MODERN STAGE OF ARTIFICIAL INTELLIGENCE (AI) DEVELOPMENT AND APPLICATION OF AI METHODS AND SYSTEMS IN POWER ENGINEERING

В статье анализируется ряд публикаций на эту тему, а также обобщаются результаты дискуссий на конференции «Знания, онтологии, теории» (Новосибирск, 8-12 ноября 2021 г.) и Круглом столе в ИСЭМ СО РАН «Искусственный интеллект в энергетике» (22 декабря 2021 г.). Рассматриваются понятия: сильный и слабый ИИ, объяснимый ИИ, доверенный ИИ. Анализируются причины «бума» вокруг машинного обучения и его недостатки. Сравниваются облачные технологии и технологии граничных вычислений. Определяется понятие «умный» цифровой двойник, интегрирующий математические, информационные, онтологические модели и технологии ИИ. Рассматриваются этические риски ИИ и перспективы применения методов и технологий ИИ в энергетике. The article analyzes a number of publications on this topic, and also summarizes the results of discussions at the conference "Knowledge, Ontology, Theory" (Novosibirsk, November 8-12, 2021) and the Round Table at the ISEM SB RAS "Artificial Intelligence in Energy" (December 22 2021). The concepts are considered: artificial general intelligence (AGI), strong and narrow AI (NAI), explainable AI, trustworthy AI. The reasons for the "hype" around machine learning and its disadvantages are analyzed. Compares cloud and edge computing technologies. The concept of "smart" digital twin, which integrates mathematical, informational, ontological models and AI technologies, is defined. The ethical risks of AI and the prospects for the application of AI methods and technologies in the energy sector are considered.

OODA-RL: A REINFORCEMENT LEARNING FRAMEWORK FOR ARTIFICIAL GENERAL INTELLIGENCE TO SOLVE OPEN WORLD NOVELTY

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

PETRO SOKALSKI’S PUBLICATIONS IN THE “NOTES OF THE IMPERIAL SOCIETY OF AGRICULTURE OF SOUTHERN RUSSIA” (1869-1872)

The second essay in the cycle of the researches of the activities of the Ukrainian composer, publicist, active participant of the national movement of the second half of the XIX-th century Petro Sokalskyi (1832 - 1887) in the Imperial Society of Agriculture of Southern Russia focuses on the analysis of the authorial articles published in the volumes of «Notes of the Imperial Society of Agriculture of Southern Russia» during 1869–1872. Therefore, the classification of publications by thematic categories and their review in chronological order are the main tasks of the study. It was found out that Petro Sokalskyi not only held the positions of the secretary of the society and editor of “Notes” (from 1869 to 1871), but also actively wrote on agricultural topics. Author's articles of this period were the reviews of the problems of agriculture in the south of the empire in different years; the discussions of measures of encouraging the sheep farming and winemaking in the region; the descriptions of the results of the exhibition of viticulture and winemaking; and so on. The publicist also responded to questions that worried the farmers, winemakers and workers throughout the empire: economic and customs policy, the “wool issue”, the labor issue and more. In fact, on all the above issues, Sokalskyi expressed professional and deep thoughts, and also drew upon the international and European experience, which underscored his general intelligence in different fields of knowledge. The characteristic of this part of the author's journalistic heritage allows not only to supplement the available biographical information, but also to draw the attention of researchers to individual members of the Imperial Society of Agriculture of Southern Russia. In addition, the authors' articles will be useful for agricultural researchers.

Adaptive Intelligence: Its Nature and Implications for Education

This article introduces the concept of adaptive intelligence—the intelligence one needs to adapt to current problems and anticipate future problems of real-world environments—and discusses its implications for education. Adaptive intelligence involves not only promoting one’s own ability to survive and thrive, but also that of others in one’s own generation and in future generations. The article opens with a discussion of some of the strengths but also the limitations of the concept of general intelligence. It then discusses the concept of adaptive intelligence. Then, it breaks down adaptive intelligence into its constituent parts—creative, analytical, practical, and wisdom-based skills and attitudes. Finally, it discusses how the concept of adaptive intelligence can be operationalized in schools.

Essential Features in a Theory of Context for Enabling Artificial General Intelligence

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.