Can Machines Think?

This paper presents a unifying framework for the study of artificial life, intelIigence and reality. By providing this framework we can give a clear and concise introduction to the fundamental arguments of all three artificial sciences and facilitate the translation of arguments from any one domain to the other two. The framework is based on a variant of functionalism that does not exclude the role of the observer.

This essay replies to some representative authors, canonic indeed, of the opposition to artificial intelligence (AI) : Hubert L. DREYFUS, with his study, systematic enough in its time, What Computers can't do. His phenomenological critic meets a numerous audience, even among computer scientists. Joseph WEIZENBAUM, research worker in artificial intelligence himself, gone over to the opposition with his book Computer Power and Human Reason. His objections are classically moral and anti-techno-scientific. The philosopher John R. SEARLE, whose book Mind, Brain and (...) Science reminds us most of the resistances of linguistic scientists against artificial intelligence, turning the real difficulties of formalization of language in ontological impossibilities. Terry WINOGRAD, another research worker of the early times of AI, and Fernando FLORES, former economy minister of Chile in 1973, both rebels against the computerization of the human's world - thanks to the phenomenological post-hedeggerian way of think -, in their common work, Understanding Computers and Cognition. -/- Phenomenological philosophy has found the perfect target with AI, which embodies the image of science for the post-romantic thinking. This image can be perfectly symbolized by the frescos, choosen for the cover of this very book, situated on the walls of the Sorbonne University Library stairs! Againts the arguments of this running down romantical thinking, this book make the effort to answer on every point. But this work invites us to give up the intellectual illusions too, against which the method of cognitive sciences and information processing can be used as riddle. Incidentally, the approach of this critical method itself can be considered too as an automatic knowledge management try. (shrink)

Artificial intelligence is a possibility that should not be ignored in any serious thinking about the future, and it raises many profound issues for ethics and public policy that philosophers ought to start thinking about. This article outlines the case for thinking that human-level machine intelligence might well appear within the next half century. It then explains four immediate consequences of such a development, and argues that machine intelligence would have a revolutionary impact on a wide range of the social, (...) political, economic, commercial, technological, scientific and environmental issues that humanity will face over the coming decades. (shrink)

The association of Wittgenstein’s name with the notion of artificial intelligence is bound to cause some surprise both to Wittgensteinians and to people interested in artificial intelligence. After all, Wittgenstein died in 1951 and the term artificial intelligence didn’t come into use until 1956 so that it seems unlikely that one could have anything to do with the other. However, establishing a connection between Wittgenstein and artificial intelligence is not as insuperable a problem as it might appear at first glance. (...) While it is true that artificial intelligence as a quasi-distinct discipline is of recent vintage, some of its concerns, especially those of a philosophical nature, have been around for quite some time. At the birth of modern philosophy we find Descartes wondering whether it would be possible to create a machine that would be phenomenologically indistinguishable from.. (shrink)

Problem-solving software that is not-necessarily infallible is central to AI. Such software whose correctness and incorrectness properties are deducible by agents is an issue at the foundations of AI. The Comprehensibility Theorem, which appeared in a journal for specialists in formal mathematical logic, might provide a limitation concerning this issue and might be applicable to any agents, regardless of whether the agents are artificial or natural. The present article, aimed at researchers interested in the foundations of AI, addresses many questions (...) related to that theorem, including differences between it and results of Gödel and Turing that have sometimes played key roles in Minds and Machines articles. This study also suggests that—if one is willing to assume a thesis due to Donald Knuth—the Comprehensibility Theorem is the first mathematical theorem implying the impossibility of any AI agent or natural agent—including a not-necessarily infallible human agent—satisfying a rigorous and deductive interpretation of the self-comprehensibility challenge. Some have pointed out the difficulty of self-comprehensibility, even according to presumably a less rigorous interpretation. This includes Socrates, who considered it to be among the most important of intellectual tasks. Self-comprehensibility in some form might be essential for a kind of self-reflection useful for self-improvement that might enable some agents to increase their success. We use the methods of applied mathematics, rather than philosophy, although some topics considered could be of interest to philosophers. (shrink)

Speaking is conceiving, not translating what has already been represented inside the brain in a hidden way.

One of the highly contraversial discussions in philosophy of mind is equivalence of human being mind and machines. Here we show that no one could prove that, in certain he is a machine.