Can computers detect the presence of life on distant planets? To some extent, they already are.
Sensors aboard spacecraft investigating other planets may discover chemicals indicative of extraterrestrial life. However, organic compounds that hint at interesting biological processes are known to decompose over time, making their presence impossible to detect with existing technology.
However, a new technology based on artificial intelligence (AI) is now capable of detecting small variations in molecular patterns that imply biological signals, even in samples hundreds of millions of years old. Even better, according to current studies, the process produces results with 90% accuracy.
In the future, this AI system could be included in smarter sensors aboard robotic space explorers, such as landers and rovers on the Moon and Mars, as well as spacecraft orbiting potentially habitable worlds such as Enceladus and Europa.
“We started with the idea that the chemistry of life differs fundamentally from that of the inanimate world; “There are ‘chemical rules of life’ that influence the diversity and distribution of biomolecules,” said Robert Hazen, co-author of the new study and a scientist at the Carnegie Institution for Science in Washington, DC. “If we could deduce those rules, we could use them to guide our efforts to model the origins of life or to detect subtle signs of life on other worlds.”
The new method is based on the concept that the chemical processes that govern the formation and function of biomolecules vary fundamentally from those that govern the formation and function of abiotic molecules in that biomolecules (such as amino acids) retain information about the processes chemicals who created them. . According to the latest study, this is also likely in the case of extraterrestrial life.
Life on any planet can produce and consume larger quantities of a few substances to function regularly. This would differentiate them from abiotic systems, and it is these characteristics that AI can detect and quantify, according to the researchers.
The team began by training the machine learning algorithm with 134 samples, 59 of which were biotic and 75 of which were abiotic. The data was then randomly divided into a training set and a test set to validate the method. The AI approach successfully detected biotic samples of living objects such as shells, teeth, bones, rice and human hair, as well as ancient life preserved in fossilized fragments made of charcoal, oil and amber.
According to the latest study, the program also detected abiotic materials, such as laboratory-created substances such as amino acids, as well as carbon-rich meteorites.
New AI technology can be used to investigate 3.5 billion-year-old rocks in the Pilbara area of Western Australia, where the world’s oldest fossils are believed to be found, almost instantly. These pebbles, discovered in 1993, were supposed to be fossilized remains of germs similar to cyanobacteria, the first living organisms to produce oxygen on Earth.
If confirmed, the discovery of bacteria so early in Earth’s history indicates that the planet was conducive to thriving life much earlier than previously assumed. However, those discoveries remain controversial, as research has repeatedly pointed out that the data could be attributable to pure geological processes unrelated to ancient life. Maybe AI has the solution.
This study was presented in a publication published in the journal Proceedings of the National Academy of Sciences on Monday (September 25).
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Categories: Technology
Source: vtt.edu.vn