Читать книгу Artificial Intelligence Glossarium: 1000 terms - Alexander Chesalov - Страница 3

A/B Testing (A/B-тестирование) – A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures [13].

Abductive logic programming (ALP) (Абдуктивное логическое программирование) – A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as adducible predicates [14].

Abductive reasoning (Also abduction) (Абдукция) — A form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. abductive inference, or retroduction [15].

Abstract data type (Абстрактный тип данных) — A mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations [16].

Abstraction (Абстракция) — The process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest.

Accelerating change (Ускорение изменений) — A perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change [17].

Access to information (Доступ к информации) – the ability to obtain information and use it.

Access to information constituting a commercial secret (Доступ к информации, составляющей коммерческую тайну) – familiarization of certain persons with information constituting a commercial secret, with the consent of its owner or on other legal grounds, provided that this information is kept confidential.

Accuracy (Точность) – The fraction of predictions that a classification model got right.

Action (Действие) – In reinforcement learning, the mechanism by which the agent transitions between states of the environment. The agent chooses the action by using a policy.

Action language (Язык действий) — A language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world. Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning [18].

Action model learning (Обучение модели действий) – An area of machine learning concerned with creation and modification of software agent’s knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners [19].

Action selection (Выбор действия) — A way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, “the action selection problem” is typically associated with intelligent agents and animats – artificial systems that exhibit complex behaviour in an agent environment [20].

Activation function (Функция активации нейрона) – In the context of Artificial Neural Networks, a function that takes in the weighted sum of all of the inputs from the previous layer and generates an output value to ignite the next layer [21].

Active Learning/Active Learning Strategy (Активное обучение/ Стратегия активного обучения) – is a special case of Semi-Supervised Machine Learning in which a learning agent is able to interactively query an oracle (usually, a human annotator) to obtain labels at new data points. A training approach in which the algorithm chooses some of the data it learns from. Active learning is particularly valuable when labeled examples are scarce or expensive to obtain. Instead of blindly seeking a diverse range of labeled examples, an active learning algorithm selectively seeks the particular range of examples it needs for learning.

Adam optimization algorithm (Алгоритм оптимизации Адам) – it is an extension of stochastic gradient descent which has recently gained wide acceptance for deep learning applications in computer vision and natural language processing [22].

Adaptive algorithm (Адаптивный алгоритм) – An algorithm that changes its behavior at the time it is run, based on a priori defined reward mechanism or criterion [23].

Adaptive Gradient Algorithm (AdaGrad) (Адаптивный градиентный алгоритм) – A sophisticated gradient descent algorithm that rescales the gradients of each parameter, effectively giving each parameter an independent learning rate [24].

Adaptive neuro fuzzy inference system (ANFIS) (Also adaptive network-based fuzzy inference system.) (Адаптивная система нейро-нечеткого вывода) – A kind of artificial neural network that is based on Takagi – Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF – THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm [25].

Adaptive system (Адаптивная система) is a system that automatically changes the data of its functioning algorithm and (sometimes) its structure in order to maintain or achieve an optimal state when external conditions change.

Additive technologies (Аддитивные технологии) are technologies for the layer-by-layer creation of three-dimensional objects based on their digital models (“twins”), which make it possible to manufacture products of complex geometric shapes and profiles.

Admissible heuristic (Допустимая эвристика) – In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e., the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path.

Affective computing (Also artificial emotional intelligence or emotion AI.) (Аффективные вычисления) – The study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Affective computing is an interdisciplinary field spanning computer science, psychology, and cognitive science [26].

Agent (Агент) – In reinforcement learning, the entity that uses a policy to maximize expected return gained from transitioning between states of the environment.

Agent architecture (Архитектура агента) – A blueprint for software agents and intelligent control systems, depicting the arrangement of components. The architectures implemented by intelligent agents are referred to as cognitive architectures [27].

Agglomerative clustering (See hierarchical clustering.) (Агломеративная кластеризация) – Agglomerative clustering first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree.

