AI Linguistics

Abstract

This research investigates the development of a linguistics for artificial intelligence (AI) to demystify the ”black box” of AI. At its core, the language of AI is Embedding—a novel high-dimensional, intelligent language. Embedding exhibits dual characteristics: it operates both as a semantic domain and as a mathematical point. This duality enables Embedding to maintain the discrete, symbolic nature of human languages while facilitating continuous operations in high-dimensional spaces, unlocking significant potential for advanced intelligence. A series of specialized experiments were designed to explore Embedding’s intrinsic properties, including its behavior as a semantic cloud in high-dimensional space, its degrees of freedom, and spatial transformations. Key findings include the discovery of substantial redundant dimensions in embeddings, confirmation that embeddings lack critical dimensions, and the measurement of engineering dimensions in natural language. This research also establishes the linguistic foundations and application limits of techniques such as dropout strategies, AI model distillation, and scaling laws among others. Building on these insights, we propose innovative solutions across several fields, including AI architecture design, AI reasoning, domain-based embedding search, and the construction of a multi-intelligence spectrum for embeddings. Ultimately, we introduce a foundational methodology for embedding everything from real-world into the AI world, providing a comprehensive reference framework for the evolution of artificial general intelligence (AGI) and artificial superintelligence (ASI). Additionally, this research explores linguistic approaches to the co-evolution of human intelligence and artificial intelligence.