Thursday, April 24, 2014   

Facebook joins university study to make sense of content
(12-10 09:39)

Facebook unveiled plans Monday on a partnership with New York University for a new center for artificial intelligence, aimed at harnessing the huge social network's massive trove of data.
The California-based tech giant named professor Yann LeCun of NYU's Center for Data Science to head up the project, AFP reports.
“As one of the most respected thinkers in this field, Yann has done groundbreaking research in deep learning and computer vision,'' said Mike Schroepfer, Facebook's chief technology officer. “We're thrilled to welcome him to Facebook.''
Facebook is building the team across three locations – New York, London and its headquarters in Menlo Park, California.
The lab will work on “machine learning,'' – a branch of artificial intelligence that involves computers “learning'' to extract knowledge from giant data sets.
LeCun, a French-born mathematician and computer scientist, said in a blog post that he was pleased to lead the project with “the ambitious, long-term goal of bringing about major advances in artificial intelligence.''
He said he I will remain a professor at New York University on a part-time basis.
Facebook chief and co-founder Mark Zuckerberg spoke of the plans during a call in October to discuss the company's quarterly earnings.
Zuckerberg said a working group was formed in September .
“The goal here is to use new approaches in AI to help make sense of all the content that people share so we can generate new insights about the world to answer people's questions,'' Zuckerberg said at the time.
He added that one of the goals was ``to build services that are much more natural to interact with and can help solve many more problems than any existing technology today.''
LeCun is a professor at NYU's Courant Institute of Mathematical Sciences and is the founding director of the university's Center for Data Science.
He is known for creating an early version of a pattern-recognition algorithm which mimics, in part, the visual cortex of animals and humans.
The algorithm helped allow AT&T's Bell Labs to deploy a check-reading system that by the late 1990s was reading about 20 percent of all the checks written in the US, according to NYU.
LeCun's recent research projects include the application of “deep learning'' methods for visual scene understanding and navigation autonomous ground robots, driverless cars, and small flying robots, as well as speech recognition, and applications in biology and medicine.
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