Generative AI refers to a class of artificial intelligence systems that have the ability to generate new content or data that is similar to, but not exactly the same as, the data they were trained on. These systems learn patterns and features from a dataset and then use that knowledge to create new examples of data that resemble the original dataset.
Generative AI techniques often involve deep learning models, particularly generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously. The generator creates new data samples, while the discriminator evaluates whether those samples are likely to have come from the original dataset or were generated by the generator. Through this adversarial process, the generator learns to produce increasingly realistic data samples.
Generative AI has a wide range of applications, including image generation, text generation, music composition, and even drug discovery. It can be used for creative purposes, such as generating art or music, as well as practical applications like data augmentation, synthetic data generation for training machine learning models, and more.