Go Attack
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About
This research project explores the robustness of superhuman Go AIs against adversarial strategies. Researchers discovered simple adversarial strategies that consistently beat state-of-the-art Go AIs, such as KataGo, at superhuman settings. The project investigates several defensive training methods, including positional and iterated adversarial training, as well as altering the network architecture to a vision transformer. Despite these defenses, new adversarial strategies are identified, revealing challenges in achieving robustness in even narrow domains. The research highlights the vulnerability of AI systems to unexpected attacks and the ongoing need for robustness improvements.
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Features
• robustness testing
• go game ai analysis
• adversarial policy training
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