Uber is in the prediction business, but it wants to get better. So the company's artificial intelligence team has proposed a new algorithm that it says improves its forecasting of demand in extreme situations like bad weather and major holidays.
Uber researchers are applying a machine-learning architecture called "long short-term memory" (LSTM) to predict demand for drivers during "holidays, concerts, inclement weather, and sporting events." In a new blog post, they said that this new approach improved the accuracy of their existing forecasting algorithm between 2% and 18%.
Why it matters: Better AI forecasting models could impact fields as diverse as medicine and earthquake prediction. The latest craze among hedge funders is using LSTM architecture to predict asset price changes.