Matt:> Logic has not solved AGI because logic is a poor model of the way
people think.
Neural networks have not solved AGI because you would need about 10^15
bits of memory and 10^16 OPS to simulate a human brain sized network.
Genetic algorithms have not solved AGI because the computational
requirements are even worse. You would need 10^36 bits just to model all
the world's DNA, and even if you could simulate it in real time, it took 3
billion years to produce human intelligence the first time.
Probabilistic reasoning addresses only one of the many flaws of first
order logic as a model of AGI. Reasoning under uncertainty is fine, but
you haven't solved learning by induction, reinforcement learning, complex
pattern recognition (e.g. vision), and language. If it was just a matter
of writing the code, then it would have been done 50 years ago.
Matt,
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.
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agi
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