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MARC状态:审校 文献类型:西文图书 浏览次数:8

题名/责任者:
AI and common sense : ambitions and frictions / edited by Martin W. Bauer and Bernard Schiele.
出版发行项:
London ; New York, NY : Routledge, Taylor & Francis Group, 2024.
ISBN:
9781032626178
ISBN:
1032626178
ISBN:
9781032626185
ISBN:
1032626186
载体形态项:
xix, 265 pages : illustrations ; 25 cm.
变异题名:
Artificial intelligence and common sense
丛编说明:
Routledge studies in science, technology and society
附加个人名称:
Bauer, Martin W., editor.
附加个人名称:
Schiele, Bernard, editor.
论题主题:
Commonsense reasoning.
论题主题:
Artificial intelligence.
中图法分类号:
TP18
书目附注:
Includes bibliographical references and indexes.
摘要附注:
"Common Sense is the endless frontier in the development of Artificial Intelligence, but what exactly is Common Sense, can we replicate it in algorithmic form, and if we can - should we? Bauer, Schiele, and their contributors from a range of disciplines, analyse the nature of Common Sense, and the consequent challenges of incorporating into Artificial Intelligence models. They look at different ways we might understand Common Sense and which of these ways are simulated within computer algorithms. These include sensory integration, self-evident truths, rhetorical common places, and mutuality and intentionality of actors within a moral community. How far are these possible features within and of machines? Approaching from a range of perspectives including Sociology, Political Science, Media & Culture, Psychology and Computer Science, the contributors lay out key questions, practical challenges and 'common sense' concerns underlying the incorporation of Common Sense within machine learning algorithms for simulating intelligence, socializing robots, self-driving vehicles, personnel selection, reading, automatic text analysis, and text production. A valuable resource for students and scholars of Science-Technology-Society studies, Sociologists, Psychologists, Media & Culture Studies, Human-Computer Interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning"--
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