Humanity has many powerful tools in its cognitive arsenal. Language, abstract thinking, the theory of mind, and many others define who we are as animals. One of our most powerful tools is pattern recognition. Pattern recognition is built-in on our ground level, a foundational brick in our cognitive structure. Pattern recognition works on a basic, fight-or-flight level, letting us respond quickly to threats to our survival. It also works much more slowly and in a more focused way, as when scientists seek patterns in large collections of data. Our pattern recognition software is prone to errors. Pareidolia is the phenomenon of seeing patterns that aren't really there. We can see what looks like a face, for example, in simple rock, as with the Man in the Moon. People have seen religious figures in pieces of bread, and found meaning listening to song lyrics backwards. Pattern recognition is, arguably, the foundation of all artificial intelligence. AI can power its way through vast amounts of data much more quickly than people can, and can ferret out significant patterns. But, alas, as new research shows, AI's pattern recognition is prone to failure the same way ours is. The research shows how easily fooled AI is when given the task of detecting life, something it'll be needed for in future missions that seek life on other worlds. The research is titled "Can AI Detect Life? Lessons from Artificial Life," and will be presented in August at the 2026 Conference on Artificial Life in Waterloo, Canada. The authors are Ankit Gupta and Christoph Adami from Michigan State University. "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," Gupta and Adami