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The paper has the Law & Gold model of (monkey) perceptual learning as one of their cases, and has a bit of discussion about how stochastic gradient descent in ML is quantitatively similar to observed characteristics of human learning. I don't think it's unfounded speculation, except perhaps on the part of the paper.

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While human learning might be similar, is it similar enough? Humans seem to require very few data points to learn something. Just a few examples are enough for a human to grasp a pattern. As far as I know, we haven't been able to do the same in ML.

I’m not so sure about that. Adult humans can learn new things with a remarkably small number of additional data points, but they’re drawing on a lifetime of human experience which is also data. Children tend to need more help.

Right. We have priors about how the world works, and that usually helps.

ML has training right?

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