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Multimodal generative AI

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multimodal generative AI/ edited by Akansha Singh, Krishna Kant Singh.
other author:	Singh, Akansha.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxii, 382 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-981-96-2355-6
ISBN:	9789819623556

Multimodal generative AI
Multimodal generative AI[electronic resource] /edited by Akansha Singh, Krishna Kant Singh. - Singapore :Springer Nature Singapore :2025. - xxii, 382 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789819623556

Standard No.: 10.1007/978-981-96-2355-6doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Multimodal generative AI
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505 0 $a 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.
520 $a 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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650 2 4 $a Machine Learning. $3 1137723
700 1 $a Singh, Akansha. $3 1487717
700 1 $a Singh, Krishna Kant. $e editor. $3 1349725
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