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 ...

Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to ...

Researchers at Korea University have developed a machine learning model for predicting sheet resistance in phosphorus oxychloride (POCl3) doping processes in solar cell manufacturing. “Our study aims ...

Validating drug production processes need not be a headache, according to AI researchers, who say machine learning could be a single answer to biopharma’s multivariate problem. The FDA defines process ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Machine learning is becoming an important tool in many industries and ...

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. (In partnership with Paperspace) In recent years, the transformer model has ...

Machine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications.

To identify and evaluate candidate materials, process engineers must analyze an enormous amount of data. Bulk properties like ...

From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models. Do you need to classify data or predict ...

Machine learning models to identify the simplest way to screen for lung cancer have been developed by researchers from UCL and the University of Cambridge, bringing personalized screening one step ...