🤖 AI Summary
This work proposes a unified framework for understanding and developing generative artificial intelligence models capable of producing multimodal content, including images, text, video, and molecular structures. Addressing the current fragmentation in generative modeling, the study integrates core methodologies—such as variational autoencoders, generative adversarial networks, diffusion models, and large language models—into a cohesive theoretical system grounded in mathematical principles, architectural design, and mechanisms for controllable generation. This framework not only advances a systematic understanding of multimodal generative processes but also provides robust theoretical foundations and practical pathways for generating high-quality, controllable digital content, with direct implications for applications in scientific discovery and beyond.
📝 Abstract
Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content. From photorealistic images to coherent text, from immersive videos to novel molecular structures, generative models now power applications that were once confined to science fiction. This book is designed to guide readers through the foundational principles, mathematical underpinnings, and practical architectures that underpin this revolution.