Meanwhile at Stanford:
Researchers at Stanford University are using 600,000 fictional stories to inform their new knowledge base called Augur. The team considers the approach to be an easier, more affordable, and more effective way to train computers to understand and anticipate human behavior. Augur is designed to power vector machines in making predictions about what an individual user might be about to do, or want to do next. The system's current success rate is 71 percent for unsupervised predictions of what a user will do next, and 96 percent for recall, or identification of human events. The researchers report dramatic stories can introduce comical errors into a machine-based prediction system. "While we tend to think about stories in terms of the dramatic and unusual events that shape their plots, stories are also filled with prosaic information about how we navigate and react to our everyday surroundings," they say. The researchers note artificial intelligence will need to put scenes and objects into an appropriate context. They say crowdsourcing or similar user-feedback systems will likely be needed to amend some of the more dramatic associations certain objects or situations might inspire.