Facebook Pursues Deep Learning AI

October 14, 2013

Facebook has assembled a new team to build a “deep learning” approach for the site’s AI, MIT Technology Review reveals in, “Facebook Launches Advanced AI Effort to Find Meaning in your Posts.” The method promises to greatly speed data analysis by simulating our own neural networks.

Google and Microsoft have both recently made strides in this area. Google is using the approach to help its algorithm recognize objects in photos (beginning with cats, of course) and to improve its voice recognition services. Microsoft employed the technique to create its real-time English-to-Mandarin translation software. For its part, Facebook hopes deep learning will improve its news feed, which must pick up the pace to keep up with changing user habits. Both the compulsion to check our feeds with our mobile devices and ballooning friends lists require Facebook’s AI to better and more quickly decide what to show each user.

So, how is deep learning different from older AI models? Reporter Tom Simonite explains:

“Conventional forms of machine learning are slower because before data can be fed into learning software, experts must manually choose which features of it the software should pay attention to, and they must label the data to signify, for example, that certain images contain cars. Deep learning systems can learn with much less human intervention because they can figure out for themselves which features of the raw data are most significant. They can even work on data that hasn’t been labeled, as Google’s cat-recognizing software did. Systems able to do that typically use software that simulates networks of brain cells, known as neural nets, to process data. They require more powerful collections of computers to run.”

Facebook engineering manager Srinivas Narayanan, who is helping assemble the deep-learning team, says some of their more general research will be shared with the public. He also notes that the project will be informed by work Facebook has done on integrating hardware and software to process large data sets.

Cynthia Murrell, October 14, 2013

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