# Information Distance -- A Primer

DevFeed: [Information Distance -- A Primer](<https://devfeed.tech/articles/information-distance-a-primer-40294.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2012/12/04/information-distance-a-primer/>)

Published: 2012-12-04T08:30:08Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Compression](<https://devfeed.tech/topics/compression.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [compression](<https://devfeed.tech/tags/compression.md>), [computing](<https://devfeed.tech/tags/computing.md>), [information-theory](<https://devfeed.tech/tags/information-theory.md>), [kolmogorov-complexity](<https://devfeed.tech/tags/kolmogorov-complexity.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [metric](<https://devfeed.tech/tags/metric.md>), [turing-machines](<https://devfeed.tech/tags/turing-machines.md>), [universality](<https://devfeed.tech/tags/universality.md>)

## AI overview

This primer introduces information distance through Kolmogorov complexity. It explains how the shortest program relating two strings can reflect their shared information and behave similarly to a metric, with applications to compression and machine learning.

## Source excerpt

This post assumes familiarity with our primer on Kolmogorov complexity. We recommend the uninformed reader begin there. We will do our best to keep consistent notation across both posts. Kolmogorov Complexity as a Metric Over the past fifty years mathematicians have been piling up more and more theorems about Kolmogorov complexity, and for good reason. One of the main interpretations of the Kolmogorov complexity function $ K$ is that for a given string $ x$, $ K(x)$ is the best theoretical compression of $ x$ under any compression scheme.