The amount of shit an NN can memorize in its parameters, and how dumb this makes it given unexpected data, dwarfs the idiocy of legacy AI/ML
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The field's term of art for memorizing shit is "overtraining". It doesn't always happen, but it's definitely a serious hazard.
I thought it was "overfitting" which in its trivial forms is avoided by having separate train & test sets, but when both are "biased", tough
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Hmm, yes, "overfitting" seems more common. It's been a while.
Thanks. Twitter will use this to make your timeline better. UndoUndo
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