The Platonic Representation Hypothesis
<|reference_start|>The Platonic Representation Hypothesis: 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.<|reference_end|>
@article{huh2024the,
    title={The Platonic Representation Hypothesis},
    author={Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola},
    journal={arXiv preprint arXiv:2405.07987},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07987},
    primaryClass={cs.LG cs.AI cs.CV cs.NE}
}