من منا لا يحب الفيزياء النظرية!
مسودة مشروع بحثي مكون من ٨٠ صفحة مشترك بين مجموعة فيزيائين نظريين وميكروسوفت يشير الى ان الكون هو عبارة عن كمبيوتر ذاتي التعلم ( كون يعلم نفسه بنفسه)
اكتشف الباحثون وراء هذه الورقة أفكارًا مشابهة للبحث الذي أجراه الفيزيائي فيتالي فانكورين الذي أكد أن الكون هو في الواقع شبكة عصبية ضخمة.
The cosmology is a machine-learning computer
“For instance, when we see structures that resemble deep learning architectures emerge in simple autodidactic systems might we imagine that the operative matrix architecture in which our universe evolves laws, itself evolved from an autodidactic system that arose from the most minimal possible starting conditions?”
Draft version of the research
The Autodidactic Universe
Stephon Alexander et al
Abstract:
We present an approach to cosmology in which the Universe learns its own physical laws. It does so by exploring a landscape of possible laws, which we express as a certain class of matrix models. We discover maps that put each of these matrix models in correspondence with both a gauge/gravity theory and a mathematical model of a learning machine, such as a deep recurrent, cyclic neural network. This establishes a correspondence between each solution of the physical theory and a run of a neural network.
This correspondence is not an equivalence, partly because gauge theories emerge from N → ∞ limits of the matrix models, whereas the same limits of the neural networks used here are not well-defined.
We discuss in detail what it means to say that learning takes place in autodidactic systems, where there is no supervision. We propose that if the neural network model can be said to learn without supervision, the same can be said for the corresponding physical theory.
We consider other protocols for autodidactic physical systems, such as optimiza- tion of graph variety, subset-replication using self-attention and look-ahead, geomet- rogenesis guided by reinforcement learning, structural learning using renormaliza- tion group techniques, and extensions. These protocols together provide a number of directions in which to explore the origin of physical laws based on putting machine learning architectures in correspondence with physical theories.
https://arxiv.org/pdf/2104.03902.pdf
The previous work of Vitaly Vanchurin entitled “The world as a neural network”.
“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.” V. Vanchurin.
