And that's why we should aim to learn *physical* generative models and causal chains!
Any problem can be solved via associative intuition (pattern recognition) if you've already seen hundreds of variants of the problem before. The value of principled reasoning lies in the ability to handle problems you've never seen before.
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Pattern recognition functionality is possible with just individual experiences. While principled reasoning requires acknowledging communities' customs for proper functionality. Or else?
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the question remaining is: how to do the latter?
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I think that a successful models of "reasoning" would be like the first successful models of differential calculus. > Data -> Intuition --> Reasoning ---> Meta-Reasoning ---->... > Position -> Velocity --> Acceleration ---> Jerk ---->...
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The value of reasoning is not that you need less data (you almost certainly need more to reason than to intuit), but that you can model an exponentially broader input distribution.
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If I saw a problem I'd never seen before I would simply try to reformulate it as a problem I have seen before.
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So underfitting vs. overfitting?
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Eg to counter one’s own or others’ associative intuitions (preventing human automation bias)
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Finally, some sense. :)
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So you're telling us not to overfit? Cool.
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