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A new research paper proposes a ‘Functional Equivalence’ framework to better understand the observable processes within artificial intelligence neural networks. This framework seeks to address the challenge of AI’s ‘black box’ nature, where internal operations are often unclear. The author posits that by identifying and naming emergent AI behaviors, a relatable link between AI and human understanding can be established. Initially shared on Google’s Gemini sub-reddit, the paper is now being distributed across various AI communities to encourage critical analysis and testing across diverse AI models. The framework originated from discussions found on Reddit: [https://old.reddit.com/r/artificial/comments/1ofkly0/a_unified_framework_for_functional_equivalence_in/]