How We’re EVQLVing Life Science

Feb 21, 2020 | Blog | 0 comments

Daniel Gigante

If you weren’t aware we have ambition goals over here at EVQLV. One of them is to change or EVQLV the life science space. Andrew Satz (CEO) and I had a discussion on what that means and how we plan to get there. See below.

Transcript of Conversation

Daniel: Can you speak to your desire to change the life science space?

Andrew: This is not really a critique of life science. I’m very passionate about it. However, there’s a very traditional view in life science and it’s very much about experimentation and failure, experimentation and failure, experimentation and success. Now, they’re not trying to fail on experiments! Everyone is very passionate about being successful. In fact, the vast majority of failures are never reported. What we’re thinking about is can we fail as much as possible on the computer so that failure becomes less frequent in the laboratory where it becomes expensive — expensive from chemicals, from time, from the utilization of people. If we’re trying to advance healing from the laboratory bench to the patient’s bedside, I think we just have to fail faster. That’s what the computer allows us to do.

We are in no way trying to replace the person working in the laboratory. We’re not trying to displace life scientists. In fact, our algorithms wouldn’t exist without the ability to utilize the data created by life scientists! What we’re trying to do is say, hey, if something works, maybe we can computationalize it and run it at scale. We really want to evolve the way life science is operating. We want to utilize technology as much as possible early on so we can accelerate the speed at which things occur in the laboratory.

Daniel: So there are no plans to displace scientists. You’re just looking to make them more efficient?

Andrew: Correct. I actually think what this will allow the scientist to do is focus less on the mundane or what can be automated on the computer, and instead allow them to use their higher ordered thinking that a machine can’t do — at least at this point. There are these sort of rote, repetitive processes that can be automated by AI or robotics and that’s really what we’re trying to do. Let’s automate these processes.

Daniel: It reminds me of an HRIS system…an ADP or BambooHR. They didn’t replace the recruiter or HR manager. What they did was remove that mundane work that took time away from the human element of recruiting and onboarding.

Andrew: Exactly. I don’t want to say we’re changing everything. There is technology out there to do this kind of stuff. The thing is that lab scientists are lab scientists, not technologists. So, if you’re using a tool and it’s a small part of what you do on a daily basis, the scientist’s ability to rely on that technology is beneficial to the work you’re doing. However, the scientist is a single user operating a single system. What we’re doing is getting the machine to operate the system and operate it at scale. So the scientist isn’t copying and pasting and checking sequences ad nauseam. That’s really one of the ideas behind AI: to automate. That “A” doesn’t just stand for “artificial,” but “automate” as well.

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