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Tony Wang

PhD Student | MIT

NIST

Tony Wang is a PhD student in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), where he is advised by Nir Shavit. Tony’s research focuses on adversarial robustness. Tony collaborated with Adam Gleave and others at FAR.AI. For more information, see his website.

Publications

Can Go AIs be adversarially robust?

Robustness

We tested three approaches to defend Go AIs from adversarial strategies. While these defenses protect against previously discovered adversaries, we uncovered qualitatively new adversaries that undermine these defenses.

June 17, 2024
Date Range

Adversarial Policies Beat Superhuman Go AIs

Robustness

We describe an attack on the state-of-the-art Go-playing AI system, KataGo. The adversaries do not win by learning to play Go better than KataGo but instead by tricking KataGo into making serious blunders, demonstrating that even superhuman AI systems may harbor surprising failure modes.

January 8, 2023
Date Range

News

Even Superhuman Go AIs Have Surprising Failure Modes

Robustness & Security

Our adversarial testing algorithm uncovers a simple, human-interpretable strategy that consistently beats superhuman Go AIs.

July 14, 2023
Date Range

Beyond the Board: Exploring AI Robustness Through Go

Robustness & Security

Achieving robustness remains a significant challenge even in narrow domains like Go. We test three approaches to defend Go AIs from adversarial strategies. We find these defenses protect against previously discovered adversaries, but uncover qualitatively new adversaries that undermine these defenses.

June 17, 2024
Date Range

Research

Our research explores a portfolio of high-potential agendas.

Events

Our events bring together global leaders in AI.

Programs

Our programs build the field of trustworthy and secure AI