Join us as we unpack KL divergence (also called relative entropy or I-divergence), the precise, always non-negative measure of how far your model Q is from the true distribution P. We explain its interpretation as the expected excess surprisal, how it shows up in data compression and cross-entropy, and why, unlike a true distance, KL divergence is asymmetric and does not satisfy the triangle inequality. We’ll see why this asymmetry matters for Bayesian updating and information gain, and how D_KL links to practical AI metrics like MAUVE. We’ll also touch a surprising physics connection: KL divergence times temperature equals thermodynamic availability. Brought to you in part by Embersilk.com.

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