Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

It’s more than just code. Scientists have found a way to "dial" the hidden personalities of AI, from conspiracy theorists to social influencers.

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

Mark Zuckerberg’s court testimony that companies should build “useful” products has reignited debate on morality of algorithms ...

Spirent Luma uses a multi-agent architecture and deterministic rule sets to automate root cause analysis in multi-technology network environments.

Researchers led by Xian-Yang Qin at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan have developed a score that predicts the risk of liver cancer. Published in the journal Proceedings ...

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, ...

Dinosaur footprints are iconic fossils, but it is challenging to identify their makers. This is illustrated by a long-standing debate about whether some footprints from the Late Triassic-Early ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

The semiconductor industry is increasingly turning to artificial intelligence as the solution for increasing complexity in test analytics, hoping algorithms can tame the growing flood of production ...