Intelligent information systems, represented by artificial intelligence (AI), are most suitable for solving unstructured problems. They are based on the identification of features, on the basis of which are drawn certain conclusions, and is planned a further strategy for problem solving. A human can possess a lot of incredibly important and valuable information. In turn a computer can bring together the professional experience of many minds of different generations, scaling the internal database of tasks through self-learning. Depending on the assigned task, is efficient to use parallel programming to process big data. Due to the more efficient use of computer capabilities, the data processing speed increases at least twice[1], which means that the execution time is also proportionally reduced. But this approach also requires a different logic for organizing the functions inside the program. It is an unchangeable fact that any data need intellectual analysis that may be impossible to provide for some reasons. As noted earlier, human capacity is limited compared to computers, and the ever-increasing amount of information being processed makes it necessary to think about other approaches that would fully cover the needs that arise. By parallelizing some processes, it is possible to speed up the training of artificial intelligence. It may also affect the possible search for new theorems or patterns not previously studied by humanity.