This kind of stuff is fairly normal actually. I don't really agree that it couldn't happen. Neural Networks train for the wrong features all of the time. It'

This kind of stuff is fairly normal actually. I don't really agree that it couldn't happen. Neural Networks train for the wrong features all of the time. It's part of what happens when you're training unsupervised. You load in a lot of data and then you find the bias. Sure, as the article purports, if done perfectly it wouldn't happen. But that's like saying "If you build a bridge perfectly it won't fall down." before building the first bridge. This was supposedly an old tale so I'm not sure why the author would assume the people working on the original theoretical NN were data experts who knew the correct ways to train NNs.