedalus) that the biological 
paradigm/metaphor is not viable for AI research, but these seem to me to be 
either overstating the case against connectionism or simply not aware
of what is being done.  Others contend that anything involving 'wetware' is 
not *really* AI at all, and thus shouldn't invade discussions on that subject.
This is, I believe, a remarkably short-sighted view that amounts to denying
the possibility of a new tool to use.

> This is symptomatic of a common fallacy.  Why should the way our brains
> work be the only way "brains" can work?  Why shouldn't *A*I workers look
> at weird and wonderful models?  We (basically) don't know anything about
> how the brain really works anyway, so who can really tell if what they're
> doing corresponds to (some part of) the brain?
> 
> Ben

I think Ben's second and following sentences here are symptomatic of a common 
fallacy, or more precisely of common misinformation and ignorance.  No one has 
said or implied that biological nervous systems have a monopoly on viable 
methodologies for sensation, perception, and/or cognition.  There probably are 
many different ways in which these types of problems can be tackled.  We do
have a considerable amount of knowledge about the human brain, and (for 
the time being more to the point) about invertebrate nervous systems and the
actions of individual neurons.  And finally, correspondence to biological
systems, while important, is by no means a single and easily acheived goal
(see below).  On the other hand, we can say at least two things about the 
current state of implemented cognition: 
  1) The methods we now call 'classical' AI, starting from about the late 
1950's or early 60's, have not made an appreciable dent in their original 
plans nor even lived up to their original claims.  To refresh your memory, 
a quote from 1958:

     "...there are now in the world machines that think, that learn and 
     that create.  Moreover, their ability to do these things is going to
     increase rapidly until --in a visible future-- the range of problems
     they can handle will be coextensive with the range to which the human
     mind has been applied."

This quote is from H. Simon and A. Newell in "Heuristic Problem Solving:
The Next Advance in Operations Research" in _Operations Research_ vol 6,
published in *1958*.  (It was recently quoted by Dreyfus and Dreyfus in the 
Winter 1988 edition of Daedalus, on page 19.)  We seem to be no closer to 
the realization of this claim than we were thirty years ago.
  2)  We do have one instance that proves that sensation, perception, and
cognition are possible: natural nervous systems.  Thus, even though there 
may be other ways of solving the problems associated with vision, for 
example, it would seem that adopting some of the same strategies used by
other successful systems would increase the likelyhood of our success.  
While it is true that there is more unknown than known about nervous 
systems, we do know enough about neurons, synapses, and small aggregates 
of neurons to begin to simulate their structure and function.

  The issue of how much to simulate is a valid and interesting one.  Natural 
nervous systems have had many millions of years to evolve their solutions 
(much longer than we hope to have to take with our artificial systems), but 
then they have been both undirected in their evolution and constrained by
the resources and techniques available to biological systems.  This would 
seem to argue for only limited biological relevance to artificial solutions: 
e.g., where neurons have axons, we can simply use wires.  On the other hand, 
natural systems also have the tendency to take a liability and make it into
a virtue.  For example, while axons are not simple 'wires', and in fact are
much slower, larger, and more complex than wires, they can also act as active
parts of the whole system, enabling such things as temporal differentiation
to occur easily and without much in the way of cellular overhead.  Thus, 
while we probably will not want to create fully detailed simulations of
neurons, synapses, and neural structures, we do need to understand what 
advantages are embodied in the natural approach and extract them for use in
our artifices while carefully excluding those things that exist only by
being carried along with the main force of the evolutionary current.
  All of this is not to say that AI researchers shouldn't look at "weird and 
wonderful models" of perception and cognition; this is after all precisely 
what they have been doing for the past thirty years.  The only assertion here 
is that this approach has not yielded much in the way of fertile results 
(beyond the notable products such as rule-based systems, windowed displays, 
and the mouse :-) ), and that with new technology, new knowledge of biological
systems, and a new generation of researchers, the one proven method for 
acheiving real-time sensation, perception, and cognition ought to be given 
its chance to fail.

Responses welcomed.

-- 
Mike Sellers                           ...!tektronix!sequent!mntgfx!msellers
Mentor Graphics Corp., EPAD            msellers@mntgfx.MENTOR.COM
"AI is simply the process of taking that which is meaningful, and making it
meaningless."  -- Tom Dietterich  (admittedly, taken out of context)