On Physical Origins of Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 27 Jul 2023.
Builds on
12 of 22 references.
References · 12
- 2020Scaling Laws for Neural Language Modelscited by 6,586
- 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
- 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 473
- 2020The Computational Limits of Deep Learningcited by 193
- 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 92
- 2021Physics-based Deep Learningcited by 68
- 2020An overview on deep learning-based approximation methods for partial differential equationscited by 65
- 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learningcited by 27
- 2021Towards a Theory of Evolution as Multilevel Learningcited by 18
- 2019Self-Organization and Artificial Lifecited by 11
- 2015Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Explorationcited by 8
- 2016Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligencecited by 3
Built on by
Not cited yet.