Artificial intelligence may seem like some amorphous, all-knowing entity that could outperform humans at even the most complex of tasks. But behind the scenes, humans must spend countless hours cleaning data and teaching these algorithms to “think.”
Hari Trivedi is an assistant professor at the department of radiology and imaging services at Emory University. In this video, he explains step-by-step why it takes a village of researchers, doctors, and students across multiple institutions to select, clean, and double-check data that’s going to be fed into AI programs.
Trivedi’s team at Emory is creating a machine learning algorithm that can detect breast cancer. But before their algorithm gets up and running, they must create a breast cancer dataset that includes mammograms, demographics, social history, and more.