Exploring the Concept of Generative Artificial Intelligence: A Narrative Review
Abstract
Abstract: This paper provides a narrative review of Generative Artificial Intelligence, exploring its evolution, underlying concepts, and diverse applications across various industries. The review is conducted by searching google and google scholar using relevant keywords which in turn leads to different published articles from different websites and databases. The introduction establishes the growing significance of AI in human lives and highlights the rise of Generative A I as a powerful force in creating, innovating, and envisioning. The paper delves into different generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Recurrent Neural Networks (RNNs), Transformer-based Models, and Diffusion Models. Foundation Models, such as BERT and GPT, are introduced as adaptable models trained on broad data for diverse downstream tasks. The significance of LLMs in Natural Language Processing (NLP) and Computer Vision is emphasized, detailing their impact on text understanding, generation, translation, and information retrieval. The benefits and challenges of LLMs, ranging from natural language understanding to content moderation, are discussed, addressing concerns such as bias, ethical considerations, misinformation, and privacy. The paper concludes with an exploration of the application of Generative AI and LLMs in healthcare and business operations, showcasing their potential in personalized treatment plans, drug discovery, medical…
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