Abstract:

Social learning is a key driver of the success of the human species (Henrich, 2015), enabling the preservation of technologies that individuals could not possibly have acquired on their own (Tennie, Call, & Tomasello, 2009). Presently, we are witnessing the rise of intelligent machines – “AI systems” – that are capable of making discoveries beyond the human horizon (Wang et al., 2023). This raises the question of whether social learning enables humans to also adopt and preserve inconceivable discoveries made by intelligent machines. In our behavioral experiment, participants’ success in a strategic task hinged on adopting a solution that humans struggle to discover due to a characteristic bias to avoid large losses. We contrast populations of humans transmitting solutions over multiple generations on their own with populations that had the opportunity to learn from AI agents in their first generation. Our findings show that an algorithmic model succeeds in discovering the adaptive strategy where humans fail on their own. Most importantly, humans are able to adopt this machine-introduced strategy and preserve it over multiple generations. Taken together, our results showcase the potential of intelligent machines to transform human cultural evolution, and an opportunity for designing them towards benefitting human progress.

Speaker's social media:

Twitter: @ThomasF_Mueller Bluesky: @thomasfmueller.bsky.social