Zoubin Ghahramani explains that intelligent systems must make decisions with limited information, so they need an explicit way to represent what they do not know. Probability theory and Bayesian updating provide a mathematical framework for combining prior beliefs with new evidence instead of producing a single unjustifiably certain answer.
He contrasts that ideal with large language models, which predict tokens probabilistically but do not maintain a coherent, explicit account of confidence in their beliefs. This helps explain confident errors, easy flip-flopping and the difficulty of deciding when a model should gather more information or defer to a person.
Ghahramani points to ensemble weather forecasting and AlphaFold confidence estimates as examples of uncertainty making AI more useful. He argues that calibrated uncertainty is especially important in medicine, robotics and autonomous driving, while continual learning, energy efficiency and new architectures remain open research areas beyond simply scaling data and compute.
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