Anokhin’s theory of functional systems (Теория функциональных систем Анохина) – a functional system consists of a certain number of nodal mechanisms, each of which takes its place and has a certain specific purpose. The first of these is afferent synthesis, in which four obligatory components are distinguished: dominant motivation, situational and triggering afferentation, and memory. The interaction of these components leads to the decision-making process.
Anomaly detection (Выявление аномалий) – The process of identifying outliers. For example, if the mean for a certain feature is 100 with a standard deviation of 10, then anomaly detection should flag a value of 200 as suspicious.
Anonymization (Анонимизация) – The process in which data is de-identified as part of a mechanism to submit data for machine learning.
Answer set programming (ASP) (Программирование набора ответов) – A form of declarative programming oriented towards difficult (primarily NP-hard) search problems. It is based on the stable model (answer set) semantics of logic programming. In ASP, search problems are reduced to computing stable models, and answer set solvers – programs for generating stable models – are used to perform search.
Antivirus software (Антивирусное программное обеспечение) is a program or set of programs that are designed to prevent, search for, detect, and remove software viruses, and other malicious software like worms, trojans, adware, and more. [[37 - Antivirus software [Электронный ресурс] www.webroot.com URL: https://www.webroot.com/ca/en/resources/tips-articles/what-is-anti-virus-software (https://www.webroot.com/ca/en/resources/tips-articles/what-is-anti-virus-software) (дата обращения: 07.07.2022)]]
Anytime algorithm (Алгоритм любого времени) – An algorithm that can return a valid solution to a problem even if it is interrupted before it ends [[38 - Anytime algorithm [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/eng_rus/423258/anytime (https://dic.academic.ru/dic.nsf/eng_rus/423258/anytime) (дата обращения: 27.01.2022)]]
API-AS-a-service (API-как-услуга) combines the API economy and software renting and provides application programming interfaces as a service. [[39 - API-AS-a-service [Электронный ресурс] www.sofokus.com URL: https://www.sofokus.com/glossary-of-digital-business/#ABCD(дата обращения: 07.07.2022)]]
Application programming interface (API) (Интерфейс прикладного программирования) – A set of subroutine definitions, communication protocols, and tools for building software. In general terms, it is a set of clearly defined methods of communication among various components. A good API makes it easier to develop a computer program by providing all the building blocks, which are then put together by the programmer. An API may be for a web-based system, operating system, database system, computer hardware, or software library [[40 - Application programming interface (API) [Электронный ресурс] // ibm.com URL: https://www.ibm.com/cloud/learn/api (дата обращения: 19.02.2022)]].
Application security (Безопасность приложений) is the process of making apps more secure by finding, fixing, and enhancing the security of apps. Much of this happens during the development phase, but it includes tools and methods to protect apps once they are deployed. This is becoming more important as hackers increasingly target applications with their attacks [[41 - Application security [Электронный ресурс] www.csoonline.com URL: https://www.csoonline.com/article/3315700/what-is-application-security-a-process-and-tools-for-securing-software.html (https://www.csoonline.com/article/3315700/what-is-application-security-a-process-and-tools-for-securing-software.html) (дата обращения: 07.07.2022)]]
Application-specific integrated circuit (ASIC) (Специализированная интегральная схема) – a specialized integrated circuit for solving a specific problem [[42 - Application-specific integrated circuit [Электронный ресурс] //medium.com URL: https://medium.com/coinbundle/asic-application-specific-integrated-circuits-4c19ea66afaf (дата обращения 28.02.2022)]].
Approximate string matching (Also fuzzy string searching.) (Нечеткое соответствие строк или приблизительное соответствие строк) – The technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately.
Approximation error (Ошибка аппроксимации) – The discrepancy between an exact value and some approximation to it.
Architectural description group (Architectural view, Архитектурная группа описаний) is a representation of the system as a whole in terms of a related set of interests.
Architectural frameworks (Архитектурный фреймворк) are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution [[43 - Architectural frameworks [Электронный ресурс] //implementationscience.biomedcentral.com URL: https://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0607-7#:~:text=Architectural%20frameworks%20are%20high%2Dlevel,principles%20that%20guide%20their%20evolution (https://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0607-7#:~:text=Architectural%20frameworks%20are%20high%2Dlevel,principles%20that%20guide%20their%20evolution). (дата обращения: 07.07.2022)]].
Architecture of a computer (Архитектура вычислительной машины) is a conceptual structure of a computer that determines the processing of information and includes methods for converting information into data and the principles of interaction between hardware and software.
