Generative Artificial Intelligence

Abstract

Generative Artificial Intelligence (GAI) is a technical term used to designate elaborate machine learning algorithms that allow the creation of human-like output text, images, audio, and biomedical data. Such methods as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models led to making GAI an initiative that promotes innovation in other fields such as healthcare, education, and creative fields. Yet with its fast development, one must wonder about algorithm bias as well as data privacy issues, misinformation, and control over AI-based content. The chapter delves into the history of the development of GAI, its theoretical background, and its fundamental implementation. It presents a bibliometric analysis to point out the interdisciplinary dynamics of GAI study. It looks at ethics implications, legal implications as well as social implications and necessity of responsible innovation. The chapter winds up with highlighting the challenges that persist and proposing future steps toward transparent and responsible use of generative technologies.

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