MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

Key opportunities in the Global App Test Automation Market include leveraging AI and machine learning to enhance testing efficiency, advancing cloud-based real device testing solutions, and ...

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

Recent study reveals machine learning's potential in predicting the strength of carbonated recycled concrete, paving the way ...

Microsoft released new open‑source quantum development tools that deepen VS Code and Copilot integration while targeting real ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

Understand why testing must evolve beyond deterministic checks to assess fairness, accountability, resilience and ...

Discover the leading AI code review tools reshaping DevOps practices in 2026, enhancing code quality, security, and team productivity with automated solutions.

WIRED spoke with Boris Cherny, head of Claude Code, about how the viral coding tool is changing the way Anthropic works.

For years, the AI community has worked to make systems not just more capable, but more aligned with human values. Researchers have developed training methods to ensure models follow instructions, ...

One of the most difficult challenges in payment card fraud detection is extreme class imbalance. Fraudulent transactions ...