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Google releases massive visual databases for machine learning
It seems like we hear about a new breakthrough using machine learning nearly every day, but it's not easy. In order to fine-tune algorithms that recognize and predict patterns in data, you need to feed them massive amounts of already-tagged information to test and learn from. For researchers, that's where two recently-released archives from Google will come in. Joining other high-quality datasets, Open Images and YouTube8-M provide millions of annotated links for researchers to train their processes on.
Microsoft's imaging tech is (sometimes) better than you at spotting objects
Many computer vision projects struggle to mimic what people can achieve, but Microsoft Research thinks that its technology might have already trumped humanity... to a degree, that is. The company has published results showing that its neural network technology made fewer mistakes recognizing objects than humans in an ImageNet challenge, slipping up on 4.94 percent of pictures versus 5.1 percent for humans. One of the keys was a "parametric rectified linear unit" function (try saying that three times fast) that improves accuracy without any real hit to processing performance.
Competition coaxes computers into seeing our world more clearly
As surely as the seasons turn and the sun races across the sky, the Large Scale Visual Recognition Competition (or ILSVRC2014, for those in the know) came to a close this week. That might not mean much to you, but it does mean some potentially big things for those trying to teach computers to "see". You see, the competition -- which has been running annually since 2010 -- fields teams from Oxford, the National University of Singapore, the Chinese University of Hong Kong and Google who cook up awfully smart software meant to coax high-end machines into recognizing what's happening in pictures as well as we can.
