NEARLY A DECADE AGO, Ian Goodfellow, then a PhD candidate at Université de Montréal, was drinking with friends at the 3 Brasseurs in Montreal’s downtown when he conceived an idea that would change machine learning—and the world of disinformation—forever.

“I don’t want to be someone who goes around promoting alcohol for the purposes of science, but in this case, I do actually think that drinking helped a little bit,” said Goodfellow in his appearance on the Lex Fridman Podcast. If the idea came to him at lunchtime rather than over a beer in the evening, he added, he might have been able to talk himself out of it. Instead, he went home and started working on the project.

Goodfellow suspected that pitting two computer systems against each other—called generative adversarial networks, or GANs—would yield more realistic outputs than the deep-learning machines that existed at the time, which would often generate blurry images of people, usually with missing facial features, did. His early model was able to create numbers that looked hand drawn, human-like faces, and photos of animals that resembled something out of a pixelated Monet painting, but as the technology evolved, it became possible to create strikingly realistic forgeries using a much less involved process. Read More