A Reminder That Machine Learning Is About Correlations Not Causation

Lost amongst the hype and hyperbole surrounding machine learning today, especially deep learning, is the critical distinction between correlation and causation. Developers and data scientists increasingly treat their creations as silicon lifeforms "learning" concrete facts about the world, rather than what they truly are: piles of numbers detached from what they represent, mere statistical patterns encoded into software. We must recognize that those patterns are merely correlations amongst vast reams of data, rather than causative truths or natural laws governing our world. As machine learning has expanded beyond its roots in the worlds of computer science and statistics into nearly every conceivable field, the data scientists and programmers building those models are increasingly detached from an understanding of how and why the models they are creating work. To them, machine learning is akin to a black box in which you blindly feed different mixes of training data in one side, twirl some knobs and dials and repeat until you get results that seem to work well enough to throw into production.