3 ms·
In grad school we bought robots because we thought they would make us faster. With very few exceptions they didn't, I was still faster with a multipipettor than
by entee 7y ago
In grad school we bought robots because we thought they would make us faster. With very few exceptions they didn't, I was still faster with a multipipettor than our robots. This was because:
1.) Getting the robot to behave means it has to see the same thing every time, that's not easy to accomplish, hence the validation people do.
2.) Even when the thing is the same as before, robots break down. Handling solvents with robots is tricky because different solvents have different viscosities. If the robot misses a well, how do you know?
If you have a team that knows the robot well, and it's set up well, then all these problems are manageable. But lets not kid ourselves that it's just trivially solved now because we have robots.
An analogy in server land, we now have AWS and GCP. Basically now it's trivial to automate things it would take weeks/months to do by hand in the past. However, they each have an army of sysadmins and engineers behind the scenes servicing all the "robots" and making sure everything runs smoothly.
- dnautics 7y agoIt's also possible your robot will be the robot equivalent of azure.
- ericmcer 7y agoSo we should have just thrown away the first computers because inputting problems into them wasn't that much faster than doing it by hand?
- mattkrause 7y agoNobody's seriously suggesting that. That said, I do worry about all the hype and overpromising and whether that's eventually going to come back to haunt the entire field, like the (1st) AI Winter.
- entee 7y agoRobots are absolutely useful, and major companies are building out massive robotics facilities to accelerate drug development. The point is that it's not a magic solution. It still takes a bunch of staff to run, and (much less appreciated) it takes a different mindset to be maximally useful. For example, maybe instead of doing a dozen informative but slightly complex assays, you do 1000 less useful but less complex assays. The end result might be just as useful if not more, but you have to structure your experiment differently. It's unlikely that technology will play out the same way in biotech as it has in pure tech. I'm heavily invested in the idea that it will be immensely useful, but given the different constraints and problem space I think the trajectory will be different.