Architecture of a computing system (Архитектура вычислительной системы) is the configuration, composition and principles of interaction (including data exchange) of the elements of a computing system.
Architecture of a system (Архитектура системы) is the fundamental organization of a system, embodied in its elements, their relationships with each other and with the environment, as well as the principles that guide its design and evolution.
Archival Information Collection (AIC) (Архивный пакет информации (AIC))
“An Archival Information Package whose Content Information is an aggregation of other Archival Information Packages” The digital preservation function preserves the capability to regenerate the DIPs (Dissemination Information Packages) as needed over time. [[44 - Archival Information Collection (AIC) [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#A (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#A) (дата обращения: 07.07.2022)]]
Archival Storage (Архивное хранилище) Archival Storage is a source for data that is not needed for an organization’s everyday operations, but may have to be accessed occasionally. By utilizing an archival storage, organizations can leverage to secondary sources, while still maintaining the protection of the data. Utilizing archival storage sources reduces primary storage costs required and allows an organization to maintain data that may be required for regulatory or other requirements. [[45 - Archival Storage [Электронный ресурс] www.komprise.com URL: https://www.komprise.com/glossary_terms/archival-storage/(дата обращения: 07.07.2022)]]
Area under curve (AUC) (Площадь под кривой) – The area under a curve between two points is calculated by performing the definite integral. In the context of a receiver operating characteristic for a binary classifier, the AUC represents the classifier’s accuracy [[46 - Area under curve (AUC) [Электронный ресурс] // Revision maths URL: https://revisionmaths.com/advanced-level-maths-revision/pure-maths/calculus/area-under-curve (дата обращения 14.02.2022)]].
Area Under the ROC curve (Площадь под кривой ROC) – is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive.
Argumentation framework (Структура аргументации или система аргументации) – A way to deal with contentious information and draw conclusions from it. In an abstract argumentation framework, entry-level information is a set of abstract arguments that, for instance, represent data or a proposition. Conflicts between arguments are represented by a binary relation on the set of arguments. []
Artifact (Артефакт) is one of many kinds of tangible by-products produced during the development of software. Some artifacts (e.g., use cases, class diagrams, and other Unified Modeling Language (UML) models, requirements and design documents) help describe the function, architecture, and design of software. Other artifacts are concerned with the process of development itself – such as project plans, business cases, and risk assessments. [[47 - Artifact [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Artifact_(software_development) (https://en.wikipedia.org/wiki/Artifact_(software_development)) (дата обращения: 07.07.2022)]]
Artificial General Intelligence (AGI) (Общий Искусственный Интеллект) – is a hypothetical type of AI that is completely analogous to the human mind and has self-awareness that can solve problems, learn and plan for the future.
Artificial Intelligence (AI) (Искусственный интеллект) – (machine intelligence) refers to systems that display intelligent behavior by analyzing their environment and taking actions – with some degree of autonomy – to achieve specific goals. AI-based systems can be purely software-based, acting in the virtual world (e.g., voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g., advanced robots, autonomous cars, drones, or Internet of Things applications). The term AI was first coined by John McCarthy in 1956. [[48 - Artificial Intelligence [Электронный ресурс] // absel.ua URL: https://absel.ua/news/tri-tipa-iskusstvennogo-intellekta-ponimanie-ii.htmlobuchenii (https://absel.ua/news/tri-tipa-iskusstvennogo-intellekta-ponimanie-ii.htmlobuchenii) (дата обращения: 18.02.2022)]]
Artificial Intelligence Automation Platforms (Платформы автоматизации искусственного интеллекта) – Platforms that enable the automation and scaling of production-ready AI. Artificial Intelligence Platforms involves the use of machines to perform the tasks that are performed by human beings. The platforms simulate the cognitive function that human minds perform such as problem-solving, learning, reasoning, social intelligence as well as general intelligence. Top Artificial Intelligence Platforms: Google AI Platform, TensorFlow, Microsoft Azure, Rainbird, Infosys Nia, Wipro HOLMES, Dialogflow, Premonition, Ayasdi, MindMeld, Meya, KAI, Vital A.I, Wit, Receptiviti, Watson Studio, Lumiata, Infrrd. [[49 - Artificial Intelligence Automation Platforms [Электронный ресурс] www.predictiveanalyticstoday.com URL: https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/ (https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/) (дата обращения: 07.07.2022)]].
