Pick up any physics textbook and you’ll find formula after formula describing how things wobble, fly, yaw, and stop. The formulas describe actions that we can observe, but behind each one there could be sets of factors that are not immediately obvious.
Now, a new artificial intelligence program developed by researchers at Columbia University has apparently discovered its own alternative physics.
After showing you videos of physical phenomena on Earth, the AI didn’t rediscover the actual variables we use; instead, he actually came up with new variables to explain what he saw.
To be clear, this does not mean that our current physics is flawed or that there is a better model to explain the world around us. (Einstein’s laws have proven to be incredibly robust.) But those laws could only exist because they were built on the basis of a preexisting “language” of theory and principles established by centuries of tradition.
Given an alternate timeline where other minds tackled the same problems with a slightly different perspective, would we still frame the mechanics that explain our Universe the same way?
Even with new black hole imaging technology and the detection of distant, alien worlds, these laws have held up time and time again (side note: quantum mechanics is another story, but let’s focus on the visible world here).
This new AI only watched videos of a handful of physical phenomena, so it is by no means in a position to create new physics to explain the Universe or try to outdo Einstein. This was not the goal here.
“I always wondered, if we ever met an intelligent alien race, would they have discovered the same physical laws as us, or might they describe the Universe in a different way?” says roboticist Hod Lipson of the Creative Machines Lab in Columbia.
“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 alternative ways to describe the Universe, and our choices may very well not be perfect.” “. .”
Beyond that, the team wanted to know if AI could actually find new variables and thus help us explain complex new phenomena emerging in our current flood of data that we currently don’t have the theoretical understanding to follow.
For example, new data emerging from giant experiments like the Large Hadron Collider hinting at new physics.
“What other laws are we missing simply because we don’t have the variables?” says mathematician Qiang Du of Columbia University.
So how does an AI find new physics? To start, the team fed the system raw video footage of phenomena they already understood and asked the program a simple question: What are the minimum fundamental variables needed to describe what’s going on?
The first video showed a swinging double pendulum that is known to have four state variables at play: the angle and the angular velocity of each of the two pendulums.
The AI pondered the images and the question for a few hours and then spat out an answer: This phenomenon would require 4.7 variables to explain, it said.
That’s close enough to the four we know of… but it still didn’t explain what the AI thought the variables were.
So the team tried to match the known variables with the variables that the AI had chosen. Two of them vaguely matched the angles of the arms, but the other two variables remained a mystery. Still, the AI could make accurate predictions about what the system would do next, so the team thought the AI must have been up to something they couldn’t understand.
“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,” says software researcher Boyuan Chen, now an assistant professor at Duke University, who directed the play.
“But nothing seemed to fit perfectly…we still don’t understand the mathematical language he’s talking about.”
The team then went on to show the AI other videos. The first featured a wave-arm ‘air dancer’ blowing in the wind (the AI said this had eight variables). The lava lamp images also produced eight variables. A llama video clip came back with 24 variables.
Each time, the variables were unique.
“Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables,” the researchers write in their paper.
This suggests that, in the future, AI could help us identify variables that underpin new concepts that we are currently unaware of. Look at this space.
The research has been published in Nature Computational Sciences.