Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, adaptability and fairness.

A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after it has already been used for training.

Snowflake announced several new enhancements, from semantic models to masking data to ensure AI is accurate at scale.

AI is only as good as the data behind it. But an AI-ready data architecture is a different beast than traditional approaches to data delivery.

Discover how Google Stax transforms AI evaluation with structured, data-driven tools for reliable performance and actionable insights.

Improved AI accuracy with diverse data: Model accuracy relies heavily on data quality and diversity. Federated learning allows agencies to learn from multiple sources, which helps reduce bias and ...

Wednesday, 06 August 2025 10:39 How to Make Agentic AI Work at Scale: 5 Ways to use Process Intelligence to Accelerate and Optimise Agentic AI Featured ...

The Cleveland Clinic and startup Piramidal are developing an AI model trained on brain wave data to monitor intensive care ...