ZusammenfassungDer Wettkampf zwischen dem Schachcomputer Deep Blue und dem damaligen Schachweltmeister Garri Kasparov im Jahr 1997 war ein medienwirksam inszeniertes Spektakel. Darüber hinaus war das Schachspiel aber, wie auch andere Spiele, ein Testfeld der Künstlichen Intelligenz-Forschung. Der Sieg Deep Blues wurde einerseits als „Meilenstein“ der KI-Forschung bezeichnet, andererseits als „Sackgasse“, da die Überlegenheit des Schachcomputers auf reiner Rechengewalt beruhe und nichts mit „wirklicher KI“ zu tun habe.Der Beitrag fragt nach den Prämissen dieser unterschiedlichen Interpretationen und ordnet Deep Blue und seine (...) Weise des Schachspielens in die Geschichte der KI ein. Dafür ist auch die Analyse der zugrundeliegenden Begriffe von Denken notwendig. Schließlich plädiert der Aufsatz dafür, von unterschiedlichen Denkweisen von Mensch und Computer auszugehen und statt der Grundsatzdiskussionen um den Begriff des Denkens nach den Konsequenzen der menschlich-maschinellen Arbeitsteilung zu fragen. (shrink)

Philosophy and Computing explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of quantam computers? Is the Internet (...) an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intelligence program? Philosophy and Computing is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises. (shrink)

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gdel, Popper, Penrose, and Lucas, to address the hotly-contested question of whether computers might (...) become intellectually superior to human beings. (shrink)

The Critique of the Power of Judgment (a more accurate rendition of what has hitherto been translated as the Critique of Judgment) is the third of Kant's great critiques following the Critique of Pure Reason and the Critique of Practical Reason. This entirely new translation of Kant's masterpiece follows the principles and high standards of all other volumes in The Cambridge Edition of the Works of Immanuel Kant. This volume includes: for the first time the indispensable first draft of Kant's (...) introduction to the work; the only English edition notes to the many differences between the first (1790) and second (1793) editions of the work; and relevant passages in Kant's anthropology lectures where he elaborated on his aesthetic views. All in all this new edition offers the serious student of Kant a dramatically richer, more complete and more accurate translation. (shrink)

What is the impact of information and communication technologies (ICTs) on the human condition? In order to address this question, in 2012 the European Commission organized a research project entitled The Onlife Initiative: concept reengineering for rethinking societal concerns in the digital transition. This volume collects the work of the Onlife Initiative. It explores how the development and widespread use of ICTs have a radical impact on the human condition.

In _Reliable Reasoning_, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory, the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade the (...) problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.