I recently proposed three reasons why Biotech ML/AI projects often struggle to have a tangible impact, and the last of these was difficulty getting data and metadata from the lab quickly and consistently. This is most acute near the end of the project, when you’re trying to build a repeatable process. But it’s also the problem that has the most technical aspect, because it relies heavily on the software you have available. So in this and the next few posts, I want to explore why this kind of software is so hard to get right. And to frame the discussion, I’ll argue that what this software really needs to do is create a “digital twin” of the lab.
Is your digital twin identical or fraternal?
Is your digital twin identical or fraternal?
Is your digital twin identical or fraternal?
I recently proposed three reasons why Biotech ML/AI projects often struggle to have a tangible impact, and the last of these was difficulty getting data and metadata from the lab quickly and consistently. This is most acute near the end of the project, when you’re trying to build a repeatable process. But it’s also the problem that has the most technical aspect, because it relies heavily on the software you have available. So in this and the next few posts, I want to explore why this kind of software is so hard to get right. And to frame the discussion, I’ll argue that what this software really needs to do is create a “digital twin” of the lab.