By 2010, the ingredients had finally come together.

The algorithm — backpropagation through deep networks — had been known since 1986. The fixes for the vanishing gradient existed. The data, in the form of ImageNet, was assembled and ready. GPU training made it practical to use all of it in a reasonable amount of time.

None of this had been true before. All of it was true now.

In 2012, a team from Toronto entered a deep neural network into the ImageNet competition. The next chapter is what happened.