Measuring intelligence in natural and artificial systems

Gamez, David ORCID: https://orcid.org/0000-0002-3075-655X (2021) Measuring intelligence in natural and artificial systems. Journal of Artificial Intelligence and Consciousness, 08 (02) . pp. 285-302. ISSN 2705-0793 [Article] (Published online first) (doi:10.1142/s2705078521500090)

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Abstract

A systematic understanding of the relationship between intelligence and consciousness can only be achieved when we can accurately measure intelligence and consciousness. In other work, I have suggested how the measurement of consciousness can be improved by reframing the science of consciousness as a search for mathematical theories that map between physical and conscious states. This paper discusses the measurement of intelligence in natural and artificial systems. While reasonable methods exist for measuring intelligence in humans, these can only be partly generalized to non-human animals and they cannot be applied to artificial systems. Some universal measures of intelligence have been developed, but their dependence on goals and rewards creates serious problems. This paper sets out a new universal algorithm for measuring intelligence that is based on a system’s ability to make accurate predictions. This algorithm can measure intelligence in humans, non-human animals and artificial systems. Preliminary experiments have demonstrated that it can measure the changing intelligence of an agent in a maze environment. This new measure of intelligence could lead to a much better understanding of the relationship between intelligence and consciousness in natural and artificial systems, and it has many practical applications, particularly in AI safety.

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