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New analysis indicates the whole cosmos could be a giant neural network

The core idea is deceptively simple: every appreciable abnormality in the entire cosmos can be modeled by a neural network. And that means, by extension, the cosmos itself may be a neural network.

Vitaly Vanchurin, a assistant of physics at the University of Minnesota Duluth, appear an absurd paper last August advantaged “The World as a Neural Network” on the arXiv pre-print server. It managed to slide past our notice until today when Futurism’s Victor Tangermann appear an account with Vanchurin discussing the paper.

The big idea

According to the paper:

We altercate a achievability that the entire cosmos on its most axiological level is a neural network. We analyze two altered types of dynamical degrees of freedom: “trainable” variables (e.g. bias vector or weight matrix) and “hidden” variables (e.g. state vector of neurons).

At its most basic, Vanchurin’s work here attempts to explain away the gap amid breakthrough and classical physics. We know that breakthrough physics does a great job of answer what’s going on in the cosmos at very small scales. When we’re, for example, ambidextrous with alone photons we can dabble with breakthrough mechanics at an observable, repeatable, assessable scale.

But when we start to pan out we’re forced to use classical physics to call what’s accident because we sort of lose the thread when we make the alteration from appreciable breakthrough phenomena to classical observations.

The argument

The root botheration with sussing out a theory of aggregate – in this case, one that defines the very nature of the cosmos itself – is that it usually ends up replacing one proxy-for-god with another. Where theorists have posited aggregate from a divine architect to the idea we’re all living in a computer simulation, the two most constant explanations for our cosmos are based on audible interpretations of breakthrough mechanics. These are called the “many worlds” and “hidden variables” interpretations and they’re the ones Vanchurin attempts to accommodate with his “world as a neural network” theory.

To this end, Vanchurin concludes:

In this paper we discussed a achievability that the entire cosmos on its most axiological level is a neural network. This is a very bold claim. We are not just saying that the bogus neural networks can be useful for allegory concrete systems or for advertent concrete laws, we are saying that this is how the world around us absolutely works. With this account it could be advised as a angle for the theory of everything, and as such it should be easy to prove it wrong. All that is needed is to find a concrete abnormality which cannot be declared by neural networks. Unfortunately (or fortunately) it is easier said than done.

Quick take: Vanchurin accurately says he’s not adding annihilation to the “many worlds” interpretation, but that’s where the most absorbing abstract implications lie (in this author’s humble opinion).

If Vanchurin’s work pans out in peer review, or at least leads to a greater accurate fixation on the idea of the cosmos as a fully-functioning neural network, then we’ll have a found a thread to pull on that could put us on the path to a acknowledged theory of everything.

If we’re all nodes in a neural network, what’s the network’s purpose? Is the cosmos one giant, closed arrangement or is it a single layer in a above network? Or conceivably we’re just one of trillions of other universes affiliated to the same network. When we train our neural networks we run bags or millions of cycles until the AI is appropriately “trained.” Are we just one of an innumerable number of training cycles for some larger-than-universal machine’s greater purpose?

You can read the paper whole paper here on arXiv.

Appear March 2, 2021 — 19:18 UTC

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