In the last three weeks, we laid down the basics of AI. To recap: Now we get to the fun part. Using one neural network is really great for learning patterns; using two is really great for creating them. Welcome to the magical, terrifying world of generative adversarial networks, or GANs. GANs are having a bit of a cultural moment. They are responsible for the first piece of AI-generated artwork sold at Christie’s, as well as the category of fake digital images known as “deepfakes.” Their secret lies in the way two neural networks work together—or rather, against each other. You start by feeding both neural networks a whole lot of training data and give each one a separate task. The first network, known as the generator, must produce artificial outputs, like handwriting, videos, or voices, by looking at the training examples and trying to mimic them. The second, known as the discriminator, then determines whether the outputs are real by comparing each one with the same training examples.
Generative adversarial network ( GAN ) technology can be used to create both original art and forgeries . Users play two neural networks against each other to create art , with the process of a picture forger and an art detective going back and forth and trying to outwit one another . Eventually one network can not tell the difference between whether an artwork is real or fake . This ability to mimic brain patterns could be helpful in sectors such as medicine , where it could improve the quality of medical imagery . But the technology could also be used for more unethical activities such as art forgeries .