Pick up any physics book and you’ll find formula after formula describing how things wobble, fly, swerve and stop. The formulas describe actions that we can observe, but behind each of them can be a series of factors that are not immediately apparent.
Now, a new AI program developed by researchers at Columbia University has seemingly discovered its own alternative physics.
After seeing videos of physical phenomena on Earth, the AI has not rediscovered the current variables we use; instead, it came up with new variables to explain what it saw.
To be clear, this does not mean that our current physics are flawed or that there is a more appropriate model to explain the world around us. (Einstein’s laws have proved incredibly robust.) But those laws could only exist because they were built on the back of a pre-existing “language” of theory and principles established by centuries of tradition.
Given an alternate timeline in which other ghosts tackled the same problems with slightly different perspectives, would we still frame the mechanics that explain our universe in the same way?
Even with new technology imaging black holes and detecting strange, distant worlds, these laws have held up time and again (note: quantum mechanics is a different story, but let’s stick to the visible world here).
This new AI has only looked at videos of a handful of physical phenomena, so it’s in no way capable of coming up with new physics to explain the universe or surpass Einstein. This was not the goal here.
“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?” says roboticist Hod Lipson of the Creative Machines Lab in Columbia.
“In the 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 ways to describe the universe and it’s very possible that our choices weren’t perfect. to be .”
In addition, the team wanted to know whether AI can actually find new variables — and therefore help us explain complex new phenomena that crop up 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 gigantic experiments like the Large Hadron Collider pointing to 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? For starters, the team gave 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 angular velocity of each of the two pendulums.
The AI brooded over the footage and the question for a few hours, then spat out an answer: This phenomenon would take 4.7 variables to explain it, 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 with the variables the AI had chosen. Two of them loosely matched the angles of the arms, but the other two variables remained a mystery. Still, the AI was able to make accurate predictions about what the system would do next, so the team thought the AI was on to something they couldn’t quite grasp.
“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,” said software researcher Boyuan Chen, now an assistant professor at Duke. University, who led the work.
“But nothing seemed to match up perfectly… we don’t understand the mathematical language it speaks yet.”
The team then showed the AI other videos. The first had an “air dancer” with an undulating arm that blew in the wind (the AI said this had eight variables). Lava lamp images also yielded 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 AI may in the future help us identify variables underlying new concepts that we are currently unaware of. Check out this space.
The research was published in Nature Computational Science.