Deep Neural Networks Are Easily Fooled
These arguments were introduced by Szegedy et. al. earlier this year in this paper: <a href="http://cs.nyu.edu/~zaremba/docs/understanding.pdf" rel="nofollow">http://cs.nyu.edu/~zaremba/docs/understanding.pdf</a>. Geoff Hinton addressed this matter in his Reddit AMA last month.<p>The results are not specific to neural networks (similar techniques could be used to fool logistic regression). The problem is that ultimately a trained network relies heavily on certain activation pathways which can be precisely targeted (given full knowledge of the network) to fool networks into misclassification on data points which might to a human seem imperceptibly changed from those which are correctly classified. It is important to understand adversarial cases, but unreasonable to get carried away with sweeping pronouncements about what this does or doesn't about all neural networks, let alone intelligence generally, or the entire enterprise of AI research, as seems to happen after a splashy headline.