It's true that the beginning of the talk is about how people abandoned backprop trained neural networks because they often underperformed SVMs, but the rest of

It's true that the beginning of the talk is about how people abandoned backprop trained neural networks because they often underperformed SVMs, but the rest of the talk is about deep learning, which introduced a new generation of neural nets (since 2006) that are the current state of the art for a lot of problems. In fact, deep neural networks are trained in an unsupervised manner at first, but then back propagation is used to "fine tune" and improve the results. Because they require unlabeled data sets, and can perform so well, research into neural networks has experienced a recent resurgence. By the way, any talk by Geoff Hinton is fantastic. If you are interested in neural networks and their capabilities, and you haven't already seen it, his Coursera course [1] builds from a simple linear perceptron to the current deep learning methods. [1] https://class.coursera.org/neuralnets-2012-001 https://class.coursera.org/neuralnets-2012-001 (You'll have to sign in to see it)