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Is the Physical World a Neural Network?

By Dr. Inés Urdaneta. Resonance Science Foundation Research Scientist

In a former RSF article entitled Between the Generalized Holographic approach and Data Science, we addressed the potential of trained artificial neural networks to replace our scientific models, and the possibility of reality being a numerical simulation was discussed. Somehow we had anticipated this next and very recent work from Vitaly Vanchurin, from the University of Minnesota Duluth, proposing that we live in a neural network. It is an audacious idea!


In our prior article we had anticipated the impact of artificial neural networks and deep machine learning … what we had not foreseen was that they would be used literally as the framework for the theory of everything! There is a saying: "better be a historian, than a prophet", meaning that a historian writes about past events, and so taking small risk, while a prophet takes a huge risk with his predictions. Though, we should not brag about this feat because given the current scientific state of the art, the proposal of a universal neural network is quite reasonable.


The most recent observations at both quantum and cosmological scales are casting serious doubts on our current models. For instance, at quantum scale, the latest electronic hydrogen proton radius measurement resulted in a much smaller radius than the one predicted by the standard model of particles physics, which now is off by 4%. At cosmological scale, the amount of observations regarding black holes and galactic formation heading in the direction of a radically different cosmological model, is overwhelming. Black holes have shown being much older than their hosting galaxies, galactic formation is much younger than our models estimates, and there is evidence of at least 64 black holes aligned with respect to their axis of rotation, suggesting the presence of a large scale spatial coherence in angular momentum that is impossible to predict with our current models. Under such scenario, it should not fall as a surprise the absence of a better alternative to unify quantum theory and relativity, and thus connect the very small to the very big, than the idea that the universe is actually a neural network. And for this reason, a theory of everything would be based on it.  


As explained in Targemann’s interview to Vanchurin on Futurism, the work of Vanchurin, proposes that we live in a huge neural network that governs everything around us.


“it’s a possibility that the entire universe on its most fundamental level is a neural network… 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”. Vitaly Vanchurin
The idea was born when he was studying deep machine learning. He wrote the book “Towards a theory of machine learning”, in order to apply the methods of statistical mechanics to study the behavior of neural networks, and he saw that in certain limits the learning (or training) dynamics of neural networks is very similar to the quantum dynamics. So, he decided to explore the idea that the physical world is a neural network.


He bases this bold idea on the following study: if he starts with a precise model of neural networks to study the behavior of the network up to the limit of a large number of neurons, that is somehow mimicking the pass from a state of quasi equilibrium (a quantum state), to a state far from equilibrium (a classical state).  And this is precisely how the world around us works, and his model too. Additionally, we know that the quantum scale works for the very small scales, while relativity works for the very large scale, so his model would solve this issue as well, connecting them fluidly.


Vanchurin argues that artificial neural networks can “exhibit approximate behaviors” of both universal theories, quantum mechanics and relativity. In the interview he also explains that there is a third phenomenon that needs to be unified to the former two, and this is the problem of the observers, which is known as the measurement problem in context of quantum mechanics, and the measure problem in context of cosmology.


Due to the success that quantum physics has had in many regimes, and given the fact that the very big is composed of the very small, most physicist would agree that quantum mechanics is the main theory and everything else emerges from it.  But we still do not know how. Vanchurin considers a different approach: that a microscopic neural network is the fundamental structure and everything else, i.e. quantum mechanics, general relativity and macroscopic observers, emerges from it.  And the main reason for it, comes from the fact that neural networks are extremely efficient to achieve emergent properties.


In his approach, the states of the individual neurons are hidden variables and the trainable variables (such as bias vector and weight matrix) are quantum variables. Since the hidden variables can be very non-local and so the 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, as seen in the figure below. This would connect his model to Bohm’s interpretation.  

The intriguing part of his idea is that to prove him wrong, it suffices, as usual, to find a physical phenomenon where this theory does not work. This is how most theories of everything have failed. Applying this same principle to his theory, being everything around a neural network, one physical phenomenon that could not be modeled with a neural network would prove him wrong. As he says, it is a very difficult task because we know very little about the behavior of neural networks and machine learning, and therefore he tries to develop a theory of machine learning on the first place.


In the original interview by Tangermann, Vanchurin also describes how his approach would address  natural selection, which is a very important topic connecting to biology. In summary, natural selection would not start at biological regime, it would come much before, deciding the sort of all subatomic particles first. Natural selection would be the result of the thermodynamical stability of the micro network, scaling up to universal size. 


Finally, in Tangermann’s article, the following surprising ending appeared:
Tangermann: “I need to ask: would this theory mean we’re living in a simulation?”
Vanchurin “No, we live in a neural network, but we might never know the difference.”
This is serendipity! The concern about living in a simulated reality, is just how this article starts … what a perfectly synchronized closure for it!

RSF in Perspective
In a previous article, Between the Generalized Holographic approach and Data Science, we had warned that the impasse in which conventional physics finds itself, including dark matter and dark energy, would lead us to opt for artificial intelligence to refine the models and close the gap. What we had not foreseen was that neural networks would be used literally, to connect the scales!


It was just a matter of time, of a very short period of time, for these ideas of using deep machine learning and artificial neural networks to emerge as the new paradigm … and the reason for it is their exceptional capacity to provide emergent properties, as we have explained in depth in our previous article.


We stress out that by means of Haramein’s fundamental holographic ratio, which is mainly a geometric solution, we can not only predict the latest proton radius within the experimental certitude of 1σ (while the results given by the standard model of particle physics is 7σ, way off experimental certitude), it also solves the vacuum catastrophe and finds the electron mass from first principle calculations. These remarkable solutions prove Haramein has achieved the quantum gravity and by doing so, has unified relativity and quantum physics. Additionally, Haramein’s model is a geometric solution and hence the physical sense can be followed all the way through. No need to resort to deep machine learning and neural networks to achieve the accuracy required by experiments. We are already there!


However, Vanchurin’s work goes in the direction of validating the unified field based on the fundamental holographic ratio and the proton entanglement network that emerges from it, including the statistical and entropic features involved in the model. It is an equivalent way of establishing a neural network, hence, both views would give an affirmative answer to the question Is the physical world a neural network? Although they will probably not provide the same network or how, because as we mentioned earlier and explained in detail here, the generalized Holographic Model provides a physical meaning which is unified to the whole.


Nevertheless, if Vanchurin’s approach converged to Haramein’s and there was a unitary transformation that could lead from one to the other, this would be a very fast, though not necessarily most appropriate, way to unify our model with the rest of the fields in physics.


The issue of the hidden variables mentioned by Vanchurin could de rephrased as follows: the physical processes or phenomenon which is being described, gets imprinted in the neural network, it becomes the neural network itself, and so now it is governed by variables that are delocalized; they are no longer assigned locally.   
Finally, the 2020 theory of everything proposed by Wolfgram, and which will be explored in a future article, is also deeply connected to Vanchurin’s works, and both aim to reach the values, predictions and conclusions that Haramein’s generalized Holographic model achieves.  

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