Utility vs Understanding: the State of Machine Learning Entering 2022

The empirical utility of some fields of machine learning has rapidly outpaced our understanding of the underlying theory: the models are unreasonably effective, but we're not entirely sure why. Conversely, other areas of research that are relatively well understood are difficult to implement or have limited applicability in practice. This article attempts to map different fields of machine learning with respect to their utility and understanding, and explores how scientific and technological progress manifests within this framework. Constructing this matrix is a highly subjective exercise, that reduces multi-faceted fields to undefined, single values on one-dimensional scales, that themselves are comprised of multiple factors. This matrix represents my personal view - one in which fields are crudely assessed only by their general characteristics.