
TL;DR
This paper proposes a universal measure of intelligence based on prediction accuracy and complexity, aiming to compare humans, animals, and AI systems on a single scale.
Contribution
It introduces a new theoretical framework for measuring intelligence through prediction, using Kolmogorov complexity, and demonstrates its practical feasibility.
Findings
Successfully measured intelligence of agents in a virtual maze
Evaluated prediction of time-series data
Proposes a unified scale for comparing different intelligent systems
Abstract
Over the last thirty years, considerable progress has been made with the development of systems that can drive cars, play games, predict protein folding and generate natural language. These systems are described as intelligent and there has been a great deal of talk about the rapid increase in artificial intelligence and its potential dangers. However, our theoretical understanding of intelligence and ability to measure it lag far behind our capacity for building systems that mimic intelligent human behaviour. There is no commonly agreed definition of the intelligence that AI systems are said to possess. No-one has developed a practical measure that would enable us to compare the intelligence of humans, animals and AIs on a single ratio scale. This paper sets out a new universal measure of intelligence that is based on the hypothesis that prediction is the most important component of…
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