HN'ers may have a good laugh at these taken from Yan LeCun's page. LeCun made it a tradition to have Hinton jokes in the lines of Chuck Norris ones (or more appropriately Doug McCllroy ones). A few will recall that neural networks all but died from the US after Minsky's damning book. Hinton gave backpropagation which is one of the foundational pillars of feed-forward neural net algorithms. With his new thrust on what…
> With his new thrust on whats called "deep belief networks" he is challenging his own early seminal contribution in the field
I don't know if I agree with that, he still uses backprop. Backprop has always been known to have problems when you scale to millions of connections, and his work on RBMs/DBNs is really quite old. What was novel more recently was showing that the contrastive divergence step need only be performed once, rather than 100 times, while the performance remained similar. The networks are generally still 'fine tuned' with backprop.
Still, the focus on generative networks (not sure if that's the right term still, been a while) and single layer training is fairly recent even if the concepts are quite old.