Understanding the limits of deep learning

I find the adversarial network examples very illuminating in showing just how different these dnn's function differently than our own brains (and how they are thus likely rather far off from generalized intelligence). These systems are still relying heavily on low-level coincidences for classification rather than more durably high-level generalizations. Kind of an overfitting of the lowest layer. Or something..