AImanities and Mirror of Collectivized Mind: Philosophy Theories of Large Language Models

Le résumé fourni par la source

This study introduces the "Mirror of Collectivized Mind" (MCM) framework within "AImanities," a newly proposed interdisciplinary paradigm integrating AI with humanistic inquiry. We reconceptualize Large Language Models (LLMs) as computational crystallizations of collective human intelligence that actively mediate cultural evolution through recursive human-AI (artificial intelligence) interactions, transcending conventional tool-agent dichotomies. The framework presents three theoretical innovations: recursive epistemic amplification, whereby LLMs transform knowledge through feedback loops that accelerate cultural evolution; distributed metacognition, emerging from dynamic interplay between human judgment and machine pattern recognition; and semantic crystallization, enabling fluid cultural knowledge to become computationally tractable while maintaining adaptive responsiveness. Our multi-scale analysis spans from quantum mechanical foundations exploring coherence and entanglement phenomena in consciousness-architecture convergence to civilizational impacts examining LLMs as cultural evolution simulators. We employ novel methodologies including cognitive archaeology for tracing reasoning patterns' cultural origins, quantum semantic analysis for examining meaning emergence through linguistic superposition, and collective intelligence metrics transcending individual model benchmarks. By bridging quantum information theory, cognitive science, and philosophical frameworks from Aristotelian thought to extended mind theory, we address fundamental questions about consciousness, understanding, and the increasingly indeterminate boundaries between human and artificial cognition. The framework delineates prospective trajectories through neural-symbolic convergence, multimodal integration, and embodied cognition, providing both theoretical foundations and practical guidance for developing AI systems that augment rather than replace human capabilities. This positions LLMs as mediators of emerging hybrid intelligence that challenges established epistemological, metaphysical, and ethical categories while contributing to novel civilizational forms of intelligence through human-AI co-evolution.

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