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Learning few-shot imitation as cultural transmission

Author

Listed:
  • Avishkar Bhoopchand

    (Google DeepMind)

  • Bethanie Brownfield

    (Google DeepMind)

  • Adrian Collister

    (Google DeepMind)

  • Agustin Dal Lago

    (Google DeepMind)

  • Ashley Edwards

    (Google DeepMind)

  • Richard Everett

    (Google DeepMind)

  • Alexandre Fréchette

    (Google DeepMind)

  • Yanko Gitahy Oliveira

    (Google DeepMind)

  • Edward Hughes

    (Google DeepMind)

  • Kory W. Mathewson

    (Google DeepMind)

  • Piermaria Mendolicchio

    (Google DeepMind)

  • Julia Pawar

    (Google DeepMind)

  • Miruna Pȋslar

    (Google DeepMind)

  • Alex Platonov

    (Google DeepMind)

  • Evan Senter

    (Google DeepMind)

  • Sukhdeep Singh

    (Google DeepMind)

  • Alexander Zacherl

    (Google DeepMind)

  • Lei M. Zhang

    (Google DeepMind)

Abstract

Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. It can be thought of as the process that perpetuates fit variants in cultural evolution. In humans, cultural evolution has led to the accumulation and refinement of skills, tools and knowledge across generations. We provide a method for generating cultural transmission in artificially intelligent agents, in the form of few-shot imitation. Our agents succeed at real-time imitation of a human in novel contexts without using any pre-collected human data. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission and develop an evaluation methodology for rigorously assessing it. This paves the way for cultural evolution to play an algorithmic role in the development of artificial general intelligence.

Suggested Citation

  • Avishkar Bhoopchand & Bethanie Brownfield & Adrian Collister & Agustin Dal Lago & Ashley Edwards & Richard Everett & Alexandre Fréchette & Yanko Gitahy Oliveira & Edward Hughes & Kory W. Mathewson & P, 2023. "Learning few-shot imitation as cultural transmission," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42875-2
    DOI: 10.1038/s41467-023-42875-2
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    References listed on IDEAS

    as
    1. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
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