[论文] A Model of Understanding in Deep Learning Systems

论文概要

研究领域: ML 作者: David Peter Wallis Freeborn

中文摘要

我提出了一个适用于机器学习系统的系统性理解模型。根据这一观点，当智能体包含一个足够的内部模型时，它就理解了目标系统的一个属性，该模型跟踪真实的规律，通过稳定的桥接原则与目标耦合，并支持可靠的预测。我认为当代深度学习系统往往能够也确实达到了这种理解。然而，它们通常达不到科学理解的理想：这种理解与目标系统在符号上不一致，不是明确还原的，且只有弱统一性。我将此称为"碎片化理解假说"。

原文摘要

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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