|Frontier Development Lab (FDL) is planning to use AI technologies to help look for alien life on other planets and detect nearby asteroids / Photo Credit: Vadim Sadovski via Shutterstock|
The space industry utilizes artificial intelligence to help astronauts in their tasks and to create tools that can help mankind observe space even more effectively. Researchers are even planning to build AI technologies into future spacecraft so that it can make real-time science decisions and not wait for instructions coming from Earth that will come only after several hours depending on the distance.
NASA has also launched an applied AI research accelerator called the Frontier Development Lab (FDL) that aims to apply AI technologies to space science to push the frontiers of research. This can prove to be of help in solving some of the biggest challenges that humanity faces. NASA gathers technology and space innovators every summer for FDL. It collaborates with companies such as Intel, IBM, and Google to develop advanced machine-learning techniques.
“FDL feels like some really good musicians with different instruments getting together for a jam session in the garage, finding something really cool, and saying, ‘Hey we've got a band here,’” said Shawn Domagal-Goldman, a NASA Goddard astrobiologist.
One of the results of this program is the plan to use AI technologies to help in looking for life on alien planets and detecting nearby asteroids. According to Space.com, a space and astronomy news website that covers the latest space exploration, innovation, and astronomy news, NASA is hoping that machine learning can interpret data that will be collected by future telescopes like the James Webb Space Telescope or the Transiting Exoplanet Survey Satellite (TESS) mission.
Giada Arney, an astrobiologist at NASA's Goddard Space Flight Center in Greenbelt, stated that AI and machine learning are important, especially for big data sets and in the exoplanet field. "Because the data we're going to get from future observations are going to be sparse and noisy, it's going to be really hard to understand. So using these kinds of tools has so much potential to help us,” she said.