I’m not going to stand on the sidelines shouting for a fight, though. This whole episode has (I hope) been instructive, because machine learning is not going away. Nor should it. But since we’re going to use it, we all have to make sure that we’re not kidding ourselves when we do so. The larger our data sets, the better our models – but the larger our data sets, the greater the danger that we don’t understand irrelevant patterns in those numbers that we didn’t intend to be there, patterns which the ML algorithms will seize on in their relentless way and incorporate into their models. I think that the adversarial tests proposed by the UCSF group make a lot of sense, and that machine-learning results that can’t get past them need to be put back in the oven at the very least. Our biggest challenge, given the current state of the ML field, is to avoid covering the landscape with stuff that’s plausible-sounding but quite possibly irrelevant.

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