Generative Adversarial Networks (GANs) are a type of artificial intelligence used to generate new, realistic data based on existing data.

They consist of two parts: the generator and the discriminator. The generator creates fake data, such as images, while the discriminator evaluates whether the data is real or fake. These two parts work against each other in a continuous loop.

The generator tries to improve its fake data to fool the discriminator, while the discriminator gets better at identifying fake data. Over time, the generator becomes so skilled that the fake data looks very realistic.

This process can be visualized as a competition where both the generator and discriminator keep improving their skills. GANs are used in various fields, including art creation, image enhancement, and the development of realistic simulations.