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Building machines that learn and think with people.

Authors :
Collins KM
Sucholutsky I
Bhatt U
Chandra K
Wong L
Lee M
Zhang CE
Zhi-Xuan T
Ho M
Mansinghka V
Weller A
Tenenbaum JB
Griffiths TL
Source :
Nature human behaviour [Nat Hum Behav] 2024 Oct; Vol. 8 (10), pp. 1851-1863. Date of Electronic Publication: 2024 Oct 22.
Publication Year :
2024

Abstract

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.<br /> (© 2024. Springer Nature Limited.)

Details

Language :
English
ISSN :
2397-3374
Volume :
8
Issue :
10
Database :
MEDLINE
Journal :
Nature human behaviour
Publication Type :
Academic Journal
Accession number :
39438684
Full Text :
https://doi.org/10.1038/s41562-024-01991-9