An artificial-intelligence model trialled in Chile's Atacama Desert could one day detect signs of life on other planets. Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet.

Artificial intelligence (AI) and machine learning could revolutionize the search for life on other planets. But before these tools can tackle distant locales such as Mars, they need to be tested here on Earth. A team of researchers have successfully trained an AI to map biosignatures -- any feature which provides evidence of past or present life -- in a three-square-kilometre area of Chile's Atacama Desert. The AI substantially reduced the area the team needed to search and boosted the likelihood of finding living organisms in one of the driest places on the planet. The results were reported on 6 March in Nature Astronomy1.

The Atacama Desert, located in South America, is one of the driest regions on Earth. Several types of endemic plants are still present at the site. After collecting several species that grow between 2,400 and 4,500 meters above sea level, scientists from INRAE, Purdue University and the Pontifical Catholic University of Santiago in Chile have been able to identify common molecular markers that allow an understanding of the mechanisms of these plants' resilience in the face of a harsh environment. The researchers used an innovative approach using artificial intelligence. The results of their work are detailed in review The new botany.

Scientists have used profoundly complex computer simulations to better understand the black hole at the heart of the galaxy M87. That black hole is probably the famous one ever to exist: it is the one shown in the first ever picture of such an object, taken in 2019. But despite that unprecedented look, much still remains about the black hole and the forces within that galaxy that feed and surround it. The galaxy in which it dwells is 55 million light years away from us. It is a giant galaxy, made up of 12,000 globular clusters – compared with only 200 in our own Milky Way.

As planetary rovers expand their capabilities, traveling longer distances, deploying complex tools, and collecting voluminous scientific data, the requirements for intelligent guidance and control also grow. This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making.

An autonomous rover named Zoë, designed and built by Carnegie Mellon University's Robotics Institute, drilled into the soil of Chile's Atacama Desert in 2013 and discovered unusual, highly specialized microbes. The NASA-funded mission demonstrated how robots might someday find life on Mars. The astrobiology mission was led by the Robotics Institute and the SETI Institute to test technologies for searching for life underground. The microbial analyses of the soil samples recovered by Zoë were published Feb. 28 in the journal Frontiers of Microbiology. Zoë was equipped with a one-meter drill that recovered samples several times each day.

NEW YORK Artificial intelligence has made history by beating humans in poker for the first time, the last remaining game in which humans had managed to maintain the upper hand. Libratus, an AI built by Carnegie Mellon University racked up over $1.7 million worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended on Tuesday in Philadelphia. While machines have beaten humans over the last two decade in chess, checkers, and most recently in the ancient game of Go, Libratus' victory is significant because poker is an imperfect information game -- similar to the real world where not all problems are laid out and the difficulty in figuring out human behavior is one of the main reasons why it was considered immune to machines. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Tuomas Sandholm, professor of computer science at CMU who created Libratus with a Ph.D student Noam Brown said on Wednesday. The victory prompted inquiries from companies all over the world seeking to use Libratus' algorithm for problem solving.

As planetary rovers expand their capabilities, traveling longer distances, deploying complex tools, and collecting voluminous scientific data, the requirements for intelligent guidance and control also grow. This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales. Specific capabilities include instrument calibration, visual targeting of selected features, an onboard database of collected data, and a long range path planner that guides the robot using analysis of current surface and prior satellite data. Field experiments in the Atacama Desert of Chile over the past decade demonstrate these capabilities and illustrate current challenges and future directions.