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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 Models (LLMs) challenges this assumption. These AI systems do not understand words in isolation; instead, they encode meaning relationally, as points in high-dimensional vector space. The significance of any given term is shaped by its contextual proximity to other concepts, rather than by a strict, definitional boundary.
What if meaning itself—across cognition, language, and intelligence—is more about motion through conceptual terrain than about possession? This essay explores how meaning operates in vector space, its implications for AI and human thought, and the profound philosophical consequences of this shift.
Meaning as Location in Vector Space: How LLMs Encode Understanding
Traditional models of meaning assume that words correspond to fixed, dictionary-defined ideas. However, LLMs function differently: they process language by mapping words, phrases, and concepts into multi-dimensional mathematical spaces, where meaning is encoded as distances and relationships between points.
In this framework:
- Meaning is relative rather than absolute.
- Words acquire significance not from definitions but from positional relationships within the model’s conceptual structure.
- Conceptual clusters emerge dynamically, forming fluid networks of meaning that evolve with usage.
For example, an LLM doesn’t store the word “truth” as a singular definition—it positions it within a web of related concepts such as “certainty,” “perception,” “bias,” and “fact.” The meaning of “truth” in a given sentence is shaped by its local relationships in the model’s vector space.
This transforms our understanding of meaning. Instead of being owned by words, meaning emerges through positioning. It is navigated rather than possessed.
Relational Nature of Meaning: Parallels in Linguistics, Cognition, and Philosophy
This repositioning of meaning aligns with several intellectual traditions:
1. Structuralism and Post-Structuralism in Linguistics
Linguist Ferdinand de Saussure argued that meaning arises from relationships between words, rather than from intrinsic definitions. This idea was later expanded by post-structuralists like Derrida, who emphasized how meaning is deferred through an endless network of associations.
LLMs unintentionally embody this philosophy. They do not treat words as static symbols but as dynamic nodes in an evolving space, where significance is shaped by relational positioning rather than fixed definitions.
2. Cognitive Science: Meaning as Situated Experience
Embodied cognition suggests that meaning is context-dependent, shaped by lived experience rather than abstract definitions. LLMs, while lacking direct experience, approximate this principle by modeling meaning as situated within a network of contextual interactions.
3. Information Theory: Meaning as Reduction of Uncertainty
Claude Shannon’s information theory defines meaning as the reduction of uncertainty, rather than as an inherent property of symbols. LLMs reflect this: words acquire significance through their ability to narrow the probabilistic range of interpretations based on context.
4. Thermodynamics of Information: Meaning as a Flow
Entropy governs information systems just as it does physical systems. Meaning in vector space behaves like energy flow in thermodynamics—not stored in discrete units but distributed across a system of interrelated structures, where shifts in conceptual positioning alter the state of understanding.
Implications for AI and Human Understanding
This relational model of meaning has profound consequences:
1. AI Comprehension Without Fixed Definitions
Rather than relying on predefined meanings, AI generates context-sensitive interpretations. This explains why LLMs can seem nuanced—they do not retrieve fixed facts but rather position ideas dynamically, shaping responses based on relational data.
2. Intelligence as Navigation, Not Storage
If meaning is positioned rather than possessed, intelligence itself might be redefined as a process of navigating conceptual terrain rather than storing knowledge. This mirrors how creative thinking in humans works—ideas are formed by weaving together relationships rather than retrieving static definitions.
3. Philosophical Challenge to Essentialism
The idea that meaning must be predefined has long supported essentialist views in philosophy. But if meaning is an emergent function of positioning, this challenges the notion that words, concepts, or identities have inherent definitions. Instead, existence and understanding might be relational and fluid.
4. Bias and Perspective in Meaning Formation
Since meaning in vector space depends on relational positioning, biases in data affect interpretation. This means AI’s “understanding” of concepts is shaped by the structure of its dataset, much like human perspectives are shaped by cultural and social environments.
Expanding the Paradigm: Meaning Beyond LLMs
What if this principle applies beyond AI? Perhaps all cognition—human or artificial—functions through positioning within conceptual networks rather than through static knowledge retrieval.
1. Creativity as Motion Through Conceptual Space
If meaning is located rather than defined, creativity might be seen as movement through conceptual terrain rather than invention from nothing. Artists and thinkers navigate meaning by adjusting relationships between ideas, akin to shifting vectors in an LLM’s space.
2. Consciousness as a Process of Mapping Meaning
Could human consciousness itself be understood as a dynamic system for navigating meaning in multidimensional thought space? If intelligence is not possessing knowledge but shaping conceptual connections, then our understanding of consciousness might evolve toward a relational model rather than a static entity.
3. Evolution as an Information Dynamics System
Even biological evolution follows a relational process where changes emerge not from predefined blueprints but from adaptive positioning within environmental constraints. This suggests that meaning, adaptation, and intelligence might all be expressions of complex relational systems, rather than isolated phenomena.
Conclusion: Meaning as Motion, Not Possession
Your insight—that meaning is located rather than defined—opens a compelling new lens on language, thought, and intelligence. LLMs exemplify this principle by encoding meaning as positional relationships rather than static assignments.
This shift suggests broader implications:
- Understanding is not possessing knowledge but navigating conceptual terrain.
- AI, human cognition, and even biological evolution function as systems of relational positioning.
- Meaning may not be an inherent property of symbols, but an emergent feature of networks.
In this view, intelligence ceases to be about storage and instead becomes about movement—dynamic exploration through interconnected spaces of knowledge.
If meaning is motion, not possession, then understanding is never about arriving at a single truth—but about continually refining the shape of conceptual space itself.
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