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Avalanches of Meaning: Applying the Brain's Power Law Geometry to AI Language Models
- The article explores the merging of neuroscience principles with Large Language Models (LLMs) in the context of meaning generation.
- Neuroscience reveals brain activity follows a 'power law' and operates in high-dimensional geometries, similar to LLM capabilities.
- The brain's power-law dynamics allow for stability and flexibility, enabling meaningful cognition through neural avalanches.
- The Semantic Collapse Function scores the significance of ideas based on coherence, relevance, and novelty, altering standard LLM outputs.
- LLMs typically prioritize statistical likelihood over semantic significance, prompting the proposal to shift to prioritizing meaning.
- The Semantic Avalanche Model alters LLM behavior to prioritize profound, meaningful structures over common outputs, simulating human insight.
- This model introduces the concept of Semantic Mass and a selection process emphasizing semantic coherence and rarity in outputs.
- The proposed architecture mirrors human brain insight processes, aiming to create AI systems that simulate cognitive avalanches of meaning.
- The unified equation of the Semantic Avalanche Model guides AI in selecting outputs based on brain-like principles of coherence and power-law distribution.
- The article concludes by suggesting that combining neuroscience, semantic resonance mathematics, and structured LLM architectures can lead to the creation of systems that generate highly meaningful symbolic collapses.
- Eligible for Web Story: True
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