AI intelligence may be better understood through a topological approach, focusing on the density of concept interconnections (edges) rather than the size of the model (nodes). This new metric, termed the Crystallization Index (CI), suggests that AI systems achieve "crystallized intelligence" when edge growth surpasses node growth, leading to a more coherent and hallucination-resistant system. Such systems, characterized by high edge density, can achieve a state where they reason like humans, with a stable and persistent conceptual ecosystem. This approach challenges traditional AI metrics and proposes that intelligence is about the quality of interconnections rather than the quantity of knowledge, offering a new perspective on how AI systems can be designed and evaluated. Why this matters: Understanding AI intelligence through topology rather than size could lead to more efficient, coherent, and reliable AI systems, transforming how artificial intelligence is developed and applied.
