An AI has just independently discovered alternative physics

Pick up a physics textbook and you’ll find formula after formula describing how things wobble, fly, oscillate, and stop. The formulas describe actions we can observe, but behind each could be sets of factors that are not immediately obvious.

Now, a new AI program developed by researchers at Columbia University has apparently discovered its own alternative physics.

After showing videos of physical phenomena on Earth, the AI ​​did not rediscover the current variables we use; instead, it actually came up with new variables to explain what it saw.

To be clear, this does not mean that our current physics is flawed or that there is a model that is better suited to explain the world around us. (Einstein’s laws have proven to be incredibly robust.) But these laws could only exist because they were built upon a pre-existing ‘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 in the same way?

Even with new technology imaging black holes and discovering strange, distant worlds, these laws have held up time and time again (side note: quantum mechanics is a whole other story, but let’s stick to the visible world here).

This new AI only looked at videos of a handful of physical phenomena, so it is in no way positioned to come up with new physics to explain the universe or try to best Einstein. That was not the goal here.

“I’ve always wondered, if we ever met an intelligent alien race, would they have discovered the same laws of physics that we have, or could 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 ​​restarted, but the specific variables were different each time. So yes, there are alternative ways to describe the universe, and it’s quite possible that our choices aren’t perfect.”

Beyond that, the team wanted to know if AI could actually find new variables – and therefore help us explain complex new phenomena emerging in our current deluge of data that we don’t currently have the theoretical understanding to keep up with .

For example, the new data coming from giant experiments such as the Large Hadron Collider that suggest new physics.

“What other laws are we missing simply because we don’t have the variables?” says mathematician Qiang Du from 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 minimal fundamental variables needed to describe what’s going on?

The first video showed a swinging double pendulum known to have four state variables at play: the angle and angular velocity of each of the two pendulums.

The AI ​​considered the footage and the question for a few hours, 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 then tried to match the known variables to the variables the AI ​​had chosen. Two of them matched loosely to 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 figured the AI ​​must have been onto something they couldn’t fully 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 led the work.

“But nothing seemed to match perfectly … we don’t yet understand the mathematical language it speaks.”

The team then proceeded to show the AI ​​other videos. The first featured an ‘air dancer’ with a wavy arm blowing in the wind (the AI ​​said this had eight variables). Lava lamp recordings also produced eight variables. A video clip of flames 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 potentially help us identify variables that support new concepts that we are currently unaware of. Watch this space.

The research is published in Natural Computational Science.

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