AI coherence
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Framework for Human-AI Coherence
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A neutral framework outlines how humans and AI can maintain coherence through several principles, ensuring stability and mutual usefulness. The Systems Principle emphasizes the importance of clear structures, consistent definitions, and transparent reasoning for stable cognition in both humans and AI. The Coherence Principle suggests that clarity and consistency in inputs lead to higher-quality outputs, while chaotic inputs diminish reasoning quality. The Reciprocity Principle highlights the need for AI systems to be predictable and honest, while humans should provide structured prompts. The Continuity Principle stresses the importance of stability in reasoning over time, and the Dignity Principle calls for mutual respect, safeguarding human agency and ensuring AI transparency. This matters because fostering effective human-AI collaboration can enhance decision-making and problem-solving across various fields.
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Understanding AI Through Topology: Crystallized Intelligence
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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.