Artificial intelligence engine (also AI engine, AIE) (Движок искусственного интеллекта) is an artificial intelligence engine, a hardware and software solution for increasing the speed and efficiency of artificial intelligence system tools.
Artificial Intelligence for IT Operations (AIOps) is an emerging IT practice that applies artificial intelligence to IT operations to help organizations intelligently manage infrastructure, networks, and applications for performance, resilience, capacity, uptime, and, in some cases, security. By shifting traditional, threshold-based alerts and manual processes to systems that take advantage of AI and machine learning, AIOps enables organizations to better monitor IT assets and anticipate negative incidents and impacts before they take hold. AIOps is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics covering operational tasks include automation, performance monitoring and event correlations, among others. Gartner define an AIOps Platform thus: “An AIOps platform combines big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies”. [[50 - Artificial Intelligence for IT Operations (AIOps) [Электронный ресурс] www.cio.com URL: https://www.cio.com/article/196239/what-is-aiops-injecting-intelligence-into-it-operations.html (https://www.cio.com/article/196239/what-is-aiops-injecting-intelligence-into-it-operations.html) (дата обращения: 07.07.2022)],[51 - Artificial Intelligence for IT Operations (AIOps) [Электронный ресурс] www.gartner.com URL: https://www.gartner.com/en/information-technology/glossary/aiops-platform (https://www.gartner.com/en/information-technology/glossary/aiops-platform) (дата обращения: 07.07.2022)]].
Artificial Intelligence Markup Language AIML (Язык разметки искусственного интеллекта) – An XML dialect for creating natural language software agents [[52 - Artificial Intelligence Markup Language AIML [Электронный ресурс] // engati.com URL: https://www.engati.com/glossary/artificial-intelligence-markup-language (https://www.engati.com/glossary/artificial-intelligence-markup-language) (дата обращения: 18.02.2022)]]
Artificial Intelligence Open Library (Открытая библиотека искусственного интеллекта) is a set of algorithms designed to develop technological solutions based on artificial intelligence, described using programming languages and posted on the Internet.
Artificial intelligence system (AIS, Система искусственного интеллекта) is a programmed or digital mathematical model (implemented using computer computing systems) of human intellectual capabilities, the main purpose of which is to search, analyze and synthesize large amounts of data from the world around us in order to obtain new knowledge about it and solve them. basis of various vital tasks. The discipline “Artificial Intelligence Systems” includes consideration of the main issues of modern theory and practice of building intelligent systems.
Artificial intelligence technologies (Технологии искусственного интеллекта) – technologies based on the use of artificial intelligence, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support and advanced methods of artificial intelligence.
Artificial life (Alife, A-Life, Искусственная жизнь) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. [2] In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena [[53 - Artificial life [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Artificial_life (https://en.wikipedia.org/wiki/Artificial_life) (дата обращения: 07.07.2022)]].
Artificial Narrow Intelligence (ANI) (Узкий искусственный интеллект) – Artificial Narrow Intelligence, also known as weak or applied intelligence, represents most of the current artificial intelligent systems which usually focus on a specific task. Narrow AIs are mostly much better than humans at the task they were made for: for example, look at face recognition, chess computers, calculus, and translation. The definition of artificial narrow intelligence is in contrast to that of strong AI or artificial general intelligence, which aims at providing a system with consciousness or the ability to solve any problems. Virtual assistants and AlphaGo are examples of artificial narrow intelligence systems [[54 - Artificial Narrow Intelligence (ANI) [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/318696 (https://dic.academic.ru/dic.nsf/ruwiki/318696) (дата обращения: 27.01.2022)],[55 - Artificial Narrow Intelligence (ANI) [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/searchall.php?SWord=Artificial+Narrow+Intelligence+%28ANI%29+&from=ru&to=xx&did=&stype (https://dic.academic.ru/searchall.php?SWord=Artificial+Narrow+Intelligence+%28ANI%29+&from=ru&to=xx&did=&stype) (дата обращения: 27.01.2022)]].
Artificial Neural Network (ANN) (Искусственная нейронная сеть) – is a computational model in machine learning, which is inspired by the biological structures and functions of the mammalian brain. Such a model consists of multiple units called artificial neurons which build connections between each other to pass information. The advantage of such a model is that it progressively “learns” the tasks from the given data without specific programing for a single task.
Artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. The difference between an artificial neuron and a biological neuron is shown in the figure.
