Deep learning is all the rage these days, and is driving a surge in interest around artificial intelligence. However, despite the advantages that deep neural networks can bring for certain applications, the actual use cases for deep learning in the real world remain narrow, as traditional machine learning methods continue to lead the way. The rapid ascent of deep learning is arguably one of the least expected technological phenomena to have occurred in the past five years. While neural networks have been around for decades, it wasn't until the University of Toronto's Geoff Hinton paired those techniques with a new computational paradigm (GPU) and the availability of huge amounts of training data to yield what we now know as deep learning. The result is that deep neural network technology has evolved "at a lightening rate," Nvidia CEO Jenson Huang said earlier this year.