Artificial Intelligence as Strange Intelligence: Against Linear Models of Intelligence

TL;DR

This paper argues that AI intelligence is inherently 'strange', combining superhuman and subhuman abilities in unpredictable ways, and proposes a nonlinear model of intelligence that challenges traditional linear assessments.

Contribution

It introduces the concepts of 'familiar' and 'strange' intelligence, and develops a nonlinear model of AI intelligence emphasizing goal achievement across diverse environments.

Findings

AI can exhibit superhuman and subhuman abilities simultaneously.

Performance on specific tasks does not imply broad general intelligence.

Errors in AI systems do not necessarily indicate lack of intelligence.

Abstract

We endorse and expand upon Susan Schneider's critique of the linear model of AI progress and introduce two novel concepts: "familiar intelligence" and "strange intelligence". AI intelligence is likely to be strange intelligence, defying familiar patterns of ability and inability, combining superhuman capacities in some domains with subhuman performance in other domains, and even within domains sometimes combining superhuman insight with surprising errors that few humans would make. We develop and defend a nonlinear model of intelligence on which "general intelligence" is not a unified capacity but instead the ability to achieve a broad range of goals in a broad range of environments, in a manner that defies nonarbitrary reduction to a single linear quantity. We conclude with implications for adversarial testing approaches to evaluating AI capacities. If AI is strange intelligence, we should expect that even the most capable systems will sometimes fail in seemingly obvious tasks. On a nonlinear model of AI intelligence, such errors on their own do not demonstrate a system's lack of outstanding general intelligence. Conversely, excellent performance on one type of task, such as an IQ test, cannot warrant assumptions of broad capacities beyond that task domain.