A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)

A new approach to machine learning has been introduced that moves away from traditional models and assumptions, focusing instead on a model-free framework that utilizes information theoretic concepts like surprisal. This innovative method allows for direct analysis of raw data, reducing bias and enhancing flexibility. By measuring uncertainty, it enables more efficient updates to training data, including edits and deletions. This advancement is significant as it could lead to more interpretable and adaptable machine learning systems, making them more effective in various applications.