The AI algorithms designed to detect life in space

In the quest to uncover signs of life on other planets, the tools at our disposal are becoming increasingly sophisticated. Spacecraft equipped with sensors have the ability to detect molecules that might suggest the presence of alien life. However, the challenge lies in identifying organic molecules that could hint at intriguing biological processes, especially as they tend to degrade over time, making them elusive to current technology.

But a promising breakthrough has emerged in the form of AI that can discern subtle variations in molecular patterns indicative of biological signals, even in samples that are hundreds of millions of years old. Recent research reports an impressive 90% accuracy rate for this innovative approach.

In the foreseeable future, this AI technology may become an integral part of advanced sensors on robotic space explorers, including moon and Mars landers and rovers, as well as spacecraft orbiting potentially habitable celestial bodies like Enceladus and Europa.

Robert Hazen, a scientist affiliated with the Carnegie Institution for Science in Washington D.C. and a co-author of the study, explained the underlying concept, stating, "We began with the idea that the chemistry of life differs fundamentally from that of the inanimate world; that there are 'chemical rules of life' that influence the diversity and distribution of biomolecules." He believes that understanding these rules can guide efforts to model the origins of life or to identify subtle signs of life on distant worlds.

The new AI-based method hinges on the principle that the chemical processes governing the creation and functioning of biomolecules are fundamentally distinct from those governing abiotic molecules. Biomolecules, such as amino acids, retain information about the chemical processes responsible for their formation. The researchers argue that this is likely to apply to alien life forms as well.

Life, whether on Earth or elsewhere, is presumed to rely on a select few compounds in higher quantities for its daily functioning. This distinction from abiotic systems can be detected and quantified using AI, as noted by the researchers.

The team behind this development trained their machine learning algorithm using 134 samples, including 59 biotic and 75 abiotic examples. To validate the algorithm, the data was randomly divided into training and test sets. The AI method successfully identified biotic samples from various sources, such as shells, teeth, bones, rice, human hair, and ancient life preserved in fossilized fragments like coal, oil, and amber. Additionally, it correctly identified abiotic samples, including lab-generated amino acids and carbon-rich meteorites.

This AI innovation opens the door to immediate applications, including the examination of 3.5 billion-year-old rocks in Western Australia's Pilbara region, where the world's oldest fossils are believed to exist. Discovered in 1993, these rocks were initially thought to be fossilized remains of microbes similar to cyanobacteria, which were responsible for producing oxygen on Earth. However, these findings have remained contentious, as some researchers have suggested that the evidence might be attributed to geological processes unrelated to ancient life. AI could potentially resolve this long-standing debate.

In summary, the emergence of AI as a tool for detecting signs of life on other planets represents a significant stride forward in our ongoing exploration of the cosmos. This technology holds the promise of unravelling age-old mysteries and reshaping our understanding of life's existence beyond Earth.