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Generative AI: Applications, Models, Challenges, Opportunities, and Future Directions
Abstract
This study provides a comprehensive view of the state of generative AI today, touching on its uses, foundational models, obstacles, prospects, and potential future courses of action. Autoregressive models like Transformers, GANs, and Variational Autoencoders (VAEs) are the backbone of generative AI. Generated AI still has a way to go before fully realizing its potential. Problems with model interpretability, training stability, and generated content bias are all examples of such challenges. Computer scientists, psychologists, and ethicists must work together to find solutions to these problems. Generative AI does, however, offer tremendous potential. Artists, designers, and storytellers have new tools at their fingertips. Improving the robustness of models, granting greater control over generated outputs, and investigating uses in interactive storytelling and real-time content production are all potential future areas for generative AI.
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