Generative AI Survey: Architectures, Applications, Challenges, and Future Directions

This survey paper presents an integrated overview of Generative Artificial Intelligence (GenAI), focusing on its fundamental concepts, major model architectures, applications, challenges, and future research directions. It reviews important generative approaches, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformer-based models, and diffusion models. The paper discusses the applications of GenAI across domains such as healthcare, education, finance, software development, scientific research, and cybersecurity. It also examines key challenges related to data privacy, bias, hallucinations, explainability, copyright, computational cost, and responsible AI governance. Furthermore, the survey highlights emerging developments in multimodal AI, human-AI collaboration, model efficiency, and AI safety. By organizing existing concepts and research directions into a unified overview, this paper aims to support students, researchers, and practitioners in understanding the current landscape of Generative AI and identifying opportunities for future investigation.