Apr 1, 2021 06:39 AM
https://iai.tv/articles/the-universe-is-...-auid-1784
EXCERPT (Vitaly Vanchurin): . . . Most scientists are not willing to conduct research outside their “comfort zone” for a very simple reason: this would mean a lot more work for a lot less recognition. That is where the real problem lies: the strategy which benefits individual researchers is the opposite to the strategy which would benefit the civilization as a whole.
My own attempt to increase the “step-size” and to find a way out of the “local minimum” employs a rather bold idea that the entire universe is a cosmological neural network. Its purpose is the same as any other neural network: to learn its training dataset or, in other words, to understand its environment. This may be trivial, but what was less trivial was to show that for learning to be effective it must be happening on all scales: from subatomic to cosmological. To check this hypothesis, I first developed a thermodynamic approach to learning (both equilibrium and non-equilibrium), and then applied it to describe natural phenomena (both quantum and classical) on a wide range of scales. Some of my calculations in this area were published in a recent paper entitled “The World as a Neural Network”. What this suggests is that the quantum, classical and gravitational effects that we observe around us might not be fundamental, but emergent behaviours of a cosmological neural network learning. If correct, then it is telling us something very deep about how nature works.
The proposal can also be viewed as a new attempt to reconcile quantum mechanics and general relativity – ‘the problem of quantum gravity’. In other words, the neural networks might be the missing link in the unification of quantum mechanics and general relativity. On the smallest scales the cosmological neural network is at equilibrium, which is very well described by quantum mechanics, but on the largest scales the neural network is still very far from an equilibrium, which is better described by general relativity. In addition, the neural network model might shed light on the problem of observers - ‘the measurement problem’ in quantum mechanics and ‘the measure problem’ in cosmology, but for that we must first develop a better understanding of macroscopic observers and, perhaps, consciousness. This is where an input from biologists might be absolutely essential.
Does this mean that neural networks give us an improved theoretical framework for doing science in the 21st century? It is too early to say for sure, but it is encouraging that a growing number of physicists, biologists and computer scientists are seriously considering this possibility... (MORE - details)
EXCERPT (Vitaly Vanchurin): . . . Most scientists are not willing to conduct research outside their “comfort zone” for a very simple reason: this would mean a lot more work for a lot less recognition. That is where the real problem lies: the strategy which benefits individual researchers is the opposite to the strategy which would benefit the civilization as a whole.
My own attempt to increase the “step-size” and to find a way out of the “local minimum” employs a rather bold idea that the entire universe is a cosmological neural network. Its purpose is the same as any other neural network: to learn its training dataset or, in other words, to understand its environment. This may be trivial, but what was less trivial was to show that for learning to be effective it must be happening on all scales: from subatomic to cosmological. To check this hypothesis, I first developed a thermodynamic approach to learning (both equilibrium and non-equilibrium), and then applied it to describe natural phenomena (both quantum and classical) on a wide range of scales. Some of my calculations in this area were published in a recent paper entitled “The World as a Neural Network”. What this suggests is that the quantum, classical and gravitational effects that we observe around us might not be fundamental, but emergent behaviours of a cosmological neural network learning. If correct, then it is telling us something very deep about how nature works.
The proposal can also be viewed as a new attempt to reconcile quantum mechanics and general relativity – ‘the problem of quantum gravity’. In other words, the neural networks might be the missing link in the unification of quantum mechanics and general relativity. On the smallest scales the cosmological neural network is at equilibrium, which is very well described by quantum mechanics, but on the largest scales the neural network is still very far from an equilibrium, which is better described by general relativity. In addition, the neural network model might shed light on the problem of observers - ‘the measurement problem’ in quantum mechanics and ‘the measure problem’ in cosmology, but for that we must first develop a better understanding of macroscopic observers and, perhaps, consciousness. This is where an input from biologists might be absolutely essential.
Does this mean that neural networks give us an improved theoretical framework for doing science in the 21st century? It is too early to say for sure, but it is encouraging that a growing number of physicists, biologists and computer scientists are seriously considering this possibility... (MORE - details)
