Anyone following DL news knows that DL alone will not lead to strong AI. The most impressive feats in the last year or so have come from combining deep artificial neural networks with other algorithms, just as DeepMind combined deep ConvNets with reinforcement learning and Monte Carlo Tree Search. There's not really an interesting conversation to be had about whether DL will get us to strong AI. It won't. It is just machine perception; that is, it classifies, clusters and makes predictions about data very well in many situations, but it's not going to solve goal-oriented learning. But it solves perception problems very well, often better than human experts. So in the not too distant future, as people wake up to its potential, we will use those infinitely replicable NNs to extract actionable knowledge from the raw data of the world. That is, the world will become more transparent. It will offer fewer surprises. We may not solve cancer with DL, but we will spot it in X-rays more consistently with image recognition, and save more lives.
Disclosure: I work on the open-source DL project Deeplearning4j: http://deeplearning4j.org/