You can read the (much more informed) opinion of Ilya Sutskever on the issue here (Yoshua Bengio also participated in the comments).

That is the most cogent, genuinely informative explanation of "Deep Learning" that I've ever heard. Most especially so regarding the bit about linear correlations: we can learn well on real problems with nothing more than stochastic gradient descent because the feature data may contain whole hierarchies of linear correlations.