More like "The Flaw Lurking in Every Machine Learning Algorithm with a Gradient"(1) IMO. For example, in a linear or logistic classifier, the derivative of the

More like "The Flaw Lurking in Every Machine Learning Algorithm with a Gradient"(1) IMO. For example, in a linear or logistic classifier, the derivative of the raw output with respect to the input is the input itself while the derivative of the input is the weight. Knowing this one can use the weights of any classifier to minimally adjust the input to produce a pathological example. As for humans, I submit we have all sorts of issues like this. It's just that we have a temporal stream of slightly different versions of the input and that keeps inputs like this from having any significant area under the curve. Have you never suddenly noticed something that was right in front of you all along? (1) And probably those that don't too, but it's harder to find cases like that without a gradient (not that it can't be done, because I've found them myself for linear classifiers using genetic algorithms, simulated annealing, and something that looked just like Thompson Sampling but wasn't).