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Everything you Want to Know About the Turing Test
An anonymous reader writes "Everything you want to know about
the Turing test provided by
Stanford Encyclopedia of Philosophy. It is their
latest entry."
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The Anti-Turing Test
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The article itself gives pretty good coverage of Turings point
of view. It gives better coverage of the Turing test than I've
read in many AI books.
I tend to agree more with Searle though, whom he cites at the
end of the article "John Searle argues against the claim that
appropriately programmed computers literally have cognitive
states". Being a programmer myself, I don't feel that
programming something so that it can perform extremely well in a
specific test is necessarily indicative of Artificial
Intelligence or Intelligence in general. I agree with Turing
that the question of "do computers think" is vague enough to be
almost meaningless in a precise sense, but I think we understand
the statement taken as a whole.
I don't particularly agree with this statement in response
to the consciousness argument: "Turing makes
the effective reply that he would be satisfied if he could
secure agreement on the claim that we might each have just as
much reason to suppose that machines think as we have reason to
suppose that other people think" The question isn't whether or
not other people think, people thinking is an axiomatic
assumption when investigating Intelligence, unless you are
investigating existence from a philosophical point of view as
Descarte did. I guess I view AI from a more practical point of
view, I am by no means an expert in AI, but I tend to think the
goal of AI research is to produce systems that can learn and
react appropriately in different situations that they were never
programmed to handle or necessarily anticipate. If that isn't
the goal of AI research, what separates it from writing programs
on a large scale?
As a whole I found the article to be a good presentation of
Turing's position, although I have a few philosophical
differences with that position.
Doug Tolton
"The destruction of a value which is, will not bring value to that which isn't." -John Galt
i wonder if any people have taken the touring test and how they did. it wouldn't surprise me and i think it would be ammusing if some people's results came back that they didn't have a human level of cognitative reasoning.
Beer Die is the game of champions Learning To walk my own path.
I think the axiomatic assumption that people think is part of the problem. If we cannot say why the claim is that people think, it's easy to just debunk any AI claims by outright statement. "People think, while computers are just machines." You can't really make any progress in the face of that.
That's part of my problem with Searle's Chinese Room thought experiment. He's saying that an automaton responding to Chinese following rules would not "understand" Chinese in the way a human who speaks the language would. But this is presupposing that the way a human who "understands" Chinese does so is not through just a very long list of rules coded in neurons, which I consider to be a rather controversial assumption.
In short, a lot of anti-AI arguments seem to start from the premise that humans are not essentially biological computers; with that premise, of course you can debunk AI. A lot of AI researchers have grown tired of the argument entirely, and instead of responding to the arguments, have just resorted to saying "ok fine, you're right, we can't make 'really' intelligent computers, but what we can do is make computers that do the same thing an intelligent person would do, which is good enough for us." The idea here being that if a computer can eventually diagnose diseases better than a doctor, pilot a plane better than a pilot, translate Russian better than a bilingual speaker, and so on, it doesn't really matter if you think it's "really" intelligent or not, because it's doing all the things an intelligent thing would do.
As a final comment, I'd agree with the AI being not that fundamentally different from large software systems. The difference is basically one of focus -- AI has been focusing on what it means to "act intelligently" for decades, whereas much CS and software engineering was focused on more low-level details (like how memory or register allocation works). At one point, the division was more clear -- AI people did stuff like write checkers programs that learned from their mistakes, which was not something any CS person not in AI would do. The fields are increasingly blending, and a lot of stuff from engineering disciplines like control logic (how to "intelligently" control chemical plants, for example) is overalapping with AI research. Part of this is because a lot of AI ideas have actually matured enough to become usable in practice.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10