Generative adversarial networks: What GANs are and how they've evolved

Perhaps you've read about AI capable of producing humanlike speech or generating images of people that are difficult to distinguish from real-life photographs. More often than not, these systems build upon generative adversarial networks (GANs), which are two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples. This unique arrangement enables GANs to achieve impressive feats of media synthesis, from composing melodies and swapping sheep for giraffes to hallucinating footage of ice skaters and soccer players. In point of fact, it's because of this prowess that GANs have been used to produce problematic content like deepfakes, which is media that takes a person in existing media and replaces them with someone else's likeness. The evolution of GANs -- which Facebook AI research director Yann LeCun has called the most interesting idea of the decade -- is somewhat long and winding, and very much continues to this day.