QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

Researchers developed a machine learning model that predicts high-yield antibody-producing cell lines early in manufacturing, ...

Researchers claim model can cut years from testing cycles Scientists have developed a machine learning method that could dramatically slash the cost and energy required to develop new lithium-ion ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Over the last several decades, urban planners and municipalities have sought to identify and better manage the socioeconomic dynamics associated with rapid development in established neighborhoods.

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has long been slow, expensive, and heavily empirical. Machine learning is now ...

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...

In essence, PAI is being used for data center optimization to support the demands of digital AI (DAI) applications like ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

Opinion: A patent case involving inventor Guillaume Desjardins has evolved into a cornerstone of modern patent eligibility ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new ...