[FDL in the news] “AI in Space” by IEEE Spectrum

30743290_10156337104031182_4918828991009259520_n.jpg

One group used Intel’s deep-learning accelerator, called Nervana, to improve the resolution of maps of the moon. This team also used a neural net to classify images—crater or no crater? Their results agreed with human image classification about 98 percent of the time, about five times the accuracy of previous image analysis systems. The group’s aim is to provide recon so that future lunar rovers don’t fall into unmapped craters while looking for water at the moon’s poles. The poles are highly shaded, so it’s difficult to distinguish crater from shadow.

Two teams working on forecasting solar flares—magnetic pulses that can cause problems with the power grid, GPS, and other systems—had support from IBM and Lockheed Martin. One group’s algorithm, called FlareNET, outperformed NOAA’s existing system for predicting solar flares.

Read more... AI in Space on IEEE Spectrum