Physicist's AI Algorithm Could Prove Our Reality Is Just A Simulation
Physicist Hong Qin from the U.S. Department of Energy¡¯s (DOE) Princeton Plasma Physics Laboratory has developed an algorithm that predicts the orbit of planets in our solar system based on training from data of planetary orbits of Mercury, Venus, Earth, Mars Ceres and Jupiter.
We've accustomed ourselves to AI algorithms making accurate predictions to enhance the comforts and convenience of our life. But just what exactly is life and this reality we all experience? Could it all be one big simulation, like they showed in the movie The Matrix?
Dr Hong Qin // Warner Bros The Matrix
However, researchers have also been applying the use of powerful, complex algorithms to decipher the mysteries of the universe. And now, one scientist has created a computer algorithm that could result in transformative discoveries in energy while also adding fuel to the notion of our reality actually being a simulation.
Are we part of a simulation?
Reported first by BigThink, Physicist Hong Qin from the U.S. Department of Energy¡¯s (DOE) Princeton Plasma Physics Laboratory has developed an algorithm that predicts the orbit of planets in our solar system based on training from data of planetary orbits of Mercury, Venus, Earth, Mars Ceres and Jupiter.
Qin claims that from his data, a serving algorithm can accurately predict other planetary orbits in the solar system -- including parabolic and hyperbolic escaping orbits. However, what made this truly remarkable was that it was able to do this without teaching it about Newton¡¯s laws of motion and universal gravitation. The algorithm learns about these phenomena by itself, based on the numbers.
Qin is now using the algorithm to predict and eventually control other behaviours with a specific focus on particles of plasma in areas that harvest fusion energy to help the Sun and star power-up.
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He explains in his paper, "Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations. What I'm doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law. Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data. There is no law of physics in the middle."
Qin has been inspired by philosopher Nick Bostrom who in his 2003 paper shared with the world an idea that revealed that we could be living in an artificial simulation of sorts. Qin is now of the belief that his algorithm provides a working example of the underlying tech that could support the simulation in the argument made by Bostrom.
Based on discrete field theories
Qin¡¯s work is based on the ¡®discrete field theory¡¯ that according to him is perfect for machine learning. He calls the ¡®discrete field theory¡¯ a sort of shell or framework with customisable parameters that can be trained with the help of observational data. After the training is complete, it turns into an algorithm of nature that a computer ¡®can run to predict new observations.
He also states that the discrete field theories are quite the opposite of the theories popularly used to study physics today -- theories that see spacetime as continuous. He is of the belief that there are serious issues with modern research that originate from conventional laws of physics that believe in continuous spacetime explained through differential equations and continuous field theories. He feels if they were based on discrete spacetime, it could overcome several problems.
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According to him, discrete field theories are more crucial than the existing laws of physics. He also states that our future generation will find the discrete field theories more natural than the conventional laws of continuous space that their ancestors stood-by in the 17-21st centuries.
