Identifying Extraterrestrial Life: U.S. Scientists’ AI System Distinguishes Life Forms with 90% Precision

An AI system, developed by American scientists, has achieved a 90% success rate in identifying signs of life, differentiating biological from non-biological materials. This innovation is pivotal for astrobiology, aiding in the quest to understand life’s emergence on Earth and Mars and in detecting life beyond our planet.

This AI model proficiently separates biological samples from non-biological ones with a 90% accuracy rate.

The quest to find extraterrestrial life has taken a leap forward with the creation of an AI system by a U.S. scientific team. This system can accurately identify life signs on other planets with 90% precision.

The system was first introduced at the Goldschmidt Geochemistry Conference in Lyon on July 14th and has garnered positive feedback from peers. Its findings are also published in the journal PNAS.

Professor Robert Hazen from the Carnegie Institution’s Geophysical Laboratory and George Mason University, the leading researcher, remarked, “This breakthrough enhances our capacity to detect life’s biochemical markers on other planets. It paves the path for using intelligent sensors on unmanned spacecraft in the search for life.”

Since the 1950s, science has recognized that simple chemicals under the right conditions can form complex molecules vital for life, like amino acids. Further discoveries include space-found nucleotides necessary for DNA. However, determining whether these molecules are biologically or abiotically formed remains a challenge. This distinction is crucial for confirming extraterrestrial life.

A photo by the Mars Curiosity rover, using the described pyrolysis-GCMS method, was taken in June 2016. Credit: NASA/JPL-Caltech/MSSS

Hazen inquired, “Is there a fundamental difference between life’s chemistry and that of the inanimate world? Are there life-specific chemical rules influencing biomolecule diversity and distribution? Our findings suggest there are.”

From an evolutionary standpoint, life sustenance is complex, involving specific viable pathways. This analysis transcends mere compound identification, focusing instead on the compound’s origin within its sample context.

Research Methodology

The team used NASA’s pyrolysis gas-chromatography mass-spectrometry (GCMS) to analyze 134 diverse carbon-rich samples, including living cells, fossil fuels, carbon-rich meteorites, and laboratory-made compounds.

Among these, 59 were biotic (like rice grains, human hair, crude oil), and 75 were abiotic (like lab-made amino acids or meteorite samples). The process involved heating samples in an oxygen-free environment (pyrolysis) and then analyzing them with a GC-MS. Machine-learning techniques then utilized three-dimensional data from each sample to accurately predict its biotic or abiotic nature with over 90% accuracy.

Conference Presentation and Response

Professor Hazen presented this research at the Goldschmidt conference in Lyon, France, on July 14th. The response from the audience was positive, with Hazen confirming the method’s potential to detect a range of biosignatures and identify extraterrestrial life forms fundamentally different from those on Earth.

Session co-chairs Anastasia Yanchilina and Fabian Gäb noted the enthusiastic reception. Dr. Yanchilina highlighted the session’s success and the significance of Hazen’s talk.

Implications and Perspectives

Hazen emphasized the profound implications of this research. It enables analysis of ancient Earth and Mars samples to determine past life existence, aiding in understanding the origins of life on Earth and the potential of life on Mars.

The research suggests a fundamental difference between biochemical and non-biological chemistry. This could lead to identifying life forms from different biospheres, thus aiding in understanding if life on Earth and other planets share a common origin or have distinct beginnings.

The machine-learning method, initially trained on biotic and abiotic attributes, unexpectedly identified three distinct groups: abiotic, living biotic, and fossil biotic. This finding indicates the potential to distinguish other life characteristics, like photosynthetic life or eukaryotes.

Professor Emmanuelle Javaux, not involved in the study, commented on its significance. She noted its potential as a groundbreaking tool in astrobiology and its application in resolving debates within the scientific community regarding the oldest traces of Earth life and organisms from the three life domains.

This study marks the beginning of what could become a transformative approach in deciphering information from complex organic mixtures.

Reference: “A robust, agnostic molecular biosignature based on machine learning” by H. James Cleaves, Grethe Hystad, Anirudh Prabhu, Michael L. Wong, George D. Cody, Sophia Economon, and Robert M. Hazen, 25 September 2023, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2307149120

Conference Abstract 18592: A Robust Molecular Biosignature Based on Machine Learning Applied to Three-Dimensional Pyrolysis

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More about Extraterrestrial Life Detection

• Original Research Paper: Read the full research paper titled “A robust, agnostic molecular biosignature based on machine learning” for in-depth information.
• Goldschmidt Geochemistry Conference: Explore the official website of the Goldschmidt Geochemistry Conference for details on the conference where the research was presented.
• Carnegie Institution for Science: Visit the Carnegie Institution for Science’s website for more information on their research and contributions to science.
• PNAS Journal: Access the Proceedings of the National Academy of Sciences (PNAS) journal for related scientific publications and articles.