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

• 成果类型: Article
• 署名作者: Musslick, Sebastian; Bartlett, Laura K.; Chandramouli, Suyog H.; Dubova, Marina; Gobet, Fernand; Griffiths, Thomas L.; Hullman, Jessica; King, Ross D.; Kutz, J. Nathan; Lucas, Christopher G.; Mahesh, Suhas; Pestilli, Franco; Sloman, Sabina J.; Holmes, William R.
• 署名单位: University Osnabruck; Brown University; University of London; London School Economics & Political Science; Aalto University; University of Alberta; Princeton University; Indiana University System; Indiana University Bloomington; Roehampton University; Princeton University; Northwestern University; University of Cambridge; Chalmers University of Technology; University of Washington; University of Washington Seattle; University of Edinburgh; University of Toronto; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Manchester
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8525
• DOI: 10.1073/pnas.2401238121
• 发表日期: 2025-02-04
• 关键词: bayesian optimization; experimental-design; DISCOVERY; generation; program; models; driven

摘要

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.