Online research in philosophy

The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help us develop new ways of understanding the mind as a whole. Craig DeLancey shows that our best philosophical and scientific understanding of the emotions provides essential insights on key issues in the philosophy of mind and artificial intelligence: intentionality, aesthetics, rationality, action theory, moral psychology, consciousness, ontology and autonomy. He provides an accessible overview of the science of emotion, explaining with minimal jargon the technical issues that arise. The book also offers new ways to understand the mind, suggesting that it is autonomy--and not cognition--that should be the core problem of the philosophy of mind, cognitive science, and artificial intelligence. DeLancey argues that the philosophy of mind has been held back by an impoverished view of naturalism, and that a proper appreciation of the complexity of the sciences of mind, readily demonstrated by the science of emotion, will overcome this. Passionate Engines provides a unique, contemporary view of the link between science and philosophy, offering a bold new way of looking at the mind for scholars in a range of disciplines. Its accessible and refreshing approach will appeal to philosophers, psychologists, computer scientists, others in the cognitive disciplines, and lay people interested in the mind.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence.

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.

John Haugeland's Mind design and Mind design II are organized around the idea that the fundamental idea of cognitive science is that, “intelligent beings are semantic engines — in other words, automatic formal systems with interpretations under which they consistently make sense”. The goal of artificial intelligence research, or the problem of “mind design” as Haugeland calls it, is to develop computers that are in fact semantic engines. This paper canvasses the changes in artificial intelligence research reflected in the different selections of essays found in each volume. While Mind design II is a worthy successor to Mind design, there are some notable developments in artificial intelligence which suggest that seemingly intelligent behavior need not be guided by semantic engines at all.

Focuses on distinguished quotations representing the best thinking in philosophy, psychology, and artificial intelligence from classical civilization to ...

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.

This book deals with the major philosophical issues in the theoretical framework of Artificial Intelligence (AI) in particular and cognitive science in general.