To identify newly formed craters on Mars, artificial intelligence spend about 40 minutes analyzing a single photo of the Martian surface taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO), looking for a dark patch that wasn't in earlier photos of the same location.
If a scientist spots the signs of a crater in one of those images, it then has to be confirmed using a higher-resolution photograph taken by another MRO instrument, the High-Resolution Imaging Science Experiment (HiRISE).
This method of spotting new craters on Mars makes it easy to determine an approximate date for when each formed if a crater wasn't in a photo from April 2016 but is in one from June 2018, for example, the scientists know it must have formed sometime between those two dates.
By studying a supercomputer cluster at NASA's Jet Propulsion Laboratory (JPL).
JPL computer scientist Gary Doran said in October, It wouldn't be possible to process over 112,000 images in a reasonable amount of time without distributing the work across many computers. The strategy is to split the problem into smaller pieces that can be solved in parallel.
With the power of all those computers combined, the AI could scan an image in an average of just five seconds. If it flagged something that looked like a fresh crater, NASA scientists could then check it out themselves using HiRISE.
In October, NASA confirmed that the AI had discovered its first fresh craters on Mars, and to date, it's helped scientists spot dozens of new impacts in the Context Camera images.
JPL computer scientist Kiri Wagstaf told Wired, The data was there all the time. It's just that we hadn't seen it ourselves.
In the future, the Artificial Intelligence might help scientists identify more craters on Mars potentially within weeks of their formation or even craters on other planets.
Ingrid Daubar, a planetary scientist who helped create the Artificial Intelligence said, the possibility of using machine learning to really delve into large data sets and find things that we otherwise wouldn't have found is really exciting. This is just beginning. We're looking forward to finding a lot more.