提出深度学习系统具备系统性理解能力，但存在符号错位与整合不足。
A Model of Understanding in Deep Learning Systems
- 构建可追踪真实规律的内部模型，通过稳定桥梁原则连接目标系统
- 当前深度学习系统已具备可靠预测能力，但符号表达与目标不一致
- 适合关注机器认知本质的研究者，揭示了模型理解的局限性
本文提出一种适用于机器学习系统的系统性理解模型：当智能体拥有能追踪真实规律的内部模型，通过稳定的桥梁原则与目标系统耦合，并支持可靠预测时，即具备理解。认为当代深度学习系统通常能够实现此类理解，但普遍达不到科学理解的理想标准——理解在符号层面与目标系统错位，非显式还原，且仅具弱统一性。这一观点被称为‘断裂理解假说’。
原文摘要 · Abstract (English)
I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction. I argue that contemporary deep learning systems often can and do achieve such understanding. However they generally fall short of the ideal of scientific understanding: the understanding is symbolically misaligned with the target system, not explicitly reductive, and only weakly unifying. I label this the Fractured Understanding Hypothesis.
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