Learning few-shot imitation as cultural transmission

Avishkar Bhoopchand, Bethanie Brownfield, Adrian Collister, Agustin Dal Lago, Ashley Edwards, Richard Everett, Alexandre Fréchette, Yanko Gitahy Oliveira, Edward Hughes (), Kory W. Mathewson, Piermaria Mendolicchio, Julia Pawar, Miruna Pȋslar, Alex Platonov, Evan Senter, Sukhdeep Singh, Alexander Zacherl and Lei M. Zhang
Additional contact information
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

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42875-2

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