https://arxiv.org/abs/2104.03902
>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.
OH NO NO NO PHYSICS IS JUST A SUB DISCIPLINE OF CS NOW AHAHAHAA HOW YOU PARTICLE MONKEYS LIKE US NOW
>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.
OH NO NO NO PHYSICS IS JUST A SUB DISCIPLINE OF CS NOW AHAHAHAA HOW YOU PARTICLE MONKEYS LIKE US NOW
