Darren McKee joins the podcast to discuss how AI might be difficult to control, which goals and traits AI systems will develop, and whether there's a unified solution to AI alignment.
Darren McKee joins the podcast to discuss how AI might be difficult to control, which goals and traits AI systems will develop, and whether there's a unified solution to AI alignment. Timestamps: 00:00 Uncontrollable superintelligence 16:41 AI goals and the "virus analogy" 28:36 Speed of AI cognition 39:25 Narrow AI and autonomy 52:23 Reliability of current and future AI 1:02:33 Planning for multiple AI scenarios 1:18:57 Will AIs seek self-preservation? 1:27:57 Is there a unified solution to AI alignment? 1:30:26 Concrete AI safety proposals
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.