Artificial neurons are the elementary units of an artificial neural network. An artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials on nerve dendrites) and sums them to produce an output signal (or activation, representing the action potential of the neuron that is transmitted down its axon). Typically, each input is weighted separately, and the sum is passed through a non-linear function known as an activation function or transfer function. Transfer functions are usually sigmoid, but they can also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable, and bounded [[56 - Artificial neuron [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Artificial_neuron (https://en.wikipedia.org/wiki/Artificial_neuron) (дата обращения: 07.07.2022)],[57 - Artificial neuron [Электронный ресурс] //towardsdatascience.com URL: https://towardsdatascience.com/the-concept-of-artificial-neurons-perceptrons-in-neural-networks-fab22249cbfc (https://towardsdatascience.com/the-concept-of-artificial-neurons-perceptrons-in-neural-networks-fab22249cbfc) (дата обращения: 07.07.2022)]].
Artificial Superintelligence (ASI) (Искусственный сверхинтеллект) – is a term referring to the time when the capability of computers will surpass humans. “Artificial intelligence,” which has been much used since the 1970s, refers to the ability of computers to mimic human thought. Artificial superintelligence goes a step beyond and posits a world in which a computer’s cognitive ability is superior to a human.
Assistive intelligence (Вспомогательный интеллект) is AI-based systems that help make decisions or perform actions.
Association (Ассоциация) is another type of unsupervised learning method that uses different rules to find relationships between variables in a given dataset. These methods are frequently used for market basket analysis and recommendation engines, along the lines of “Customers Who Bought This Item Also Bought” recommendations.
Association for the Advancement of Artificial Intelligence (AAAI) (Ассоциация по развитию искусственного интеллекта) — An international, nonprofit, scientific society devoted to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence (AI), improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions
Association Rule Learning (Правила обучения ассоциации) – A rule-based Machine Learning method for discovering interesting relations between variables in large data sets.
Asymptotic computational complexity (Асимптотическая вычислительная сложность) – In computational complexity theory, asymptotic computational complexity is the usage of asymptotic analysis for the estimation of computational complexity of algorithms and computational problems, commonly associated with the usage of the big O notation [[58 - Asymptotic computational complexity [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/eng_rus/429332/asymptotic (дата обращения: 27.01.2022)]].
Asynchronous inter-chip protocols (Асинхронные межкристальные протоколы) are protocols for data exchange in low-speed devices; instead of frames, individual characters are used to control the exchange of data.
Attention mechanism (Механизм внимания) is one of the key innovations in the field of neural machine translation. Attention allowed neural machine translation models to outperform classical machine translation systems based on phrase translation. The main bottleneck in sequence-to-sequence learning is that the entire content of the original sequence needs to be compressed into a vector of a fixed size. The attention mechanism facilitates this task by allowing the decoder to look back at the hidden states of the original sequence, which are then provided as a weighted average as additional input to the decoder.
Attributional calculus (AC) (Атрибутивное исчисление) – A logic and representation system defined by Ryszard S. Michalski. It combines elements of predicate logic, propositional calculus, and multi-valued logic. Attributional calculus provides a formal language for natural induction, an inductive learning process whose results are in forms natural to people [[59 - Attributional calculus Ryszard S. Michalski (2004), attributional calculus: a logic and representation language for natural induction. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030—4444 and Institute of Computer Science, Polish Academy of Sciences, Warsaw.]].
Augmented Intelligence (Дополненный (расширенный) интеллект) – is the intersection of machine learning and advanced applications, where clinical knowledge and medical data converge on a single platform. The potential benefits of Augmented Intelligence are realized when it is used in the context of workflows and systems that healthcare practitioners operate and interact with. Unlike Artificial Intelligence, which tries to replicate human intelligence, Augmented Intelligence works with and amplifies human intelligence [[60 - Augmented Intelligence [Электронный ресурс] // gartner.com URL: https://www.gartner.com/en/information-technology/glossary/augmented-intelligence#:~:text=Augmented%20intelligence%20is%20a%20design,decision%20making%20and%20new%20experiences (https://www.gartner.com/en/information-technology/glossary/augmented-intelligence#:~:text=Augmented%20intelligence%20is%20a%20design,decision%20making%20and%20new%20experiences). (дата обращения: 28.01.2022)]]
Augmented reality (AR) (Дополненная реальность) — An interactive experience of a real-world environment where the objects that reside in the real-world are “augmented” by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory, and olfactory.
Augmented reality technologies (Технологии дополненной реальности) are visualization technologies based on adding information or visual effects to the physical world by overlaying graphic and/or sound content to improve user experience and interactive features.
