Work

Exploring generative artificial intelligence: a comprehensive guide

Year: 2026

Type: article

Abstract: Generative artificial intelligence (GAI), a specialized branch of artificial intelligence, has developed as a dynamic discipline that drives innovation and creativity across several domains. It is concerned with creating models that can autonomously produce novel text, images, videos, music, 3D, code, and more. GAI is distinguished by its capacity to acquire knowledge from extensive datasets, identify recurring patterns, capture distributions, and produce novel content demonstrating similar features. This review presents a comprehensive historical overview of the development and progression of GAI techniques over the years. Various essential methodologies employed in developing GAI are also discussed, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), transformers, and diffusion models. Moreover, a detailed overview of the technologies used in GAI is provided for generating images, videos, music, code, and text, such as ChatGPT, DALL-E, Midjourney, Claude, Bard, GitHub Copilot, and others. The research subsequently introduces the different datasets used to train the GAI models and the evaluation metrics for evaluating their performances. Ultimately, the research investigates the diverse applications of GAI across various domains, challenges, and ethical implications.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.3276

Authors: Rasha Shoitan, Mona M. Moussa, Nahed Tawfik, Young-Im Cho, Mohamed S. Abdallah

Institutions: Electronics Research Institute, Gachon University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,395

Funder: Ministry of Oceans and Fisheries