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

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMusslick, Sebastian
dc.contributor.authorBartlett, Laura K.
dc.contributor.authorChandramouli, Suyog H.
dc.contributor.authorDubova, Marina
dc.contributor.authorGobet, Fernand
dc.contributor.authorGriffiths, Thomas L.
dc.contributor.authorHullman, Jessica
dc.contributor.authorKing, Ross D.
dc.contributor.authorKutz, J. Nathan
dc.contributor.authorLucas, Christopher G.
dc.contributor.authorMahesh, Suhas
dc.contributor.authorPestilli, Franco
dc.contributor.authorSloman, Sabina J.
dc.contributor.authorHolmes, William R.
dc.contributor.departmentDepartment of Information and Communications Engineeringen
dc.contributor.organizationOsnabrück University
dc.contributor.organizationLondon School of Economics and Political Science
dc.contributor.organizationIndiana University
dc.contributor.organizationPrinceton University
dc.contributor.organizationNorthwestern University
dc.contributor.organizationUniversity of Cambridge
dc.contributor.organizationUniversity of Washington
dc.contributor.organizationUniversity of Edinburgh
dc.contributor.organizationUniversity of Toronto
dc.contributor.organizationUniversity of Texas at Austin
dc.contributor.organizationUniversity of Manchester
dc.date.accessioned2025-02-24T21:38:31Z
dc.date.available2025-02-24T21:38:31Z
dc.date.issued2025-02-04
dc.descriptionPublisher Copyright: Copyright © 2025 the Author(s).
dc.description.abstractAutomation 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.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.mimetypeapplication/pdf
dc.identifier.citationMusslick, 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, vol. 122, no. 5, e2401238121. https://doi.org/10.1073/pnas.2401238121en
dc.identifier.doi10.1073/pnas.2401238121
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.otherPURE UUID: af8a7f1b-e9f6-42cf-b44f-33f479024b1a
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/af8a7f1b-e9f6-42cf-b44f-33f479024b1a
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/173767554/musslick-et-al-2025-automating-the-practice-of-science-opportunities-challenges-and-implications.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/134271
dc.identifier.urnURN:NBN:fi:aalto-202502242541
dc.language.isoenen
dc.publisherNational Academy of Sciences
dc.relation.fundinginfoS. 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).
dc.relation.ispartofseriesProceedings of the National Academy of Sciences of the United States of Americaen
dc.relation.ispartofseriesVolume 122, issue 5en
dc.rightsopenAccessen
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.keywordAI for science
dc.subject.keywordautomation
dc.subject.keywordcomputational scientific discovery
dc.subject.keywordmetascience
dc.titleAutomating the practice of science: Opportunities, challenges, and implicationsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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