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The two volumes on Wittgenstein and AI aim to trace and suggest Wittgensteinian influences in some of the most cutting-edge areas of research in Artificial Intelligence (such as Computation, Machine Learning, Natural Language Processing and the use of automation in legal settings). The collection is driven by an essentially interdisciplinary approach, featuring articles from philosophers, computer scientists and legal scholars, writing on a broad range of topics in AI. The chapters across these two volumes are grouped into two sets of themes: Mind and Language and Value and Governance .…mehr
The two volumes on Wittgenstein and AI aim to trace and suggest Wittgensteinian influences in some of the most cutting-edge areas of research in Artificial Intelligence (such as Computation, Machine Learning, Natural Language Processing and the use of automation in legal settings). The collection is driven by an essentially interdisciplinary approach, featuring articles from philosophers, computer scientists and legal scholars, writing on a broad range of topics in AI.
The chapters across these two volumes are grouped into two sets of themes: Mind and Language and Value and Governance. These themes correspond to two major areas of research in the philosophical study of AI: the computational mind and the ethics of artificial intelligence. These volumes make a significant and unprecedented contribution to the question of what Wittgenstein's philosophy can offer to the ever-growing field of AI. It aims to provide insight for both philosophers and non-philosophers alike, offering reflection on the significance of Wittgenstein's work for AI, and on the implications of advancements in AI technology for Wittgenstein's philosophy and philosophy influenced by Wittgenstein.
VOLUME I: Wittgenstein and AI (Volume I): Mind and Language. This volume includes chapters on Wittgenstein and Turing's views on AI, the question of whether machines can think, intentionality, AI language models, analogical reasoning and logic.
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Autorenporträt
Brian Ball is Associate Professor in Philosophy at Northeastern University London. His research spans a range of areas, notably the philosophy of mind, language and artificial intelligence, and he has taught the early history of analytic philosophy, including Wittgenstein.
Alice Helliwell is Assistant Professor in Philosophy at Northeastern University London. Her research is focused on computational creativity and AI art, including questions of aesthetics and ethics.
Alessandro Rossi is Assistant Professor in Philosophy at Northeastern University London. His work focuses on logic, metaphysics and the areas in which they intersect.
Inhaltsangabe
Introduction Brian Ball, Alice C. Helliwell and Alessandro Rossi Chapter 1. Wittgenstein and Turing on AI: Myth Versus Reality, Diane Proudfoot Chapter 2. Between Wittgenstein and Turing: Enactive Embodied Thinking Machines, Tomi Kokkonen and Ilmari Hirvonen Chapter 3. Wittgenstein, Psychological Language and AI, Arturo Vázquez Hernández Chapter 4. The Metonymical Trap, Éloïse Boisseau Chapter 5. The Forms of Artificially Intelligent Life: Brandom, Chomsky and Wittgenstein on the Possibility of Strong-AI, Laith Abdel-Rahman Chapter 6. Black Boxes, Beetles and Beasts, Ian Ground Chapter 7. Language Models and the Private Language Argument: A Wittgensteinian Guide to Machine Learning, Giovanni Galli Chapter 8. Simplification without Falsification: The Problem of Relevance in Logic and AI, Oskari Kuusela Chapter 9. Modelling Analogical Reasoning: One-Size-Fits-All?, Ioannis Votsis Notes on Contributors Index
Introduction Brian Ball, Alice C. Helliwell and Alessandro Rossi Chapter 1. Wittgenstein and Turing on AI: Myth Versus Reality, Diane Proudfoot Chapter 2. Between Wittgenstein and Turing: Enactive Embodied Thinking Machines, Tomi Kokkonen and Ilmari Hirvonen Chapter 3. Wittgenstein, Psychological Language and AI, Arturo Vázquez Hernández Chapter 4. The Metonymical Trap, Éloïse Boisseau Chapter 5. The Forms of Artificially Intelligent Life: Brandom, Chomsky and Wittgenstein on the Possibility of Strong-AI, Laith Abdel-Rahman Chapter 6. Black Boxes, Beetles and Beasts, Ian Ground Chapter 7. Language Models and the Private Language Argument: A Wittgensteinian Guide to Machine Learning, Giovanni Galli Chapter 8. Simplification without Falsification: The Problem of Relevance in Logic and AI, Oskari Kuusela Chapter 9. Modelling Analogical Reasoning: One-Size-Fits-All?, Ioannis Votsis Notes on Contributors Index
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