AI-Driven Research Workflows: Lessons learned from a million automated experiments - Paul Jensen

“Lessons learned from a million automated experiments”
Presentation abstract: We developed an automated platform (called BacterAI) that learns the nutrient requirements of bacteria entirely on its own. Most importantly, our system requires no prior knowledge of the bacteria it studies; it learns using only the results of experiments it designs and executes autonomously. Our insight was to accelerate automated science by converting biology into a game. Recasting a scientific question as a puzzle makes it amenable to the powerful reinforcement learning techniques that underlie AI’s advances in gameplay. The agents in our automated system think they are simply playing a game by making moves and collecting points. The agents are unaware that each move corresponds to a scientific experiment.
One million experiments later, our team has much about planning, running, and analyzing automated experiments. This talk discusses lessons learned and open problems when running experiments in a closed loop. In short, most of the challenges we encountered were unexpected, and many of our worries were unfounded.
About this event: Significant advancements in Artificial Intelligence (AI), including Generative AI, and the hardware and software research environment are enabling researchers to develop AI-driven research workflows (ARWs): AI and Generative AI for hypothesis generation, experimental design and monitoring, as well as data acquisition, processing and analytics. Such ARWs will not only significantly accelerate research, but also enable new research possibilities.
This mini-symposium featured a keynote by Ian Foster (Argonne National Lab) and presentations from University of Michigan faculty members showcasing how they have developed and embedded AI-driven components in their research workflows.
For more information about the series and speakers, please visit the event page at: midas.umich.edu/ai-driven-res...
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