Google DeepMind explores why AI should recognize uncertainty
Google DeepMind research chief Zoubin Ghahramani and Hannah Fry discuss probabilistic reasoning and how AI systems can better identify the limits of their knowledge. The conversation highlights potential benefits for applications such as weather forecasting and robotics.
In a Google DeepMind discussion, VP of Research Zoubin Ghahramani and Hannah Fry examine how artificial intelligence can deal with situations where the available information is incomplete or uncertain. They argue that systems should not always respond with unwarranted confidence, but should be able to represent probabilities and communicate how reliable their conclusions are.
That capability could matter in real-world settings such as weather forecasting and robotics, where decisions must be made despite changing conditions and limited information. The discussion presents uncertainty modeling as a way to support safer and more dependable decision-making, rather than treating uncertainty solely as a weakness.
The material is an interview and research discussion, not an announcement of a new product or a reported benchmark improvement. It frames probabilistic reasoning as a long-term ingredient for more robust and appropriately cautious AI systems.
Source evidence
Transcript: The Mathematics of AI Uncertainty with Zoubin Ghahramani on DeepMind Podcast – The Singju Postsingjupost.com · supportingEDITOR’S NOTE: In this insightful interview, host Hannah Fry talks with Zoubin Ghahramani—Cambridge professor and VP of Research at Google DeepMind—about a problem he has been working on for three decades: teaching machines to know what they don’t know. Drawing on Bayesian thinking and real-world systems from weather models to AlphaFold, he argues that uncertainty isn’t a flaw in AI so much as a missing ingredient for more reliable, humble, and useful intelligence. ## Introduction HANNAH FRY (00:00:00 – 00:01:06): Welcome to Google DeepMind, the podcast. Now, if you ask an AI a question, then it will usually give you an absolute answer with unwavering authority, even if that answer turns out to be wrong. In fact, today’s AI seems to be missing a fundamental human trait, self-doubt. [...] ## Closing Thoughts: Embracing Uncertainty Hannah Fry (00:43:58 – 00:44:36): For years, we tried to build AI that was focused on accuracy, right? Building systems that crunch through enormous amount
Instagraminstagram.com · supportingLog In Sign Up googledeepmind's profile picture Never miss a post from googledeepmind Sign up for Instagram to stay in the loop. By continuing, you agree to Instagram's Terms of Use and Privacy Policy. Sign up Log in Video by Google DeepMind on August 27, 2026. May be an image of text. googledeepmind's profile picture googledeepmind • Follow googledeepmind's profile picture googledeepmind Edited• From weather forecasting to robotics, intelligent problem-solving relies on understanding the unknown. Our VP Research Zoubin Ghahramani explores with @fryrsquared why teaching systems self-doubt and probability can lead to safer, more reliable real-world decision-making. 🧠 Watch the full episode on our YouTube 🔗 vuve.ai's profile picture vuve.ai [...] Reply nikunj_bende's profile picture nikunj\_bende Always insightful @fryrsquared 🙌 Reply boringmanager's profile picture boringmanager 30 years working and what exactly to show for it? Reply ich_bin_batman
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