The development represents 'an important advance in extraterrestrial research, in which biology has often lagged behind chemistry and geology.'
Kimberley Warren-Rhodes, a senior research scientist at the SETI Institute in Mountain View, California, and lead author of the paper, combined her background in statistical ecology with AI to help mission scientists "who are under a lot of pressure to find biosignatures."In 2016, Warren-Rhodes’ group collected drone footage, geochemical analyses, and DNA sequences from an elevation of around 3,500 meters in the Chilean Andes, also the "proposed" Mars analog.
The team fed the data into an AI-based convolutional neural network and a machine-learning algorithm. This then predicted whereThe AI helped the researchers reduce the search area by up to 97 percent and increase their likelihood of finding life by up to 88 percent."I'm very impressed and very happy to see this suite of work,” says Kennda Lynch, an astrobiologist at the Lunar and Planetary Institute in Houston, Texas, who studies biosignatures, told Nature.
Further work is needed to be done. The new method will need to be verified across various ecosystems. as the Atacama is relatively simple when it comes to habitats and the types of lives found. According to Warren-Rhodes, the team's advance represents "an important advance in extraterrestrial research, in which biology has often lagged behind chemistry and geology."
"To have our team make one of these first steps towards reliably detecting biosignatures using AI is exciting," she said.In the search for biosignatures on Mars, there is an abundance of data from orbiters and rovers to characterize global and regional habitability, but much less information is available at the scales and resolutions of microbial habitats and biosignatures.
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