Generative Adversarial Networks, or GANs, are a type of artificial intelligence model used to generate new data. They consist of two neural networks: a generator that creates new data samples and a discriminator that evaluates them. The generator tries to produce data that is indistinguishable from real data, while the discriminator attempts to identify which samples are real and which are generated. The two networks work in opposition, hence the term "adversarial." As the generator improves its ability to create realistic data, the discriminator also becomes better at detecting fakes. This process continues until the generator produces high-quality data that the discriminator can no longer reliably distinguish from real data.