Generative AI: A Chronological Review
Byong Hyon Chong · Asia-pacific Journal of Convergent Research Interchange · 2024
This paper provides a comprehensive and chronological account of the development of generative Artificial Intelligence (AI) from its inception to the present day.Through a detailed examination of key advances and key technologies, it traces the evolution of generative AI technology, uncovering important milestones in neural network design, language model development, and image generation techniques.Beginning with foundational theories in the 1950s and ending with breakthrough innovations such as Generative Adversarial Neural Networks (GANs) and transformer models in the 2010s, it examines the exponential growth of AI capabilities and the expansion of its range of applications, from simple pattern recognition to complex natural language processing and beyond.Methodologically, it uses a chronological analysis with a matching of seminal papers and major technological innovations that have shaped the landscape of generative AI.The implications of these developments are critically analyzed, focusing on the impact of AI on different industries, ethical considerations, and future technological potential.The results show that generative AI has greatly improved computational efficiency and creativity but also poses challenges, such as ethical dilemmas and the need for enormous computational resources.The conclusion discusses the future of generative AI and suggests that it will require continued innovation balanced with strong ethical standards.This study is significant not only for its historical trajectory but also for its call to action for policymakers, developers, and academics to find ways to mitigate the risks while converging on the benefits of generative AI.