Mitigate the risks posed by autonomous AI-driven labs

Early in 2020, as cities around the world began locking down in response to covid, a few researchers were still able to continue to run their experiments.

- RAHUL MATTHAN

Even though they, like everyone else, had been prohibited from entering their labs, they were able to log into 'cloud laboratories' and submit their trials remotely, leaving it to robotic arms and automated instruments to execute their instructions from a distance.

What was a quaint convenience in the midst of a crisis is now a widespread reality, as software, robotics and artificial intelligence (AI) have come together to bring the concept of 'work-from-home' to scientific experimentation. Around the world, commercial cloud labs have already begun to invert traditional scientific workflows to the point where, instead of researchers moving between their instruments, samples travel through robotic pathways.

Self-driven laboratories take this one step further. By embedding AI directly into these autonomous laboratories, they can move beyond just executing instructions to actively generating them. These intelligent automated systems are not only able to identify new experiments and carry them out using robotic infrastructure, but also analyse their results and, based on the feedback, decide what needs to be done next. In the process, the long cycle of experimentation can be collapsed into a continuous feedback loop.