Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time. Whether someone is trying to predict tomorrow's weather, forecast future stock prices, identify missed opportunities for sales in retail, or estimate a patient's risk of developing a disease, they will likely need to interpret time-series data, which are a collection of observations recorded over time. Making predictions using time-series data typically requires several data-processing steps and the use of complex machine-learning algorithms, which have such a steep learning curve they aren't readily accessible to nonexperts. To make these powerful tools more user-friendly, MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds. MIT researchers created a tool that enables people to make highly accurate predictions using multiple time-series data with just a few keystrokes.

Researchers from MIT have developed new self-driving car system capable of navigating in low visibility settings, including in fog and snow. The system relies on Localizing Ground Penetrating Radar (LGPR), which takes readings the shape and composition of the road directly below and around the car with electromagnetic pulses. Other self-driving car systems use a combination of Lidar, radar, and cameras to develop a real-time topographical model of where the car is in space. These systems are generally reliable but have been vulnerable to visual tricks like fake road signs and lane makers, and can become significantly less reliable during bad weather conditions. The LGPR system aims to improve on these vulnerabilities by focusing on the road itself and not the open space in front of the car.