Artificial Intelligence: Monitoring the Monitors

Time for a sobering moment of truth -- very little of what's being positioned as the "new" in Machine Learning is really "New Math" at all. Artificial Neural Networks (ANN) themselves are a decades-old concept, and even then the basis of that approach shares large commonalities with other statistical techniques that are themselves decades older. Consider that the most common task for most ANNs today is classification (fraud detection, threat detection, etc.), which is germane to any number of previously established supervised classification techniques such as logistic regression, support vector machines, etc., that have been circulating in the statistical realm for decades. While the computational approaches have algebraic differences, they can generally be shown to (in the limit) converge to a small set of solutions that are largely interchangeable or at least statistically indistinguishable insofar as the models are used appropriately, underlying data structures are respected by the model choice, and each model is shown the same set of information. What actually IS new is the ability to quickly and easily perform these modeling computations at the scale to which we now can.