探讨机器学习表征与人类心智表征的哲学关联，揭示当前结论的局限性。
The Concept of Representation in ML: Beyond Plato and Aristotle
- 引入心灵哲学视角分析AI模型表征的共性
- 指出对齐证据不足以支撑形而上结论
- 适合关注AI本质与认知哲学交叉研究者
表示是现代机器学习的核心概念，通常指支持学习和泛化的内部编码。随着模型规模扩大、能力接近人类水平，这种表征语言有时从工程语境转向更具哲学意味的心智表征领域。本文认为，近期关于不同人工智能模型表征属性趋同的主张即属此类。特别是，我们评估了《柏拉图式表征假说》中的论点——该假说认为这种趋同源于现实的统一结构。通过引入心灵哲学中关于心智表征的争论观点，我们论证这些哲学资源有助于澄清此类主张的本质，解释为何对齐证据不足以得出强形而上的结论，并为未来研究提供方向。
原文摘要 · Abstract (English)
Representation is a central concept in modern machine learning, where it usually refers to internal encodings that support learning and generalization. As models scale and their capabilities become increasingly human-level, this representational language sometimes shifts from an engineering context into the more philosophically loaded domain of mental representation. We argue that this is the case for recent claims about the convergence of representational properties across different AI models. In particular, we assess the arguments developed in The Platonic Representation Hypothesis, according to which this convergence is driven by a unified structure of reality. We examine this claim by introducing arguments and ideas from debates about mental representation in the philosophy of mind. We argue that these philosophical resources can clarify what is at stake in such claims, explain why alignment evidence alone is insufficient for strong metaphysical conclusions, and suggest directions for future research.
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