Aggregate (Агрегат) A total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc., that comprise the county. To total data from smaller units into a large unit. [28]

Aggregator (Агрегатор) A feed aggregator is a type of software that brings together various types of Web content and provides it in an easily accessible list. Feed aggregators collect things like online articles from newspapers or digital publications, blog postings, videos, podcasts, etc. A feed aggregator is also known as a news aggregator, feed reader, content aggregator or an RSS reader. [29]

AI benchmark (Исходная отметка (Бенчмарк) ИИ) is an AI benchmark for evaluating the capabilities, efficiency, performance and for comparing ANNs, machine learning (ML) models, architectures and algorithms when solving various AI problems, special benchmarks are created and standardized, initial marks. For example, Benchmarking Graph Neural Networks – benchmarking (benchmarking) of graph neural networks (GNS, GNN) – usually includes installing a specific benchmark, loading initial datasets, testing ANNs, adding a new dataset and repeating iterations.

AI chipset market (Рынок чипсетов ИИ) is the market for chipsets for artificial intelligence (AI) systems.

AI acceleration (ИИ ускорение) – acceleration of calculations encountered with AI, specialized AI hardware accelerators are allocated for this purpose (see also artificial intelligence accelerator, hardware acceleration).

AI acceleration (Ускорение ИИ) is the acceleration of AI-related computations, for this purpose specialized AI hardware accelerators are used.

AI accelerator (ИИ ускоритель) – A class of microprocessor or computer system designed as hardware acceleration for artificial intelligence applications, especially artificial neural networks, machine vision, and machine learning.

AI benchmark (ИИ бенчмарк) – is benchmarking of AI systems, to assess the capabilities, efficiency, performance and to compare ANNs, machine learning (ML) models, architectures and algorithms when solving various AI problems, special benchmark tests are created and standardized, benchmarks. For example, Benchmarking Graph Neural Networks – benchmarking (benchmarking) of graph neural networks (GNS, GNN) – usually includes installing a specific benchmark, loading initial datasets, testing ANNs, adding a new dataset and repeating iterations (see also artificial neural network benchmarks).

AI Building and Training Kits (Комплекты для создания и обучения искусственного интеллекта) – applications and software development kits (SDKs) that abstract platforms, frameworks, analytics libraries, and data analysis appliances, allowing software developers to incorporate AI into new or existing applications.

AI camera (ИИ камера) – a camera with artificial intelligence, digital cameras of a new generation – allow you to analyze images by recognizing faces, their expression, object contours, textures, gradients, lighting patterns, which is taken into account when processing images; some AI cameras are capable of taking pictures on their own, without human intervention, at moments that the camera finds most interesting, etc. (see also camera, software-defined camera).

AI chipset (ИИ чипсет) is a chipset for systems with AI, for example, AI chipset industry is an industry of chipsets for systems with AI, AI chipset market is a market for chipsets for systems with AI.

AI chipset market (ИИ рыное чипов) – chipset market for systems with artificial intelligence (AI), see also AI chipset.

AI cloud services (Облачные сервисы искусственного интеллекта) – AI model building tools, APIs, and associated middleware that enable you to build/train, deploy, and consume machine learning models that run on a prebuilt infrastructure as cloud services. These services include automated machine learning, machine vision services, and language analysis services.

AI CPU (Центральный процессор ИИ) is a central processing unit for AI tasks, synonymous with AI processor.

AI engineer (ИИ-инженер) – AI systems engineer.

AI engineering (ИИ-инжиниринг) – transfer of AI technologies from the level of R&D, experiments and prototypes to the engineering and technical level, with the expanded implementation of AI methods and tools in IT systems to solve real production problems of a company, organization. One of the strategic technological trends (trends) that can radically affect the state of the economy, production, finance, the state of the environment and, in general, the quality of life of a person and humanity

AI hardware (also AI-enabled hardware) (ИИ-аппарат) – AI hardware, AI hardware, artificial intelligence infrastructure [system] hardware, AI infrastructure. Explanations in the Glossary are usually brief

AI hardware (Аппаратное обеспечение ИИ) is infrastructure hardware or artificial intelligence system, AI infrastructure.

