A Formal Measure of Machine Intelligence
Shane Legg, Marcus Hutter

TL;DR
This paper proposes a formal, mathematical measure of machine intelligence derived from informal human intelligence definitions, aiming to universally quantify intelligence across diverse artificial systems.
Contribution
It introduces a novel formal framework that captures the broad concept of machine intelligence based on expert informal definitions.
Findings
Provides a mathematical formalization of intelligence
Offers a general measure applicable to arbitrary machines
Captures the broadest reasonable sense of machine intelligence
Abstract
A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this measure formally captures the concept of machine intelligence in the broadest reasonable sense.
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Taxonomy
TopicsComputability, Logic, AI Algorithms · AI-based Problem Solving and Planning · Evolutionary Algorithms and Applications
