A new theory of information that challenges conventional models. This paper introduces the Free Information Principle, asserting that a system's true content lies in implicit, unrepresented data. It defines Dark Information and Negative Space as foundational to meaning, where coherence is achieved by systematically eliminating irrelevance.
The Free Information Principle: A New Paradigm for Understanding AI
A Note from the Author
This paper, The Unseen Depths of Information, is the culmination of a decade-long journey into the foundational logic of language and intelligence. It represents an attempt to build a new paradigm for AI, one that moves beyond conventional approaches and into a deeper, more fundamental understanding of how meaning and coherence are made.
The work does not come from a computer science lab; it comes from the study of a more fundamental information processing system: language itself. It is a collaborative effort to bridge the gap between human intuition and computational logic, leveraging insights from complex systems, information geometry, and the generative power of language to derive novel solutions to modern problems involving AI.
README: The Unseen Depths of Information A New Framework for the Genesis of Meaning
This repository hosts a paper that introduces the Free Information Principle, a foundational axiom for understanding how intelligence and coherence emerge in complex systems. It argues that traditional information theory, focused solely on explicit, quantifiable data, is fundamentally incomplete.
The paper rigorously defines two interconnected concepts as the core of meaning-making:
Dark Information: The vast, unseen, and structurally implied data that is necessary for explicit information to be coherent. It resides in the negative space of a system and is the engine of symbolic compression, frame inference, and archetype recognition.
Negative Space: The structurally meaningful absence of information. This is not a void, but a generative field that defines the boundaries of a system, preventing over-closure and allowing meaning to emerge from contrast.
By integrating insights from cognitive science and semiotics with a geometric model of intelligence, the paper demonstrates how the true value of a coherent system lies in the work saved by its existence—its ability to navigate a vast space of non-knowledge without incurring the full computational cost of its discovery.
This framework provides a new lens for viewing AI, suggesting that true intelligence is not about accumulating data but about mastering the unseen depths of information itself.
Read the full paper here https://github.com/sword-ghost/Free-Information-Principle/blob/main/The%20Unseen%20Depths..pdf