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Philosophy of Artificial Intelligence: Optimistic and Pessimistic Views [Kõva köide]

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  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, 2 Illustrations, color; 6 Illustrations, black and white; IX, 278 p. 8 illus., 2 illus. in color., 1 Hardback
  • Sari: Studies in Applied Philosophy, Epistemology and Rational Ethics 75
  • Ilmumisaeg: 10-Oct-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031996534
  • ISBN-13: 9783031996535
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  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, 2 Illustrations, color; 6 Illustrations, black and white; IX, 278 p. 8 illus., 2 illus. in color., 1 Hardback
  • Sari: Studies in Applied Philosophy, Epistemology and Rational Ethics 75
  • Ilmumisaeg: 10-Oct-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031996534
  • ISBN-13: 9783031996535
Teised raamatud teemal:

This book discusses, from various angles, the new trends in Artificial intelligence (AI), given the current so-called golden period that this field is undergoing. Recent progress in machine learning applications, such as image recognition and natural language processing, have raised the level of optimism that one day an AI can exhibit genuine intelligence. In games like Go and chess, human players have been surpassed by computers. As during earlier periods of AI optimism, there is increasing talk about domain-general artificial intelligence being possible. But just how intelligent are current AI systems, and what can we expect in the future? What will a world with an increasingly important role for AI systems be like? Which shifts with regard to the concept of intelligence as well as the societal order do these developments have? Authors with different views on the present and future of AI research and the role of AI in society discuss these questions, illustrating the diversity of discussions on AI and the many ways our lives are influenced by it.

.- Conceptual Disconnect: the prospect of concept-based AI
explanations.- Understanding the relationship between fairness and
explainability for algorithmic decision-making.- Inductive assumptions in ML
always required, often ignored.- Beyond Control: Will to Power in AI, etc.