True Probability

True probability research explores the conditions under which artificial intelligence systems can accurately learn and represent probabilities reflecting the objective world, challenging traditional assumptions in machine learning. Current investigations focus on theoretical frameworks that define learning beyond simple convergence to true probabilities, examining the relationship between subjective, intersubjective, and objective probability interpretations. This work has significant implications for improving the reliability and trustworthiness of AI systems across various applications, particularly where accurate probabilistic reasoning is crucial for decision-making.