As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models is learning without crossing ethical lines.

Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...

Overview Machine learning offers efficiency at scale, but trust depends on understanding how decisions are madeAs machine ...

The software tool uses self-supervised learning to detect long-term defects in solar assets weeks or years before ...

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...

With such increased predictive knowledge of solar systems, these anomaly detectors can significantly reduce costs of O&M, a major component of project economics in solar development. There is great ...

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

University of Idaho receives over $6M in DoD grants to advance machine learning research for PTSD diagnosis and military ...