Knowledge-Infused Learning: Neurosymbolic AI by Manas Gaur (.PDF)
File Size: 10.4 MB
Knowledge-Infused Learning: Neurosymbolic AI for Explainability, Interpretability, and Safety by Manas Gaur, Amit P. Sheth
Requirements: .PDF reader, 10.4 MB
Overview: 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. Neurosymbolic AI and Knowledge-Infused Learning (KiL) exemplify techniques within this broader framework. KiL, in particular, emphasizes the integration of various forms of knowledge – lexical, domain-specific, commonsense, or constraint-based – into AI. This integration helps overcome challenges commonly associated with symbolic or statistical AI approache. Compared to powerful statistical AI that exploits data, KiL benefits from data and knowledge. For instance, in autonomous driving, combining knowledge graphs with Machine Learning has led to significant advancements. This book is ideal for readers with a background in Computer Science, especially in AI, data mining, natural language processing (NLP), and information retrieval. A basic understanding of linear algebra, probability, and statistics will enhance comprehension. It serves as a primer on KiL, a form of neurosymbolic AI in which knowledge enhances neural networks. This book targets Computer Science students and faculty, as well as interdisciplinary centers on Data Science.
Genre: Non-Fiction > Tech & Devices
Free Download links: