A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
This research introduces a model-free machine learning framework that uses surprisal, an information theory metric, to directly infer knowledge from raw data instead of building explicit models. It achieves this by quantifying uncertainty to determine relevance between data points. This approach allows for generalizable inference across many tasks while maintaining high traceability and human interpretability.