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Today in @nature, with @EPFL, the first deep reinforcement learning system that can keep nuclear fusion plasma stable inside its tokamaks, opening new avenues to advance nuclear fusion research. Paper: dpmd.ai/fusion-paper
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This bit caught my eye… behind the jargon, the conventional approach is a *very* primitive strategy. SISO PID+gain scheduling is basically 1950s era controls. There would be no stability guarantees which removes one on the big reasons to use control theoretic over AI approaches.
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I always liked the idea of throwing AI at control problems, even though conservative friends would whine about lack of stability and convergence guarantees. But most controls as practiced in industry doesn’t use the advanced techniques that offer such guarantees anyway.
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yeah but "AI improves upon your industrial control systems" is almost exactly the Nanotronics AIPC pitch. (This is an easy case to make in situations where e.g. there's complex chemistry that nobody has a half-decent model of.)
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I remember thinking of this point like in 1999-2000 during a couple of controls talks on control of atomic force microscopes and semiconductor lithography processes. My reaction was "this is the wrong way to do this"
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