为什么让 AI 理解世界的前提是读懂因果？

TL;DR - This analysis argues that next-token prediction learns statistical correlations but lacks causal understanding. Causal structures, intervention reasoning, and counterfactual analysis could improve AI’s generalization, planning, and reliability in open environments.

• Current data-driven AI largely occupies Pearl’s association layer, with weaker intervention and counterfactual reasoning.
• World models, spatial intelligence, and embodied AI aim to model environmental dynamics and the consequences of actions.
• Structural causal models and causal graphs may help systems remain robust under distribution shifts and unfamiliar interactions.
• Practical causal reasoning remains limited by verification and deployment challenges.