Generative adversarial networks (GANs) are a type of artificial intelligence system that consists of two neural networks: a generator and a discriminator. The generator is trained to generate new data samples, such as images or text, that are similar to a dataset of real examples. The discriminator, on the other hand, is trained to distinguish between real and generated data. The generator and discriminator are trained simultaneously in a competitive process, with the generator trying to produce increasingly realistic data samples and the discriminator trying to correctly classify them. This adversarial training process pushes both networks to improve, leading to the generation of high-quality, realistic data samples. GANs have been successfully applied in a variety of domains, including image generation, natural language processing, and video synthesis. They have also been used for tasks such as generating photo-realistic images, creating new artwork, and generating realistic human faces. Overall, GANs have shown great potential for generating high-quality, diverse, and realistic data samples, making them a popular research area in the field of artificial intelligence.