The problem with #machinelearning in a nutshell, via extensional vs intensional logic:
A: {2, 4, 6, 8, 10, ... X}
B: {2x: x ∈ N}
There is no way for a statistical, asymbolic machine to arrive at B from A, no matter how large you choose X @GaryMarcus @filippie509 #ai
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Ok, but how does that change the argument? Evolution is machine learning with a longer time span, e.g. an optimization process. It is still machine learning. The bias is learned by evolution.
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If I am not mistaken, your paper responds to my first reply (we are not born blank), but not to my second reply, which says that "innateness" is not a prior appearing from nowhere. What you call "innate" is the result of an additional learning process, performed by evolution.
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actually, i address exactly that
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