Robin Hanson joins the podcast to discuss AI forecasting methods and metrics. Timestamps: 00:00 Introduction 00:49 Robin's experience working with AI 06:04 Robin's views on AI development 10:41 Should we care about metrics for AI progress? 16:56 Is it useful to track AI progress? 22:02 When should we begin worrying about AI safety? 29:16 The history of AI development 39:52 AI progress that deviates from current trends 43:34 Is this AI boom different than past booms? 48:26 Different metrics for predicting AI
Peter Wildeford discusses methods for forecasting AI progress and why he sees AI as neither a bubble nor a normal technology, covering economic effects, national security, cyber capabilities, robotics, export controls, and prediction markets.
Inria researcher Carina Prunkl discusses why AI evaluation struggles to keep pace with general-purpose systems, including jagged capabilities, missed real-world behavior, misuse risks, de-skilling, red teaming, and layered safeguards.
Li-Lian Ang from Blue Dot Impact discusses how to build a workforce to defend against AI-driven risks, including engineered pandemics, cyber attacks, job disempowerment, and concentrated power, using a defense-in-depth framework for uncertain AI progress.
