Dan explains how attention heads learn to read variables and how this process can be interpreted through a probabilistic framework. He emphasizes the importance of making each module's calculations interpretable, ensuring that the logic behind the model can be translated into understandable rules. The discussion highlights the balance between complexity and interpretability in AI systems.
