How do we categorize the world around us? When we see a fluffy cat and a slithery snake, our brains instantly categorize them differently. But what about artificial intelligence? New research dives into the minds of large language models (LLMs) to explore how *they* conceptualize objects. Researchers put LLMs like Gemini Pro Vision through a massive object recognition test, showing them millions of object combinations and asking them to pick the odd one out. The surprising result? These AIs develop object representations strikingly similar to humans, spontaneously clustering objects into categories like "animal," "food," or "tool." The study also compared how LLMs represent objects to how human brains do, using fMRI data. While not a perfect match, the patterns revealed a significant overlap, especially for the multimodal AI, Gemini Pro Vision, which combines text and image processing. This suggests that giving AIs access to multiple sensory modalities, like vision and language, helps them develop a richer, more human-like understanding of the world. This research opens exciting new doors to developing more human-like AI and building more intuitive interfaces between humans and machines. It also highlights the importance of multimodal learning in shaping how AI perceives and interacts with the world around it.