Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

Machine learning is often key to success for today’s institutions that rely heavily on data. But often, data science teams can have a difficult time convincing their organizations of the breadth and ...

Gengo, a leader in expert, high-scale crowdsourced translation services, is taking aim at the growing need for high-quality multilingual data to train tomorrow’s advanced AI (artificial intelligence) ...

One of the big challenges of developing a machine learning project can be simply getting enough relevant data to train the algorithms. That’s where Superb AI, a member of the Y Combinator Winter 2019 ...

eWeek content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More Machine learning (ML) uses advanced mathematical models ...

Mathematicians have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...