The winner got 6.7% in top-5 classification error. 2013 was 11%, 2012 was 15%. This may be better than exponential, since there is an upper limit of 0% error, a

The winner got 6.7% in top-5 classification error. 2013 was 11%, 2012 was 15%. This may be better than exponential, since there is an upper limit of 0% error, and every additional percentage point is harder than the last. Exponential improvement in this area, and it's a pretty significant field that will affect everyone. The results will likely help improve neural networks in other areas as well, as NNs are extremely general purpose. Here is an example from a system from 2012 in the same competition to give an example of what this is: http://mappingignorance.org/fx/media/2013/04/Deep-learning-5.png http://mappingignorance.org/fx/media/2013/04/Deep-learning-5... A bunch of images were scraped from Flickr and then given a single label by a human. The computer gets 5 guesses to guess what label the human used. As you can see, even when the computer fails, it's guesses are usually semantically very good. This specific system got an error rate of 17%. You can explore the images of ImageNet here: http://image-net.org/explore http://image-net.org/explore. Here are some image recognition systems online you can play with: * Toronto Deep Learning Demo: http://deeplearning.cs.toronto.edu/ http://deeplearning.cs.toronto.edu/ * clarifai: http://www.clarifai.com/ http://www.clarifai.com/ * AlchemyVision: http://www.alchemyapi.com/products/demo/alchemyvision/ http://www.alchemyapi.com/products/demo/alchemyvision/ Note these aren't related to the winner of this competition and probably are no where near as good. Just examples of this kind of system you can play with.