I co-lead the Carpenter-Singh Lab at the Broad. Our work built much of the foundation behind Cell Painting and image-based profiling - turning microscopy into fingerprints of genes, chemicals, and disease states. It's now in 100+ labs, at the core of several techbios, and used across every major pharma.
I've now pivoted to agents for science: apprentices that learn from how we work, distill what they see into runbooks you can read and edit, and eventually run the loop on their own.
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Apprentices
We stand at an unmissable moment in the history of technology: to build systems that can learn how scientists do their work, make new tools to do it, and capture that knowledge in forms scientists can read and refine. A system that looks over your shoulder as you do a task, distills what it observes into a runbook, uses that runbook to perform the task on its own, then refines through feedback and reflection, many times over. Apprenticeship learning1 is not a new concept, but for the first time we have all the pieces to make it practical, scalable, and customizable for individual scientists at an organizational level. Most important is that these runbooks are an intermediate representation, a human-readable artifact, made up of composable pieces now called agent skills, which makes the whole system inspectable, editable by any scientist, and persistent as institutional knowledge. The models that have come out in the last few months, their instruction-following, their ability to distill and reflect and build tools and use tools, all of it is already here.