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Introduction: From Randomness to Meaning We live in a world filled with information. Every time we read a sentence, see a tree, or hear a melody, we are processing signals. These signals come from an underlying sea of possibilities. But how do raw data and random events become meaningful? What determines whether a signal is…

Abstract The emergence of Large Language Models (LLMs) challenges traditional notions of meaning, which rely on static definitions assigned to words and concepts. This paper argues that meaning is not an inherent property but an emergent phenomenon arising from positional relationships within high-dimensional conceptual spaces. Drawing on linguistics, cognitive science, information theory, philosophy, and evolutionary…

I. Introduction To understand the fundamental origins of life, consciousness, and adaptive complexity, we must look beyond isolated metrics and toward their interplay across multiple domains. The “Entropy State Space” graph—layered with Shannon entropy, Boltzmann entropy, systemic complexity contours, and a modeled free energy landscape—offers a conceptual bridge spanning physics, information theory, thermodynamics, and evolutionary…

Introduction Big AI models like ChatGPT work by turning words into numbers—called embeddings—and figuring out how those numbers relate to one another. These numbers help the model understand the meaning of words, sentences, and even long conversations. At the same time, there’s a deep concept from information theory called entropy. It’s a way to measure…

Introduction: The End of Static Meaning? Human thought has long relied on definitions—structured, contained units of meaning assigned to words, concepts, and experiences. Dictionaries cement words into fixed meanings, and conventional linguistic theories assume that meaning is something possessed by words rather than positioned dynamically in a conceptual space. But the emergence of Large Language…

🧠 Introduction: The Illusion of Understanding Artificial General Intelligence (AGI) conjures images of synthetic minds with human-like reasoning, introspection, and adaptability. As researchers edge closer to crafting machines that can converse, reason, and reflect, a tempting narrative emerges: these systems “understand” the world. But peel back the layers of memory modules, planning algorithms, and tool…