The "holy grail of astrobiology" - a new machine learning technique can determine whether a sample is of biological or non-biological origin with 90% accuracy.
Scientists have developed an innovative method based on artificial intelligence to detect signs of life on other planets. This method, with an accuracy of 90%, differentiates between biological and non-biological samples by analyzing molecular patterns. It promises to revolutionize space exploration and our understanding of the origins of life, with potential applications in various fields including biology and archaeology.
The "holy grail of astrobiology" - a new machine learning technique can determine whether a sample is of biological or non-biological origin with 90% accuracy.
Scientists have discovered a simple and reliable test for signs of past and present life on other planets - the "holy grail of astrobiology".
In a recent paper published in the journal Proceedings of the National Academy of Sciences, a team of seven researchers, funded by the John Templeton Foundation and led by Jim Cleaves and Robert Hazen of the Carnegie Institution for Science, reported that their artificial intelligence method distinguished modern and ancient biological samples from those from Non-biological, 90% accurate.
A revolution in space exploration and earth sciences
"This routine analytical method has the potential to revolutionize the search for extraterrestrial life and deepen our understanding of both the origin of life and the chemistry of the earliest life on Earth," says Dr. Hazen. "This paves the way for the use of smart sensors on spacecraft, landers and robotic rovers to look for signs of life before the samples return to Earth."
Most immediately, the new test could reveal the history of mysterious ancient rocks on Earth, and possibly that of samples already collected by the Mars Sample Analysis Instrument (SAM) of the Persistence Mars lander survey rover.
"We will have to adapt our method to the SAM protocols, but we may already have data to determine if there are molecules on Mars from the organic volatile biosphere."
Key points from the new study
"The search for extraterrestrial life remains one of the most important endeavors in modern science," says principal investigator Jim Cleves of the Earth and Planetary Laboratory, Carnegie Institution for Science, Washington, DC.
“This new research has many implications, but there are three main points: first, biological organic chemistry is different from non-biological organic chemistry; Second, we can look at samples from Mars and the ancient Earth and find out if they were once alive; And third, it is likely that this new method will be able to differentiate the biospheres of the Earth from alternative biospheres, with significant implications for future astrobiological missions."
The role of artificial intelligence in distinguishing between biotic and abiotic samples
The innovative analytical method does not simply rely on identifying a specific molecule or group of compounds in the sample.
Instead, the researchers showed that artificial intelligence can distinguish between biotic and abiotic samples by detecting subtle differences in the sample's molecular patterns as revealed by pyrolysis gas chromatography analysis (which separates and identifies the sample's constituents), and then by mass spectrometry (which determines the molecular weights of those components).
Extensive multidimensional data from molecular analyzes of 134 carbon-rich samples, known as abiotic or biotic, were used to train artificial intelligence so that it could predict the origin of a new sample. Artificial intelligence successfully identified with an accuracy of about 90%, samples originating from living things, such as shells, teeth, bones, insects, leaves, rice, human hair and cells preserved in fine-grained rocks; Remains of ancient life that have been geologically processed (eg coal, oil, amber and carbon-rich fossils), or samples of abiotic origin, such as laboratory-pure chemicals (eg amino acids) and carbon-rich meteorites.
The authors add that until now it has been difficult to determine the provenance of many ancient carbon samples because collections of organic molecules, whether biotic or abiotic, tend to break down over time.
To their surprise, despite significant decay and alteration, the new analytical method revealed signs of biology that had survived in some cases over hundreds of millions of years.
Deciphering the chemistry of life and the potential for future discoveries
Says Dr. Robert Hazen of the Carnegie Institution for Science: "We started with the idea that the chemistry of life is fundamentally different from that of the inanimate world; that there are "chemical rules of life" that affect the diversity and distribution of biomolecules. If we can characterize these rules, we can use them to direct our efforts to model the origins of life or detect subtle signs of life on other worlds."
"The meaning of these results is that we may be able to find a life form from another planet, from another biosphere, even if it is very different from the life we know on Earth. If we find signs of life in other places, we will be able to know if life on Earth and other planets have a common or different origin."
"In other words, the method should be able to detect alien biochemistry, as well as life on Earth. This is a big deal because the molecular biomarkers of life on Earth are relatively easy to detect, but we cannot assume that alien life will use DNA, amino acids, etc. Our method looks for patterns in molecular distribution resulting from life's requirement for "functional" molecules.
"What really amazed us was that we trained our machine learning model to predict only two types of samples - biotic or abiotic - but the method automatically discovered three separate populations: abiotic, living biotic and fossilized biotic. In other words, it can distinguish between contemporary biological samples and fossil samples—a freshly picked leaf or vegetable, for example, versus something that died a long time ago. This surprising finding gives us optimism that other features such as photosynthetic or eukaryotic life (cells with a nucleus) can also be observed."
The analysis capabilities of artificial intelligence in revealing complex patterns
To explain the role of artificial intelligence, one of the study's authors, Anirudh Prabhu, of the Carnegie Institution for Science, uses the idea of separating coins by different attributes — monetary value, metal, different, weight or radius, for example — and then goes further to find combinations of attributes. that create more nuanced separations and groupings. "When hundreds of such features are involved, artificial intelligence algorithms are invaluable to gather the information and create highly nuanced insights."
