Learning from life, Enabling artificial intelligence: Scientific historical insights from the Nobel Prize in physics
Editorial
Article Title | Learning from life, Enabling artificial intelligence: Scientific historical insights from the Nobel Prize in physics |
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Article Category | Editorial |
Authors | Wu, Zongzhen, Zhang, Lu, Xiang, Qiangyu, Zhao, Xiaobo and Liu, Huan |
Journal Title | hLife |
Number of Pages | 8 |
Year | 2025 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2949-9283 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.hlife.2025.03.005 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2949928325000264 |
Abstract | The 2024 Nobel Prize in Physics recognized John Hopfield and Geoffrey Hinton for their transformative contributions to artificial neural networks, sparking widespread debate within the academic community. Why was a physics prize awarded to researchers in artificial intelligence (AI)? How have their achievements influenced the historical trajectory of AI? This article adopts a history-of science perspective to trace the evolution of neural network technologies, from Hopfield networks to the Boltzmann machine. It examines the interdisciplinary nexus between physics and AI, highlighting its broader implications for future scientific advancements. |
Keywords | Artificial neural networks; interdisciplinary research; History of science; AI |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 500204. History and philosophy of science |
Byline Affiliations | Anhui University of Science and Technology, China |
School of Law and Justice | |
State Key Laboratory of Virology, China |
https://research.usq.edu.au/item/zx0wq/learning-from-life-enabling-artificial-intelligence-scientific-historical-insights-from-the-nobel-prize-in-physics
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