Three years ago, after an argument at a bar with some fellow artificial intelligence researchers, Ph.D student Ian Goodfellow cobbled together a new way for AI to think about creating images. The idea was simple: one algorithm tries to generate a realistic image of an object or a scene, while another algorithm tries to decide whether that image is real or fake.
The two algorithms are adversaries–each trying to beat the other in the interest of creating the final best image–and this technique, now called “generative adversarial networks” (GANs) has quickly become a cornerstone of AI research. Goodfellow is now building a group at Google dedicated to studying their use, while Facebook, Adobe, and others are figuring out how to use the technique for themselves. Uses for data generated this way span from healthcare to fake news: machines could generate their own realistic training data so private patient records don’t need to be used, while photo-realistic video could be used to falsify a presidential address.