DeepLearning
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Robot learns skills through trial and error, like you do
As a rule, robots have to learn through explicit instruction, whether it's through new programming, watching videos or holding their hands. UC Berkeley's BRETT (Berkeley Robot for the Elimination of Tedious Tasks) isn't nearly that dependent, however. The machine uses neural network-based deep learning algorithms to master tasks through trial and error, much like humans do. Ask it to assemble a toy and it'll keep trying until it understands what works. In theory, you'd rarely need to give the robot new code -- you'd just make requests and give the automaton enough time to figure things out.
Computers are learning to size up neighborhoods using photos
Us humans are normally good at making quick judgments about neighborhoods. We can figure out whether we're safe, or if we're likely to find a certain store. Computers haven't had such an easy time of it, but that's changing now that MIT researchers have created a deep learning algorithm that sizes up neighborhoods roughly as well as humans. The code correlates what it sees in millions of Google Street View images with crime rates and points of interest; it can tell what a sketchy part of town looks like, or what you're likely to see near a McDonald's (taxis and police vans, apparently).
Facebook hires NYU professor to lead its artificial intelligence efforts
Facebook's fledgling artificial intelligence group now has a leader -- the social network has hired New York University professor Yann LeCun to run the research division from New York City. He'll remain at the university part time, but most of his energy will now be spent researching data science, deep learning and other technologies that could refine Facebook's social stream. While LeCun's hire won't pay off for some time, it already suggests that Zuckerberg and crew are serious about competing with Google's Ray Kurzweil in the AI space.
Facebook developing brain-like AI to find deeper meaning in feeds and photos
Facebook's current News Feed ranking isn't all that clever -- it's good at surfacing popular updates, but it can miss lower-profile updates that are personally relevant. The company may soon raise the News Feed's IQ, however, as it recently launched an artificial intelligence research group. The new team hopes to use deep learning AI, which simulates a neural network, to determine which posts are genuinely important. The technology could also sort a user's photos, and it might even select the best shots. While the AI work has only just begun, the company tells MIT Technology Review that it should release some findings to the public; those breakthroughs in social networking could help society as a whole.
