Analyze the theoretical challenges associated with interpreting 'black box' deep learning models. Discuss the difference between post-hoc explanations (e.g., saliency maps) and mechanistic interpretability (understanding internal circuits). Is it theoretically possible to fully comprehend a super-human model?