CYBIONT
The Cybiont Knowledge Series presents a foundational exploration of knowledge systems as we know them, viewed through the lens of thermodynamics and information theory. This research informs our approach to designing verifiable and robust Artificial Intelligence architectures.
Core Thesis
We posit that knowledge systems are fundamentally entropy-reducing amplification loops. Compact encodings (such as DNA, algorithms, or protocols) expand into vast ordered outcomes while exporting thermodynamic entropy to maintain the second law.
This framework explains the accelerating evolution from genetic inheritance to artificial intelligence.
Papers in the Series
1. Knowledge and Entropy: From Living Systems to AI
An analysis of the core principle and the universal amplification pattern. It explores the thermodynamic basis (Gibbs Free Energy Constraint, Shannon Entropy Reduction) that differentiates active knowledge from passive order.
Read Paper 1: Knowledge and Entropy
2. The Historical Evolution of Knowledge Systems
A timeline and analysis of the major epochs of knowledge evolution, from genetic systems (3.8 billion years ago) to the current AI era.