The details do not matter

[Jordan B Pollack]

Neurobiologists often write grossly inefficient programs, showing that
Computer Science is merely a handservant to neuroscience, providing a
catalog of algorithms, data-structures, and fancy jargon.:)

Jim Bower writes:
>> 	In conclusion, in my view, AI and connectionism (neural nets) should
>>work out their own definitions on their own merits without reference to
>>biology.  Then, if there is a difference, both should fight it out based on
>>real world performance.

I fully agree with Jim Bower that biological plausibility is the wrong
yardstick for the AI/Connectionist debate, but I refuse to have my 
fields trivialized. Maybe "Neural Networks" and "expert systems" are
mere engineering, but Connectionism and AI are not! 

The subject of study is not brain, but MIND, and the mind is not a
steam engine, or a telephone switching system, or a stored program
computer, and it isn't a brain either.  The dispute between AI and
Connectionism is not about neural plausibility, or vague notions of
intelligence, but about the PRINCIPLES OF ORGANIZATION of a
computational system at the upper limit of biological complexity.

With limited research resources and the impetus of its own history and
living legends, AI has focused primarily on one particular
organization of computation which is postulated to be able to support
mind-like systems: the sequential/recursive rule-interpreter.  There
are very good reasons for this postulate, but no convincing argument
that it is true or false. But there are several compelling deficits of
rule-interpretation when taken literally as a model for mind:

    learnability   (changes in rules are unstable)
    scaling  (no systems with more than 1000's of rules)
    behavioral profile  (no temporal behavior which is psych. plausible)
    parallelization  (no easy way to distribute memory)
    biological plausibility (not evolutionarily or neurally justified)

These deficits are all being addressed in multiple ways, both within AI
and more broadly in the computer and cognitive sciences.
Connectionism can be viewed as a unified attack on these problems,
although any single connectionist model today only addresses one or
two of these at a time.  But the connectionist approach is based on a
very different postulate, that the organization of the brain is
extremely relevant to the organization of the mind. Again, there is
no convincing argument that it is true or false.

However, to compete against rule-interpretation as a literal theory of
mind, connectionism cannot afford to be constrained by biological
detail.  So, while we may be interested in the coarse organization of
the brain, such as its layers and columns, population codes, diameters
and densities of connections, and so on, we ignore the "details" such
as the lack of bidirectional synapses or the use of calcium in some
synaptic modification event.  Why? Because the complexity of a theory
cannot be greater than the complexity of the mental faculty it purports
to explain, or the theory would fail the parsimony test of science itself.

On another note, this lack of attention to detail is also a
conditioned response, for whenever a connectionist has crossed the
line by taking a bold stand on a detailed model of some piece of the
brain, X, they face the angry voices of the biologists:

   "No, you are doing it all wrong! Believe me, I'm the world's
   expert on X!"
or
   "No, you can't model X yet, the `basic science' data isn't in and
   it will take me another 10 years of slicing and dicing ratbrains to
   get it!

So, in conclusion: 
   The AI/connectionist debate is about science, not engineering. 
   What is involved is mind, not brain.
   The brain details don't matter as do its principles of organization.

Jordan Pollack                            Assistant Professor
CIS Dept/OSU                              Laboratory for AI Research
2036 Neil Ave                             Email: pollack at cis.ohio-state.edu
Columbus, OH 43210                        Fax/Phone: (614) 292-4890