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Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032626178 ISBN 13: 9781032626178
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Añadir al carritoPaperback. Condición: New. 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 and 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, socialising 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 and 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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Añadir al carritoPaperback or Softback. Condición: New. AI and Common Sense: Ambitions and Frictions. Book.
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Añadir al carritoCondición: New. Martin W. Bauer is Professor of Social Psychology and Research Methodology at the London School of Economics and Political Science (LSE). He investigates common sense in relation to science and emerging technologies in the internationa.
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Añadir al carritoTaschenbuch. Condición: Neu. AI and Common Sense | Ambitions and Frictions | Martin W. Bauer (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Routledge | EAN 9781032626178 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032626178 ISBN 13: 9781032626178
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Añadir al carritoPaperback. Condición: New. 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 and 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, socialising 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 and 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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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 and 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, socialising 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 and Culture Studies, human-computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning. 288 pp. Englisch.
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 and 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, socialising 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 and Culture Studies, human-computer interaction with an interest in the post-human, and programmers tackling the contextual questions of machine learning.