August 10, 2022

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Artificial intelligence discovers alternative physics

Artificial intelligence discovers alternative physics

Latent weddings from our frame colored by physical condition variables. Credit: Boyuan Chen / Columbia Engineering

new[{” attribute=””>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=MC2. 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 picture shows a chaotic dynamic system swaying in motion. Our work aims to identify and extract the minimum state variables needed to describe such a system directly from high-dimensional video footage. Credit: Yinuo Qin / Columbia Engineering

Scientists began feeding the system raw video footage of physical phenomena that they already knew the solution to. For example, they fed a videotape of a swinging double pendulum that is known to have exactly four “state variables” – the angle and the angular velocity of each of the arms. After several hours of analysis, the AI ​​came out with its 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.”

Next, the researchers set out to visualize the actual variables that the program had identified. Extracting the variables themselves was difficult because the program could not describe them in any intuitive way that could be understood by humans. After some investigation, it turns out that two of the variables chosen by the program correspond loosely to the angles of the arms, but the other two variables remain a mystery.

“We tried to relate the other variables to anything and everything we could think of: angular and linear velocities, kinetic and potential energy, various combinations of known quantities,” explained Boyuan Chen PhD ’22, now an assistant professor at Duke University. “But it seems like nothing quite matches.” 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.

Boyuan Chen explains how a new artificial intelligence program observed physical phenomena and revealed related variables – a necessary precursor to any physical theory. Credit: Boyuan Chen / Columbia Engineering

After validating a number of other physical systems with known solutions, the scientists inserted videos of systems to which they did not know the explicit answer. One of these videos showed an “air dancer” swaying in front of a local used car yard. After several hours of analysis, the program returned 8 variables. Similarly, a video of the Lava 8 lamp produced eight variants. When they presented a video of the flames from the Holiday Fireplace episode, the program 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 the experiments, the number of variables was the same each time the AI ​​was restarted, but the specific variables were different each time. So yes, there are indeed alternative ways to describe the universe and it is very likely that our choices may not be perfect.

According to the researchers, this type of AI could help scientists uncover complex phenomena whose theoretical understanding does not align with the vast amount of data — fields ranging from biology to cosmology. “While we have used video data in this work, any type of array data source can be used—radar arrays, or[{” attribute=””>DNA arrays, for example,” explained Kuang Huang PhD ’22, who coauthored the paper.

The work is part of Lipson and Fu Foundation Professor of Mathematics Qiang Du’s decades-long interest in creating algorithms that can distill data into scientific laws. Past software systems, such as Lipson and Michael Schmidt’s Eureqa software, could distill freeform physical laws from experimental data, but only if the variables were identified in advance. But what if the variables are yet unknown?

Hod Lipson explains how the AI ​​program was able to discover new physical variables. Credit: Hod Lipson/Columbia Engineering

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. Since July 1, 2022, Boyuan Chen has been an assistant professor at Duke University. Work is part of a joint[{” attribute=””>University of Washington, Columbia, and Harvard NSF AI institute for dynamical systems, aimed to accelerate scientific discovery using AI.

Reference: “Automated discovery of fundamental variables hidden in experimental data” by Boyuan Chen, Kuang Huang, Sunand Raghupathi, Ishaan Chandratreya, Qiang Du and Hod Lipson, 25 July 2022, Nature Computational Science.
DOI: 10.1038/s43588-022-00281-6

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