AI Definitions: Compression-meaning tradeoff

Compression-meaning tradeoff – This is the balance between reducing data size (compression) and preserving the original information (meaning). To manage information overload, humans group items into categories. For instance, we think of poodles and bulldogs as dogs. We balance this compression with details that separate them: size, nose, tales, fur types, etc. LLMs, on the other hand, attempt to maintain this balance between compressing information and original meaning differently. LLMs have an aggressive compression approach which allows them to store vast amounts of knowledge. However, it also contributes to unpredictability and failures. This tension has led many data scientists to conclude that better alignment with human cognition would result in more capable and reliable AI systems.

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