Can the universe improve itself?

The universe could be learning how to evolve into a better, more stable cosmos. This is the unusual idea proposed by a group of scientists who claim to be reimagining the universe in the same way that Darwin reimagined our view of the natural world.

The unusual new idea attempts to explain why the laws of physics are as we see them, using a mathematical framework to describe various proposed theories in physics, such as quantum field theory and quantum gravity. The result is a system that looks like a machine learning program.

Scientists have discovered many physical laws and quantities with fixed values to define the universe. From the mass of an electron to the force of gravity, there are many defined constants in the universe that seem arbitrary to some, given their precise and seemingly non-sequential values.

“One of the goals of fundamental physics these days is not just to understand what the laws of physics are, but why they turn out to be what they are, why they take the forms they do,” says author William Cunningham, a physicist and software manager at quantum computing company Agnostiq. “There’s really no obvious reason why one set of laws would be preferable to another.”

A self-learning system

To answer this question, the group wondered whether the way we see the universe today is just one way the universe exists. Perhaps the laws we see today are just one of many possible forms. Perhaps the universe is evolving.

To get a universe that evolves, researchers have proposed an idea called an autodidactic universe – a universe that is self-learning. In this case, learning would occur similar to the way a machine learning algorithm works, where feedback at one stage affects the next, in order to achieve a more stable energy state.

Following this idea, the group developed a possible scheme by which the universe could learn, relying on matrix mathematics–a way of mathematical modeling in the form of rows and columns–neural networks and other principles of machine learning. In short, they investigated whether the universe could be a self-learning computer.

The Darwinian Universe

Just as a moth might evolve to have better camouflage, the universe might evolve to a higher state – which in this case might mean that it is in a more stable energy state. According to a mathematical scheme developed by the researchers, this system can only move forward, with each iteration creating a better or more stable universe than before. The physical constants we measure today are only valid now and may have had different values in the past.

The team found that certain quantum theories of gravity and quantum field theories, known as gauge theories – a class of theories that seek to form a bridge between Einstein’s theory of special relativity and quantum mechanics to describe subatomic particles – could be mapped or translated into matrix mathematics, creating a machine learning system model. This connection showed that in each iteration or cycle of the machine learning system, the result could be the physical laws of the universe.

According to the group, the learning system described in their work, published in the arXiv preprint database, represents the first “baby steps” toward realizing the idea. With further work, however, the group could create a full-fledged model of the universe that would open new doors to understanding our cosmos.

“One exciting prospect is that you can use one of these models and maybe extract something new,” Cunningham said. It could be the discovery of physics for a new type of black hole or a new law describing a physical system that hasn’t yet been explained, such as dark energy.”

The authors of the new study acknowledge that their work is only preliminary and is not a definitive theory, but rather a way to start thinking about things in new ways. Ultimately, while the paper makes no conclusions about what kind of model might be used to describe our universe, it does allow for the possibility that the universe can learn.

“I think at the end of the paper we were left with a lot of open questions, and we certainly weren’t able to prove anything,” Cunningham told Live Science. “But really our goal is to start a discussion.”

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