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Multimodal Artificial Intelligence and Large Language Models [Kumar] - mklabgr - 09-23-2026
Multimodal Artificial Intelligence and Large Language Models: A Comprehensive Guide from Theory to Practice, edited by L. Ashok Kumar, D. Karthika Renuka, Tanvi Banerjee, and C. Deisy, is a technical overview of how AI systems can combine text, images, audio, and video rather than operating on a single type of data. The book covers multimodal large language models, conversational AI, cross-modal processing, data fusion, speech recognition, image captioning, real-time synchronization, and architectures for building scalable multimodal systems.
It also examines generative AI and the practical challenges involved in deploying these systems, including computational resources, latency, memory management, and distributed processing using frameworks such as PyTorch and TensorFlow.
The second major emphasis is on applications and emerging research problems. The chapters discuss multimodal emotion recognition, sign-language recognition, user-behavior analysis and recommendation systems, multimodal biological data, privacy and data security, cryptography and blockchain, and ethical considerations.
Thus, the book is positioned less as a general introduction to AI and more as a bridge between theory, implementation, and research applications of multimodal AI, aimed particularly at advanced undergraduate and graduate students, engineers, and researchers in computer science, AI, electronics, and information technology.
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