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A social path to human-like artificial intelligence

Abstract

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in artificial intelligence (AI) advancement is shifting from data assimilation to novel data generation. We bring together evidence showing that natural intelligence emerges at multiple scales in networks of interacting agents via collective living, social relationships and major evolutionary transitions, which contribute to novel data generation through mechanisms such as population pressures, arms races, Machiavellian selection, social learning and cumulative culture. Many breakthroughs in AI exploit some of these processes, from multi-agent structures enabling algorithms to master complex games such as Capture-The-Flag and StarCraft II, to strategic communication in the game Diplomacy and the shaping of AI data streams by other AIs. Moving beyond a solipsistic view of agency to integrate these mechanisms could provide a path to human-like compounding innovation through ongoing novel data generation.

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Fig. 1: Learning quality depends on the richness and size of the dataset.
Fig. 2: Social interactions drive compounding innovation.

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We thank A. Anand, D. Parkes, T. Schaul and K. Tuyls for helpful comments on early versions of this manuscript.

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Duéñez-Guzmán, E.A., Sadedin, S., Wang, J.X. et al. A social path to human-like artificial intelligence. Nat Mach Intell 5, 1181–1188 (2023). https://doi.org/10.1038/s42256-023-00754-x

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