Multimodal Generative AI
Dr. Akansha Singh, Bennett University ,Greater Noida, India. Email: akanshasing@gmail.com
Dr. Krishna Kant Singh, Director (Academics), Delhi Technical Campus, Greater Noida, India.
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. The impetus for Multimodal Generative AI is the emergent necessity 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 essential. This book addresses 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.
For this new book volume, We welcome chapters on following topics:
Introduction to Multimodal Generative AI
Overview of AI evolution towards multimodality.
The significance of integrating language and visual models.
Evolution of Language Models: From BERT to GPT
Detailed exploration of the development of language models.
Case studies on their real-world applications and impacts.
Visual Generative Technologies: GANs and VAEs
In-depth analysis of visual generative models.
Exploration of their creative and practical applications.
Text-to-Image Synthesis: Techniques and Applications
Image-to-Text Generation: Bridging Visual and Linguistic Worlds
Exploration of the technology enabling image-to-text generation.
Detailed case studies illustrating the application of these technologies.
Exploration of future trends and potential societal impacts.
AI in Education and E-Learning: A Multimodal Approach
Discuss the use of Multimodal in Education
Exploration of opportunities and challenges
Interfacing Multimodal AI with IoT: Expanding Capabilities
Exploring how multimodal AI can interpret and respond to diverse data from IoT devices
Multimodal AI in Autonomous Systems: Cars, Drones, and Robotics
Examining the role of multimodal AI in enhancing the safety, efficiency, and decision-making, capabilities of self-driving cars.
Examining the role of multimodal AI in drones.
Examining the role of multimodal AI in robotics.
Speculating on the future trajectory of multimodal AI.
Full Chapter Submission: 30th March 2024
Final Acceptance/Rejection Notification: 20th April 2024
We invite researchers, practitioners, and scholars to submit original chapters that contribute to the understanding of generative and responsive AI in creative applications. Chapters should be well-researched, grounded in academic literature, and present insightful analyses. Practical examples, case studies, and ethical considerations are encouraged to enrich the discussions.
The minimum length of the chapter should not be less than 20-25 pages (7,000 to 10,000 words).
The full chapter includes Title of paper, Authors Name, Authors affiliations, email ID, Corresponding author details, department, Authors Bio, etc. with (Font Size 12, Font Style: Times New Roman, Line Space: 1 Point, Headings: 12+Bold) with APA reference style.
All submitted chapters will be reviewed on a double-blind review basis.
There is no fee for inclusion of a chapter. Any queries or issues can be addressed to akanshasing@gmail.com