NN, AI, biology

[Joshua Alspector]

On the question of NN vs. AI, I think one point that needs emphasizing is
that much of the difference between these two views of intelligence
arises from physical implementation, and the convenience of modeling
based on this underlying structure.
A computer program has the capability of simulating the behavior
of a physical system including neural models but it may take a long
time.  Traditional symbolic AI is much better suited to the processes
that an arithmetic and logic unit in a computer can perform.
On the other hand, a computer's logic and memory are implemented using
transistors arranged in circuits in such a way that they act digitally
but are really fundamentally analog and messy like neurons (well not
THAT messy).  One could build a digital computer from biological neurons
if the technology existed to manipulate them.

Since neural systems and symbol-manipulating computers can simulate
each other, NN and AI are fundamentally equivalent in their descriptive
powers.  But each description has its advantages.
Because logic circuits can be implemented in a wide variety of technologies
(CMOS, bipolar, optical, neural), it is natural to ignore this
level of description.  One can further abstract away the logical structure
(bus width, registers, instruction set) by a compiler that allows us to
work in a high-level language.  Here, we get into the doctrine
of software separability.  Software is everything, hardware doesn't matter.

Neural networks are much messier, and the levels of description cannot
be separated so easily.  Slips of the tongue confuse low-level phonetic
and articulatory information with high-level linguistic information, 
something you would have to sully an AI program to do.
The computation here reflects the hardware (wetware).

Because of the equivalence of NN and AI, NN cannot do anything
that AI (computers) can't except to do it faster in a parallel implementation.
As has been pointed out, the NN models are not new mathematically.
Neural networks are a biological inspiration for how to physically do parallel
processing of information.  The qualities of NN that are awkward (but not
impossible) to model with AI have to do with the physical nature of networks.
These include speed in a parallel implementation, natural time scales,
network dynamics, and yes, also a functional description of relevance to
biological neural networks.  Being an experimental neuroscientist
is not the only way to understand brains.  Suppose we had an intelligent
AI system implemented on a digital computer and had no idea
how it worked.  We would not get far by sticking many oscilloscope
probes into it and watching the results as we ask it questions.

Josh Alspector
josh at bellcore.com