The idea of having multiple levels of representation (deep learning) goes beyond neural networks. A good example is the recent work (award-winning at NIPS 2012) on sum-product networks, which are graphical models whose partition function is tractable by construction. Several important things have been added since 2006 (when deep learning was deemed to begin) to the previous wave of neural networks research, in particular powerful unsupervised learning algorithms (which allow very successful semi-supervised and transfer learning - 2 competitions won in 2011), often incorporating advanced probabilistic models with latent variables, a better understanding (although much more remains to be done) of the optimization difficulty of training gradient-based systems through many composed non-linearities, and other improvements to regularize better (such as the recent dropouts) and to rationally and efficiently select hyper-parameters (random sampling and Bayesian optimization). It is also true that sheer improvements in computing power and amounts of training data are in part responsible for the impressively good results recently obtained in speech recognition (see recent New York Times article, 24 nov, J. Markoff) and object recognition (see NIPS 2012 paper by Krizhesky et al).