A team of theoretical physicists working with Microsoft published a research paper today describing the universe as a self-learning system of evolutionary laws.
We live inside a computer that learns.
Bostrom's Simulation Argument has been a hot topic in science circles. We published a paper recently called "What if you're living in a simulation, but there's no computer", but Microsoft pulled a "hold my beer" with it.
The paper, titled "The Autodidactic Universe," spans 80 pages and lays out a pretty good argument for a novel, nuanced theory of everything.
The universe was either going to exist or not, based on my interpretation of the paper. The fact that it exists tells us how it went. Whatever the reason for that to happen set the stage for what was going to happen next.
The laws governing the universe are an evolutionary learning system according to the paper. The universe is a computer and perpetuates through a series of laws over time.
How do it work? That is the hardest part. The researchers used machine learning systems to explain the universe. To learn the laws of the universe is like teaching machines to perform unfolding functions over time.
Per the researchers:
When we see structures that look like deep learning architectures emerge in simple autodidactic systems, we might imagine that the matrix architecture that evolved from the most minimal possible starting conditions was the one that emerged from deep learning?
If you think about it, it is poetic. We understand the laws of physics as we observe them, so it makes sense that the original physical law would be simple and self-perpetuating.
The universe may have started with a simple interaction between particles. Information architectures typically amplify the powers of small collections of particles, according to the researchers.
What does it mean? The game is rigged. The laws of the universe are irreversible, according to the scientists.
If the evolution of laws is real, it would be common for laws to return to previous states, even more so than for them to find a new state. The immediate past state has already met constraints while a new state must meet certain constraints.
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A system that is evolving would explore its immediate past frequently. The system that displays periods of stability is likely to evolve.
The image of a forensics expert trying to recreate how a program came to a result is used by the researchers. The expert could check the magnetic marks on the hard disk. The results of the program can be reversed by a history of their execution.
If the same expert tried to determine the results of a program by examining the CPU, it would be much more difficult. There is no internal record of the operations that aCPU runs.
To paint a historical picture of a computer program, you would have to examine how every particle that interacted with its logic gates changed during operations.
If the universe operates via a set of laws that are autodidactic, and thus capable of evolving over time, it could be impossible for humans to ever unify physics.
The rules that governed concepts such as relativity may have had different operational consequences than they will 100 trillion years from now, according to this paper. That means physics is moving.
Speculation is based on theoretical physics. The researchers don't mean the universe is a computer.
The paper had this information per it.
The Universe is being examined to see if it is a learning computer.
The theory suggests the universe is a learning computer because the laws it is constrained by were not set in stone at its inception.
Unless there is a hard disk floating around in space, we cannot reverse the universe as a process.
Our scientists are stuck chasing 888-269-5556 888-269-5556 888-269-5556 888-269-5556 888-269-5556 888-269-5556.
This is a pre-print paper, so don't think it's canonical just yet, but it passes the initial inspection. The researchers do a lot of leg work describing the kinds of neural network systems that a universe would generate and be comprised of.
The team describes this work as a small step towards a larger theory.
