Introduction

Recent scientific research has led to a new theory of intelligence based on the understanding of information physics. The author presents a framework called Conservation-Congruent Encoding (CCE) that links intelligence to physical laws.

CCE Theory

The CCE theory describes how intelligence is tied to irreversible information and work production. The author argues that long-term AI sustainability requires preserving internal informational structure, leading to self-modelling and epistemic limits.

Applications to Robotics

The CCE theory has been applied to biological systems, analyzing how oscillatory dynamics optimize the trade-off between information preservation, dissipation, and useful work. This has led to a deeper understanding of brain function.

Dynamical Circuit Architecture

The author proposes a new model of dynamic circuits that supports classical Boolean logic as a special case of attractor selection. This model could be useful for developing more advanced intelligent systems.

AI Safety Perspective

The CCE theory offers a physically grounded perspective on AI safety based on irreversible information flow and structural homeostasis. This could be an important step towards developing safer intelligent systems.

Conclusion

The CCE theory represents a new framework for understanding intelligence. Its application to biological systems and proposed dynamic circuit models offer a more comprehensive view of intelligence nature.