WIRED: How do you explain the difference between deep learning and ordinary machine learning? A lot of people are familiar with the sort of machine learning that Google did over the first tens of its life, where it would analyze large amounts of data in an effort to, say, automatically identify web-spam. LeCun: That’s relatively simple machine learning. There’s a lot of effort that goes into creating those machine learning systems, in the sense that the system is not able to really process raw data. The data has to be turned into a form that the system can digest. That’s called a feature abstractor. Take an image, for example. You can’t feed the raw pixels into a traditional system. You have to turn the data into a form that a classifier can digest. This is what a lot of the computer vision community has been trying to do for the last twenty or thirty years — trying to represent images in the proper way. But what deep learning allows us to do is learn this representation process as well, instead of having to build the system by hand for each new problem.

Facebook's 'Deep Learning' Guru Reveals the Future of AI | Wired Enterprise | Wired.com Friday, December 13, 2013 @ 10:26pm