Online research in philosophy

Contributors: Rodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin...

Buchanan and Darden have provided compelling reasons why philosophers of science concerned with the nature of scientific discovery should be aware of current work in artificial intelligence. This paper contends that artificial intelligence is even more than a source of useful analogies for the philosophy of discovery: the two fields are linked by interfield connections between philosophy of science and cognitive psychology and between cognitive psychology and artificial intelligence. Because the philosophy of discovery must pay attention to the psychology of practicing scientists, and because current cognitive psychology adopts a computational view of mind with AI providing the richest models of how the mind works, the philosophy of discovery must also concern itself with AI models of mental operations. The relevance of the artificial intelligence notion of a frame to the philosophy of discovery is briefly discussed.

Nowadays, it has become almost a matter of course to say that human mind is like a computer. Folks in all walks of life talk of ‘programming’ themselves, ‘multi-tasking’, running different ‘operating systems’, and sometimes of ‘crashing’ and being ‘rebooted’. Few who have used computers have not been touched by the appeal of the idea that our inner workings somehow resemble computing machines. The success of modern day computational psychology appears to bears witness to the explanatory and predictive pay-off in positing a connection between computers and minds. Among its other virtues, the computational framework has rendered theorising about inner processes respectable, it has provided a unified and naturalistic arena in which to conduct debates about psychological models, and it provides the tantalising possibility of accurately simulating and reproducing psychological processes. There is almost universal agreement that the mind is in some sense like a computer. But consensus quickly ends once we ask how the mind is supposed to be like a computer. Even after more than thirty years of model building, and a wealth of empirical work, surprisingly little consensus exists in cognitive science on the correct answer to this question. What is more, disagreement tends to lie at a relatively deep level. There is little agreement about the content of the notion of computation, what it means for a physical system, like the brain, to implement a computation, the broad-brush computational architecture of the mind, or how computational models fit with other models of the mind, such as control theoretic models, statistical models, or dynamical systems theory.

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 (artificial intelligence). 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..

This book is concerned with the teaching and understanding of history with the aid of a computer. It draws on ideas and experience from a wide range of disciplines including philosophy, social science, artificial intelligence and computer science.

In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the Philosophy Department’s AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature.

This collection by a distinguished group of philosophers, psychologists, and physiologists reflects an interdisciplinary approach to the central question of cognitive science: how do we model the mind? Among the topics explored are the relationships (theoretical, reductive, and explanatory) between philosophy, psychology, computer science, and physiology; what should be asked of models in science generally, and in cognitive science in particular; whether theoretical models must make essential reference to objects in the environment; whether there are human competences that are resistant, in principle, to modelling; whether simulated thinking and intentionality are really thinking and intentionality; how semantics can be generated from syntactics; the meaning of the terms "representations" and "modelling;" whether the nature of the "hardware" matters; and whether computer models of humans are "dehumanizing." Contributors include Donald Davidson, Daniel C. Dennett, Margaret A. Boden, Adam Morton, Dennis Noble, T. Poggio, Colin Blakemore, K.V. Wilkes, P.N. Johnson-Laird, and Jonathan St. B.T. Evans.

Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper briefly describes a computational model of problem solving and learning that has been used to simulate several kinds of scientific reasoning.

Since 1991 the author has been Professor of Artificial Intelligence and Cognitive Science in the School of Computer Science at the University of Birmingham, UK.