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Befuddling AI Go Systems: MIT, UC Berkeley & FAR AI’s Adversarial Policy Achieves a >99% Win Rate Against KataGo
Nov. 2, 2022, 10:39 p.m. | Synced
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In the new paper Adversarial Policies Beat Professional-Level Go AIs, a research team from MIT, UC Berkeley, and FAR AI employs a novel adversarial policy to attack the state-of-the-art AI Go system KataGo. The team believes theirs is the first successful end-to-end attack against an AI Go system playing at the level of a human professional.
The post Befuddling AI Go Systems: MIT, UC Berkeley & FAR AI’s Adversarial Policy Achieves a >99% Win Rate Against KataGo first appeared on …
ai artificial intelligence deep-neural-networks machine learning machine learning & data science mit ml policy rate reinforcement learning research systems technology uc berkeley
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