# On Physical Origins of Learning

arXiv: https://arxiv.org/abs/2310.02375 · HTML: https://mlsanity.com/paper/2310.02375/

## 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 (most cited first, 12 of 12)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://mlsanity.com/paper/2211.08064/) (2022)
- [Physics-based Deep Learning](https://mlsanity.com/paper/2109.05237/) (2021)
- [An overview on deep learning-based approximation methods for partial differential equations](https://mlsanity.com/paper/2012.12348/) (2020)
- [Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2011.01734/) (2020)
- [Towards a Theory of Evolution as Multilevel Learning](https://mlsanity.com/paper/2110.14602/) (2021)
- [Self-Organization and Artificial Life](https://mlsanity.com/paper/1903.07456/) (2019)
- [Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration](https://mlsanity.com/paper/1509.06824/) (2015)
- [Can Turing machine be curious about its Turing test results? Three informal lectures on physics of intelligence](https://mlsanity.com/paper/1606.08109/) (2016)

## 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.