Word in technogenic multidimensional space

Abstract

Today, artificial intelligence is actively mastering natural languages, becoming an interlocutor and partner of human in various aspects of activity. However, the symbolic approach, which implies the transfer of rules and logic, has failed, the number of rules and exceptions of the language does not allow its formalization, so modern «deep learning» of artificial neural networks involves an independent search for patterns in extensive databases. During training, artificial intelligence puts a word into a sentence so that the syntagmatic relationships are as close as possible to those of the target word in the base, taking into account both the semantic relationships of words and the relationships between words in the sequence of presentation. The «language» of information technologies is digital. During natural language processing, words are represented in vector form as a sequence of numbers. The idea of representing words mathematically is familiar to people and is usually associated with logical consistency. Visualization of the position of words in a multidimensional space created by artificial intelligence demonstrates a number of patterns, obvious semantic and syntactic relationships, but the essence of other relationships between words is not obvious. The mathematical representation of words, created by artificial intelligence, can allow you to look at the language from a new non-human point of view.