The purpose of the lecture course in Artificial Intelligence (AI) is to review and analyze the role and place of AI in the framework of the scientific-technical revolution and to explain, how it performs the creative functions that are traditionally considered the prerogative of a person. During the lecture course, the history of Artificial Intelligence development, different tests of the logical character, fields of business and economy, and other sciences, where the AI is used, main scientific centers of AI, and other interesting topics will be discussed. At the same time, an intelligent system is a technical or software system capable of solving problems traditionally considered creative, belonging to a specific subject area, knowledge about which is stored in the memory of such a system. The structure of an intelligent system includes three main blocks - a knowledge base, a solver, and an intelligent interface that allows you to communicate with a computer without special programs for data entry. One of the particular definitions of intelligence, common to humans and "machines", can be formulated as follows: "Intelligence is the ability of a system to create, in the course of self-learning, programs (primarily heuristic) for solving problems of a certain class of complexity and to solve these problems."

The purpose of the lecture course in Artificial Intelligence (AI) is to review and analyze the role and place of AI in the framework of the scientific-technical revolution and to explain, how it performs the creative functions that are traditionally considered the prerogative of a person. During the lecture course, the history of Artificial Intelligence development, different tests of the logical character, fields of business and economy, and other sciences, where the AI is used, main scientific centers of AI, and other interesting topics will be discussed. One of the particular definitions of intelligence, common to humans and "machines", can be formulated as follows: "Intelligence is the ability of a system to create, in the course of self-learning, programs (primarily heuristic) for solving problems of a certain class of complexity and to solve these problems."

This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise distribution by constructing tensors with different types of dimensions. We call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple textures from datasets of one or more complex large images. Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset. In addition, we can also accurately learn periodical textures. We make multiple experiments which show that PSGANs can flexibly handle diverse texture and image data sources. Our method is highly scalable and it can generate output images of arbitrary large size.