A physicist creates an AI algorithm that predicts natural events and may prove the simulation hypothesis.
- Princeton physicist Hong Qin creates an AI algorithm that can predict planetary orbits.
- The scientist partially based his work on the hypothesis which believes reality is a simulation.
- The algorithm is being adapted to predict behavior of plasma and can be used on other natural phenomena.
A scientist devised a computer algorithm which may lead to transformative discoveries in energy and whose very existence raises the likelihood that our reality could actually be a simulation.
The algorithm was created by the physicist Hong Qin, from the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL).
The algorithm employs an AI process called machine learning, which improves its knowledge in an automated way, through experience.
Qin developed this algorithm to predict the orbits of planets in the solar system, training it on data of Mercury, Venus, Earth, Mars, Ceres, and Jupiter orbits. The data is “similar to what Kepler inherited from Tycho Brahe in 1601,” as Qin writes in his newly-published paper on the subject. From this data, a “serving algorithm” can correctly predict other planetary orbits in the solar system, including parabolic and hyperbolic escaping orbits. What’s remarkable, it can do so without having to be told about Newton’s laws of motion and universal gravitation. It can figure those laws out for itself from the numbers.
Qin is now adapting the algorithm to predict and even control other behaviors, with a current focus on particles of plasma in facilities built for harvesting fusion energy powering the Sun and stars. Along with Eric Palmerduca, a Ph.D. graduate student at PPPL, Qin is using his technique “to learning an effective structure-preserving algorithm with long-term stability to simulate the gyrocenter dynamics in magnetic fusion plasmas,” as he elaborated. He also plans to utilize the algorithm to study quantum physics. [ … ]
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