Can Machines Learn the True Probabilities?

When there exists uncertainty, AI machines are The outline of the proof is as follows. After defining some designed to make decisions so as to reach the main concepts, we identify the Success Criterion and the best expected outcomes. Expectations are based necessary condition for any machine to learn the true objective on true facts about the objective environment the probabilities. From these conditions, we derive machines interact with, and those facts can be the theorem that learning implies the true guarantee of encoded into AI models in the form of true objective well-calibration. Roughly speaking, "truly guaranteed wellcalibration" probability functions.