> This summary completely neglects the fact that the paper also recommends that -- just like distorted images are added to training sets today (you wouldn't wan

> This summary completely neglects the fact that the paper also recommends that -- just like distorted images are added to training sets today (you wouldn't want something common like optical aberration from the camera lens screwing up your classifier) -- in the future, these adversarial examples should be added to training sets to mitigate their effects. I've seen several articles citing this paper as proof that deep learning is deeply flawed, yet they all seem to miss the point you make above. The other interesting result is that the neurons are not in fact individual features you can just grab and drop into another algo--the entire space defined by the model works together, through all the layers. Honestly, that was a more interesting result for me, although I don't know that it negates anything, I've just got to stop telling people that the individual units are features. Not that deep learning is the end-all-be-all of machine learning--it's not. It's just that this paper isn't saying what reporters are saying it's saying... As per usual? Never let the truth get in the way of a good story...