Utah-based HireVue uses video interviews to examine candidates' word choice, voice inflection, and micro gestures for subtle clues, such as whether their facial expressions contradict their words. Yale School of Management professor Jason Dana, who has studied hiring for years, recently made waves with a high-profile article in the New York Times that excoriated job interviews as useless. But when Google examined its internal evidence, it found that grades, test scores, and a school's pedigree weren't a good predictor of job success. Google created a program called qDroid, which drafts questions for interviewers based on how qDroid parses the data the applicant provided on the qualities Google emphasizes.
A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 percent of the time, explained Khosla, adding, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "But the truly exciting thing was when we combined the pathologist's analysis with our automated computational diagnostic method, the result improved to 99.5 percent accuracy," said Beck.