A neural network requires the inputs of the calculator to take on the form of a vector. Therefore, the world must be coded in advance in the form of a purely digital vectorial representation. While certain objects such as images are naturally broken down into vectors, other objects need to be ‘embedded’ within a vectorial space before it is possible to calculate or classify them with neural networks. This is the case of text, which is the prototypical example. To input a word into a neural network, the Word2vec technique ‘embeds’ it into a vectorial space that measures its distance from the other words in the corpus. Words thus inherit a position within a space with several hundreds of dimensions. The advantage of such a representation resides in the numerous operations offered by such a transformation. Two terms whose inferred positions are near one another in this space are equally similar semantically; these representations are said to be distributed: the vector of the concept ‘apartment’ [-0.2, 0.3, -4.2, 5.1...] will be similar to that of ‘house’ [-0.2, 0.3, -4.0, 5.1...]. […] While natural language processing was pioneering for ‘embedding’ words in a vectorial space, today we are witnessing a generalization of the embedding process which is progressively extending to all applications fields: networks are becoming simple points in a vectorial space with graph2vec, texts with paragraph2vec, films with movie2vec, meanings of words with sens2vec, molecular structures with mol2vec, etc. According to Yann LeCun, the goal of the designers of connectionist machines is to put the world in a vector (world2vec).39
