How To Fool A Neural Network

It's an extreme rhetorical, but it illustrates one of the biggest challenges facing machine learning today. Neural networks are only as good as the information they're trained on, which had led to high-profile examples of how susceptible they are to bad data riddled with bias. But these technologies are also vulnerable to another kind of weakness known as "adversarial examples." An adversarial example occurs when a neural net identifies an image as one thing–while any person looking at it sees something else. The phenomenon was discovered in 2013, when a group of researchers from Google and OpenAI realized they could slightly shift the pixels in an image so that it would appear the same to the human eye, but a machine learning algorithm would classify it as something else entirely.