  The growth of machine learning as a field has been accelerating with
increasing interest and publications across fields, including statistics, but
predominantly in computer science. How can we parse this vast literature for
developments that exemplify the necessary rigor? How many of these manuscripts
incorporate foundational theory to allow for statistical inference? Which
advances have the greatest potential for impact in practice? One could posit
many answers to these queries. Here, we assert that one essential idea is for
machine learning to integrate maximum likelihood for estimation of functional
parameters, such as prediction functions and conditional densities.
