The first frontier is that of mathematical induction, whereby domain experts have historically labored to develop useful closed-form analytic functions that map sets of features and observations (x→y). This activity, which was once solely the province of domain experts, is now routinely solved algorithmically using ML strategies and being applied to problems that previously eluded generations of modelers. The second frontier is that of emulating the deductive reasoning processes typical of experts. This frontier interfaces with a non-overlapping set of opportunities for ML; it also poses several fundamental challenges to the way ML models are designed and trained.