Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem.

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Minds, machines, and Searle

Any attempt to explain the mind by building machines with minds must confront the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is “everything” a body with a mind can do? Turing's original “pen-pal” version of the Turing Test (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls instead for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body.

Other bodies, other minds: a machine incarnation of an old philosophical problem

Parsing the Turing Test is a landmark exploration of both the philosophical and methodological issues surrounding the search for true artificial intelligence. Will computers and robots ever think and communicate the way humans do? When a computer crosses the threshold into self-consciousness, will it immediately jump into the Internet and create a World Mind? Will intelligent computers someday recognize the rather doubtful intelligence of human beings? Distinguished psychologists, computer scientists, philosophers, and programmers from around the world debate these weighty issues and, in effect, the future of the human race in this important volume.

Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer

................................................................................................................................................... iii Acknowledgments .................................................................................................................................... v Table of Figures ...................................................................................................................................... xii “Theories Thus Become Instruments”: An Introduction ......................................................... 1 1. “The Present Dilemma in Philosophy” ................................................................................................. 1 2. Historiographical Considerations ......................................................................................................... 6 3. Bounded Rationality in History .......................................................................................................... 17 4. The Revolt Against Certitude ............................................................................................................. 25 5. The Plan of the Dissertation ............................................................................................................... 31 “Many Curious Experiments”: From Natural Theology to Natural Selection ..................... 36

A Method Only: The Evolving Meaning of Science in the United States, 1830-1910

Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them.

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Artificial intelligence and personal identity

Programs, language understanding, and searle

Recent philosophical accounts of progress in science describe science as essentially a problem-solving activity. Laudan's Progress and Its Prob? lems,1 the most recent account to achieve prominence, holds that the solved problem is the unit of scientific progress. This view is more extreme than those of Kuhn and Lakatos, and is fundamentally at odds with such accounts as that of Toulmin. But Laudan's view is false, since there are examples of scientific progress in the absence of solving any previously recognised problem. The rise of functionalism in psychology, centered at the University of Chicago at the end of the nineteenth century, is the example I discuss in what follows. For Kuhn, "Paradigms gain their status because they are more success? ful than their competitors in solving a few problems that the group of practitioners has come to regard as acute." But "To be more successful is not... to be either completely successful with a single problem or notably successful with any large number. The success of a paradigm... is at the start largely a promise of success.. ."2 Lakatos requires that "each step of a research programme be consistently content-increasing" for the pro? gramme to be progressive, but only that "at least every now and then the increase in content should be seen to be retrospectively corroborated".3 Toulmin characterises scientific progress rather as the production and se? lection of new concepts.4 Both Kuhn and Toulmin have even pointed out historical examples of what they take to be scientific progress in the ab? sence of solutions to previously-recognised problems. Kuhn asserts. "No fundamental astronomical discovery, no new sort of astronomical observa? tion, persuaded Copernicus of ancient astronomy's inadequacy or of the necessity for change;" and his new theory initially offered no improvement in problem-solving effectiveness, and resolved no external conceptual problems. For Toulmin the biological innovation of identifying genes with DNA was successfully introduced in the absence of any crisis in the field of biology, simply "from a desire to bring physical and biological under? standing into closer relation."5 Somehow this point gets overlooked or its importance underestimated. So strong is our natural inclination to sup

https://www.jstor.org/stable/info/20010792

Lawrence Richard Carleton

Papers

Programs, language understanding, and searle

The rise of Chicago functionalism