The Physics of Learning: From Autoencoders to Truly Autonomous Learning Machines

TL;DR

This paper explores a theoretical framework where autonomous learning systems derive energy from their own predictions, enabling self-sustaining, energy-driven learning processes exemplified by autoencoders, aiming to redefine intelligence as an energy-seeking phenomenon.

Contribution

It introduces a theoretical approach for transforming unsupervised learning models into self-sustaining physical systems through simple architectural modifications.

Findings

Autoencoders can be viewed as energy-seeking systems.

Predicted information can serve as an energy source for learning.

Theoretical basis for autonomous, energy-driven learning systems.

Abstract

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.