Artificial intelligence could help to spot life on Mars, alien hunters say
Artificial intelligence (AI) could spot the likeliest places where life might be lurking in environments such as Mars – helping scientists to ‘zoom in’ quickly on alien microbes or other life forms.
Future space robots might carry AI algorithms to help them focus on areas where microbes might survive, the researchers say.
Researchers used a machine learning model to recognise the patterns and rules of where life lurks in a harsh environment on Earth – the salt domes, rocks and crystals of the Chilean Atacama desert.
The researchers, led by Kim Warren-Rhodes of the alien-hunting SETI Institute trained the model to home in on the sparse signs of life in the Atacama desert.
Using AI, the scientists could locate and detect biosignatures up to 87.5% of the time (versus round 10% by random search) and decrease the area needed for search by up to 97%.
Read more: There might once have been life on the moon
The same technique could help to spot life on Mars, the researchers believe.
Rhodes said: “Our framework allows us to combine the power of statistical ecology with machine learning to discover and predict the patterns and rules by which nature survives and distributes itself in the harshest landscapes on Earth.
“We hope other astrobiology teams adapt our approach to mapping other habitable environments and biosignatures.
“With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harbouring past or present life—no matter how hidden or rare.”
Similar algorithms and machine learning models for many different types of habitable environments and biosignatures could be automated onboard planetary robots to efficiently guide mission planners to areas with the highest probability of containing life.
Read more: Hubble spots something very strange flying through the solar system
Rhodes and the SETI Institute NASA Astrobiology Institute (NAI) team used the Salar de Pajonales, as an analogue of the surface of Mars.
Pajonales is a high altitude, high UV, hyper arid, dry salt lakebed, considered inhospitable to many life forms but still habitable.
The team collected more than 7,765 images and 1,154 samples and tested instruments to detect photosynthetic microbes living within the salt domes, rocks and alabaster crystals.
The study’s findings confirm (statistically) that microbial life at the Pajonales terrestrial analogue site is not distributed randomly but concentrated in patchy biological hotspots strongly linked to water availability.
Next, the team trained convolutional neural networks (CNNs) to recognise and predict macro-scale geologic features at Pajonales – some of which, like patterned ground or polygonal networks, are also found on Mars – and micro-scale substrates (or ‘micro-habitats’) most likely to contain biosignatures.
Nathalie A Cabrol, the principal investigator of the SETI Institute NAI team, said: “While the high rate of biosignature detection is a central result of this study, no less important is that it successfully integrated datasets at vastly different resolutions from orbit to the ground, and finally tied regional orbital data with microbial habitats.
“With it, our team demonstrated a pathway that enables the transition from the scales and resolutions required to characterise habitability to those that can help us find life. In that strategy, drones were essential, but so was the implementation of microbial ecology field investigations that require extended periods (up to weeks) of in situ (and in place) mapping in small areas, a strategy that was critical to characterise local environmental patterns favourable to life niches.”
