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arXiv · 2405.07987

The Platonic Representation Hypothesis

Minyoung Huh·Brian Cheung·Tongzhou Wang·Phillip Isola
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Abstract

We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato's concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, we discuss the implications of these trends, their limitations, and counterexamples to our analysis.

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Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola. 2024-05-13. The Platonic Representation Hypothesis. https://arxiv.org/abs/2405.07987

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