The hosts lay out a three-stage framework for how AI work connects to physical experiments: first, an AI model designs and prioritizes targets (using the scorpion antivenom project as the example); second, physical automation labs like robotic systems carry out the actual bench work; and third, some efforts try to close the loop by feeding lab measurements back into the AI model to guide the next design iteration. They note that most current players operate in just one of these stages, while a few are attempting the full closed-loop cycle.
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The scorpion antivenom design prompt was described as about 35 pages long, run on roughly 300 GPUs, costing an estimated $40,000 to $60,000.
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Named physical-automation players include MIT, Emerald Cloud Lab, Strateos, and Ginkgo.
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Lila Sciences is cited as a company attempting the full closed-loop cycle of AI design, robotic execution, and feedback into the model.
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Carnegie Mellon and SLAC (Stanford Linear Accelerator Lab) are mentioned as providers of shared infrastructure supporting these automated lab efforts.
612 words · auto-generated from the episode video
1:19:40an AI lab ecosystem. Let's say that that's exactly right. like like this this is there's so many parts of this that are are still sort of not even optimized I would say just like catering to human time scales like PhD student time scales >> right right and and I think part of what we're trying to talk about in this story is looking at each of the stages of this process >> and where is AI but also the combination of automation and robotics ultimately interact with the real world. >> That's right. >> And so this is where the AI and robotics
1:20:22fit in that process. So I I wanted to again I was trying to >> help myself understand what are we looking at here. And so I created this map of how AI is going to like meet the physical lab and where the current state of play is and some of the sort of this is not a uh complete map of every player in the space but it's just to give a kind of representative example. >> So it kind of breaks down into these three stages. There's the design and prioritize. So we just talked about that in the centropic story with the scorpion antivenenom. So the AI is going to design and prioritize >> correct >> in this model. >> We we may still define the question,
1:21:04>> right? >> Scorpion antivenenom, >> right? Be like, give me an antivenenom to all of scorpions, >> right? >> And I think the original prompt for this was about 35 pages that they fed. They had I think 300 GPUs. It costs a total of about $40 to $60,000 to run this design and prioritize process, >> which is not crazy. >> No, but it's a little expensive, but it's, you know, not crazy. >> No, it's it's not the usual stuff that I ask Claude and Chad GPT to do, right? >> My prompts are not 35 pages, >> right? And if one of those ultimately became something that was able to get to the end of process, that would be
1:21:44fantastic. Okay. So, a variety of the frontier labs are doing that that that design and prioritize, but we're talking about this anthropic story in combination with Catrini. So, then sort of stage two is this idea of physical automation, not necessarily AI, anything. Uh we have a couple of players in here, MIT, uh Emerald, Cloud Lab, Stratios, and GKO. They're trying to now take the physical process that humans do in a lab >> and have it be run by a robotic automation. Obviously, it's a connective tissue because you can take part one and then send it to part two. >> Now, there are some companies that are trying to do the full
1:22:26closed loop cycle going back to our previous story of the learn and repeat. This is Lila science madiraa where it is the combination of both the AI designing prioritizing the robotic lab executing getting the measurements beating that back directly into the AI to then decide what to change and then retry right human oversight but yep >> kind of a closed loop cycle these processes are also using a variety of shared infrastructure for example Carnegie Melon is providing some of this shared infrastructure as well as Slack, the uh national lab, the uh Stanford linear accelerator lab. Yep. Is is
1:23:08giving some of the infrastructure around these pieces. So that's that's where it's sort of where we're trying to go is where Llaya Sciences is effectively saying we are right now. And so the
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