Knowledge-Infused Learning: Neurosymbolic AI for Explainability, Interpretability, and Safety - Rilegato
Sinossi
Introduces an emerging field that blends statistical models with symbolic knowledge to make AI safer and more explainable.
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Informazioni sugli autori
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.
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Hardcover. Condizione: new. Hardcover. 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. This practical guidebook introduces an emerging field that confronts the opacity of current 'black-box' AI models. Knowledge-infused learning combines machine learning and symbolic AI to make AI safer and more explainable, which is vital for applications in human-critical contexts such as healthcare, law, and finance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781009513746
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Knowledge-Infused Learning
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Hardback. Condizione: New. 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. Codice articolo LU-9781009513746
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