Over the past decade, the 19th century science-of-counting was resurrected to provide a combinatorial derivation of conventional Machine Learning that uniquely generalizes statistics to probability theory, allows energy to enter or exit the system, and processes any time-series to return a complete set of scientific (thermodynamic) measurements as deductive reality [1]. Conventional machine learning is effective in static, closed world applications, but does not accommodate changing environments, where energy can enter or exit, or, where energy is stored temporarily for later release. Energy that enters or exits a system should also include emotional energy, as it is the principle and obvious source of human energy that can both affect the dynamics of counting and anticipate a need.