Evidence map›Paper›PMID 38016945›Full record

ArticleNature communications2023

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 and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Avishkar BhoopchandGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.ORCID http://orcid.org/0009-0004-0465-4244
Bethanie BrownfieldGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Adrian CollisterGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Agustin Dal LagoGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.ORCID http://orcid.org/0000-0001-5187-2448
Ashley EdwardsGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Richard EverettGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.ORCID http://orcid.org/0000-0002-9404-6338
Alexandre FréchetteGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Yanko Gitahy OliveiraGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Edward HughesGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK. edwardhughes@google.com.ORCID http://orcid.org/0000-0002-2434-2334
Kory W MathewsonGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Piermaria MendolicchioGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Julia PawarGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Miruna PȋslarGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Alex PlatonovGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Evan SenterGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Sukhdeep SinghGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Alexander ZacherlGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Lei M ZhangGoogle DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID38016945
PMCPMC10684502

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.