Teaching AI to learn from non-experts

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.

Read the whole article on Physorg

More from Physorg