He Got Into Bed And His Package Was So Long And Hard | ||||||||||||||||||||||||||||||||||||||||
Jack, give any man this to get hard in under 9 seconds We started at 9pm and didnt finish until 12am - It was incredible | ||||||||||||||||||||||||||||||||||||||||
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Researchers at Massachusetts Institute of Technology and New York University have begun to teach computers an algorithm that mirrors the learning techniques practiced by humans – a big step toward reducing the amount of time needed for machines to learn and then practice new concepts. "It has been very difficult to build machines that require as little data as humans when learning a new concept," Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto, said in a news release. "Replicating these abilities is an exciting area of research connecting machine learning, statistics, computer vision, and cognitive science." The researchers created a probability-based algorithm called a "Bayesian Program Learning" framework. Designed to be self-programming, the algorithm generated new code that produced a new output of the computer program the researchers wanted their software to learn in each successive variation. The results were published online Thursday and in Friday's edition of the journal Science. Recommended: Despite sci-fi tropes, robots make better managers, study says This practice mirrors the way humans learn; most human beings require a few brief examples of a new concept before they latch onto it and understand. Despite sci-fi tropes, robots make better managers, study says PHOTOS OF THE DAY Photos of the day 12/10 Over the course of the self-generating program, the software was able to identify correctly and to draw a handwritten character after seeing just one example. The researchers tested this accuracy by drawing on a database of the world's written languages, including Sanskrit and Tibetan. When the researchers showed the results of what their software had drawn to a panel of judges, asking them to compare the computer's drawings to those made by humans, the judges were unable to distinguish between the humans' drawings and the computer's only 25 percent more than chance in each example. The researchers are excited to explore what the results of this study could portend for other developments in artificial intelligence. | ||||||||||||||||||||||||||||||||||||||||
Sunday, December 13, 2015
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