GENERATIVE AI A COMPREHENSIVE EXAMINATION OF TECHNIQUES, APPLICATIONS, AND DIFFICULTIES

Abstract

Generative Artificial Intelligence (Generative AI) has emerged as a transformative technology capable of generating realistic text, images, audio, video and code in a wide range of application areas. The review covers several important Generative AI methodologies, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformer-based models, Diffusion Models, and Hybrid Architectures. This also delves into their applications in the fields of healthcare, NLP, computer vision, finance, cybersecurity, climate science, and digital twin technologies. Also covered are key ethical, social, and economic concerns, and new research areas like multimodal AI, explainable AI, AI safety, and foundation models. This research paper reveals the latest developments, current challenges, and future research directions in creating reliable, effective, and ethical Generative AI systems.