The fact that accurately predicted information can serve as an energy source paves the way for new approaches to autonomous learning. The energy derived from a sequence of successful predictions can be recycled as an immediate incentive and resource, driving the enhancement of predictive capabilities in AI agents. We propose that, through a series of straightforward meta-architectural adjustments, any unsupervised learning apparatus could achieve complete independence from external energy sources, evolving into a self-sustaining physical system with a strong intrinsic 'drive' for continual learning. This concept, while still purely theoretical, is exemplified through the autoencoder, a quintessential model for unsupervised efficient coding. We use this model to demonstrate how progressive paradigm shifts can profoundly alter our comprehension of learning and intelligence. By reconceptualizing learning as an energy-seeking process, we highlight the potential for achieving true autonomy in learning systems, thereby bridging the gap between algorithmic concepts and physical models of intelligence.

通过对无监督学习装置进行简单的元架构调整，我们提出了精确预测信息可以作为能源的概念，从而使无人驾驶学习的新方法得以实现，这种能量源自一系列成功的预测，可以作为即时的激励和资源循环利用，在AI代理中推动预测能力的提升。通过以自动编码器为例，我们阐述了这一概念示例，并展示了渐进的范式转变如何深刻改变我们对学习和智能的理解，将学习重新构想为寻求能量的过程，从而强调实现学习系统真正自主的潜力，从而弥合算法概念与智能物理模型之间的差距。

学习的物理学：从自动编码器到真正的自主学习机器