"For instance, on the planet Earth, man had always assumed that he was more intelligent than dolphins because he had achieved so much--the wheel, New York, wars and so on--whilst all the dolphins had ever done was muck about in the water having a good time. But conversely, the dolphins had always believed that they were far more intelligent than man--for precisely the same reasons." Douglas Adams, The Hitchhiker's Guide to the Galaxy One of the big obstacles in the field of artificial intelligence is not having a definition of intelligence based on solid mathematical and physical principles that could inspire the design and implementations of efficient intelligent algorithms. For instance, consider the most widely accepted definition of intelligence, signed by 52 specialist on the field [2]: "A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings..." A more recent definition [3] provided by Shane Legg, chief scientist of Deep Mind, and Marcus Hutter, founder of AIXI, is the following: "Intelligence measures an agent's ability to achieve goals in a wide range of environments." Although there are many other definitions of intelligence, they are too fuzzy to help us develop a theory of intelligent behaviour or give us an insight on how a general, computable and efficient algorithm for generating intelligent behaviour should look like. This document is an effort to present such a definition based on entropic principles deeply inspired by the concept of "Causal Entropic Forces" introduced by Alexander Wissner-Gross in 2013 [1] and to propose a generic implementation of those principles.