What it is: A Generative Adversarial Network (GAN) is a neural network that, after being trained to understand the structure of a given data set, can generate new, realistic examples of it. For example, a GAN trained on photographs of human faces can generate realistic photographic portraits of people that don’t exist. These networks are called ‘adversarial’ because their training takes the form of a competition between two neural networks.
What it does: Making a GAN involves training two neural networks, a generator and a discriminator, until the point at which they can train each other in opposition. In the example of a portrait-making GAN, the generator would be trained to make portraits of imaginary people. Meanwhile, the discriminator would be trained to tell the difference between the images generated by its ‘opponent’ network and the real thing. Having completed this initial training, the networks then face off, with the generator trying to fool the discriminator repeatedly, using any failures to improve itself. Trained in this fashion, the generator algorithm will, theoretically, eventually create realistic fakes on demand.
Why it matters: GANs, though developed only recently, can already do a number of impressive things. GANs can create high-resolution photos of faces from blurry ones, or generate street schematics from aerial photos of urban environments, or create plausible paintings of objects from pencil sketches. Positive applications include enhancing the utility of security footage, detecting malware more effectively, and creating powerful artistic tools. Nefarious applications include automatic malware generation, as well as the production of fake images, video, and audio of real people, so-called Deepfakes.
What to do about it: For most people, GANs represent a scientific curiosity at this point. However, enterprises whose applications perform functions that might be accomplished by GANs–image processing, for example–may need to prepare for significant disruption alongside opportunity.