Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control.

Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data ...

The second approach uses generative AI to produce data. Modern generative models are trained on vast amounts of data and can ...

The hybrid model is emerging as the framework for trustworthy AI in test analytics. It retains traceability and supports ...

To overcome two challenges in training AI – scarce or hard-to-get data and data privacy – researchers have come up with a ...

While AI and shifting industry trends have affected this particular end of the job market, programmers and engineers continue ...

While self-healing agentic test suites can help eliminate the manual intervention consuming engineering cycles, there are key strategies to make this approach successful.

For rare diseases, AI-driven repurposing fills a critical gap. With more than 7000 rare diseases and only a small percentage ...

Researchers have developed a powerful new software toolbox that allows realistic brain models to be trained directly on data.

Tests at Scala's Tamboré campus confirmed a latency reduction of approximately 32%, bringing data transmission closer to the speed of light ...