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• Consciousness: An Introduction
• About Dr.d
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And when you leave out the mental and the social, you've just kissed psychology and the rest of the social sciences good-bye. That is because psychology isn't just positioned between the biological sciences and the social sciences. Psychology is both a biological science and a social science.

That is part of its beauty and it is part of its tension. In her recent memoir, entitled Touching a Ne rve , Patri c ia Churchland writes that she turned from "pure" philosophy to neuroscience when she realized that "if mental processes are actually processes of the brain, then you cannot understand the mind without understanding how the brain works". If mental processes are actually processes of the brain, and brain-processes are electrochemical processes, then she should have skipped over the neuroscience and gone straight to physics.

And so should everyone else, including historians and literary critics. But if historians and literary critics don't have to be neuroscientists, why is this an obligation for psychologists? Interestingly, it's possible to be a materialist monist but not a reductionist. D avidson agreed that the world consists only of material entities, thus rejecti n g Descartes' substance dualism ; and therefore, all events in the world are physical events. At the same time, D avidson denied that mental events, such as believing and desiring, could be explained in purely physical terms.

So, his theory is ontologically materialistic bec ause t he world consists only of physical entities but explanatorily dualistic because the network of causal relations is different for mental events than for physical events. In fact, the histories of psychology and of neuroscience show exactly the opposite of what the Churchlands discuss.

In every case, whether it is concerned with visual perception or with memory or anything else, theoretical developments in neuroscience have followed theoretical developments in psychology, not the other way around. Consider, for example, the amnesic syndrome, as exemplified by patient H. But what exactly is that role? The fact is, our interpretation of the amnesic syndrome, and thus of hippocampal function, has changed as our conceptual understanding of memory has changed. Here, clearly, neuroscientific data hasn't done much constraining: the psychological interpretation of this neurological syndrome, and its implication for cognitive theory, changed almost wantonly, as theoretical fashions changed in psychology, while the neural evidence stayed quite constant.

Here's another example: what might be called The Great Mental Imagery Debate -- that is, the debate over the representation of mental imagery -- or, more broadly, whether there are two distinct forms of knowledge representation in memory, propositional verbal and perceptual imagistic.

So, in the final analysis, neuroscientific evidence was neither necessary nor sufficient to resolve the theoretical dispute over the nature of knowledge representation. And, of course, interpretation of PET and fMRI brain images requires a correct description of the subject's task at the psychological level of analysis. Without a correct psychological theory in hand, neuroscience can't find out anything about the neural substrates of mental life, because they don't know what the neural substrates are neural substrates of.

As someone once put it unfortunately, not me :. But I have said Kihlstrom, :. Other cognitive scientists prefer token identity theories. They don't seek correspondence between mental events and brain events. Instead, they classify mental events in terms of their functional roles and ignore the physical systems in which these functions are implemented. Philosophical functionalism is closely related to psychological and philosophical behaviorism, in that all that matters are the functional relations between inputs and outputs -- what goes on in between doesn't much matter.

This philosophical functionalism lies at the basis of many programs of artificial intelligence AI , which attempt to devise computer programs that will carry out the same operations as minds. John Searle has famously distinguished between two views of AI:. Colby, one of the pioneers of artificial intelligence, was a psychiatrist who graduated from the Yale School of Medicine in , and computer scientist at Stanford and later UCLA.

While at Stanford, Colby developed Parry, a computer simulation of paranoia that is sometimes counted as the only computer program to pass the Turing test.

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It turns out that there's also a prize for the "Most Human Human", and Christian's book charts his quest to win it. Although I agree with Searle, I believe it is important to distinguish yet another form of artificial intelligence. What I call Pure AI is not concerned with psychological theories or mental states at all, but only with the task of getting machines to carry out "intelligent" behaviors. Most robotics take this form.

So does most work on chess-playing by machine. It is just programmed with the rules of the game and the capacity to perform incredibly fast information-processing operations. For pure AI, programs are programs. Kasparov, commenting on his loss, and on chess as a goal for artificial intelligence research, writes:.

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The AI crowd Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating million possible moves on the chess board per second and winning with brute number-crunching force. By the mids the number of people with some experience of using computers was many orders of magnitude greater than in the s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.

It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Watson, Sr. Watson, the behaviorist , based on the company's Blue Gene supercomputer, intended to answer questions on "Jeopardy!

The software must interact with humans on their own terms, and fast The system must be able to deal with analogies, puns, double entendres, and relationships like size and location, all at lightning speed" "IBM Computer Program to Take On 'Jeopardy! The IBM Jeopardy Challenge was taped in January and aired on February , the first game of the two-day match was spread out over two days, to allow for presentations on how Watson worked.

The machine made only two mistakes: responding "Toronto" to a question about U. Milliseconds count in Jeopardy, and if so it would be interesting to see a re-run of the contest with Watson constrained to something like humans' average response latencies. In the contest, Watson displayed a remarkable ability to parse language and retrieve information. In a later match, however, Watson was beaten by Rep. Rush Holt D. One category that Watson failed in was "Presidential rhymes" as in "Herbert's military operations": What were Hoover's maneuvers?

The point is not that Watson is always right, or that he wins all the time, or that he doesn't win all the time. The point is that Watson is a pretty good question-answering machine. It is not clear what kind of AI Watson represents. Probably not Strong AI: nobody claims that Watson will "think", and the newspaper article uses the word "understand" in quotes.

Many of the principles undergirding Watson's software are based on studies of human cognition, which suggests that it is not Pure AI either, and might be a version of Weak AI. IBM engineers, interviewed after the Challenge was completed, were reluctant to claim that Watson mimicked human thought processes see "on 'Jeopardy! Watson is nowhere near passing the Turing test -- the famous benchmark proposed by Alan Turing in , in which a program demonstrates its intelligence by duping a human being, in the course of conversation over the Web or some other network, into believing that it's human and not software.

But when a program does pass the Turing test, it's likely to resemble a gigantic Watson. It will know lots about the superficial structure of language and conversation.

It won't bother with such hard topics as meaning or consciousness. Watson is one giant leap for technology, one small step for the science of mind. But this giant leap is a major milestone in AI history. Which strengthens the case for Pure AI. But even if it had been only remotely successful, instead of spectacularly successful, the article notes that it will be a "great leap forward" in "building machines that can understand language and interact with humans".

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For example, IBM is using an improved version of Watson's technology to develop a language-based interactive system for medical diagnosis. Will Watson play again? Probably not. Deep Blue never played chess again after defeating Kasparov part of him is on display in the Smithsonian. He's made his point, it's unlikely that any human will beat him when Jennings and Rutter couldn't, and Watson may have a slight mechanical advantage in reaction time at the buzzer.

HAL's not the focus, the focus is on the computer on S tar Trek , where you have this intelligence information seek[ing] dialog, where you can ask follow-up questions and the computer can look at all the evidence and tries to ask follow-up questions. That's very cool.



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