Why AI Needs Uncertainty: A Conversation with Google DeepMind's Zoubin Ghahramani

In this podcast episode, we explore the critical role of uncertainty in artificial intelligence with Zoubin Ghahramani, professor at Cambridge and co-lead of frontier AI at Google DeepMind. The conversation examines why AI systems must represent and reason about uncertainty to make safe, reliable decisions, and how Bayesian thinking can help.

■ The case for uncertainty in AI
- Why decision-making requires representing uncertainty
- Different types of uncertainty and probability theory

■ Uncertainty in current AI systems
- Overconfidence and adversarial examples
- Large language models and hallucinations

■ Bayesian thinking and real-world applications
- How Bayesian updating models learning and perception
- Weather forecasting and AlphaFold as examples

This episode is for anyone interested in AI safety, machine learning research, or the philosophical questions behind intelligent systems. Viewers will gain a clearer understanding of why uncertainty is a key ingredient for trustworthy AI.