Wittgensteins Remarks On The Foundations Of Ai

Wittgenstein's Remarks on the Foundations of AI is a valuable contribution to the study of Wittgenstein's theories and his controversial attack on artifical intelligence, which successfully crosses a number of disciplines, including philosophy, psychology, logic, artificial intelligence and cognitive science, to provide a stimulating and searching analysis.

This collection brings together work on the relevance of Wittgenstein’s philosophy to the field of Artificial Intelligence (AI). Over two volumes, our contributors cover a wide range of topics from different disciplinary approaches. In this Volume (I), contributions are centred on two major themes in the philosophy of AI: questions of mind and language. Contributions include chapters on AI thought, intentionality, logic and language, as well as the relationship between Wittgenstein’s thought and Turing’s.

Volume II This collection brings together work on the relevance of Wittgenstein’s philosophy to the field of Artificial Intelligence (AI). Over two volumes, our contributors cover a wide range of topics from different disciplinary approaches. In this Volume (II), contributions are centred on two major themes in the philosophy of AI: questions of value and governance. Contributions include chapters on both ethics and aesthetics and AI, as well as questions of the governance of AI systems, including legal and policy issues.

“Tell me," Wittgenstein once asked a friend, "why do people always say, it was natural for man to assume that the sun went round the earth rather than that the earth was rotating?" His friend replied, "Well, obviously because it just looks as though the Sun is going round the Earth." Wittgenstein replied, "Well, what would it have looked like if it had looked as though the Earth was rotating?” What would it have looked like if we looked at all sciences from the viewpoint of Wittgenstein’s philosophy? Wittgenstein is undoubtedly one of the most influential philosophers of the twentieth century. His complex body of work has been analysed by numerous scholars, from mathematicians and physicists, to philosophers, linguists, and beyond. This volume brings together some of his central perspectives as applied to the modern sciences and studies the influence they may have on the thought processes underlying science and on the world view it engenders. The contributions stem from leading scholars in philosophy, mathematics, physics, economics, psychology and human sciences; all of them have written in an accessible style that demands little specialist knowledge, whilst clearly portraying and discussing the deep issues at hand.

Can humans and artificial intelligences share concepts and communicate? One aim of Making AI Intelligible is to show that philosophical work on the metaphysics of meaning can help answer these questions. Cappelen and Dever use the externalist tradition in philosophy of to create models of how AIs and humans can understand each other. In doing so, they also show ways in which that philosophical tradition can be improved: our linguistic encounters with AIs revel that our theories of meaning have been excessively anthropocentric. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (e.g. creditworthiness, recidivism, cancer, and combatants.) If AIs can share our concepts, that will go some way towards justifying this reliance on AI. The book can be read as a proposal for how to take some first steps towards achieving interpretable AI. Making AI Intelligible is of interest to both philosophers of language and anyone who follows current events or interacts with AI systems. It illustrates how philosophy can help us understand and improve our interactions with AI.

An historical analysis of main topics of Wittgenstein's work. Part 1 deals with the "game" of mathematics. Part 2 discusses Wittgenstein's development up to 1930 and Part 3 looks at philosophical and psychological problems arising from the possiblity of artificial intelligence.

In this volume we witness Wittgenstein in the act of composing and experimenting with his new visions in philosophy. The book includes key explanations of the origin and background of these previously unknown manuscripts. It investigates how Wittgenstein’s philosophical thought-processes are revealed in his dictation to, as well as his editing and revision with Francis Skinner, in the latter’s role of amanuensis. The book displays a considerable wealth and variety of Wittgenstein’s fundamental experiments in philosophy across a wide array of subjects that include the mind, pure and applied mathematics, metaphysics, the identities of ordinary and creative language, as well as intractable problems in logic and life. He also periodically engages with the work of Newton, Fermat, Russell and others. The book shows Wittgenstein strongly battling against the limits of understanding and the bewitchment of institutional and linguistic customs. The reader is drawn in by Wittgenstein as he urges us to join him in his struggles to equip us with skills, so that we can embark on devising new pathways beyond confusion. This collection of manuscripts was posted off by Wittgenstein to be considered for publication during World War 2, in October 1941. None of it was published and it remained hidden for over two generations. Upon its rediscovery, Professor Gibson was invited to research, prepare and edit the Archive to appear as this book, encouraged by Trinity College Cambridge and The Mathematical Association. Niamh O’Mahony joined him in co-editing and bringing this book to publication.

In the 11 contributions, theorists historically associated with each position identify the basic tenets of their position.Have the classical methods and ideas of AI outlived their usefulness? Foundations of Artificial Intelligence critically evaluates the fundamental assumptions underpinning the dominant approaches to AI. In the 11 contributions, theorists historically associated with each position identify the basic tenets of their position. They discuss the underlying principles, describe the natural types of problems and tasks in which their approach succeeds, explain where its power comes from, and what its scope and limits are. Theorists generally skeptical of these positions evaluate the effectiveness of the method or approach and explain why it works - to the extent they believe it does - and why it eventually fails.ContentsFoundations of AI: The Big Issues, D. Kirsh - Logic and Artificial Intelligence, N. J. Nilsson - Rigor Mortis: A Response to Nilsson's 'Logic and Artificial Intelligence, ' L. Birnbaum - Open Information Systems Semantics for Distributed Artificial Intelligence, C. Hewitt - Social Conceptions of Knowledge and Action: DAI Foundations and Open Systems Semantics, L. Gasser - Intelligence without Representation, R. A. Brooks - Today the Earwig, Tomorrow Man? D. Kirsh - On the Thresholds of Knowledge, D. B. Lenat, E. A. Feigenbaum - The Owl and the Electric Encyclopedia, B. C. Smith - A Preliminary Analysis of the Soar Architecture as a Basis for General Intelligence, P. S. Rosenbloom, J. E. Laird, A. Newell, R. McCarl - Approaches to the Study of Intelligence, D. A. Norman

Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (for example, creditworthiness, recidivism, cancer, and combatants). If AIs can share our concepts, that will go some way towards justifying this reliance on AI. This ground-breaking study offers insight into how to take some first steps towards achieving Interpretable AI.