Название: Multimodal Generative AI
Автор: Akansha Singh, Krishna Kant Singh
Издательство: Springer
Год: 2025
Страниц: 398
Язык: английский
Формат: pdf (true), epub
Размер: 35.6 MB

This book stands at the forefront of AI research, offering a comprehensive examination of multimodal generative technologies. Readers are taken on a journey through the evolution of generative models, from early neural networks to contemporary marvels like GANs and VAEs, and their transformative application in synthesizing realistic images and videos. In parallel, the text delves into the intricacies of language models, with a particular on revolutionary transformer-based designs. A core highlight of this work is its detailed discourse on integrating visual and textual models, laying out state-of-the-art techniques for creating cohesive, multimodal AI systems. “Multimodal Generative AI” is more than a mere academic text; it’s a visionary piece that speculates on the future of AI, weaving through case studies in autonomous systems, content creation, and human-computer interaction. The book also fosters a dialogue on responsible innovation in this dynamic field. Tailored for postgraduates, researchers, and professionals, this book is a must-read for anyone vested in the future of AI. It empowers its readers with the knowledge to harness the potential of multimodal systems in solving complex problems, merging visual understanding with linguistic prowess. This book can be used as a reference for postgraduates and researchers in related areas.

Multimodal Generative AI is meticulously designed for a readership well-versed in the intricacies of machine learning and artificial intelligence. This text delineates itself by delving into the fusion of two traditionally distinct AI disciplines: generative models for visual data and natural language processing.

Multimodal Generative AI is driven by the growing need for AI systems that not only process but also synthesize novel content that spans both visual and linguistic elements. In an era where digital information is overwhelmingly multimodal, the development of AI that can interpret and generate such content is not only revolutionary but also essential. This book fills a gap in the current literature, providing a holistic exploration of how disparate generative technologies can be interlinked to produce more sophisticated and versatile AI systems.

The core contents of Multimodal Generative AI include:
• An examination of the evolution of generative models, from early neural networks to contemporary architectures like GANs and VAEs and their application in creating realistic images and videos.
• A thorough analysis of language models, particularly transformer-based designs, and their unprecedented success in understanding and generating human-like text.
• A detailed discourse on the integration of visual and textual models, presenting state-of-the-art techniques for creating cohesive multimodal systems.
• Case studies showcasing the application of multimodal generative AI across various sectors, highlighting breakthroughs in areas such as autonomous systems, content creation, and human–computer interaction.
• An insightful discussion on the ethical and societal ramifications of generative AI, promoting a dialogue on responsible innovation.

What sets Multimodal Generative AI apart is its focus on the intersectionality of generative visual and language models. It offers readers a unique vantage point on how these models can be harmoniously combined to engender AI with a more profound understanding and creative capability. This book is designed to catalyze further research and development in the field, serving as a springboard for innovation. The intended readership stands to benefit from a comprehensive understanding of how multimodal systems can be employed to solve complex problems that require an amalgamation of visual understanding and language proficiency. It is a text that will not only inform but also inspire its audience to push the boundaries of what is possible in AI.

Prerequisites for fully grasping the contents of Multimodal Generative AI include a foundation in Machine Learning concepts, familiarity with neural network architectures, and an understanding of the basics of computer vision and natural language processing (NLP). This ensures that readers are equipped to appreciate the advanced methodologies and novel insights presented in this book.




ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШЕМ СЮДА!