Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
EurekAlert!

New uncertainty-aware AI framework could improve fuel cell degradation forecasting

Researchers have developed an uncertainty quantification-based framework for predicting degradation trends in proton exchange ...
GitHub

Deepfake Detection with Bayesian Uncertainty Quantification

This project applies Bayesian Machine Learning to deepfake image detection, going beyond simple binary classification by providing calibrated uncertainty estimates for every prediction. Instead of ...
Harvard Business Review

Leaders, It’s Time to Build Your Tolerance for Uncertainty

As we move into 2026, many leaders are fretting about the uncertain business environment. Given the transformational nature of AI, geopolitical instability, or economic disruption, it’s hard to plan ...
IEEE

Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging

Abstract: Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural ...
International Monetary Fund

Even as Global Uncertainty Surges, Economic Sentiment Remains Positive

Major policy shifts this year have been adding to unknowns about the future and policy decisions, according to our World Uncertainty Index, which has doubled since January. Uncertainty has surged to ...
CNBC

‘Uncertainty is fundamental reality,’ says Deepak Chopra: Here's how to cope if you're feeling afraid or anxious

For millions of Americans, it is an uncertain time. Amazon, General Motors and Paramount have all been making layoffs, and U.S.-based employers announced 153,074 job cuts in October altogether, ...
Oak Ridge National Lab

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an ...