Superhuman AI Bots Are Surprisingly Vulnerable to Exploits

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By learning exploits from adversarial AI, people could defeat a superhuman Go-playing system

is heating up. But research has revealed weaknesses in one of the most successful AI systems — a bot that plays the board game Go and can beat the world’s best human players — showing that such superiority can be fragile. The study raises questions about whether more general AI systems will suffer from vulnerabilities that could compromise their safety and reliability, and even their claim to be ‘superhuman’.

In Go, two players take turns placing black and white stones on a grid to surround and capture the other player’s stones. In 2022, researchers reported, the best open-source Go-playing AI system, which typically beats the best humans handily . Their bots found exploits that regularly beat KataGo, even though the bots were otherwise not very good — human amateurs could beat them. What’s more, humans could understand the bots’ tricks and adopt them to beat KataGo.

The second defensive strategy that Gleave’s team tried was iterative: training a version of KataGo against adversarial bots, then training attackers against the updated KataGo and so on, for nine rounds. But this didn’t result in an unbeatable version of KataGo either. Adversaries kept finding exploits, with the final one beating KataGo 81% of the time.

 

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