Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. They consist of two neural networks, a generator and a discriminator, which compete against each other. The generator creates fake data, aiming to mimic real data, while the discriminator evaluates the data and distinguishes between real and generated. This adversarial process continues until the generator produces highly realistic data. GANs are used in various applications, including image synthesis, video generation, and data augmentation, revolutionizing fields like computer vision and creative arts.