IRG lab member Joseph Kim's recently developed an intelligent machine that listens to a team’s conversation and infers whether there is consensus. The machine then provides feedback, alerting the team to review "weak" discussion points that could later result in confusion. Joseph designed a computational model that processed the team’s utterances to extract a set of dialogue features that are indicative of a team’s shared consensus, according to well-established cognitive models. The machine used this model to participate in live team meetings, improving the team’s consistency in understanding by up to 17%. The article is published in the IEEE Transactions on Human-Machine Systems (THMS). You can read the full paper here. You can also check out the system demonstration video here.
Are we on the same page? Improving Teams’ Consistency of Understanding
June 30, 2016