Generative Artificial Intelligence: Transforming the Future

We are at the dawn of a new area of AI: Generative Artificial Intelligence (AI), which holds the potential to automate and generate in ways never before possible and can be applied across countless disciplines from natural language processing (NLP) to image synthesis, interactive simulations, and more. Generative models like Generative Adversarial Networks and transformers, as well as cutting edge AI tools such as GitHub Copilot, DALL-E, Gemini, and Bing Copilot have made content generation a different game. From realizing intelligent code generation, realistic image synthesis, or personalized search, these tools are transforming industries to software development and digital art, to search engines. In this paper we present a comprehensive survey of generative AI technologies and the methodologies behind these technologies, including attention mechanisms, variational autoencoders, diffusion models, and transformer-based models such as GPT and BERT. The paper also discusses the implementation of generative AI across Augmented Reality and Virtual Reality, highlighting how the use of AI to generate content is revolutionizing the interactive experiences in gaming, education, and training. In the study we also perform performance evaluations of generative AI models on benchmark datasets using BLEU, ROUGE scores etc. We discuss some of the challenges facing generative AI on ethical, computational, and too-often opaque lines and predict that generative AI can transform industries by creating autonomous creativity and personalized learning.