An Introduction to Generative AI

Abstract

The chapter serves as an intricate exploration into the domain of Generative AI, offering a comprehensive understanding of its fundamental principles, diverse methodologies, and wide-ranging applications. Beginning with a clear definition and overview, it progresses to clarify key concepts such as probability distributions and sampling while providing an overview of various generative models, including autoregressive models, variational autoencoders, and generative adversarial networks. Architectural components, training methods, and optimization techniques are thoroughly examined, alongside an in-depth analysis of challenges and considerations in model design and selection. Through real-world examples, the chapter showcases the transformative potential of Generative AI across domains such as image generation, text processing, music composition, creative arts, etc. Addressing current limitations and outlining future directions offers valuable insights into the growing landscape of Generative AI, positioning it as a catalyst for innovation and creative expression.