A social path to human-like artificial intelligence
This addresses the problem of achieving human-like AI for researchers by shifting focus from individual agents to social processes, though it is incremental in building on existing multi-agent AI work.
The paper argues that the bottleneck in AI progress is shifting from data assimilation to novel data generation, and proposes that integrating social mechanisms like multi-agent interactions and collective learning can provide a path to human-like compounding innovation.
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.