Automated science uses AI to plan, execute, and interpret experiments without human intervention. Automated science platforms combine three technologies: laboratory robots that perform physical experiments, a machine learning model that predicts results, and an AI agent that plans future experiments. This course introduces all three technologies and shows how they fit together into a closed-loop, autonomous system. The course emphasizes the interfaces between technologies, e.g. how to modify machine learning models for use with a planning agent, or the liquid handling challenges that arise from AI-planned experiments. Case studies in biology and chemistry will highlight state-of-the-art automated science systems.
Attendees will learn from instructors who have assembled automated science pipelines in biology and chemistry. The course requires only a basic understanding of statistics and laboratory automation. Practitioners with expertise in one technology (e.g. machine learning or lab automation) are encouraged to attend and learn how to combine their knowledge with other parts of an automated science platform.