An experiment on a Martian analogue in northern Chile tested the utility of teaming planetary robots with artificial intelligence to focus the search for life in the most efficient way.
In an article published in Nature Astronomy, an interdisciplinary study led by Kim Warren-Rhodes, Principal Investigator at the SETI Institute, mapped the scant life hidden in salt domes, rocks and crystals in the Salar de Pajonales, on the border between the Chilean desert of Atacama and the Altiplano.
Next, Warren-Rhodes worked with co-investigators Michael Phillips (Johns Hopkins Applied Physics Laboratory) and Freddie Kalaitzis (University of Oxford) to train a machine learning model that would recognize the patterns and rules associated with their distributions so that they could learn to predict and find those same distributions in data on which he had not been trained.
In this case, by combining statistical ecology with artificial intelligence/machine learning, scientists were able to locate and detect biosignatures up to 87.5 % of the time (versus 10 % using random search) and decrease the area needed to search by up to 97 %.
(Reference image source: file)