Why Machine Learning Needs Semantics Not Just Statistics

The deep learning revolution has ushered in a new generation of machine learning tools capable of identifying the patterns in massive noisy datasets with accuracy that often exceeds that of human domain experts. In turn, as machines have achieved human or even superhuman accuracy across an increasing number of tasks, we have increasingly described them in the same terms we describe ourselves, as silicon incarnations of life, learning about the world. Yet, a critical distinction between machines and humans is the way in which we reason about the world: humans through high order semantic abstractions and machines through blind adherence to statistics. The problem with likening machine learning to human learning is that when humans learn, they connect the patterns they identify to high order semantic abstractions of the underlying objects and activities. In turn, our background knowledge and experiences give us the necessary context to reason about those patterns and identify the ones most likely to represent robust actionable knowledge.