Philosophy of Artificial Intelligence

Donald Gillies studied mathematics and philosophy at Cambridge as an undergraduate. In 1966 he began graduate studies in Professor Sir Karl Popper's department at the London School of Economics, and he completed his PhD on the Foundations of Probability in 1970 with Professor Imre Lakatos as supervisor. From 1968 to 1971, he was a Fellow of King's College, Cambridge, and thereafter followed a career in London University until his retirement in 2009. He is currently Emeritus Professor of Philosophy of Science and Mathematics at University College London. Donald Gillies developed an interest in the philosophy of AI in the 1990s, participating in two AI projects as well as organizing two further projects in the philosophy of AI.Marco Gillies is a Professor of Computing at Goldsmiths, University of London and an expert on AI virtual humans for virtual reality. After completing his PhD at the University of Cambridge he did post-doctoral work where, in 2005, he published one of the first papers to apply machine learning to virtual humans. At Goldsmiths, a creative university famous for the Young British Artists Movement, he has helped pioneer the application of AI to the arts, including founding the Creative Computing Degree in 2008. His work since then has focused on human-centre and interdisciplinary AI including working with dancers, psychologists, game developers, clinicians and education researchers. His current work focuses on augmenting LLMs with the body language for natural communication.

'From its opening contention that Turing was a philosopher-mathematician like Descartes before him, to its discussions of the epistemology of machine learning, and the aesthetics and ethics of AI, this excellent book remains informative, thoughtful, and nuanced throughout. Gillies and Gillies clearly show how illuminating and productive the interaction between philosophy and artificial intelligence has been historically, and will continue to be.' - Professor Brian Ball, Computational Philosophy Lab, Northeastern University London

• Acknowledgements Preface Part 1: Turing and the Project for AI (1950s and 1960s) 1. Turing, Wittgenstein, and the Imitation Game 2. The First Attempts at AI Part 2: The First Successes of AI and the Development of the Logic-Based Approach (c. 1970 to c. 2010) 3. The Success of Expert Systems 4. Philosophical Views of Induction 5. Induction and Logic-based Machine Learning 6. Successes and Criticisms of the Logic-Based Approach 7. The Dominance of Probability and Statistics in AI from the late 1990s to the early 2010s Part 3: Neural Networks and Deep Learning 8. The Development of Neural Networks 9. Implications of Deep Learning for the Philosophy of Induction and Knowledge 10. Generative AI, Literature and the Visual Arts 11. Ethical Problems of Contemporary AI Part 4: The Future of AI and Current Challenges 12. Is an AI Superintelligence Possible? 13. The Real Danger from AI: Mass Unemployment. Index