Learning From Pseudo-Randomness With an Artificial Neural Network–Does God Play Pseudo-Dice?

Abstract

Inspired by the fact that the neural network, as the mainstream method for machine learning, has brought successes in many application areas, here we propose to use this approach for decoding hidden correlation among pseudo-random data and predicting events accordingly. With a simple neural network structure and a typical training procedure, we demonstrate the learning and prediction power of the neural network in pseudo-random environments. Finally, we postulate that the high sensitivity and efficiency of the neural network may allow learning on a low-dimensional manifold in a high-dimensional space of pseudorandom events and critically test, if there could be any fundamental difference between quantum randomness and pseudo randomness, which is equivalent to the classic question: Does God play dice? (Note that this analogy was first made by Einstein in his famous quotation “God does not play dice with the universe”. He believed in laws of nature, and hence, the meaning of God in this context was some mechanism responsible for the ways by which the universe evolves.)