I know there are ways to mitigate overfitting, but I figured explaining it wasn't important for a comment made for the layman (I also have my doubts about the real world effectiveness of some of these techniques anyway, but that's just based on my own experiences, so I didn't mention it). Things have gotten better than when I first started playing with NNs, but overfitting is still an issue, especially since good, large datasets are hard to come by. The lack of generalization, whether through overfitting or as a natural consequence of NNs is a very large issue if there were to be used in a potentially life threatening scenario. But again, without the ability to know why a neural network has made given it's answer they cannot be trusted with decision making.