Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

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 ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

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 ...

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.

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 ...

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...

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

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