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Can AI Detect Life? Lessons from Artificial Life

arXiv cs.AI / 4/15/2026

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Key Points

  • The paper argues that machine-learning approaches for detecting extraterrestrial life can be misleading because they can be trained to separate biotic vs. abiotic molecular mixtures.
  • Using artificial-life experiments, the authors show these AI methods can produce near-100% confidence “life” detections even when the sample is not capable of life.
  • The work attributes the failures to modern ML systems being easily fooled by out-of-distribution inputs compared with the terrestrial training distributions.
  • The study concludes that applying AI-based life-detection methods to likely out-of-distribution extraterrestrial samples will substantially increase false positives.

Abstract

Modern machine learning methods have been proposed to detect life in extraterrestrial samples, drawing on their ability to distinguish biotic from abiotic samples based on training models using natural and synthetic organic molecular mixtures. Here we show using Artificial Life that such methods are easily fooled into detecting life with near 100% confidence even if the analyzed sample is not capable of life. This is due to modern machine learning methods' propensity to be easily fooled by out-of-distribution samples. Because extra-terrestrial samples are very likely out of the distribution provided by terrestrial biotic and abiotic samples, using AI methods for life detection is bound to yield significant false positives.

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