I think that DeepXplore [0] is on the right track. I think that perhaps some methodological improvements could likely be made, but applying an adversarial, secu

I think that DeepXplore [0] is on the right track. I think that perhaps some methodological improvements could likely be made, but applying an adversarial, security-like approach to deep neural networks is a way to build fault-tolerance into these methods. I'll keep hoping that things like capsules and "smarter" network design/training and data augmentation will eventually, de novo, help add "safe" generalizability. Perhaps optimizing accuracy/minimizing loss in a more broadly, uniformly random way rather than in optimizing accuracy, weighted by frequency of observations. [0]: https://arxiv.org/abs/1705.06640 https://arxiv.org/abs/1705.06640