Soon, you might not need anything more specialized than a readily accessible touchscreen device and any existing data sets you have access to in order to build powerful prediction tools. A new experiment from MIT and Brown University researchers have added a capability to their'Northstar' interactive data system that can "instantly generate machine-learning models" to use with their exiting data sets in order to generate useful predictions. One example the researchers provide is that doctors could make use of the system to make predictions about the likelihood their patients have of contracting specific diseases based on their medial history. Or, they suggest, a business owner could use their historical sales data to develop more accurate forecasts, quickly and without a ton of manual analytics work. Researchers are calling this feature the Northstar system's "virtual data scientist," (or VDS) and it sounds like it could actually replace the human equivalent, especially in settings where one would never actually be readily available or resourced anyway.

When engineers or designers want to test the aerodynamic properties of the newly designed shape of a car, airplane, or other object, they would normally model the flow of air around the object by having a computer solve a complex set of equations–a procedure that usually takes hours or even an entire day. Nobuyuki Umetani from Autodesk research (now at the University of Tokyo) and Bernd Bickel from the Institute of Science and Technology Austria (IST Austria) have now significantly sped up this process, making streamlines and parameters available in real-time. Their method, which is the first to use machine learning to model flow around continuously editable 3D objects, will be presented at this year's prestigious SIGGRAPH conference in Vancouver, where IST Austria researchers are involved in a total of five presentations. Machine learning can make extremely time-consuming methods a lot faster. Before, the computation of the aerodynamic properties of cars usually took a day.

When engineers or designers want to test the aerodynamic properties of the newly designed shape of a car, airplane, or other object, they would normally model the flow of air around the object by having a computer solve a complex set of equations--a procedure that usually takes hours, or even an entire day. Nobuyuki Umetani from Autodesk research (now at the University of Tokyo) and Bernd Bickel from the Institute of Science and Technology Austria (IST Austria) have now significantly sped up this process, making streamlines and parameters available in real time. Their method, which is the first to use machine learning to model flow around continuously editable 3-D objects, will be presented at this year's prestigious SIGGRAPH conference in Vancouver, where IST Austria researchers are involved in a total of five presentations. Machine learning can make extremely time-consuming methods a lot faster. Before, the computation of the aerodynamic properties of cars usually took a day.