"Most artificial-intelligence (AI) models can reliably identify patterns in data and make predictions, but struggle to use that data to come up with broad scientific concepts, such as the laws of gravity. Now, a team in China has developed a system called AI-Newton that, after being fed experimental data, can autonomously 'discover' key physics principles, such as Newton's second law describing the effect of force and mass on acceleration."
"The model mimics the human scientific process by incrementally building a knowledge base of concepts and laws, says Yan-Qing Ma, a physicist at Peking University in Beijing who helped to develop the system. Being able to identify useful concepts means that the system can potentially discover scientific insights without human pre-programming, Ma adds. Keyon Vafa, a computer scientist at Harvard University in Cambridge, Massachusetts, explains that AI-Newton uses an approach called symbolic regression, in which the model hunts for the best mathematical equation to represent physical phenomena. This technique is a promising method for scientific discovery, he adds, because the system is programmed in a way that encourages it to deduce concepts."
AI-Newton is a system that autonomously identifies physical concepts and laws from experimental data by incrementally building a knowledge base of concepts and equations. The model employs symbolic regression to search for mathematical equations that represent physical phenomena and is programmed to deduce concepts. A simulator generated data for 46 physics experiments covering the free motion of balls and springs, collisions, vibrations, oscillations and pendulum-like motion, with deliberate statistical errors to mimic real-world noise. The system inferred velocity from position-time data, retained that concept, and later used it to derive mass via Newton's second law. The results have not yet been peer reviewed.
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