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.
Introduction.- Collaborative Commonsense Reasoning.- Adaptive Commonsense Reasoning.- Responsible Commonsense Reasoning.- Explainable Commonsense Reasoning.- Hybrid Intelligence with Common Sense.- Conclusions and Outlook.
Filip Ilievski, Ph.D., is a Senior Assistant Professor of Commonsense AI at the VrijeUniversiteit (VU) Amsterdams Computer Science department. Dr. Ilievski is an affiliated scientist at the USC Information Sciences Institute (ISI), where he was previously a Research Lead and played a key role in a team within the DARPAMachine Common Sense (MCS) program. Dr. Ilievski holds a Ph.D. in Natural Language Processing. 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. Ilievski has also made extensive contributions in identifying long-tail entities in text, interpretation of internet memes, and enabling access to large-scale knowledge resources.