On Physical Origins of Learning
This work addresses a foundational problem in understanding intelligence for researchers in AI, physics, and biology, but it appears incremental as it builds on existing physical models rather than introducing a new paradigm.
The paper tackles the problem of understanding the origins of learning by proposing that it may have non-biological and non-evolutionary roots, showing that key learning properties can be explained and reproduced in simple physical models of energy accumulation in open resonant systems with dissipation.
The quest to comprehend the origins of intelligence raises intriguing questions about the evolution of learning abilities in natural systems. Why do living organisms possess an inherent drive to acquire knowledge of the unknown? Is this motivation solely explicable through natural selection, favoring systems capable of learning due to their increased chances of survival? Or do there exist additional, more rapid mechanisms that offer immediate rewards to systems entering the "learning mode" in the "right ways"? This article explores the latter possibility and endeavors to unravel the possible nature of these ways. We propose that learning may have non-biological and non-evolutionary origin. It turns out that key properties of learning can be observed, explained, and accurately reproduced within simple physical models that describe energy accumulation mechanisms in open resonant-type systems with dissipation.