AI industry (Индустрия ИИ) – for example, commercial AI industry – commercial AI industry, commercial sector of the AI industry.

AI industry trends (Тренды индустрии ИИ) are promising directions for the development of the AI industry.

AI infrastructure (also AI-defined infrastructure, AI-enabled Infrastructure) (Инфраструктура ИИ) – artificial intelligence infrastructure [systems], AI infrastructure, AI infrastructure, for example, AI infrastructure research – research in the field of AI infrastructures (see also AI, AI hardware).

AI server (ИИ сервер) – artificial intelligence server – is a server with (based on) AI; a server that provides solving AI problems.

AI shopper (ИИ-покупатель) is a non-human economic entity that receives goods or services in exchange for payment. Examples include virtual personal assistants, smart appliances, connected cars, and IoT-enabled factory equipment. These AIs act on behalf of a human or organization client.

AI supercomputer (ИИ суперкомпьютер) – a supercomputer for artificial intelligence tasks, a supercomputer for AI, characterized by a focus on working with large amounts of data (see also artificial intelligence, supercomputer).

AI term (ИИ термин) – a term from the field of AI (from terminology, AI vocabulary), for example, in AI terms – in terms of AI (in AI language).

AI term (Термин ИИ) is a term from the field of AI (from terminology, AI vocabulary), for example, in AI terms – in terms of AI (in AI language).

AI terminology (ИИ терминология) – artificial intelligence terminology, is a set of special terms related to the field of AI (see also AI term).

AI terminology (Терминология ИИ) is the terminology of artificial intelligence, a set of technical terms related to the field of AI.

AI TRiSM (Управление доверием, рисками и безопасностью ИИ) is the management of an AI model to ensure trust, fairness, efficiency, security, and data protection.

AI vendor (ИИ вендор) – is a supplier of AI tools (systems, solutions).

AI vendor (Поставщик ИИ) is a supplier of AI tools (systems, solutions).

AI winter (Winter of artificial intelligence, Зима искусственного интеллекта) is a period of reduced interest in the subject area, reduced research funding. The term was coined by analogy with the idea of nuclear winter. The field of artificial intelligence has gone through several cycles of hype, followed by disappointment and criticism, followed by a strong cooling off of interest, and then followed by renewed interest years or decades later [30].

AI workstation (ИИ рабочая станция) – a workstation (PC) with means (based on) AI; AI PC, a specialized desktop PC for solving technical or scientific problems, AI tasks; usually connected to a LAN with multi-user operating systems, intended primarily for the individual work of one specialist.

AI workstation (Рабочая станция ИИ) is a workstation (PC) with (based on) AI; AI RS, a specialized computer for solving technical or scientific problems, AI tasks; usually connected to a LAN with multi-user operating systems, intended primarily for the individual work of one specialist.

AI-based management system (Система управления на основе искусственного интеллекта) – the process of creating policies, allocating decision-making rights and ensuring organizational responsibility for risk and investment decisions for an application, as well as using artificial intelligence methods.

AI-based systems (Системы на основе ИИ) are information processing technologies that include models and algorithms that provide the ability to learn and perform cognitive tasks, with results in the form of predictive assessment and decision making in a material and virtual environment. AI systems are designed to work with some degree of autonomy through modeling and representation of knowledge, as well as the use of data and the calculation of correlations. AI-based systems can use various methodologies, in particular: machine learning, including deep learning and reinforcement learning; automated reasoning, including planning, dispatching, knowledge representation and reasoning, search and optimization. AI-based systems can be used in cyber-physical systems, including equipment control systems via the Internet, robotic equipment, social robotics and human-machine interface systems that combine the functions of control, recognition, processing of data collected by sensors, as well as the operation of actuators in the environment of functioning of AI systems.

AI-complete (Сложный/завершенный искусственный интеллект) – In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem – making computers as intelligent as people, or strong AI. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm [31]

AI-enabled (ИИ-совместимый) is AI-enabled hardware or software that uses AI-enabled AI, such as AI-enabled tools.

