Abstract:

In this submission, we explore the concept of "machine culture", describing culture that is mediated or generated by machines. In particular, we review existing and foreseeable instances where intelligent machines are transforming evolutionary processes of culture at the levels of variation, transmission, and selection. Empirical evidence highlights that machine culture is already occurring: models using reinforcement learning are enhancing variation when they inspire humans to develop novel behaviors, as has been demonstrated for the ancient game of Go (Shin et al. 2023); Large Language Models are facilitating the transmission of cultural knowledge and redefining the value of human creative skills (Epstein et al. 2023); and recommender systems are rewiring pathways of cultural transmission by selecting what and from whom humans learn. Compared to humans, machine intelligence is structurally different, trained differently, and subject to different computational constraints (Gershman et al. 2015, Griffiths 2020). We investigate how these idiosyncrasies of machines might reshape cultural variation, transmission, and selection. Ultimately, our contribution presents a research agenda and a conceptual framework that can be leveraged for deeper investigations of machine culture.

Speaker's social media:

@LevinBrinkmann