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自主“自动驾驶”实验室:技术与政策影响综述

Autonomous 'self-driving' laboratories: a review of technology and policy implications.

作者信息

Tobias Alexander V, Wahab Adam

机构信息

Department of Biotechnology and Life Sciences, The MITRE Corporation, McLean, VA, USA.

出版信息

R Soc Open Sci. 2025 Jul 16;12(7):250646. doi: 10.1098/rsos.250646. eCollection 2025 Jul.

DOI:10.1098/rsos.250646
PMID:40852582
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12368842/
Abstract

This article reviews and provides perspective on the emerging technology of autonomous, 'self-driving' laboratories (SDLs) that combine artificial intelligence (AI) and laboratory automation to perform research in chemistry, materials science and biological sciences. Today's most capable SDLs automate nearly the entire scientific method, from hypothesis generation, experimental design, experiment execution and data analysis, to drawing conclusions and updating hypotheses for subsequent rounds of optimization or discovery. 'Cloud labs' offer subscription-based remote-control access to experimental capabilities. Reports of AI-directed experiments executed in cloud labs are appearing in the literature, previewing a democratization of science that intrigues but inspires concern. Indeed, SDLs have potential implications for society far beyond the academy. Inventions emerging from AI-driven science pose a grand challenge, as patent laws across the world recognize only human inventors. If the inventions they generate remain unpatentable, funding for SDLs may be constrained. SDLs raise safety and security concerns. We deem them surmountable with a proactive approach, ultimate human accountability and robust cybersecurity measures. Finally, we estimate the impacts of SDLs on the technical labour force. Our analysis suggests that SDLs may displace some scientific roles but are likely to create many new opportunities.

摘要

本文回顾并展望了新兴的自主“自动驾驶”实验室(SDL)技术,该技术将人工智能(AI)与实验室自动化相结合,用于化学、材料科学和生物科学领域的研究。当今最先进的SDL几乎能自动化整个科学方法,从假设生成、实验设计、实验执行和数据分析,到得出结论以及为后续的优化或发现更新假设。“云实验室”提供基于订阅的远程控制实验功能。在云实验室中执行的人工智能指导实验的报告已出现在文献中,预示着科学的民主化,这既引人入胜又引发担忧。事实上,SDL对社会的潜在影响远远超出学术界。人工智能驱动的科学产生的发明带来了巨大挑战,因为世界各国的专利法只认可人类发明者。如果它们产生的发明仍然无法获得专利,那么对SDL的资金投入可能会受到限制。SDL引发了安全方面的担忧。我们认为通过积极主动的方法、最终的人类问责制和强大的网络安全措施,这些问题是可以克服的。最后,我们估计了SDL对技术劳动力的影响。我们的分析表明,SDL可能会取代一些科学岗位,但也可能创造许多新机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed72/12368842/a6261dc0d7b1/rsos.250646.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed72/12368842/9c117f966af4/rsos.250646.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed72/12368842/a6261dc0d7b1/rsos.250646.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed72/12368842/9c117f966af4/rsos.250646.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed72/12368842/a6261dc0d7b1/rsos.250646.f002.jpg

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