AI-enabled (Оснащенный ИИ) – using AI and equipped with AI, for example, AI-enabled tools – tools with AI (see also AI-enabled device).

AI-enabled device (ИИ-совместимое устройство) is a device supported by an artificial intelligence (AI) system, such as an intelligent robot.

AI-enabled device (Устройство, оснащенное ИИ) – A device supported by an artificial intelligence (AI) system, such as an intelligent robot (see also AI-enabled healthcare device).

AI-enabled healthcare device (ИИ-совместимое медицинское устройство) is an AI-enabled healthcare device.

AI-enabled healthcare device (Оснащенное ИИ медицинское устройство) – is an AI-enabled device for healthcare (medical care), see also AI-enabled device.

AI-optimized (ИИ-оптимизированный) is one that is optimized for AI tasks or optimized using AI tools, for example, an AI-optimized chip is a chip that is optimized for AI tasks.

AI-optimized (Оптимизированный для задач ИИ) – optimized for AI tasks; AI-optimized chip, for example, an AI-optimized chip is a chip optimized for AI tasks (see also artificial intelligence).

AlexNet (Нейронная сеть AlexNet) – The name of a neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012. It is named after Alex Krizhevsky, then a computer science PhD student at Stanford University. See ImageNet.

Algorithm (Алгоритм) – an exact prescription for the execution in a certain order of a system of operations for solving any problem from some given class (set) of problems. The term “algorithm” comes from the name of the Uzbek mathematician Musa Al-Khorezmi, who in the 9th century proposed the simplest arithmetic algorithms. In mathematics and cybernetics, a class of problems of a certain type is considered solved when an algorithm is established to solve it. Finding algorithms is a natural human goal in solving various classes of problems.

Algorithmic Assessment (Алгоритмическая оценка) is a technical evaluation that helps identify and address potential risks and unintended consequences of AI systems across your business, to engender trust and build supportive systems around AI decision making.

AlphaGo (Программа AlphaGo) – is the first computer program that defeated a professional player on the board game Go in October 2015. Later in October 2017, AlphaGo’s team released its new version named AlphaGo Zero which is stronger than any previous human-champion defeating versions. Go is played on 19 by 19 board which allows for 10171 possible layouts (chess 1050 configurations). It is estimated that there are 1080 atoms in the universe [32]

Ambient intelligence (AmI) (Окружающий интеллект) – Ambient intelligence (AmI) represents the future vision of intelligent computing where explicit input and output devices will not be required; instead, sensors and processors will be embedded into everyday devices and the environment will adapt to the user’s needs and desires seamlessly. AmI systems, will use the contextual information gathered through these embedded sensors and apply Artificial Intelligence (AI) techniques to interpret and anticipate the users’ needs. The technology will be designed to be human centric and easy to use. [33]

An AI accelerator (Ускоритель ИИ) is a specialized chip that improves the speed and efficiency of training and testing neural networks. However, for semiconductor chips, including most AI accelerators, there is a theoretical minimum power consumption limit. Reducing consumption is possible only with the transition to optical neural networks and optical accelerators for them.

An integrated GPU (Интегрированный ГП) is an integrated graphics processing unit, integrated GPU, a GPU located on the same chip or on the same chip as the CPU, such as the one implemented in Apple’s M1 processor.

Analogical Reasoning (Рассуждение по аналогии) – Solving problems by using analogies, by comparing to past experiences [34].

Analysis of algorithms (AofA) (Анализ алгоритмов) – The determination of the computational complexity of algorithms, that is the amount of time, storage and/or other resources necessary to execute them. Usually, this involves determining a function that relates the length of an algorithm’s input to the number of steps it takes (its time complexity) or the number of storage locations it uses (its space complexity) [35].

Annotation (Аннотация) – A metadatum attached to a piece of data, typically provided by a human annotator [36].

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]

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]

API-AS-a-service (API-как-услуга) combines the API economy and software renting and provides application programming interfaces as a service. [39]

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 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-specific integrated circuit (ASIC) (Специализированная интегральная схема) – a specialized integrated circuit for solving a specific problem [42].

