Matt,
Thanks. But how do you see these:
"Pattern recognition in parallel, and hierarchical learning of increasingly
complex patterns by classical conditioning (association), clustering in
context space (feature creation), and reinforcement learning to meet evolved
goals."
as fundamentally different from logicomathematical thinking? ("Reinforcement
learning" strikes me as literally extraneous and not a mode of thinking).
Perhaps you need to explain why conditioned association is different.
It may help if I set up a pole of comparison. I see the brain, for example,
as working primarily by "free association." I can start right now with a
thought -
"COW" - and proceed - DOG - TAIL - CURRENT CRISIS - LOCAL VS GLOBAL
THINKING - WHAT A NICE DAY - MUST GET ON- CANT SPEND MUCH MORE TIME ON
THIS...." etc. etc.
and that literally was an ad hoc and ad lib chain and form of reasoning.
Free association. In no way was the whole programmed. Parts of it certainly
were - my spelling of different words, use of phrases etc. but not the
whole - I could have gone off at different points on very different
tangents. (Try it for yourself).
Also, of course, each association is indeed an *association* with and not a
*logical/ necessary sequitur" from the previous idea.
Now free association is clearly antithetical to logicomathematical thinking
which do indeed represent forms of routines and programs. I would have
thought that it is also antithetical to any kind of thinking you would
advocate.
Matt/MT:>
What then do you see as the way people *do* think? You
surprise me, Matt, because both the details of your answer
here and your thinking generally strike me as *very*
logicomathematical - with lots of emphasis on numbers and
compression - yet you seem to be acknowledging here, like
Jim, the fundamental deficiencies of the logicomathematical
form - and it is indeed only one form - of thinking.
Pattern recognition in parallel, and hierarchical learning of increasingly
complex patterns by classical conditioning (association), clustering in
context space (feature creation), and reinforcement learning to meet
evolved goals.
You can't write a first order logic expression that inputs a picture and
tells you whether it is a cat or a dog. Yet any child can do it. Logic is
great for abstract mathematics. We regard it as the highest form of
thought, the hardest thing that humans can learn, yet it is the easiest
problem to solve on a computer.
-------------------------------------------
agi
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