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Computer Chess: The Drosophila of AI

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The domain of computer chess playing is suggested as a general means for quantifying the distance by which we have not yet achieved our stated objectives in artificial intelligence. The game of chess traditionally has been considered, at least in Western societies, as the epitome of intellectual skill and accomplishment. Herbert Simon and later John McCarthy, among the cofounders of AI, have referred to chess as the Drosophila of AI, speaking metaphorically about the importance for genetics of Thomas Morgan's early research with fruit flies, for which he won the Nobel Prize in 1933. This metaphor is appropriate, since the quantification of human chess play has been institutionalized over the last 40 years by giving every tournament player a numerical rating, a metric that also can be used to measure progress in machine performance. In 1993, world champion Gary Kasparov unilaterally created his own world chess organization (The Professional Chess Association or PCA) with the aim of displacing The International Chess Federation (FIDE), which traditionally has supervised tournaments for the world title.


Monster in a Box

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This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue. The inside story of an ingenious chess-playing machine that thrilled crowds, terrified opponents, and won like clockwork. One autumn day in 1769, a 35-year-old civil servant was summoned to the imperial court in Vienna to witness a magic show. Wolfgang von Kempelen – well versed in physics, mechanics, and hydraulics – was a trusted servant of Maria Theresa, the empress of Austria-Hungary.


Better Face-Recognition Software

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For scientists and engineers involved with face-recognition technology,the recently released results of the Face Recognition Grand Challenge–more fully, the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006–have been a quiet triumph. Sponsored by the National Institute of Standards and Technology (NIST), the match up of face-recognition algorithms showed that machine recognition of human individuals has improved tenfold since 2002 and a hundredfold since 1995. Indeed, the best face-recognition algorithms now perform more accurately than most humans can manage. Overall, facial-recognition technology is advancing rapidly. Jonathon Phillips, program manager for the NIST tests and lead author of the agency's report, says that the intended goal of the Face Recognition Grand Challenge was always an order-of-magnitude improvement in recognition performance over the results from 2002.


Using Artificial Neural Networks to Predict the Quality and Performance of Oilfield Cements

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Inherent batch to batch variability, ageing and contamination are major factors contributing to variability in oilfield cement slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods which allow the identification, characterisation and prediction of the variability of oilfield cements. Our approach involves predicting cement compositions, particle size distributions and thickening time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders.


Deep Learning How I Did It: Merck 1st place interview

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What was your background prior to entering this challenge? We are a team of computer science and statistics academics. Ruslan Salakhutdinov and Geoff Hinton are professors at the University of Toronto. George Dahl and Navdeep Jaitly are Ph.D. students working with Professor Hinton. Christopher "Gomez" Jordan-Squire is in the mathematics Ph.D. program at the University of Washington, studying (constrained) optimization applied to statistics and machine learning.


The Creative Processor

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With a souped-up reproducing piano and some ingenious learning machines, AI maestro Gerhard Widmer is discovering how performers unlock the art in Mozart. A gray-blue dusk is settling over the Gothic cathedrals, palatial opera houses, and labyrinthine streets of Vienna's First District. Here in the Austrian capital, music is an almost elemental force. You're just as likely to overhear the sound of a young diva practicing operatic vocal scales as the thump of a sleek techno-innovator like Patrick Pulsinger playing in some ancient cellar just out of sight. It's a place where very old and very new musical traditions collide and intermingle – the perfect setting for a computer scientist obsessed with examining the blips and fault lines, deviations and inventions, that transform music into something more than code and just slightly less than magic.


The hottest new technologies are coming to cars

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People gather in the Nvidia booth at the Mobile World Congress, the world's largest mobile phone trade show, in Barcelona on Feb. 27. (Photo: Manu Fernandez, AP) Tesla, the electric car maker, (TSLA) saw it shares dip 4.1% to $243.45, down $10.43, on a day that the overall market was rising. Ask them where those technologies will have an impact, on the other hand, and the responses will likely be all over the map. Smartphones, smart cities and intelligent assistants are just a few of the many options you might hear. Ironically, the one answer you probably won't hear is the category that all of these technologies are either already in or quickly coming to: cars. Today's automobiles have some of the most advanced tech available, and over the next several model years, the amount and capabilities of that technology is going to increase dramatically.


IBM's Watson morphs into big business

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Mike Rhodin is senior vice president of IBM Watson. DETROIT -- IBM Watson initially won fame as the artificially intelligent computer system that won $1 million for whipping former Jeopardy! Since then, under the leadership of 1984 University of Michigan graduate Mike Rhodin, Watson has morphed into a muscular big business with lots of tentacles and more than 2,000 employees. Earlier this month in Ann Arbor, I interviewed Rhodin, the New York-based senior vice president of IBM Watson who was in town to speak with two groups of University of Michigan business students and budding entrepreneurs. Rhodin smiled when I asked the sci-fi question he hears often: When will machines turn on humans and take over the world?


Why Cars Won't be Driverless Anytime Soon

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Driving on Interstate 495 toward Boston in a Ford Fusion one chilly afternoon in March, I did something that would've made even my laid-back long-ago driving instructor spit his coffee over the dashboard: I took my hands off the steering wheel, lifted my foot off the gas pedal, and waited to see what would happen. To a degree, the car was already driving itself. Sensors were busy tracking other vehicles and road markings; computer systems were operating the accelerator, the brake, and even the steering wheel. The car reduced its speed to keep a safe distance from the vehicle ahead, but as that car sped up again, mine did so too. I tried nudging the steering wheel so that we drifted toward the dotted line on my left.


By Observing Humans in Slow Motion, Robots Learn to Collaborate with Us

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Roomba has a new friend. Researchers have developed a robot that can help clean the kitchen. In a paper presented at Robotics Science and Systems in Rome in July, scientists at the University of Wisconsin-Madison describe how they taught a Kinova Mico robot arm to help people do the dishes. The key, apparently, is slowing down and letting human team members take charge. "We want robots to follow our lead, or at least plan their actions with an awareness of ours," says Bilge Mutlu, associate professor of computer science, psychology, and industrial engineering and an author of the paper.