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.

翻译：本文提出一种适用于机器学习系统的系统性理解模型。在此框架下，当智能体具备以下条件时，即被认为理解目标系统的某个属性：包含能够追踪真实规律性的充分内部模型，通过稳定的桥接原理与目标系统耦合，并能支持可靠预测。本文论证当代深度学习系统通常能够且确实实现了这种理解。然而，这些系统普遍未能达到科学理解的理想标准：其理解在符号层面与目标系统存在偏差，缺乏显式的还原性解释，且仅能实现弱统一性。我将此称为"碎片化理解假说"。