In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Black-box optimization, particularly Bayesian optimization, is a practical approach for weather-intervention design, achieving meaningful rainfall ...

Food System Innovations has launched an open-source Food Intelligence Lab to use AI to create better-tasting alternative ...

Abstract: This article proposes a novel constrained multiobjective evolutionary Bayesian optimization algorithm based on decomposition (named CMOEBO/D) for expensive constrained multiobjective ...

Personalized algorithms may quietly sabotage how people learn, nudging them into narrow tunnels of information even when they start with zero prior knowledge. In the study, participants using ...

This article was co-authored with Emma Myer, a student at Washington and Lee University who studies Cognitive/Behavioral Science and Strategic Communication. In today’s digital age, social media has ...

Abstract: This paper proposes a fast expectation-maximization (EM)-like algorithm for visualizing radioactive sources within a target region using maximum a posteriori (MAP) estimation and a Gaussian ...

Bayesian methods have emerged as a powerful framework for interpreting functional magnetic resonance imaging (fMRI) data by combining prior knowledge with observed signals to yield probabilistic ...

Speaking at WSJ Opinion Live in Washington, D.C., WSJ Editorial Page Editor Paul Gigot and SandboxAQ CEO Jack Hidary discuss Large Quantitative Models (LQMs) and their role in AI applications, the ...