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.

Data science and machine learning teams face a hidden productivity killer: annotation errors. Recent research from Apple analyzing production machine learning (ML ...

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

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, ...

In many embodiments, machine classifiers may process the set of data to identify particular features within the data. Scoring data can be generated, based on annotations provided by other adjuster ...