Multimodal Generative AI
Description
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.
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Prof. Akansha Singh , Professor at the School of Computer Science and Engineering, Bennett University, Greater Noida, boasts a comprehensive academic background with a B.Tech, M.Tech, and Ph.D. in Computer Science. Her doctoral studies, conducted at the prestigious IIT Roorkee, were focused on the cutting-edge fields of image processing and machine learning. A prolific author and scholar, Dr. Singh has contributed over 100 research papers and penned several books, alongside a multitude of conference papers. Her editorial expertise is recognized by leading publishers such as Elsevier, Taylor and Francis, and Wiley, where she has edited books on a variety of emerging topics. Her research interests are diverse and influential, spanning image processing, remote sensing, the Internet of Things (IoT), Blockchain, and machine learning. Prof. Singh's work in these areas not only advances the field of computer science but also significantly contributes to the broader scientific and technological community.
Prof. Dr. Krishna Kant Singh , currently the esteemed Director of Delhi Technical Campus in Greater Noida, India, is a highly experienced educator and researcher in the field of engineering and technology. Dr. Singh is a B.Tech and M.Tech degree, a Postgraduate Diploma in Machine Learning and Artificial Intelligence from IIIT Bangalore, a Master of Science in Machine Learning and Artificial Intelligence from Liverpool John Moores University, United Kingdom, and a Ph.D. from IIT Roorkee. Currently serving as the Director of Delhi Technical Campus in Greater Noida, Dr. Singh has made significant contributions to the academic and research community. With over 18 years of teaching experience, he has played a vital role in educating and mentoring future professionals. Dr. Singh's scholarly contributions are remarkable, with over 150 research papers published in prestigious Scopus and SCIE indexed journals. His commitment to knowledge dissemination is further evidenced by his authorship of 25 technical books, which serve as significant resources in the field of engineering and technology. Dr. Singh's dedication to his field is evident through his involvement in various capacities, from research and writing to editorial responsibilities. His work not only advances the field of engineering and technology but also inspires future generations of engineers and researchers.
Content
Chapter 1. Introduction to Multimodal Generative AI.- Chapter 2. ChatGPT and BERT: Comparative Analysis of Various Natural Language Processing Applications.- Chapter 3. Large Language Model on Multi-Modal Data.- Chapter 4. Adaptive Learning Technologies: Navigating the Road from Hype to Reality.- Chapter 5. Generative Artificial Intelligence in Visual Content: A Review of the Influence on Consumer Perception and Perspective.- Chapter 6. Text-to-Image Synthesis: Techniques and Applications.- Chapter 7. Image-to-Text Generation: Bridging Visual and Linguistic Worlds.- Chapter 8. Sustainability in the Metaverse: Challenges, Implications, and Potential Solutions.- Chapter 9. Transcendent Artificial Intelligence in Education.- Chapter 10. Chat GPT in Academia and Research - A Comprehensive Review of Integrating AI in Higher Education.- Chapter 11. Exploring Multimodal Hate Speech Detection Using Machine Learning and Deep Learning Models.- Chapter 12. Multimodal Generative AI for People with Disabilities.- Chapter 13. Single-Modality to Multimodality: The Evolutionary Trajectory of Artificial Intelligence in Integrating Diverse Data Streams for Enhanced Cognitive Capabilities.- Chapter 14. Interfacing Multimodal AI with IoT: Unlocking New Frontiers.-Chapter 15. Enhancing Safety and Reliability in VANETs for Autonomous Vehicles by M-XAI (Multi Model- Explainable-AI) .- Chapter 16. Future Directions In Multimodal Genrative AI.