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From: msellers@mentor.com (Mike Sellers)
Newsgroups: comp.ai.philosophy
Subject: Re: Imitations of Humanity
Message-ID: <1990Nov29.181511.2942@mentor.com>
Date: 29 Nov 90 18:15:11 GMT
References: <129155@tiger.oxy.edu> <1990Nov28.165033.26351@ncsuvx.ncsu.edu>
Organization: Mentor Graphics Corp.
Lines: 112

Gary Fostel writes:
>Winebarger, at Occidental College asked (essentially) if there was a
>Holy Grail:
>
>   I'm relativity new to this net, so I'm not sure if this question will
>   really be appropriate. My question is:  has the possibility of a neural 
>   net hooked up to 2 cameras, connected for stereo vision, with other
>attempts 
>   at equivalency for the other 4 physical senses, been considered for
>attempts 
>   to produce intelligence?  I have been thinking that if a neural net
>was given 
>   the same senses (as far as we can tell, of course we can't be sure they are 
>   the same as our own) as a human, and given something to make it open it's 
>   eyes (a proverbial slap in the behind) then the flood of information it 
>   would recieve, somewhat like a human baby would recieve, would force it to
>   somehow deal with the flood, and hopefully, eventually, enable intelligence.
>
>This is a very seductive expectation, but it founders a bit on the
>oversimplification of what happens during human development.  This is likely
>at the root of some people's inflated expectations of artificial neural nets.
>They feel that an artificial net is the same as "brain stuff" and since there
>is an existence proof for Brain stuff supporting intelligent behavior, then
>artificial nets should be able to behave intelligently as well.  There are
>a lot of issues that the "flood of data to the gigantic net" idea miss: 

This is an excellent point:  artificial neural nets are not magic.  Biological
neural nets probably aren't either. :-)   The extension from a small network 
to a large self-developing neural mass (made of many nets) is not a straight-
forward one.  However, some research (and three book reviews in this month's
AI Expert) is beginning to show a change in this as ANNs are applied to 
problems that require more real-world interactivity.

I agree with Gary's thrust in general, but would take exception on a few 
points:

>   1) There is no "blank slate".  We are not born with a vast neural net
>      devoid of all structure, ready to be programmed. Different areas of
>      the brain have strikingly different connectivity, and it is becomming
>      clear that this fine structure is optimized for the role that part of
>      the brain is supposed to perform. 

True, but large parts of the mammalian brain (most notably the frontal, 
parietal, and parts of (I believe) the superior temporal cortex) do not 
appear to be strongly differentiated at birth.  This process takes place 
over the next several years at least.  This implies something of a "blank
slate" that is developed via interaction with the environment through the
other, already more developed structures.  

>   3) The structure of the net evolves in direct response to appropriate
>      stimulation.  This is not a question of setting weights in response to
>      stimulation, but rather the growth of new net-stuff.  If the stimulation
>      is wrong during the critical period, the growth either does not occur
>      or goes awry

Actually, "setting weights" is not that bad an abstraction for synapse LTP
given the correct view.  If you look at the processing nodes in an ANN as 
a small ensemble of neurons that generally respond as a group rather than 
as each node being a single neuron, and if you start with complete 
connectivity to all the other ensembles in the net, then altering the weights
(especially to 0) simulates the long-term development of synapses, with a 0
weight corresponding to the degeneration of the connections between ensembles.  
  It is true, however, that during the last trimester before birth and during
the first year or two after birth, new neurons are being generated at an
average rate of about 100,000 *per minute*, something that no current model
that I know of has begun to address.  These cells then migrate into place 
within the neuronal matrix by way of a very complex and still mysterious 
mechanism (the brain is not just a mass of neurons blobbed together!), and
begin to form synapses.  In some parts of the brain only a few of the 
synapses created this way will degenerate, while in other parts (notably 
those like the prefrontal cortex that are the most susceptible to environmental
conditions), upwards of 90% of all the synapses and the neuronal soma will 
degenerate, presumably from disuse.  Still, the major as-yet unaddressed 
conditions have to do with level of abstraction that we can get away with 
and still have a good model, the rate of new neurons being generated in 
the early development of the human brain, and the sheer scale and complexity
of the structures that we are hoping to model.

>   4) There are external factors that influence behavior that are not part
>      of the net, e.g. glandular action and the mix of chemicals in your food.

But these ultimately have an effect on synaptic behavior, which may be
modellable by altering synaptic weights.  The primary difference between 
chemically-mediated and neurally-mediated effects are that chemicals can 
be quickly diffused throughout a large part of the neural system (as by the
cerebrospinal fluid), affecting a wide variety of synapses more quickly 
and uniformly than if the change had to be propogated synapse to synapse, 
cell by cell.  

>   5) Artificial neurones are to real neurons as paper airplanes are to birds.
>      There are an incredible array of things going on inside a real neurone
>      that are abstracted away by the usual "neurone" in artificial net.

This is probably the most philosophical of all the issues you've raised.
When does the simulation of neurons correspond enough to the "real thing"
that we are no longer simply doing a simulation?  How much of the structure
and function of neurons are just nature's expedients, and which parts are
expedients that have since evolved into crucial parts of the neural 
mechanism?  I think there may be some (Searle?) who believe that we will
never get to the point where we are no longer _simulating_ neural systems,
but are actually building their functioning analogs.  For myself, I'm not
so sure.

>----GaryFostel----                           Department of Computer Science
>                                             North Carolina State University   


-- 
Mike Sellers     msellers@mentor.com     Mentor Graphics Corp.

"I used to think that the brain was the most wonderful organ in my
body.  Then I realized who was telling me this." -- Emo Phillips