This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...

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, ...

The future of work is here. Professionals who master AI tools are gaining an edge, not losing jobs. Employers now value ...

Nurses use an AI-powered tool to identify and address social determinants of health that influence patient outcomes.

At least 1,357 medical devices using AI are now authorized by the FDA – double the number it had allowed through 2022 ...

What sets Codeflash apart, he argues, is that it operates not just as a one-time audit or consultancy (as many optimization firms do) but as a continuous engine: “Codeflash has beaten us at optimizing ...

A firm that wants to use a large language model (LLM) to summarize sales reports or triage customer inquiries can choose between hundreds of unique LLMs with dozens of model variations, each with ...

Digital twins revolutionize drug discovery by integrating AI and biological data, enhancing prediction, trial design, and decision-making in precision medicine.

Despite the continued hype surrounding AI adoption, many overlook one of the biggest factors for AI success: data quality.

One of the promises of AI is that it can reduce workloads so employees can focus more on higher-value and more engaging tasks ...

The signals that drive many of the brain and body's most essential functions—consciousness, sleep, breathing, heart rate and motion—course through bundles of "white matter" fibers in the brainstem, ...