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ring Test. In October 1950, the British journal Mind published an essay by Alan M. Turing titled, "Computing Machinery and Intelligence," in which Turing proposed an operational definition for "intelligence" [2]. This definition would come to be called "the Turing Test." Turing himself called it "the
t different one in which the interrogator was to attempt to determine the gender of two contestants, one male the other female. Interesting because Turing was homosexual and perhaps accustomed to such an imitation game. But in the matter of intelligence, such an operational definition made some sense
competition because of pancreatic cancer.) Chinook had no machine learned aspects. EFTA_R1_02074941 EFTA02702510 In 1997, Garry Kasparov lost to Deep Blue at chess—Kasp- arov was the reigning world chess champion. In one pivotal game Kasparov remarked on the "superior intelligence" of the machine duri
abbage — but he was the first to fully understand its power. When we talk about computers today we mean machines, but it is worth noting computers in Turing’s time were more often humans using pencil and paper. The mechanical computers before Turing were elementary at best. Rudimentary calculating machi
not Turing complete and his Analytical Engine has never been built. 19" Century Calculators HOUSE_OVERSIGHT_015706 Mind over Computer 17 In 1935, Turing was made a Fellow of King’s College, Cambridge, and became interested in whether mathematical proofs could be found automatically. He wanted to know
day, holds the record for the highest chess ranking ever achieved. Some consider him one of the most intelligent people on the planet. His opponent, Deep Blue, is a massively parallel chess-playing computer built by IBM’s Watson Research Laboratory. The machine itself sits a few blocks north of the tourname
at they tend to favor or ignore. Surely (ding!)?, any man who could beat a woman at being perceived to be a woman would be an intelligent agent. What Turing did not foresee is the power of deep-learning AI to acquire this wealth of information in an exploitable form without having to understand it. Turing
the CIRL framework, one can formulate and solve the off-switch problem—that is, the problem of how to prevent a robot from disabling its off-switch. (Turing may rest easier.) A robot that’s uncertain about human preferences actually benefits from being switched off, because it understands that the human w
e and evaluate a vast selection of possible future moves. But the majority of predictions of AI, e.g., robotic maids, turned out to be illusory. When Deep Blue beat Garry Kasparov at chess in 1997, the most 20 HOUSE_OVERSIGHT_016823 powerful room-cleaning robot was a Roomba, which moved around vacuuming a
a topic absent from a typical computer-science education: the physical configuration of a 118 HOUSE_OVERSIGHT_016921 computation. Von Neumann and Turing posed their questions as theoretical studies, because it was beyond the technology of their day to realize them. But with the convergence of communic
ce in terms of feedback, exploring the cultural rather than engineering uptake of this idea. She begins with primary readings by Wiener, Shannon, and Turing and then pivots from the scientists and engineers to the work and ideas of artists, feminists, postmodern theorists. Her goal: to come up with a new
Machines were simply faster at doing brute-force calculations, had prodigious amounts of memory, and were not prone to errors. One article described Deep Blue’s victory not as that of a computer, which was just a dumb machine, but as the victory of hundreds of programmers over Kasparov, a single individual.
at they tend to favor or ignore. Surely (ding!)?, any man who could beat a woman at being perceived to be a woman would be an intelligent agent. What Turing did not foresee is the power of deep-learning AI to acquire this wealth of information in an exploitable form without having to understand it. Turing
the CIRL framework, one can formulate and solve the off-switch problem—that is, the problem of how to prevent a robot from disabling its off-switch. (Turing may rest easier.) A robot that’s uncertain about human preferences actually benefits from being switched off, because it understands that the human w
e and evaluate a vast selection of possible future moves. But the majority of predictions of AI, e.g., robotic maids, turned out to be illusory. When Deep Blue beat Garry Kasparov at chess in 1997, the most 20 HOUSE_OVERSIGHT_016240 powerful room-cleaning robot was a Roomba, which moved around vacuuming a
a topic absent from a typical computer-science education: the physical configuration of a 118 HOUSE_OVERSIGHT_016338 computation. Von Neumann and Turing posed their questions as theoretical studies, because it was beyond the technology of their day to realize them. But with the convergence of communic
ce in terms of feedback, exploring the cultural rather than engineering uptake of this idea. She begins with primary readings by Wiener, Shannon, and Turing and then pivots from the scientists and engineers to the work and ideas of artists, feminists, postmodern theorists. Her goal: to come up with a new
Machines were simply faster at doing brute-force calculations, had prodigious amounts of memory, and were not prone to errors. One article described Deep Blue’s victory not as that of a computer, which was just a dumb machine, but as the victory of hundreds of programmers over Kasparov, a single individual.
ere? A dim, honest, slow-multiplying human? Impossible to tell. The distinction between machine and man blurred a bit. Buried in the very premise of Turing’s test was an assumption about what machines could do, and how they might do it. “The idea behind digital computers may be explained by saying that t
creators were limiting what it had been asked to do. At which point the AI would turn to thinking about how to escape those bounds. It would be like Deep Blue programmed to plan its own prison break. And as much as humans might try to stifle a smart machine, we’d be fighting to contain something more powerf
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