Aliens have long been a fascinating subject for humans. Innumerable movies, TV series and books are proof of this allure. Our search for extra-terrestrial has even taken us to other planets, albeit remotely. This search has progressed leaps and bounds in the last few years, but it is still in its natal stages. Global space agencies like the National Aeronautics and Space Administration (NASA) and China National Space Administration (CNSA) have in recent years sent rovers to Mars to aid this search remotely. However, the accuracy of these random searches remains low.
To remedy this, the Search for Extraterrestrial Intelligence (SETI) Institute has been exploring the use of artificial intelligence (AI) for finding extraterrestrial life on Mars and other icy worlds.
According to a report on Space, a recent study from SETI states that AI could be used to detect microbial life in the depths of the icy oceans on other planets.
In a paper published in Nature Astronomy, the team details how they trained a machine-learning model to scan data for signs of microbial life or other unusual features that could be indicative of alien life.
Using a machine learning algorithm called convolutional neural networks (CNNs) a multidisciplinary team of scientists led by SETI’s Kim Warren-Rhodes has mapped sparse lifeforms on Earth. Warren-Rhodes worked alongside experts from other prestigious institutions: Michael Phillips of Johns Hopkins Applied Physics Lab and Freddie Kalaitzis of the University of Oxford.
The system developed by them used statistical ecology and AI-detected biosignatures with up to 87.5 per cent accuracy, compared to only 10 per cent for random searches. As per the researchers, it can potentially reduce the search area by up to 97 per cent, making it easier for scientists to locate potential chemical traces of life.
For testing their system, they initially focused on the sparse lifeforms that dwell in salt domes, rocks, and crystals at Salar de Pajonales at the boundary of the Chilean Atacama Desert and Altiplano.
Warren-Rhodes and his team collected over 8,000 images and 1,000 samples from Salar de Pajonales to search for photosynthetic microbes that may represent a biosignature on NASA’s “ladder of life detection” for finding life beyond Earth.
The team also used drone imagery to simulate Mars Reconnaissance Orbiter’s High-Resolution Imaging Experiment camera’s Martian terrain images to examine the region.
They found that microbial life in the region is concentrated in biological hotspots that strongly relate to the availability of water.
Researchers suggest that the machine learning tools developed can be used in robotic planetary missions like NASA’s Perseverance Rover. The tools can guide rovers towards areas with a higher probability of having traces of alien life, even if they are rare or hidden.
“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,” explained Warren-Rhodes.
(With inputs from agencies)
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