This paper proposes a novel perspective on learning, positing it as the pursuit of dynamical invariants -- data combinations that remain constant or exhibit minimal change over time as a system evolves. This concept is underpinned by both informational and physical principles, rooted in the inherent properties of these invariants. Firstly, their stability makes them ideal for memorization and integration into associative networks, forming the basis of our knowledge structures. Secondly, the predictability of these stable invariants makes them valuable sources of usable energy, quantifiable as kTln2 per bit of accurately predicted information. This energy can be harnessed to explore new transformations, rendering learning systems energetically autonomous and increasingly effective. Such systems are driven to continuously seek new data invariants as energy sources. The paper further explores several meta-architectures of autonomous, self-propelled learning agents that utilize predictable information patterns as a source of usable energy.

翻译：本文提出了一种关于学习的新视角，将其视为对动力学不变量的追寻——即系统演化过程中保持恒定或变化最小的数据组合。这一概念基于信息与物理双重原理，根植于这些不变量的固有特性。首先，其稳定性使其成为记忆存储与整合至联想网络的理想载体，构成我们知识结构的基础。其次，这些稳定不变量的可预测性使其成为可用能量的宝贵来源，每比特准确预测的信息可量化为kTln2。这种能量可被用于探索新的变换，使学习系统具备能量自主性并持续增强效能。此类系统被驱动去不断寻找作为能量来源的新数据不变量。本文进一步探讨了多种自主推进学习代理的元架构，这些架构利用可预测信息模式作为可用能量来源。