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.

翻译：当存在不确定性时，人工智能机器被设计为通过决策实现最佳预期结果。预期基于机器所交互的客观环境的真实事实，这些事实可以以真实客观概率函数的形式编码到AI模型中。因此，AI模型涉及概率机器学习，其中的概率应被客观解释。我们在若干基本假设下证明了：机器何时能够学习真实客观概率（如果存在的话），以及机器何时无法学习它们。