Artificial Intellect with Artificial Neural Networks

В настоящее время не существует единого определения искусственного интеллекта. Требуется такая классификация задач, которые должны решать системы искусственного интеллекта. В сообщении дана классификация задач при использовании искусственных нейросетей (в виде получения субъективно и объективно новой информации). Показаны преимущества таких нейросетей (неалгоритмизируемые задачи) и показан класс систем (третьего типа — биосистем), которые принципиально не могут изучаться в рамках статистики (и всей науки). Для изучения таких биосистем (с уникальными выборками) предлагается использовать искусственные нейросети, которые решают задачи системного синтеза (отыскание параметров порядка). Сейчас такие задачи решает человек в режиме эвристики, что не моделируется современными системами искусственного интеллекта. Currently, there is no single definition of artificial intelligence. We need a Such categorization of tasks to be solved by artificial intelligence. The paper proposes a task categorization for artificial neural networks (in terms of obtaining subjectively and objectively new information). The advantages of such neural networks (non-algorithmizable problems) are shown, and a class of systems (third type biosystems) which cannot be studied by statistical methods (and all science) is presented. To study such biosystems (with unique samples) it is suggested to use artificial neural networks able to perform system synthesis (search for order parameters). Nowadays such problems are solved by humans through heuristics, and this process cannot be modeled by the existing artificial intelligence systems.

Automated System Synthesis of Structured Educational Content

At present the study of computer science is almost impossible imagine without electronic educational resources usage. These resources are most often represents at learning package. Synthesis of learning packages is labour and resource intensive process. The results of this process influence to students work efficient. Article is devoted to creating automated system synthesis of structured educational content. This system is the universal shell for computer science learning package. This system allow to unify educational process approach, apply regardless of course content, increase student work efficient in university local network. Developed system is include six independent module. These modules realize system load properties choose, service, system load, authentification and tools, student competence control. This article contains modules work concept. Module work concept of system load properties choose is describes in detail and it's function algorithm is presented. Educational content is structured to computer science teaching program. All content is divided to two logical section. Which section is contains several subsection. Those section and subsection are TreeView hierarchical tree nodes. They are included educational content elements by special algorithm. Universal test system and special developed on VBA interactive trainers are included to competence control subsection. Those trainers are program's which can generate task for several topic. The system which considering in this article have some advantages. It's occupied small memory on hard disk, it's worked in network and on local computer. The educational content can be flexible included in this system without on course volume and student basic knowledge level.

Intermetallic phases in the Sc–Ir–In system – synthesis and structure of Sc1.024Ir2In0.976 and Sc3Ir1.467In4

The intermetallic scandium compounds Sc1.024Ir2In0.976 and Sc3Ir1.467In4 were synthesized by reactions of the elements in sealed tantalum ampoules at high temperature followed by annealing for crystal growth. Both structures were refined from single-crystal X-ray diffractometer data: MnCu2Al type, F m 3 ‾ m $Fm‾{3}m$ , a = 639.97(19) pm, wR2 = 0.0376, 41 F 2 values, seven variables for Sc1.024Ir2In0.976 and P 6 ‾ $P‾{6}$ , a = 769.99(5), c = 684.71(4) pm, wR2 = 0.0371, 967 F 2 values, 33 variables for Sc3Ir1.467In4. Sc1.024Ir2In0.976 is a new Heusler phase with a small homogeneity range due to Sc/In and In/Sc mixing. The structure of Sc3Ir1.467In4 is closely related to that of Sc3Rh1.594In4 and belongs to the large family of ZrNiAl superstructures. The striking structural motif is the ordered stacking of empty In6 and filled Ir@In6 prisms with Ir–In distances of 269 pm.