What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

Mindware: An Introduction to the Philosophy of Cognitive Science invites readers to join in up-to-the-minute conceptual discussions of the fundamental issues, problems, and opportunities in cognitive science. Written by one of the most renowned scholars in the field, this vivid and engaging introductory text relates the story of the search for a cognitive scientific understanding of mind. This search is presented as a no-holds-barred journey from early work in artificial intelligence, through connectionist (artificial neural network) counter-visions, and on to neuroscience, (...) artificial life, dynamics, and robotics. The journey ends with some wide-ranging and provocative speculation about the complex coadaptive dance between mind, culture, and technology. Each chapter opens with a brief sketch of a major research tradition or perspective, followed by short yet substantial critical discussions dealing with key topics and problems. Ranging across both standard philosophical territory and the landscape of cutting-edge cognitive science, Clark highlights challenging issues in an effort to engage readers in active debate. Topics covered include mental causation; machine intelligence; the nature and status of folk psychology; the hardware/software distinction; emergence; relations between life and mind; the nature of perception, cognition, and action; and the continuity (or otherwise) of high-level human intelligence with other forms of adaptive response. Numerous illustrations, text boxes, and extensive suggestions for further reading enhance the text's utility. Helpful appendices provide background information on dualism, behaviorism, identity theory, consciousness, and more. An exceptional text for introductory and more advanced courses in cognitive science and the philosophy of mind, Mindware is also essential reading for anyone interested in these fascinating and ever-changing fields. (shrink)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...) three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

Behaviourism is the view that preferences, beliefs, and other mental states in social-scientific theories are nothing but constructs re-describing people's behaviour. Mentalism is the view that they capture real phenomena, on a par with the unobservables in science, such as electrons and electromagnetic fields. While behaviourism has gone out of fashion in psychology, it remains influential in economics, especially in ‘revealed preference’ theory. We defend mentalism in economics, construed as a positive science, and show that it fits best scientific practice. (...) We distinguish mentalism from, and reject, the radical neuroeconomic view that behaviour should be explained in terms of brain processes, as distinct from mental states. (shrink)

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do.The basic assumption is that vision depends on the eye which is connected to the brain. The (...) author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision—and what this book is about. (shrink)

Preface 1 Introduction: The Persistence of the Attitudes 2 Individualism and Supervenience 3 Meaning Holism 4 Meaning and the World Order Epilogue Creation Myth Appendix Why There Still Has to be a Language of Thought Notes References Author Index.

Introduction: representationalismMost theorists of cognition endorse some version of representationalism, which I will understand as the view that the human mind is an information-using system, and that human cognitive capacities are representational capacities. Of course, notions such as ‘representation’ and ‘information-using’ are terms of art that require explication. As a first pass, representations are “mediating states of an intelligent system that carry information” (Markman and Dietrich 2001, p. 471). They have two important features: (1) they are physically realized, and so (...) have causal powers; (2) they are intentional, in other words, they have meaning or representational content. This presumes a distinction between a representational vehicle—a physical state or structure that has causal powers and is responsible for producing behavior—and its content. Consider the following characterization of a device that computes the addition functionReaders will recognize the similarity t. (shrink)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

David Marr's three-level method for completely understanding a cognitive system and the importance he attaches to the computational level are so familiar as to scarcely need repeating. Fewer seem to recognize that Marr defends his famous method by criticizing the “reductionistic approach.” This sets up a more interesting relationship between Marr and reductionism than is usually acknowledged. I argue that Marr was correct in his criticism of the reductionists of his time—they were only describing, not explaining. But a careful metascientific (...) account of reductionistic neuroscience over the past two decades reveals that Marr's criticisms no longer have force. Contemporary neuroscience now explains cognition directly, although in a fashion—causal-mechanistically—quite different than Marr recommended. So while Marr was correct to reject the reductionism of his day and offer an alternative method for genuinely explaining cognition, contemporary cognitive scientists now owe us a new defense of Marr's famous method and the advantages of its explanations over the type now pursued successfully in current reductionist neuroscience. There are familiar reasons for thinking that this debt will not be paid easily. (shrink)

In the past century, nearly all of the biological sciences have been directly affected by discoveries and developments in genetics, a fast-evolving subject with important theoretical dimensions. In this rich and accessible book, Paul Griffiths and Karola Stotz show how the concept of the gene has evolved and diversified across the many fields that make up modern biology. By examining the molecular biology of the 'environment', they situate genetics in the developmental biology of whole organisms, and reveal how the molecular (...) biosciences have undermined the nature/nurture distinction. Their discussion gives full weight to the revolutionary impacts of molecular biology, while rejecting 'genocentrism' and 'reductionism', and brings the topic right up to date with the philosophical implications of the most recent developments in genetics. Their book will be invaluable for those studying the philosophy of biology, genetics and other life sciences. (shrink)

Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.

To make progress on the problem of consciousness, we have to confront it directly. In this paper, I first isolate the truly hard part of the problem, separating it from more tractable parts and giving an account of why it is so difficult to explain. I critique some recent work that uses reductive methods to address consciousness, and argue that such methods inevitably fail to come to grips with the hardest part of the problem. Once this failure is recognized, the (...) door to further progress is opened. In the second half of the paper, I argue that if we move to a new kind of nonreductive explanation, a naturalistic account of consciousness can be given. I put forward my own candidate for such an account: a nonreductive theory based on principles of structural coherence and organizational invariance, and a double-aspect theory of information. (shrink)

The main message of the paper is that there is a disconnect between what many philosophers of mind think of as the scientific practice of reductive or reductionist explanation, and what the most relevant scientific work is actually like. I will sketch what I see as a better view, drawing on various ideas in recent philosophy of science. I then import these ideas into the philosophy of mind, to see what difference they make.1 At the end of the paper I (...) address a possible objection: the familiar package of ideas I reject in the philosophy of science should not be lightly discarded, because other popular views on fundamental issues depend on positions that I want to reject. I reply that those apparently attractive further ideas are not worth holding onto. (shrink)

I argue that the traditional distinction between hard and easy problems rests on some inaccurate views about explanation in cognitive science. We should distinguish the question of what gives rise to a phenomenon (the generative question) from what that phenomenon is (the nature question). In many cases throughout the special sciences, an answer to the generative question will not shed significant light on the nature question, nor will it eliminate all conceptually possible alternatives. Meanwhile, the apparent easiness of explaining consciousness (...) functions is due to an oversimplification of these problems akin to what is often called 'substitution bias'. Once these issues are clarified, we see that the hard problem is not so hard, the easy problems are not so easy, and the meta-problem is neither a traditional easy problem nor should we expect it to play a special role in illuminating the natural basis of consciousness. (shrink)