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].

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 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]

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 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]

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 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 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”. [50,51].

Artificial Intelligence Markup Language AIML (Язык разметки искусственного интеллекта) – An XML dialect for creating natural language software agents [52]

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 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 [54,55].

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 [56,57].

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].

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].

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 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.

Auto Associative Memory (Автоассоциативная память) is a single layer neural network in which the input training vector and the output target vectors are the same. The weights are determined so that the network stores a set of patterns. As shown in the following figure, the architecture of Auto Associative memory network has “n’ number of input training vectors and similar “n’ number of output target vectors [61].

Autoencoder (Автокодер) – а type of Artificial Neural Network used to produce efficient representations of data in an unsupervised and non-linear manner, typically to reduce dimensionality [62].

Automata theory (Теория автоматов) – The study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). [63] Automata theory (part of the theory of computation) is a theoretical branch of Computer Science and Mathematics, which mainly deals with the logic of computation with respect to simple machines, referred to as automata [64].

Automated control system (Автоматизированная система управления) – a set of software and hardware designed to control technological and (or) production equipment (executive devices) and the processes they produce, as well as to control such equipment and processes.

Automated planning and scheduling (Also simply AI planning.) (Планирование ИИ) – A branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory [65].

Automated processing of personal data (Автоматизированная обработка персональных данных) – processing of personal data using computer technology.

Automated reasoning (Автоматизированное мышление) – An area of computer science and mathematical logic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science, and even philosophy [66].

Automated system (Автоматизированная система) is an organizational and technical system that guarantees the development of solutions based on the automation of information processes in various fields of activity.

Automation (Автоматизация) is a technology by which a process or procedure is performed with minimal human intervention.

Automation bias (Предвзятость автоматизации) – When a human decision maker favors recommendations made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors [67].

Autonomic computing (Автономные вычисления) is the ability of a system to adaptively self-manage its own resources for high-level computing functions without user input.

Autonomous (Автономность) – A machine is described as autonomous if it can perform its task or tasks without needing human intervention.

Autonomous artificial intelligence (Автономный искусственный интеллект) is a biologically inspired system that tries to reproduce the structure of the brain, the principles of its operation with all the properties that follow from this.

Autonomous artificial intelligence systems (Системы автономного искусственного интеллекта) – simulate the work and structure of the brain (thinking, creativity, emotions, will, freedom of choice and decision-making, search for new knowledge and making optimal decisions, memory, etc.). Such systems are also called adaptive artificial intelligence or neuromorphic artificial intelligence.

Autonomous car (Also self-driving car, robot car, and driverless car.) (Автономный автомобиль) – A vehicle that is capable of sensing its environment and moving with little or no human input [68].

Autonomous robot (Автономный робот) — A robot that performs behaviors or tasks with a high degree of autonomy. Autonomous robotics is usually considered to be a subfield of artificial intelligence, robotics, and information engineering [69].

Autonomous vehicle (Автономное транспортное средство) is a mode of transport based on an autonomous driving system. The control of an autonomous vehicle is fully automated and carried out without a driver using optical sensors, radar and computer algorithms.

Autoregressive Model (Авторегрессионная модель) – An autoregressive model is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. In statistics and signal processing, an autoregressive model is a representation of a type of random process. It is used to describe certain time-varying processes in nature, economics, etc. [70].

Auxiliary intelligence (Дополнительный интеллект) – systems based on artificial intelligence that complement human decisions and are able to learn in the process of interacting with people and the environment.

Average precision (Средняя точность) – A metric for summarizing the performance of a ranked sequence of results. Average precision is calculated by taking the average of the precision values for each relevant result (each result in the ranked list where the recall increases relative to the previous result) [71].

Ayasdi (Платформа Ayasdi) is an enterprise scale machine intelligence platform that delivers the automation that is needed to gain competitive advantage from the company’s big and complex data. Ayasdi supports large numbers of business analysts, data scientists, endusers, developers and operational systems across the organization, simultaneously creating, validating, using and deploying sophisticated analyses and mathematical models at scale.