1. A method of machine learning of written natural languages comprising: receiving a string of natural language texts in a first computing system having at least one application module installed thereon; forming, with the at least one application module in the first computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of rules, the 2-D symbol being a matrix of N×N pixels of data that contains a super-character, the matrix being divided into M×M sub-matrices with each of the sub-matrices containing (N/M)×(N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where N and M are positive integers, and N is a multiple of M; and learning the meaning of the super-character in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed with the at least one application module in the first computing system and transmitted to the second computing system.