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

A fundamental divide between data engineering and business analytics complicates how organizations operate in a rapidly evolving digital environment. Enterprises manage unprecedented volumes of ...

AI transformation cannot be "AI for everything." Successful enterprises focus on a limited set of high-impact use cases with measurable outcomes.

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

In 2026, data engineering isn't just about managing data-it's about building intelligent systems that power business strategy. Companies are moving beyond batch warehouses to real-time, cloud-native ...

Schneider Electric's Vance Peterson and Gia Wiryawan explain why power distribution and thermal management—not compute—are ...

"An AI system can be technically safe yet deeply untrustworthy. This distinction matters because satisfying benchmarks is necessary but insufficient for trust." ...

Digital twins revolutionize drug discovery by integrating AI and biological data, enhancing prediction, trial design, and ...

When severe weather strikes, the National Weather Service's (NWS) Office of Water Prediction (OWP) makes critical flood ...

By Steven Vaccaro, Maj. Apoorv Vohra, and Chris LovatoU.S. Army Combined Arms Support Command (CASCOM) and U.S. Army Combat ...

The consolidation of SpaceX and xAI could lead to more adaptive use of robots, data, and AI in manufacturing, says ...

How an AI-first approach, co-engineered with AWS, is helping airlines move from reactive operations to proactive, predictive ...