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Among all 2^2^n Boolean functions of n variables, almost none is "learnable" or susceptible to generalization, regardless of the method. Any family of functions can only learn an exponentially small # of functions with a less-than-exponentially large # of samples.
For sure, we should not benchmark our models on artificial, meaningless tasks. But it seems there is no shortage of perfectly reasonable tasks where good generalization requires abstraction and reasoning. And where humans perform well, while ML does not.
I beleive the biggest problem with the AI development is the focus is towards application of XYZ technology in AI than the core requirements of AI. Its like ppl trying to fit a problem to some or the other available solution. #wait4vicki
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