strong compression across modalities reflects an understanding of images, audio and more at a deep statistical level.
There are inherent tradeoffs between model scale, datasets, and compression performance. Bigger datasets allow bigger models, but size must match.
The results provide new perspective on model scaling laws - compression considers model size unlike log loss. Scaling hits limits.
The equivalence between prediction and compression means these models could have practical applications for compressing images, video and more. However, model size may be prohibitive compared to current methods.
The compression viewpoint offers new insights into model generalization, failure modes, tokenization, and other aspects of deep learning.
In summary, this research shows large language models have become adept general-purpose learners. Their exceptional compression capabilities demonstrate an expansive understanding of patterns in textual, visual and audio data. There is still progress to be made, but these models show increasing competence as general systems for automating prediction and compression across modalities.
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