AI may uncover new scientific concepts that defy human intuition, but will we be able to understand and operate with them? This scenario might seem like science fiction, but physicists have faced it before.
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Anderson, P. W. More Is Different: Broken symmetry and the nature of the hierarchical structure of science. Science 177, 393–396 (1972).
Schwartz, M. D. Modern machine learning and particle physics. Harvard Data Sci. Rev. https://doi.org/10.1162/99608f92.beeb1183 (2021).
Schrouff, J. et al. Best of both worlds: local and global explanations with human-understandable concepts. Preprint at https://arxiv.org/abs/2106.08641 (2021).
Kim, B. et al. Neural networks trained on natural scenes exhibit gestalt closure. Comput. Brain Behav. 4, 251–263 (2021).
Liu, Z. & Tegmark, M. Machine learning hidden symmetries. Phys. Rev. Lett. 128, 180201 (2022).
Udrescu, S. M. & Tegmark, M. AI Feynman: A physics-inspired method for symbolic regression. Sci. Adv. 6, eaay2631 (2020).
Greydanus, S. et al. Hamiltonian neural networks. Preprint at https://arxiv.org/abs/1906.01563 (2019)
Cranmer, M. et al Lagrangian neural networks. Preprint at https://arxiv.org/abs/2003.04630 (2020).
Liu, Z. et al. Machine-learning nonconservative dynamics for new-physics detection. Phys. Rev. E 104, 055302 (2021).
Lemos, P. et al. Rediscovering orbital mechanics with machine learning. Preprint at https://arxiv.org/abs/2202.02306 (2022).
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Georgescu, I. How machines could teach physicists new scientific concepts. Nat Rev Phys 4, 736–738 (2022). https://doi.org/10.1038/s42254-022-00497-5
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