Adds Dr. Cleves: "From a chemical point of view, the differences between biotic and abiotic samples are related to things like water solubility, molecular weights, volatility and so on."
“The simple way I would think about it is that a cell has a membrane and an interior, called the cytosol; Normally the membrane is insoluble in water, while the cell contents are soluble in water. This arrangement keeps the membrane assembled as it tries to minimize the contact of its components with water, and also keeps the 'internal components' from leaking on the surface of the membrane."
"The internal components can also remain dissolved in water despite being very large molecules such as chromosomes and proteins," he says.
"Therefore, if you break down a living cell or tissue into its components, you get a mixture of very water-soluble molecules and water-insoluble molecules spread over a spectrum. Things like oil and coal have lost most of their water-soluble matter during their long history."
"Abiotic samples can have a unique distribution across this spectrum relative to each other, but they also differ from the biological distribution."
3.5 billion year old black sediments
The technique may soon solve a number of scientific mysteries on Earth, including the origin of 3.5 billion-year-old black sediments from Western Australia. These rocks are hotly contested, with some researchers claiming they hold the oldest fossil microbes on Earth, while others claim they are devoid of any signs of life.
Other samples from ancient rocks in northern Canada, South Africa and China are sparking similar debates.
"We are applying our methods now to answer these long-standing questions about the biogenicity of the organic matter in these rocks," Hazen says.
New ideas were raised about the potential contributions of this new approach in other fields such as biology, paleontology and archaeology.
"If artificial intelligence can easily distinguish between biotic and non-biotic, as well as between modern and ancient life, then what additional insights can we gain? For example, can we find out if an ancient fossilized cell had a nucleus, or if it was photosynthetic?" says Dr. Hazen.
More of the topic in Hayadan:
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We will see more slowly if the AI can also be "consciously engineered" behaviorally or if the whole world will find out how much NACHA is a false organization that deceives people on a daily basis.
I saw a picture here of land with an ant nest in the middle and it seems to me that it is a picture taken from a space telescope lol
So many words to swallow the immortal phrase "garbage in, garbage out". The only use I see for the tool is to sort through hundreds of thousands of samples from extraterrestrial sources (which we currently don't have), only to be left with a small number of positive samples for further testing. All this while running the risk of rejecting interesting samples. And we haven't even begun to discuss the possibilities of erroneous extrapolation on the part of the algorithm trained on a terrestrial environment.
Besides, an example of the black rock was given, which is currently unclear if it actually contains a sample of life. You feed the algorithm into it and it gives an answer. then what? How can it be refuted or confirmed? The algorithm did not provide any new information, except for the fact that "the algorithm determined, with a 10% error". You have to go back to the desk and plan a new way to determine - and you have to do that anyway, without the help of the algorithm.
"The reason for searching for life on other stars is not clear. If there was other life - it would have been discovered a long time ago, and if other life exists thousands of light years away from us - it's not interesting either... billions are being spent to support scientists living "Star Wars" dreams, instead of using the funds to improve the quality of life on Earth.
Placing formidable telescopes in space that "see" 300 million light-years away is also really not interesting. Waste of time and money"
Accurate and completely correct. And in my opinion, the day will come when the world will treat these scientists as they are ……!?
The reason for searching for life on other stars is not clear. If there was other life - it would have been discovered a long time ago, and if other life exists thousands of light years away from us - it's not interesting either... billions are being spent to support scientists living "Star Wars" dreams, instead of using the funds to improve the quality of life on Earth.
Placing formidable telescopes in space that "see" 300 million light-years away is also really not interesting. A waste of time and money.
And there is another method of the type called "How come we didn't think of that?" This method is called output analysis. During the Cuban Missile Crisis, American spy planes flew over Cuba equipped with cameras that photographed the island from a height of 20 km and identified missile silos that the Russians had placed there. Intelligence satellites can photograph objects with resolutions of tens of centimeters per pixel. If I'm not mistaken, there are some that are capable of 10 cm per pixel. After the launch of Ofek 1, a number of its photographs were published on Channel 1 news. 3 photographs of an Iraqi port are clearly visible, with cranes, roads and even vehicle traffic. I remember these photos very well. The MRO Mars spacecraft from an altitude of 400 km takes pictures with a resolution of 25 cm per pixel. The Curiosity vehicle takes pictures with a resolution of 33 cm per pixel. Quite a few objects from zero distance. Any body for which no geological explanation can be given, another non-natural explanation must be checked. Methodologically, everything that applies to intelligence satellites also applies here. As for Mars there are many surprises. As for future spacecraft that will be launched to planets and moons in the solar system, it will be necessary to have cameras with very high resolutions. In the 70s, a new field called astroarchaeology was developed. This field is done using satellites. The cameras of these spacecraft are not pointed at the stars, but at the Earth's ground. They are intended for the preservation and identification of archaeological sites. There was even an article about it in the late Science magazine. There is no reason why the same method should not be used elsewhere in the solar system.