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

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.

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

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 ...

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

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...