Automating the practice of science: Opportunities, challenges, and implications

Sebastian Musslick; Laura K. Bartlett; Suyog H. Chandramouli; Marina Dubova; Fernand Gobet; Thomas L. Griffiths; Jessica Hullman; Ross D. King; J. Nathan Kutz; Christopher G. Lucas; Suhas Mahesh; Franco Pestilli; Sabina J. Sloman; William R. Holmes

doi:10.1073/pnas.2401238121

Automating the practice of science: Opportunities, challenges, and implications

Sebastian Musslick^*
, Laura K. Bartlett
, Suyog H. Chandramouli
, Marina Dubova
, Fernand Gobet
, Thomas L. Griffiths
, Jessica Hullman
, Ross D. King
, J. Nathan Kutz
, Christopher G. Lucas
, Suhas Mahesh
, Franco Pestilli
, Sabina J. Sloman
, William R. Holmes

^*Corresponding author for this work

Department of Information and Communications Engineering

Research output: Contribution to journal › Article › Scientific › peer-review

19 Citations (Scopus)

110 Downloads (Pure)

Abstract

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.

Original language	English
Article number	e2401238121
Number of pages	10
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	122
Issue number	5
DOIs	https://doi.org/10.1073/pnas.2401238121
Publication status	Published - 4 Feb 2025
MoE publication type	A1 Journal article-refereed

Funding

S. Musslick and S. Mahesh were supported by Schmidt Science Fellows, in partnership with the Rhodes Trust. S. Musslick was also supported by the Carney BRAINSTORM program at Brown University and the NSF (2318549). S. Mahesh also acknowledges the support of the Acceleration Consortium fellowship. S.J. Sloman acknowledges support from the UK Research and Innovation (UKRI) Turing AI World-Leading Researcher Fellowship [EP/W002973/1]. S.H.C. was supported by the Finnish Center for Artificial Intelligence, and Academy of Finland (328813); he also acknowledges the support from the Jorma Ollila Mobility Grant by Nokia Foundation. L.K.B. and F.G. were supported by European Research Council Grant ERC-ADG-835002GEMS. T.L.G. was supported by a grant from the NOMIS Foundation. R.D.K. was supported by the Wallenberg AI, Autonomous Systems and Software Program funded by the Knut and Alice Wallenberg Foundation, by Chalmers Artificial Intelligence Research Centre, and by the UK Engineering and Physical Sciences Research Council (EPSRC) Grants EP/R022925/2 and EP/W004801/1. W.R.H. was supported by the NSF (SES-2242962). J.N.K. acknowledges support from the National Science Foundation AI Institute in Dynamic Systems (2112085). We thank Solomon Oyakhire for valuable feedback. F.R. acknowledges support by National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering (R01EB029272, R01EB030896NSF and R01EB030896), National Science Foundation Behavior and Cognitive Science (1734853, 1636893), Advanced Cyberinfrastructure (1916518), and Information and Intelligent Systems (1912270). 13. A. Velasquez, Foundation Models for Scientific Discovery (FoundSci) (Defense Advanced Research Projects Agency DARPA Program Solicitation, 2023). ACKNOWLEDGMENTS. S. Musslick and S. Mahesh were supported by Schmidt Science Fellows, in partnership with the Rhodes Trust. S. Musslick was also supported by the Carney BRAINSTORM program at Brown University and the NSF (2318549). S. Mahesh also acknowledges the support of the Acceleration Consortium fellowship. S.J. Sloman acknowledges support from the UK Research and Innovation (UKRI) Turing AI World-Leading Researcher Fellowship [EP/W002973/1]. S.H.C. was supported by the Finnish Center for Artificial Intelligence, and Academy of Finland (328813); he also acknowledges the support from the Jorma Ollila Mobility Grant by Nokia Foundation. L.K.B. and F.G. were supported by European Research Council Grant ERC-ADG-835002- GEMS. T.L.G. was supported by a grant from the NOMIS Foundation. R.D.K. was supported by the Wallenberg AI, Autonomous Systems and Software Program funded by the Knut and Alice Wallenberg Foundation, by Chalmers Artificial Intelligence Research Centre, and by the UK Engineering and Physical Sciences Research Council (EPSRC) Grants EP/R022925/2 and EP/W004801/1. W.R.H. was supported by the NSF (SES-2242962). J.N.K. acknowledges support from the National Science Foundation AI Institute in Dynamic Systems (2112085). We thank Solomon Oyakhire for valuable feedback. F.R. acknowledges support by National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering (R01EB029272, R01EB030896NSF and R01EB030896), National Science Foundation Behavior and Cognitive Science (1734853, 1636893), Advanced Cyberinfrastructure (1916518), and Information and Intelligent Systems (1912270).

Keywords

AI for science
automation
computational scientific discovery
metascience

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10.1073/pnas.2401238121Licence: CC BY

musslick-et-al-2025-automating-the-practice-of-science-opportunities-challenges-and-implicationsFinal published version, 1.07 MBLicence: CC BY

Cite this

Musslick, S., Bartlett, L. K., Chandramouli, S. H., Dubova, M., Gobet, F., Griffiths, T. L., Hullman, J., King, R. D., Kutz, J. N., Lucas, C. G., Mahesh, S., Pestilli, F., Sloman, S. J., & Holmes, W. R. (2025). Automating the practice of science: Opportunities, challenges, and implications. Proceedings of the National Academy of Sciences of the United States of America, 122(5), Article e2401238121. https://doi.org/10.1073/pnas.2401238121

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abstract = "Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.",

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Musslick, S, Bartlett, LK, Chandramouli, SH, Dubova, M, Gobet, F, Griffiths, TL, Hullman, J, King, RD, Kutz, JN, Lucas, CG, Mahesh, S, Pestilli, F, Sloman, SJ & Holmes, WR 2025, 'Automating the practice of science: Opportunities, challenges, and implications', Proceedings of the National Academy of Sciences of the United States of America, vol. 122, no. 5, e2401238121. https://doi.org/10.1073/pnas.2401238121

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AU - Chandramouli, Suyog H.

AU - Dubova, Marina

AU - Gobet, Fernand

AU - Griffiths, Thomas L.

AU - Hullman, Jessica

AU - King, Ross D.

AU - Kutz, J. Nathan

AU - Lucas, Christopher G.

AU - Mahesh, Suhas

AU - Pestilli, Franco

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AB - Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.

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Automating the practice of science: Opportunities, challenges, and implications

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Keywords

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Human Automata: Simulator-based Methods for Collaborative AI

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