"To help create high-quality machine learning data more effectively, we've developed technology that streamlines the annotation process.

Researchers on Microsoft's Bing team have developed a novel way of generating high-quality data for training machine learning models. In a blog post and paper published ahead of the Computer Vision and Pattern Recognition Conference (CVPR) in Salt Lake City, they describe a system that can discriminate between accurately labeled data and poorly labeled data with impressive consistency. "Getting enough high-quality training data is often the most challenging piece of building an AI-based service," the researchers wrote. "Typically, data labeled by humans is of high quality (has relatively few mistakes) but comes at high cost -- both in terms of money and time. On the other hand, automatic approaches allow for cheaper data generation in large quantities but result in more labeling errors ('label noise')."