Generative Adversarial Networks (GANs) consist of two neural networks, a generator and a discriminator, that compete with each other. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. Over time, the generator improves, producing data that becomes increasingly difficult to distinguish from real data, commonly used in image synthesis, video generation, and other creative AI applications.