Artificial Intelligence Discovers Alternative Physics
A new Columbia University AI program observed physical phenomena and uncovered relevant variables—a necessary precursor to any physics theory. But the variables it discovered were unexpected.
Energy, Mass, Velocity. These three variables make up Einstein’s iconic equation E=MC. But how did Albert Einstein know about these concepts in the first place? Before understanding physics you need to identify relevant variables. Not even Einstein could discover relativity without the concepts of energy, mass, and velocity. But can variables like these be discovered automatically? Doing so would greatly accelerate scientific discovery.
This is the question that Columbia Engineering researchers posed to a new artificial intelligence program. The AI program was designed to observe physical phenomena through a video camera and then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published in the journal Nature Computational Science on July 25.
The image shows a chaotic swing stick dynamical system in motion. Our work aims at identifying and extracting the minimum number of state variables needed to describe such system from high-dimensional video footage directly. Credit: Yinuo Qin/Columbia Engineering
The scientists started by feeding the system raw video footage of physics phenomena for which they already knew the solution. For example, they fed a video of a swinging double-pendulum known to have exactly four “state variables”—the angle and angular velocity of each of the two arms. After several hours of analysis, the AI outputted its answer: 4.7.
“We thought this answer was close enough,” said Hod Lipson, director of the Creative Machines Lab in the Department of Mechanical Engineering, where the work was primarily done. “Especially since all the AI had access to was raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number.”
Next, the researchers proceeded to visualize the actual variables that the program identified. Extracting the variables themselves was difficult because the program cannot describe them in any intuitive way that would be understandable to humans. After some investigation, it appeared that two of the variables the program chose loosely corresponded to the angles of the arms, but the other two remain a mystery.
“We tried correlating the other variables with anything and everything we could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities,” explained Boyuan Chen PhD ’22, now an assistant professor at Duke University, who led the work. “But nothing seemed to match perfectly.” The team was confident that the AI had found a valid set of four variables, since it was making good predictions, “but we don’t yet understand the mathematical language it is speaking,” he explained.
Boyuan Chen explains how a new AI program observed physical phenomena and uncovered relevant variables—a necessary precursor to any physics theory. Credit: Boyuan Chen/Columbia Engineering
After validating a number of other physical systems with known solutions, the scientists inputted videos of systems for which they did not know the explicit answer.
