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Compression is prediction
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Overview
Compression techniques and large language models (LLMs) both aim to predict and represent data efficiently. Both fields utilize similar principles to achieve their respective goals of data reduction and understanding.
Summary from ngrok.com.
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Hacker News · ngrok.com
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Based on 5 sampled comments from this thread. Analyzed 2026-08-12.
- Praise: The connection between information theory and machine learning is a revelation, showcasing how they are fundamentally intertwined, much like cybernetics of the past.
- Praise: Viewing training as optimization over a vast family of compression algorithms gives a fresh perspective on how LLMs can generate new ideas, challenging the narrow view of them as mere next-token predictors.
- Praise: The historical context provided by Shannon's information theory lends credibility to the thesis, emphasizing the importance of rigorous education in these concepts.
- Concern: There's a troubling trend of authors presenting established ideas as their own groundbreaking discoveries, which undermines the depth of knowledge in the field.
- Concern: The argument that 'new ideas' can emerge from LLMs is not intuitive for many; it raises skepticism about the true innovative capacity of these models beyond their training data.