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Hey Jamie and Judith,
Interesting posts. I
studied neural networks for several years as opposed to what's now called
Classical AI, which is I suppose that disembodied super-intelligence that you
refer to sometimes Judith. People who gravitate towards neural networks
are, I agree, much more likely in my opinion to stumble upon hidden
entailments. Especially if they are simultaneously looking at studies of
real brains and their functioning. I know that I did. I spent
several years thinking that the classical AI guys were way off, still do.
Now I feel that even many neural network approaches are likely to also be
off the mark. Mind and body are inseparable. I now feel that if the
mind is to be understood then this will likely entail moving the object of study
from mind to brain and body and then on to life and organism. Convenient
huh?
I "built" several neural networks (simulated) and
ultimately came to the conclusion that, though the networks did in fact do
something akin to learning, the networks themselves (as opposed to their
synaptic weights) were unentailed. How do we account for the
"architecture" of any given network? I also now believe that if learning
(which seems to me must involve anticipation) is to be embodied in computational
models that this absolute distinction between hardware and software will have to
be circumvented. Anybody got any ideas?
David
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