Artificial intelligence systems are only as powerful as the data they are trained on. High-quality labeled datasets determine whether a model performs with precision or fails in production.

Labeling and annotation are the foundation of context setting and the invisible backbone of AI, which are quietly shaping the world around us.

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make ...

Autonomous vehicles rely on high-quality data annotation to train AI/ML models for safe and intelligent driving. This blog explores how sensor data from cameras, LiDAR, and radar is annotated using ...

is a senior reporter who has covered AI, robotics, and more for eight years at The Verge. A new report from Reuters reveals that contract workers are looking at private posts on Facebook and Instagram ...