Why AI needs uncertainty, according to Zoubin Ghahramani

Zoubin Ghahramani argues that AI systems need explicit, calibrated representations of uncertainty to make safer real-world decisions, especially in settings such as medicine, autonomous driving, weather forecasting, and science. He says today’s language models can be useful and increasingly grounded, but their apparent confidence may not reflect coherent beliefs; the discussion presents Bayesian methods as promising but computationally difficult. The distinction matters because overconfident systems can mislead users when consequences are high.