A lot of successful movies have been made about people saving the planet from a rogue meteor or some other non-Earthly object. Deep Impact, Armageddon, Asteroid, Meteor…. There is always some impending doom via a rock and there is always some good-looking hero to save us. As you might imagine, the truth is slightly more complicated: If scientists do see a giant rock heading this way, identifying and (hopefully) fixing the problem requires a whole lot of time. But perhaps not anymore.
The same kind of technology that speeds up your legal research with accuracy via ROSS Intelligence, can also speed up saving Earth with accuracy. Katherine Bourzac writes, “machine learning algorithms can more quickly identify and cluster the debris that comets leave in their wake. By speeding up analysis of meteor showers, researchers hope to pinpoint the orbits of distant, but potentially dangerous, comets. This project is one of five being explored as part of an artificial intelligence pilot research program sponsored by NASA.”
Great, you say. We’re saved! But what else can AI do in space? Turns out, quite a bit. A recent Science: Robotics article looked at the different ways NASA is relying on AI to further exploration. The article, written by Steve Chien and Kiri Wagstaff of NASA’s Jet Propulsion Laboratory (JPL), “suggests that autonomy will be a key technology for the future exploration of our solar system, where robotic spacecraft will often be out of communication with their human controllers.” (here)
“The goal is for A.I. to be more like a smart assistant collaborating with the scientist and less like programming assembly code,” said Chien, a senior research scientist on autonomous space systems. “It allows scientists to focus on the ‘thinking’ things — analyzing and interpreting data — while robotic explorers search out features of interest.”
According to NASA, “Science is driven by noticing the unexpected, which is easier for a trained human who knows when something is surprising. For robots, this means having a sense of what’s ‘normal’ and using machine learning techniques to detect statistical anomalies.”
“We don’t want to miss something just because we didn’t know to look for it,” said Wagstaff, a principal data scientist with JPL’s machine learning group. “We want the spacecraft to know what we expect to see and recognize when it observes something different.”
Among the many ways NASA’s JPL has led the way for space AI: “Dust devils swirling across the Martian surface were imaged by NASA’s Opportunity rover using a program called WATCH. That program later evolved into AEGIS, which helps the Curiosity rover’s ChemCam instrument pick new laser targets that meet its science team’s parameters without needing to wait for interaction with scientists on Earth. AEGIS can also fine-tune the pointing of the ChemCam laser.”
Plus, according to NASA, AI allows spacecraft to prioritize the data it collects, balancing other needs like power supply or limited data storage. Autonomous management of systems like these is being prototyped for NASA’s Mars 2020 rover.
The Futurism site reports that “Any AI that we use in the future of space exploration will allow us to retrieve data from the places we send probes to, as well as allow us to explore them further, and collect better data. Since humans aren’t yet able to traverse these locales ourselves, unless we’re willing to hand at least some of that responsibility over to AI, it’s unlikely any of these missions could happen.”
Hiro Ono, a NASA engineer, gives the example of a spacecraft on a Jovian moon Europa, which is covered by a 10km thick icy crust: “The probe might be trapped in ice if it waits for the instruction of human operators. Without an advanced autonomy, exploration of such a remote world would be severely limited, if not impossible.” (here)
“The vast majority of space exploration is conducted by robotic probes. Increasing the autonomy in future missions is essential to both increasing the effectiveness of space exploration as well as exploring more distant, challenging environments, such as sub-ice oceans,” Chien concluded.
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