Roman Yampolskiy on Shoggoth, Scaling Laws, and Evidence for AI being Uncontrollable
Roman Yampolskiy joins the podcast again to discuss whether AI is like a Shoggoth, whether scaling laws will hold for more agent-like AIs, evidence that AI is uncontrollable, and whether designing human-like AI would be safer than the current development path.
Roman Yampolskiy joins the podcast again to discuss whether AI is like a Shoggoth, whether scaling laws will hold for more agent-like AIs, evidence that AI is uncontrollable, and whether designing human-like AI would be safer than the current development path. You can read more about Roman's work at http://cecs.louisville.edu/ry/ Timestamps: 00:00 Is AI like a Shoggoth? 09:50 Scaling laws 16:41 Are humans more general than AIs? 21:54 Are AI models explainable? 27:49 Using AI to explain AI 32:36 Evidence for AI being uncontrollable 40:29 AI verifiability 46:08 Will AI be aligned by default? 54:29 Creating human-like AI 1:03:41 Robotics and safety 1:09:01 Obstacles to AI in the economy 1:18:00 AI innovation with current models 1:23:55 AI accidents in the past and future
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.
