Artificial intelligence discovers new physics variables!

An artificial intelligence tool has explored physical systems and, unsurprisingly, found new ways to describe what it found.

How do we give meaning to the universe? There is no manual. There is no recipe.

In its most basic sense, physics helps us understand the relationships between “observable” variables – these are things we can measure. Velocity, energy, mass, position, angles, temperature, charge. Some variables, such as acceleration, can be reduced to more fundamental variables. These are all variables in physics that shape our understanding of the world.

These variables are related to each other through equations.

Albert Einstein’s most famous equation, E = mc2sums up the relationship between the variables energy (E) and mass (m), the habits constant: the speed of light (c). In fact, all of Einstein’s very complicated special theory of relativity can be reduced to relationships between three variables: energy, mass, and velocity.

There is nothing sacred about our variable choice. The variables and math we choose have stood the test of time as the ones that make sense for a particular theory or physical system.

But what if we found other physical variables to solve the same problems? It wouldn’t change the problem… or the solution. But it can give us new insights into the inner workings of the universe and accelerate scientific discovery.

Read more: Machine learning identifies the origin of the most famous Mars meteorite to land on Earth

Now an artificial intelligence (AI) tool developed at Columbia University in New York has done just that. The results of the experiments are: published in Nature Computational Science.

Robotics at Columbia Engineering developed an AI program to review raw video data and search for the minimal set of fundamental variables that fully describe the system’s observed physical dynamics, the swing of a pendulum.

To test their AI, the team first showed the tool videos of a phenomenon they already knew the answer to.

Double pendulum can be described by exactly four “state variables” – the angle and angular velocity of each of the two arms. After staring at the videos for a few hours, the AI ​​gave its answer for the number of variables in the system: 4.7.

“We thought this answer was close enough,” said senior author Hod Lipson, director of the Creative Machines Lab in Columbia University’s Department of Mechanical Engineering. “Especially because the AI ​​had access to raw video footage, without any knowledge of physics or geometry. But we wanted to know what the variables actually were, not just their number.”

So the next challenge was to try and visualize the variables the AI ​​had identified. This was not easy because the program does not describe the variables in language intuitively for people. However, the researchers correlated two of the variables with the angles of each pendulum’s arm.

“We tried to correlate the other variables with everything we could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities,” explains lead author Boyuan Chen, now an assistant professor at Duke University. . “But nothing seemed to match perfectly.”

Boyuan Chen explains the team’s research

Boyuan Chen, now an assistant professor at Duke University, says the team tried to correlate other variables with anything they could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities. “But nothing seemed to match perfectly.”

The team was confident that the AI’s good predictions about the dynamics of the system meant it found a valid set of four variables, and what the others could be.

“We don’t understand the mathematical language it speaks yet,” Chen says.

Still, the AI ​​delivered good calculations on other physical systems with known solutions.

Already confused, what would the team have to lose by showing the AI ​​something for which there is no known answer?

The team showed the AI ​​a video of “crazy wavy inflatable arm flailing tube men” from Family man fame, flogging for a used car lot. A few hours of analysis yielded eight variables. A lava lamp video also gave eight variables. A looped video of a fireplace produced 24 variables.

The researchers wondered whether the sets of variables were different each time the program was restarted, or whether it found the same unique set of variables for each system.

“I’ve always wondered if we would ever meet an intelligent alien race, would they have discovered the same laws of physics that we do, or could they describe the universe in a different way?” asks Lipson. “Perhaps some phenomena seem puzzlingly complex because we’re trying to understand them using the wrong set of variables.”

In the experiments, the number of variables remained unchanged, but the specific variables varied each time the AI ​​restarted. This indicates that there are alternative ways of describing the systems – and by extension the universe – and our choices may not be perfect.

Such AI tools can help scientists understand complex phenomena that escape current theoretical understanding. “While we used video data in this work, any kind of array data source could be used — radar arrays or DNA arrays, for example,” explains co-author Kuang Huang.

Lipson argues that scientists may misinterpret or misunderstand many phenomena simply because they don’t have a good set of variables. “For thousands of years, people knew about fast or slow moving objects, but it wasn’t until the notion of speed and acceleration was formally quantified that Newton was able to discover his famous law of motion. F = ma.”

Likewise, the variables of temperature and pressure had to be described before the laws of thermodynamics could be formalized. The same goes for any scientific theory: to develop a theory, you first need the variables. “What other laws are we missing simply because we don’t have the variables?” asks co-author and math professor Qiang Du.

Leave a Comment

Your email address will not be published.