THEORETICAL FOUNDATIONS AND PRACTICAL APPLICATIONS OF GENERATIVE MODELS IN ARTIFICIAL INTELLIGENCE

Parker Furlong Anderson Journal

THEORETICAL FOUNDATIONS AND PRACTICAL APPLICATIONS OF GENERATIVE MODELS IN ARTIFICIAL INTELLIGENCE

Authors

Parker Furlong Anderson Journal

Independent Researcher, Japan.

Keywords:

Generative Models, AI, GANs, VAEs, Diffusion Models, Synthetic Data, Machine Learning

Synopsis

Generative models have emerged as transformative tools in artificial intelligence (AI), enabling machines to generate complex data such as images, text, music, and even synthetic scientific data. This paper explores the theoretical underpinnings of generative models, particularly focusing on probabilistic reasoning and neural network-based architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models. We also review practical applications across domains such as healthcare, art, drug discovery, and natural language generation. While recent advancements in models such as OpenAI’s GPT-4 and Google's Imagen have demonstrated unprecedented generative capabilities, critical evaluation reveals theoretical limitations and practical constraints. This paper draws upon literature to contextualize the current state and trajectory of generative AI.

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