Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning. Our approach is designed to enable AI systems to learn just as well from non-experts as they do from expert-generated training data. We developed a platform, called Quanti.us, that allows non-experts to analyze images (a common task in biomedical research) and create an annotated dataset. The platform is complemented by a set of algorithms specifically designed to interpret this kind of “noisy” and incomplete data correctly. Used together, these technologies can expand applications of machine learning in biomedical research.