Machine learning models that could recognize objects in images – and even create entirely new images – were once no more than a pipe dream. Although the AI world discussed various strategies, a satisfactory solution proved illusive. Then in 2014, after an animated discussion in a Montreal bar, Ian Goodfellow came up with a bright idea.

At a fellow student's doctoral party, Goodfellow and his colleagues were discussing a project that involved mathematically determining everything that makes up a photograph. Their idea was to feed this information into a machine so that it could create its own images. At first, Goodfellow declared that it would never work. After all, there were too many parameters to consider, and it would be hard to include them all. But back home, the problem was still on Goodfellow's mind, and he actually found the solution that same night: Neural networks could teach a computer to create realistic photos.

His plan required two networks, the generator and the discriminator, interacting as counterparts. The best way to understand this idea is to consider an analogy. On one side is an art forger (generator). The art forger wants to, say, paint a picture in the style of Vincent van Gogh in order to sell it as an original to an auction house. On the other hand, an art detective and a real van Gogh connoisseur at the auction house try to identify forgeries. At first, the art expert is quite inexperienced, but the detective immediately recognizes that it is not a real van Gogh. Nevertheless, the counterfeiter does not even think of giving up. The forger keep practicing and trying to foist new and better paintings off on the detective. In each round, the painting looks more like an original by a famous painter, until the detective finally classifies it as genuine.

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