A social path to human-like artificial intelligence

TL;DR

This paper argues that social interactions and collective behaviors are crucial for advancing AI towards human-like intelligence by enabling continuous novel data generation and complex learning processes.

Contribution

It introduces a social perspective on AI development, emphasizing the role of multi-agent interactions and evolutionary mechanisms in fostering intelligence.

Findings

Multi-agent systems improve game mastery and strategic reasoning.

Social learning and cultural evolution contribute to AI complexity.

AI progress benefits from mechanisms like arms races and social relationships.

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) progress 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 like Capture-The-Flag and StarCraft II, to strategic communication in Diplomacy and the shaping of AI data streams by other AIs. Moving beyond a solipsistic view of agency to integrate these mechanisms suggests a path to human-like compounding innovation through ongoing novel data generation.