Overview

This book enables readers to understand the challenges and opportunities of developing trustworthy AI with commonsense reasoning skills.  Commonsense knowledge is often implicit and presents a challenge for automated methods in natural language processing and question answering as the extraction and learning algorithms cannot count on the commonsense knowledge being available directly in text. As such, commonsense knowledge and reasoning has been considered the “black matter” of AI, raising concerns about the trustworthiness and applicability of AI methods in automated and hybrid applications, especially social good applications in misinformation, traffic, health, and education.  This book presents dominant methods that combine neural and symbolic advances to achieve adaptivity, collaboration, explainability, and responsibility through commonsense reasoning.  In addition, the book describes how these socio-technical properties of AI can facilitate a range of social-good applications like misinformation, traffic, education, and health.  What makes commonsense reasoning such a unique and impactful challenge? What do cognitive and AI perspectives bring to the table? How can we approach building responsible, adaptive, collaborative, and explainable AI with common sense? And finally, what is the impact of this work on hybrid human-AI intelligent systems?  This book provides an accessible introduction and exploration of these topics. 

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9783031699733

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Filip Ilievski, Ph.D., is a Research Assistant Professor of Computer Science at the University of Southern California (USC) and Research Lead at the Information Sciences Institute (ISI) at the USC Viterbi School of Engineering.  Dr. Filip holds a Ph.D. in Natural Language Processing from the Vrije Universiteit (VU) in Amsterdam, where he also worked as a postdoctoral researcher before joining USC. His research focuses on developing robust and explainable neuro-symbolic technology with positive real-world impact, based on neural methods and high-quality knowledge. Dr. Filip has made extensive contributions in identifying long-tail entities in text, performing robust and explainable commonsense reasoning, and managing large-scale knowledge resources.

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