  When there exists uncertainty, AI machines are designed to make decisions so
as to reach the best expected outcomes. Expectations are based on true facts
about the objective environment the machines interact with, and those facts can
be encoded into AI models in the form of true objective probability functions.
Accordingly, AI models involve probabilistic machine learning in which the
probabilities should be objectively interpreted. We prove under some basic
assumptions when machines can learn the true objective probabilities, if any,
and when machines cannot learn them.
