Plasma Trained to Play Tic-Tac-Toe: A Programmable Intelligent Material

The rate coefficient of an inelastic collision in plasmas can be determined by an Arrhenius-like mathematical relation. This is similar to the weights in an artificial neural network (ANN) determined by the activation functions. Therefore, a chemical pathway network (CPN) that massive chemical species are connected by the cause-effect chemical reaction pathways shares a similar mathematical ability with ANN: a functional mapping between the input space and output space. Based on such a similarity, we propose a programmable material intelligence by training a He-air plasma, considering the CPN as an ANN, to play a board game Tic-Tac-Toe. In each turn, the board information is sent to the plasma discharge units by feeding a gas combination, and the plasma returns spectra to show its next move. After training, the plasma shows a significantly high winning rate when playing against a random-move player in a 500-game test. This work thus reveals the potential of any matter that has a complicated chemical reaction system that can be used as a carrier of artificial intelligence. In other words, a material can be programmed and process data through its own molecular collisions, and thus can be considered as an analog computer at the molecular level.