'Professor Amit Sheth is a leading expert in knowledge-infused learning. The topics covered by this book are important to advance state-of-the-art AI. As our understanding of generative AI deepens, we ask what the next frontiers of AI are. This timely book offers a refreshing answer that explores AI research beyond large language models.' Huan Liu, Arizona State University

'This timely and insightful book by Manas Gaur and Amit Sheth combines data-driven AI with structured human knowledge, creating a practical pathway toward transparent and safe AI. Addressing critical gaps in AI's explainability and interpretability, especially in healthcare and crisis management, the authors introduce 'Knowledge-infused Learning'-an essential approach for human-centric AI. Their innovative frameworks, like CREST, are thoughtfully designed for real-world impact. For anyone deeply engaged in multimodal AI, digital health, or responsible technology use, this book is a must-read guide, offering robust technical foundations and thoughtful ethical considerations crucial for equitable AI solutions.' Ramesh Jain, University of California, Irvine

'Knowledge-Infused Learning is a timely and essential guide to building AI systems that are not only powerful, but also interpretable and trustworthy. Gaur and Sheth brilliantly show how integrating human knowledge with machine learning leads to more explainable, safer, and more responsible AI. A must-read for anyone shaping the future of intelligent systems.' Craig Knoblock, Information Sciences Institute, University of Southern California

Knowledge-infused learning directly confronts the opacity of current 'black-box' AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. This guidebook introduces the pioneering techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. This is critical in high-stakes AI applications in healthcare, law, finance, and crisis management. The book brings readers up to speed on advancements in statistical AI, including transformer models such as BERT and GPT, and provides a comprehensive overview of weakly supervised, distantly supervised, and unsupervised learning methods alongside their knowledge-enhanced variants. Other topics include active learning, zero-shot learning, and model fusion. Beyond theory, the book presents practical considerations and applications of neurosymbolic AI in conversational systems, mental health, crisis management systems, and social and behavioral sciences, making it a pragmatic reference for AI system designers in academia and industry.
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1. Introduction; 2. Knowledge graphs for explainability and interpretability; 3. Knowledge-infused learning: the subsumer to neurosymbolic AI; 4. Shallow infusion of knowledge; 5. Semi-deep infusion learning; 6. Deep knowledge-infused learning; 7. Process knowledge-infused learning; 8. Knowledge-infused conversational NLP; 9. Neurosymbolic large language models; References; Index.
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Introduces an emerging field that blends statistical models with symbolic knowledge to make AI safer and more explainable.

Produktdetaljer

ISBN
9781009513746
Publisert
2026-04-30
Utgiver
Cambridge University Press
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
310

Biografisk notat

Manas Gaur is an assistant professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He earned his Ph.D. in 2022 from the University of South Carolina's Artificial Intelligence Institute, studying under Dr. Amit P. Sheth. A pioneer in knowledge-infused learning (2016–2022), Gaur's research has earned multiple best paper awards and recognition through USC Eminent Profiles and AAAI New Faculty Highlights. His cutting-edge work continues to attract major funding, including grants from NSF and EPSRC-UKRI in partnership with the Alan Turing Institute. Amit P. Sheth is the NCR Chair and Professor of Computer Science and Engineering at the University of South Carolina, where he founded the university-wide AI Institute in 2019 and grew it to nearly 50 AI researchers in four years. He is a fellow of IEEE, AAAI, AAAS, ACM, and AIAA. His awards include the IEEE CS Wallace McDowell Award and the IEEE TCSVC Research Innovation Award. He has co-founded four companies, run two of them, and advised or mentored over 45 Ph.D. candidates and postdocs to exceptional careers in academia, industry, and as entrepreneurs.