Deep Learning, Part 3: Too Deep or Not Too Deep? That is the Question

In my previous two posts in this series, I've essentially argued both sides of the same issue. In the first, I explained why deep learning is not a panacea, when machine learning systems (now and likely always) will fail, and why deep learning in its current state is not immune to these failures. In the second post, I explained why deep learning, from the perspective of machine learning scientists and engineers, is an important advance: Rather than a learning algorithm, deep learning gives us a flexible, extensible framework for specifying machine learning algorithms. Many of the algorithms so far expressed in that framework give orders of magnitude-level improvement on the performance of previous solutions. In addition, it's a tool that allows us to tackle some problems heretofore unsolvable directly by machine learning methods.