A team of researchers funded by the John Templeton Foundation, led by Jim Cleaves and Robert Hazen of the Carnegie Institution for Science, has developed an artificial intelligence-based method that can distinguish between modern and ancient biological samples and those of abiotic origin with a remarkable 90% accuracy. This breakthrough has significant implications for the search for extraterrestrial life and our understanding of the origins and chemistry of early life on Earth.
The researchers believe that their innovative analytical method has the potential to revolutionize the quest for extraterrestrial life. It can be employed on smart sensors integrated into robotic spacecraft, landers, and rovers to search for signs of life on distant celestial bodies before the samples return to Earth.
Jim Cleaves, the lead author of the study, highlights three major takeaways from their research:
- Biochemistry fundamentally differs from abiotic organic chemistry.
- The method can assess samples from Mars and ancient Earth to determine if they were once alive.
- It has the potential to distinguish alternative biospheres from Earth’s, which is crucial for future astrobiology missions.
Unlike traditional methods that rely on identifying specific molecules or compound groups in a sample, this AI-driven approach focuses on detecting subtle differences within a sample’s molecular patterns. It utilizes pyrolysis gas chromatography analysis to separate and identify a sample’s components, followed by mass spectrometry to determine their molecular weights. Vast multidimensional data from molecular analyses of 134 known abiotic or biotic carbon-rich samples trained the AI to predict a sample’s origin.
One of the significant applications of this method is deciphering the origins of mysterious ancient rocks on Earth. It could also be used to analyze samples collected by the Mars Curiosity rover’s Sample Analysis at Mars (SAM) instrument.
The researchers suggest that this method could detect not only Earth-based life but also alien biochemistries. It doesn’t rely on specific biomarkers like DNA or amino acids but rather identifies patterns in molecular distributions that arise from the functional molecules required for life.
The technique may resolve longstanding scientific mysteries, such as the origin of 3.5 billion-year-old black sediments in Western Australia, where the debate continues about whether they contain Earth’s oldest fossil microbes or are devoid of life signs.
This AI-powered approach has the potential to make significant contributions to various fields, including biology, paleontology, and archaeology. It could help identify the presence of nuclei or photosynthetic capabilities in ancient fossil cells and differentiate between charred remains from archaeological sites.