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

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.