The hosts describe cloud laboratories such as Emerald Cloud Lab and Strateos, which let researchers run physical wet-lab experiments like mass spectrometry or centrifugation remotely through a software interface, with automation and robotics handling execution while results come back in real time. They compare this to how cloud computing gave machine learning researchers remote access to GPUs, and argue the bigger shift is that specialized lab equipment can now be rented rather than owned. They also raise questions this creates, including who gets authorship credit when a cloud lab generates the underlying data for a published experiment.
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The hosts note this could let under-resourced research teams, including those working on neglected diseases in global health, ask questions that previously required access to high-end equipment they could not afford.
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They compare the model to a patient getting a blood test sent to an outside lab like Quest Diagnostics, where one party orders the work and another executes it remotely.
873 words · auto-generated from the episode video
1:28:04>> history about how we've done these things for quite some time right >> so this is from Emerald Cloud Lab and Stratios and they kind of address a slightly different part of this execution which is making physical laboratories accessible through software through the cloud remotely. So I'm in one location, I need to do X, Y or Z in another location and the entirety of the process that I need to be done is remotely executed by automation and robotics. But I have a software interface that I can interact with in real time to execute. So like a lot of folks might go go to the doctor
1:28:45and then they need to get, you know, a blood test and then they send it to Quest Diagnostics, right? So this idea that you have one person doing the question, one person doing the execution and then it comes back to your physician to give you the result. The difference here again with still some elements of human involvement for some of the component parts many of the components are now directly cloud connectable and fully automated. >> This is a I think a a big area. >> I think this is very big. >> No, this is crazy. This is absolutely and you get how long it takes to run each. You get the real-time data back in the format. It's
1:29:26a It's a It's a wet lab that you can access through the cloud. >> This is >> Isn't that crazy? >> I mean, no, they're doing mass spec, I mean, or something, you know? This seems like >> this seems like stuff that would require specialized equipment, but there you go. Like Right. >> Um, >> no, I mean, that's actually incredible, right? Because for the longest time, we've seen machine learning researchers do this. you just access GPUs from the cloud AWS and then and then you know you're doing machine learning experiments. Well, now you're actually doing like real biological experiments. You've got pipets that are automated. You've got >> um I think I saw a centrifuge that was
1:30:08automated, right? And they did it correctly unlike the Ryan Gosling movie. >> Um I >> look it was it was one of the only flaws I brought up. I was wondering if I was gonna get if you were gonna say or miss it. But >> but like they did it right. Like the the AI knows to to balance the centrifuge >> unlike Ryan Gosling. >> So but no, this is incredible. >> And what again this is not a promo for any particular entity. There are variety of companies that are trying to do something. >> No, I'm just saying this is a new era in uh in scientific. >> Correct. >> Yeah. It's like so I mean one one thing
1:30:48I I want to understand is like is Emerald Cloud Lab going to be a co-author on I I wonder like as a as a former researcher in like a PhD or a postoc setting it's like I'm always worried about like who's going to be co-author on the paper if I were to use this data right do they have some kind of thing in their contract being like we got to be acknowledgements are fine co-author it's a bit you know like okay Maybe a maybe a footnote. Yeah, m may maybe a footnote. And one of the things that this brings up that I was kind of thinking about that's not necessarily as obvious cuz like the idea is like oh you know a lot
1:31:30of the thoughts around these AI implementations is it's going to allow the rich to get richer which is true in some >> in some in a lot of context that's true but what it does also like what type of research teams that might not have access to these types of resources who are in underserved environments. What kind of questions can they ask >> now that they and it's the idea of like AWS create and all this cloud computing allowed people to build websites and do video and we can do this show because of >> cloud computing and distributed networking and you know this wouldn't exist without that. So it does bring up the question like what kind of research
1:32:11questions and what access that teams that otherwise would not have been able to access these kind of highfidelity resources particularly in my family who's worked in you know global health and neglected diseases. um it becomes a very interesting democratization of the access to the types of tooling when you have this cloud accessibility and that that was a non-obvious conclusion uh after kind of digging into this is like it it is a different paradigm that allows us to ask some different questions. One last sort of note I want to bring up going to the third stage because we talked about design and how to prioritize what questions to ask. We
1:32:51then looked at automation at two different levels. right? One where the humans are still involved in the lab, one that's cloud accessible. Um, Llaya Sciences is trying
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