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.

通过证据的汇集，展示了自然智能是通过不同尺度的互动网络中的智能集体、社会关系和主要进化转变而产生的，这些过程通过种群压力、军备竞赛、马基雅维利选择、社会学习和积累文化等机制促进了新数据的产生，将这些机制整合到智能代理的视角中，表明通过持续的新数据生成实现类人复合创新的可能路径。