Human-Centric AI with Common Sense
Springer International Publishing (Verlag)
978-3-031-69973-3 (ISBN)
This book enables readers to understand the challenges and opportunities of developing human-centered AI with commonsense reasoning abilities. Despite apparent accuracy improvements brought by large neural models across task benchmarks, common sense is still lacking. The lack of common sense affects many tasks, including story understanding, decision-making, and question answering. Commonsense knowledge and reasoning have long been considered the "black matter" of AI, raising concerns about the trustworthiness and applicability of AI methods in both autonomous and hybrid applications. This book describes how to design a more robust, collaborative, explainable, and responsible AI through incorporating neuro-symbolic commonsense reasoning. In addition, the book provides examples of how these properties of AI can facilitate a wide range of social-good applications in digital democracy, traffic monitoring, education, and robotics. What makes commonsense reasoning such a unique and impactful challenge? What can we learn from cognitive research when designing and developing AI systems? How can we approach building responsible, robust, collaborative, and explainable AI with common sense? And finally, what is the impact of this work on human-AI teaming? This book provides an accessible introduction and exploration of these topics.
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.
Introduction.- Collaborative Commonsense Reasoning.- Adaptive Commonsense Reasoning.- Responsible Commonsense Reasoning.- Explainable Commonsense Reasoning.- Hybrid Intelligence with Common Sense.- Conclusions and Outlook.
| Erscheinungsdatum | 21.12.2024 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Computer Science |
| Zusatzinfo | XV, 137 p. 30 illus., 27 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 168 x 240 mm |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Schlagworte | Adaptive AI • Collaborative AI • Combining Language Models and Knowledge Graphs • Commonsense Reasoning • Explainable AI • Hybrid intelligence • Machine Common Sense • Neuro-Symbolic Commonsense Reasoning • responsible AI • trustworthy AI |
| ISBN-10 | 3-031-69973-4 / 3031699734 |
| ISBN-13 | 978-3-031-69973-3 / 9783031699733 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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