AI has a long history, but only recently has Generative AI risen to prominence as a form that makes notable progress on some of the long-standing goals of the field. Whereas classic machine learning models typically predict a category label or a number, a Generative AI model may produce an entire text, a photorealistic picture, or other complex outputs. This chapter provides a brief introduction to the techniques that enable this, starting from basic ideas such as learning from training data and building foundation models. The chapter explains how large language models operate internally, introducing the transformer neural network architecture. It also provides a high-level explanation of common generative models for images and further kinds of data. Finally, it concludes with a discussion of agentic AI and possible future developments.