Drawing a parallel with API -- phenomenon, this was natural, but confusing at the same time.

Machine learning (ML) has become one of the hottest areas in data, with computational systems now able to learn patterns in data and act on that information. The applications are wide-ranging: from autonomous robots, to image recognition, drug discovery, fraud detection, etc. At the cutting edge is deep learning, which draws its inspiration from the networks of neurons that comprise the cerebral cortex. These networks are massively parallel. As such, it's no surprise that an increasing number of ML approaches are turning to graphical processing units (GPUs)--a key hardware component for general-purpose parallel computation.