Tim Hwang

Miles Brundage and Tim Hwang discuss how AI could be misused, how emerging capabilities may change the scale and reach of those risks, and why predictions about future applications require humility. They examine public perceptions of AI, interpretability, fairness, accountability, transparency, and robustness, including how these concerns arise in health care and across different regulatory settings. They describe the need for policy work to connect technical research with government, industry, and civil society, and discuss shared goals between near-term AI governance and long-term AI safety. The talk closes with debate about open publication and predictions for advances in game-playing systems and meta-learning.

Tim Hwang describes how his Google public policy role connects AI research with governments, regulators, and social impacts. The talk explores fairness and privacy, adversarial vulnerabilities, automation, and the challenge of making machine learning understandable and useful in products. Hwang discusses opportunities in creative tools, small niche applications, cloud machine learning, and on-device learning, while emphasizing that good interfaces and domain knowledge matter alongside research. He closes with recommendations for learning about machine learning and its history, including past cycles of AI hype.