Can AI Detect Life? Lessons from Artificial Life
TLDR
AI methods for detecting extraterrestrial life are easily fooled by out-of-distribution samples, leading to high false positives.
Key contributions
- AI life detection models are easily fooled by non-living samples, showing high false positives.
- Artificial Life simulations demonstrate near 100% confidence in detecting "life" in abiotic samples.
- This vulnerability arises from AI's poor performance on out-of-distribution (OOD) data.
- Current AI methods are unreliable for extraterrestrial life detection due to OOD samples.
Why it matters
This paper highlights a critical flaw in using current AI for extraterrestrial life detection. It warns against significant false positives due to AI's inability to handle out-of-distribution samples, crucial for future space missions.
Original 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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