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020 _a9783031699740
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024 7 _a10.1007/978-3-031-69974-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
100 1 _aIlievski, Filip.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989236
245 1 0 _aHuman-Centric AI with Common Sense
_h[electronic resource] /
_cby Filip Ilievski.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXV, 137 p. 30 illus., 27 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
490 1 _aSynthesis Lectures on Computer Science,
_x1932-1686
505 0 _aIntroduction -- Collaborative Commonsense Reasoning -- Adaptive Commonsense Reasoning -- Responsible Commonsense Reasoning -- Explainable Commonsense Reasoning -- Hybrid Intelligence with Common Sense -- Conclusions and Outlook.
520 _aThis 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. In addition, this book: Features cognitive, symbolic, and neural theories and methods for commonsense reasoning Integrates neural and symbolic methods for open-domain tasks built on robustness, adaptivity, collaboration, and responsibility principles Synthesizes lessons learned and provides open challenges that future research and engineering efforts need to tackle About the Author Filip Ilievski, Ph.D., is a Senior Assistant Professor of Commonsense AI at the Vrije Universiteit (VU) Amsterdam’s 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 DARPA Machine 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.
650 0 _aArtificial intelligence.
_958441
650 0 _aNatural language processing (Computer science).
_965131
650 0 _aReasoning.
_976426
650 0 _aComputer science.
_958552
650 0 _aUser interfaces (Computer systems).
_964688
650 0 _aHuman-computer interaction.
_964854
650 0 _aApplication software.
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aNatural Language Processing (NLP).
_966164
650 2 4 _aFormal Reasoning.
_977299
650 2 4 _aComputer Science.
_958552
650 2 4 _aUser Interfaces and Human Computer Interaction.
_966577
650 2 4 _aComputer and Information Systems Applications.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031699733
776 0 8 _iPrinted edition:
_z9783031699757
776 0 8 _iPrinted edition:
_z9783031699764
830 0 _aSynthesis Lectures on Computer Science,
_x1932-1686
_965520
856 4 0 _uhttps://doi.org/10.1007/978-3-031-69974-0
912 _aZDB-2-SXSC
999 _c65875
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