It’s not every day that we come across a paper that attempts to redefine reality.

But in a preprint uploaded
to arXiv this summer, a physics professor at the University of Minnesota Duluth
named Vitaly Vanchurin attempts to reframe reality in a particularly
eye-opening way — suggesting that we’re living inside a massive neural network
that governs everything around us. In other words, he wrote in the paper, it’s
a “possibility that the entire universe on its most fundamental level is a
neural network.”

For years, physicists have attempted to
reconcile quantum mechanics and general relativity. The first posits that
time is universal and absolute, while the latter argues that time is relative,
linked to the fabric of space-time.

In his paper, Vanchurin argues that artificial neural networks can “exhibit approximate behaviors” of both universal theories. Since quantum mechanics “is a remarkably successful paradigm for modeling physical phenomena on a wide range of scales,” he writes, “it is widely believed that on the most fundamental level the entire universe is governed by the rules of quantum mechanics and even gravity should somehow emerge from it.

“We are not just saying that the artificial neural networks can be useful for analyzing physical systems or for discovering physical laws, we are saying that this is how the world around us actually works,” reads the paper’s discussion. “With this respect it could be considered as a proposal for the theory of everything, and as such it should be easy to prove it wrong.”

The concept is so bold that most physicists
and machine learning experts we reached out to declined to comment on the
record, citing skepticism about the paper’s conclusions. But in a Q&A with
Futurism, Vanchurin leaned into the controversy — and told us more about his
idea.

Futurism: Your paper argues that the
universe might fundamentally be a neural network. How would you explain your reasoning
to someone who didn’t know very much about neural networks or physics?

The first way is to start with a precise
model of neural networks and then study the behavior of the network in the
limit of a large number of neurons. What I have shown is that equations of
quantum mechanics describe pretty well the behavior of the system near
equilibrium and equations of classical mechanics describe pretty well how the
system is further away from the equilibrium. Coincidence? Maybe, but as far as we
know quantum and classical mechanics are exactly how the physical world works.

The second way is to start with physics. We
know that quantum mechanics works pretty well on small scales and general relativity
works pretty well on large scales, but so far we were not able to reconcile the
two theories in a unified framework. This is known as the problem of quantum
gravity. Clearly, we are missing something big, but to make matters worse we do
not even know how to handle observers. This is known as the measurement problem
in the context of quantum mechanics and the measure problem in the context of
cosmology.

Then one might argue that there are not two,
but three phenomena that need to be unified: quantum mechanics, general
relativity, and observers. 99% of physicists would tell you that quantum
mechanics is the main one and everything else should somehow emerge from it,
but nobody knows exactly how that can be done. In this paper, I consider another
possibility that a microscopic neural network is a fundamental structure and
everything else, i.e. quantum mechanics, general relativity, and macroscopic
observers, emerges from it. So far things look rather promising.

__What first gave you this idea?__

First I just wanted to better understand how
deep learning works and so I wrote a paper entitled “Towards a theory of
machine learning”. The initial idea was to apply the methods of statistical
mechanics to study the behavior of neural networks, but it turned out that in
certain limits the learning (or training) dynamics of neural networks is very
similar to the quantum dynamics we see in physics. At that time I was (and
still is) on a sabbatical leave and decided to explore the idea that the
physical world is actually a neural network. The idea is definitely crazy, but
if it is crazy enough to be true? That remains to be seen.

*In the paper you wrote that to
prove the theory was wrong, “all that is needed is to find a physical
phenomenon which cannot be described by neural networks.” What do you mean by
that? Why is such a thing “easier said than done?”*

Well, there are many “theories of everything”
and most of them must be wrong. In my theory, everything you see around you is
a neural network and so to prove it wrong all that is needed is to find a
phenomenon that cannot be modeled with a neural network. But if you think
about it it is a very difficult task mainly because we know so little about how
the neural networks behave and how machine learning actually works. That
was why I tried to develop a theory of machine learning in the first place.

The idea is definitely crazy, but if it is
crazy enough to be true? That remains to be seen.

There are two main lines of thought Everett’s (or many-worlds) interpretation of quantum mechanics and Bohm’s (or
hidden variables) interpretation. I have nothing new to say about the
many-worlds interpretation, but I think I can contribute something to the
hidden variables theories. In the emergent quantum mechanics which I
considered, the hidden variables are the states of the individual neurons and
the trainable variables (such as bias vector and weight matrix) are quantum
variables. Note that the hidden variables can be very non-local and so Bell’s inequalities are violated. An approximated space-time locality is
expected to emerge, but strictly speaking, every neuron can be connected to
every other neuron and so the system need not be local.

Do you mind expanding on the way this theory
relates to natural selection? How does natural selection factor into the
evolution of complex structures/biological cells?

What I am saying is very simple. There are
structures (or subnetworks) of the microscopic neural network which are more
stable and there are other structures that are less stable. The more stable
structures would survive the evolution, and the less stable structure would be
exterminated. On the smallest scales, I expect that the natural selection should
produce some very low complexity structures such as chains of neurons, but on
larger scales, the structures would be more complicated. I see no reason why
this process should be confined to a particular length scale and so the claim
is that everything that we see around us (e.g. particles, atoms, cells,
observers, etc.) is the outcome of natural selection.

I was intrigued by your first email when you
said you might not understand everything yourself. What did you mean by that?
Were you referring to the complexity of the neural network itself, or to
something more philosophical?

Yes, I only refer to the complexity of
neural networks. I did not even have time to think about what could be
philosophical implications of the results. I need to ask: would this theory
mean we’re living in a simulation?

No, we live in a neural network, but we
might never know the difference.

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