Energy, mass and velocity. These three variables make up Einstein’s iconic equation E = MC2. But how did Einstein learn about these concepts in the first place? A preliminary step to understanding physics is to identify the relevant variables. Without the concept of energy, mass, and velocity, not even Einstein could discover relativity. But can such variables be detected automatically? Doing so can greatly speed up scientific discovery.
That’s the question researchers at Columbia Engineering are asking a new artificial intelligence program. The program is designed to observe physical phenomena through a video camera, and then try to search for the minimum set of basic variables that fully describe the observed dynamics. The study was published on July 25 in computational natural sciences.
The researchers began feeding the system raw video footage of phenomena they already knew the answer to. For example, they fed a videotape of a swinging double pendulum known to have exactly four “state variables” – the angle and the angular velocity of each of the arms. After a few hours of analysis, the AI produced the answer: 4.7.
“We thought this answer was close enough,” said Hood Lipson, director of the Creative Machines Laboratory in the Department of Mechanical Engineering, where the work was primarily done. “Especially since all the AI could access was raw video footage, without any knowledge of physics or engineering. But we wanted to know what the variables actually were, not just how many.”
The researchers then proceeded to visualize the actual variables identified by the program. Extracting the variables themselves was not easy, as the program cannot describe them in any intuitive way that can be understood by humans. After some investigation, it turns out that two of the variables chosen by the program loosely correspond to the angles of the arms, but the other two variables remain a mystery.
Boyuan Chen Ph.D. explained. , who is now an assistant professor at Duke University, said, “We’ve tried to relate other variables to anything and everything we can think of: angular and linear velocities, kinetic and potential energy, various combinations of known quantities.” who led the work. “But nothing seems to be a perfect match.” The team was confident the AI had found a valid set of four variables, because it was making good predictions, “but we didn’t yet understand the mathematical language it was speaking,” he explained.
After validating a number of other physical systems with known solutions, the researchers fed videos of systems to which they didn’t know the explicit answer. In the first videos, she appeared as an “air dancer” swaying in front of a local used car yard. After a few hours of analysis, the program returned eight variables. A video of the lava lamp also produced eight variants. Then they fed a flame video from a holiday fireplace episode, and the show brought back 24 variables.
A particularly interesting question was whether the set of variables was unique to each system, or whether a different set was produced each time the program was restarted.
“I’ve always wondered, if we ever met an intelligent alien race, would they discover the same laws of physics that we have, or would they describe the universe differently?” Lipson said. “Perhaps some phenomena seem vaguely complex because we are trying to understand them using the wrong set of variables. In experiments, the number of variables was the same every time the AI restarted, but the specific variables were different each time. So yes, there are alternative approaches. to describe the universe and it is entirely possible that our choices may not be perfect.”
Researchers believe this type of AI could help scientists uncover complex phenomena for which theoretical understanding doesn’t keep pace with vast amounts of data — fields ranging from biology to cosmology. “While we used video data in this work, any type of array data source can be used — radar arrays, or DNA arrays, for example,” explained Kwang Huang, PhD, who co-authored the paper.
The work is part of Chiang Du, a professor of mathematics at the Lipson and Fu Foundation, who has long been interested in creating algorithms that can turn data into scientific laws. Previous software systems, such as Lipson’s and Michael Schmidt’s Eureqa, could extract free physical laws from experimental data, but only if the variables were previously defined. But what if the variables are not yet known?
Lipson, who is also the James and Sally Scapa Professor of Innovation, argues that scientists may misinterpret or fail to understand many phenomena simply because they do not have a good set of variables to describe the phenomenon.
Lipson noted: “For thousands of years, people have known that objects move quickly or slowly, but only when the concept of velocity and acceleration was formally defined did Newton discover his famous law of motion F = MA.” The variables describing temperature and pressure must be identified before the laws of thermodynamics can be formulated, and so in every corner of the scientific world. Variables are a precursor to any theory.
“What other laws are we missing simply because we don’t have the variables?” asked Doe, who co-led the work.
The paper was also co-authored by Sunand Raghupathi and Ishaan Chandratreya, who helped collect data for the trials.
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Boyuan Chen et al, Automated detection of baseline variables hidden in experimental data, computational natural sciences (2022). DOI: 10.1038 / s43588-022-00281-6
Submitted by Columbia University College of Engineering and Applied Sciences
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