I'm sure a Gaussian process could model the equivalent of feedback control; they're pretty flexible (if not as flexible as NNs). The trick is coming up with something that can learn the feedback controls, or lack thereof, on its own. Unsurprisingly, they're reinventing reinforcement learning.
This is a neat application of ML! In the same way that automated theorem provers have had huge impact on doing mechanized proofs, perhaps "automated lab assistant" could have a similar impact on the experimental sciences.
It looks like they've hooked up standard function optimization techniques to their lab equipment. Nothing new from an ML perspective. Is there something clever in the way they parameterized the space of possible experiments?
'This AI is extremely specific in its design, of course, ... for more flexible automation, physicists will still have to rely on the general-purpose research units called “graduate students.”'
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[ 3.3 ms ] story [ 28.2 ms ] threadThis sounds suspiciously like slapping the term "AI" on to a oscillating feedback control loop.
You're not far from wrong, but look at the original article.
This is a neat application of ML! In the same way that automated theorem provers have had huge impact on doing mechanized proofs, perhaps "automated lab assistant" could have a similar impact on the experimental sciences.
'This AI is extremely specific in its design, of course, ... for more flexible automation, physicists will still have to rely on the general-purpose research units called “graduate students.”'