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

Area of Science:

• Chemistry
• Materials Science
• Biological Sciences

Background:

• Emerging technology of autonomous, 'self-driving' laboratories (SDLs) combining artificial intelligence (AI) and laboratory automation.
• SDLs automate nearly the entire scientific method, from hypothesis generation to data analysis and conclusion updating.
• 'Cloud labs' provide subscription-based remote access to experimental capabilities, enabling AI-directed experiments.

Purpose of the Study:

• To review and provide perspective on the emerging technology of SDLs.
• To explore the implications of SDLs for scientific research, patent law, safety, security, and the workforce.
• To assess the potential societal impacts of AI-driven scientific discovery.

Main Methods:

• Review of current literature and capabilities of SDLs.
• Analysis of AI's role in automating the scientific method.
• Examination of legal, ethical, and societal challenges posed by SDLs.

Main Results:

• SDLs are increasingly capable of automating the full scientific method.
• AI-driven science presents challenges for patent law, as only human inventors are recognized.
• SDLs raise safety, security, and workforce concerns, though deemed surmountable.
• Analysis indicates potential displacement of some scientific roles but creation of new opportunities.

Conclusions:

• SDLs represent a significant advancement in scientific research automation.
• Addressing patent, safety, security, and workforce implications is crucial for SDLs' responsible development.
• Proactive measures, human accountability, and cybersecurity are essential for managing SDL risks.
• The integration of AI in laboratories is poised to reshape the scientific landscape and labor market.