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Artificial Intelligence Glossarium: 1000 terms
Matvey Bakanach

Alexander Vlaskin

Alexander Chesalov


Dear reader!Your attention is invited to a unique book!A modern glossary of over 1000 popular terms and definitions for artificial intelligence.This book is also unique in that it was written by practicing experts who worked together on the Program of the Center for Artificial Intelligence of the Bauman Moscow State Technical University.This text was previously published as part of the book «Glossary on artificial intelligence: 2500 terms» (Russian and English versions of the book).





Artificial Intelligence Glossarium: 1000 terms



Alexander Chesalov

Alexander Vlaskin

Matvey Bakanach



Illustrator Abidal | Dreamstime.com



© Alexander Chesalov, 2022

© Alexander Vlaskin, 2022

© Matvey Bakanach, 2022

© Abidal | Dreamstime.com, illustrations, 2022



ISBN 978-5-0059-0164-4

Created with Ridero smart publishing system




FROM AUTHORS-CREATORS




Alexander Yurievich Chesalov,

Vlaskin Alexander Nikolaevich,

Bakanach Matvey Olegovich

Experts in information technology and artificial intelligence, developers of the program of the Center for Artificial Intelligence, the programs “Artificial Intelligence” and “Deep Analytics” of the project “Priority 2030” of the Bauman Moscow State Technical University in 2021—2022.


Good afternoon, dear Friends and Colleagues!



The last couple of years for us, the authors of this book, have been not only “hot”, but also generous with various events and activities.



Undoubtedly, the most significant event for us that took place in 2021 is participation in the Competition held by the Analytical Center under the Government of the Russian Federation for the selection of recipients of support for research centers in the field of artificial intelligence, including in the field of “strong” artificial intelligence, trusted artificial intelligence systems and ethical aspects of the use of artificial intelligence. We were faced with an extraordinary and still at that time unsolved task of creating a Center for the Development and Implementation of Strong and Applied Artificial Intelligence of the Bauman Moscow State Technical University. All the authors of this book took a direct part in the development and writing of the program and action plan of the new Center. You can learn more about this story from Alexander Chesalov’s book How to Create an Artificial Intelligence Center in 100 Days. You can also find information about it on the chesalov.com blog and ridero.ru website.



The first international forum “Ethics of artificial intelligence: the beginning of trust”, which took place on October 26, 2021, and within the framework of which the solemn signing ceremony of the National Code of Ethics of Artificial Intelligence was organized, which establishes general ethical principles and standards of behavior that should guide the participants in relations in the field of artificial intelligence in his activities, also had a certain influence on us. In fact, the forum became the first specialized platform in Russia, where about one and a half thousand developers and users of artificial intelligence technologies discussed steps to effectively implement the ethics of artificial intelligence in priority sectors of the economy of the Russian Federation.



We did not pass by the AI Journey International Conference on Artificial Intelligence and Data Analysis, within which, on November 10, 2021, IT market leaders joined the signing of the National Code of Ethics for Artificial Intelligence. The number of conference speakers was amazing – there were more than two hundred of them, and the number of online visits to the site was more than forty million.



Summarizing our active work over the past couple of years, the experience that has already been accumulated, we can say that wherever we discuss the topic of “artificial intelligence”, there have always been heated debates among the participants of certain events, among various specialists and scientists, what is, for example, “strong artificial intelligence” (“Artificial general intelligence”) and how to translate and interpret the word “general” – (“strong” or “general”, or maybe “applied”? There have been many disputes over the definition of the term “trusted artificial intelligence” and many others.



Undoubtedly, we have found answers to these and many other questions of interest to a wide range of specialists.



For example, we have defined for ourselves that Artificial Intelligence is a computer system based on a complex of scientific and engineering knowledge, as well as technologies for creating intelligent machines, programs, services and applications (for example, machine learning and deep learning), imitating human thought processes or living beings, capable of perceiving information with a certain degree of autonomy, learning and making decisions based on the analysis of large amounts of data, the purpose of which is to help people solve their daily routine tasks.








Or, one more example. We have determined that the Trusted Artificial Intelligence System is a system that ensures the fulfillment of the tasks assigned to it, taking into account a number of additional requirements and / or restrictions that ensure confidence in the results of its work:








And also the fact that Machine learning is one of the areas (subsets) of artificial intelligence, thanks to which the key property of intelligent computer systems is embodied – self-learning based on the analysis and processing of large heterogeneous data. The greater the amount of information and its diversity, the easier it is for artificial intelligence to find patterns and the more accurate the result will be.








And the fact that machine learning is a very interesting, multifaceted and relevant area of science and technology:








Have you ever heard of “Transhumanists”?



On the one hand, as an idea, Transhumanism is the empowerment of man through science. On the other hand, it is a philosophical concept and an international movement, whose adherents wish to become “post-humans” and overcome all kinds of physical limitations, illness, mental suffering, old age and death through the use of the possibilities of nano- and biotechnologies, artificial intelligence and cognitive science.

In our opinion, the ideas of “transhumanism” intersect very closely with the ideas of “digital human immortality”.



Undoubtedly, you have heard and, of course, you know who a “Data Scientist” is – a scientist and data scientist.



Have you ever heard of “data-satanics”? :-)

Data Satanists is a definition invented by the authors, but reflecting modern reality (along with, for example, the term “infogypsyism”), which developed during the period of popularization of the ideas of artificial intelligence in the modern information society. Data Satanists are people (essentially scammers and criminals) who very skillfully disguise themselves as scientists and specialists in the field of AI and ML, but at the same time use other people’s merits, knowledge and experience, for their own selfish purposes and for the purposes of illegal enrichment. Their actions can be interpreted under Article 159 of the Criminal Code of the Russian Federation Fraud, Article 174 of the Criminal Code of the Russian Federation Legalization (laundering) of money or other property acquired by other persons by criminal means, Article 285 of the Criminal Code of the Russian Federation Abuse of official powers, Article 286 of the Criminal Code of the Russian Federation Abuse of official powers, etc.



How do you like the term “bibleoclasm”?

Biblioclasm is a person who, due to his transformed worldview and overly inflated ego, out of envy or some other selfish goal, destroys the books of other authors.

You won’t believe it, but there are a lot of people like “data-satanists” or “biblioclasms” now.

We can give many more such examples of “amazing terms”. But in our work, we did not waste time on the “harsh reality” and shifted the focus to a constructive and positive attitude.

In a word, we have done a great job for you and have collected more than 1000 terms and definitions on machine learning and artificial intelligence based on our experience, data from Internet articles, books, magazines and analytical reports.



Also, this book includes basic terms and definitions from the books of one of the authors-compilers – Alexander Chesalov: “Glossary on artificial intelligence and information technology”, “Glossary on the digital economy” (distributed free of charge on Ridero.ru), “Digital transformation” [[1 - Чесалов А. Ю. Цифровая трансформация. -М.: Ridero. 2020.-302c. URL: https://ridero.ru/books/cifrovaya_transformaciya_2/ (https://ridero.ru/books/cifrovaya_transformaciya_2/)]], “The Digital Ecosystem of the Ombudsman Institute: Concept, Technologies, Practice” [[2 - Чесалов А. Ю. Цифровая экосистема Института омбудсмена: концепция, технологии, практика. -М.: Ridero. 2020.-320c.]], as well as terms and definitions from the following additional sources:

– Decree of the President of the Russian Federation dated May 7, 2018 №204 “On national goals and strategic objectives for the development of the Russian Federation for the period up to 2024” [[3 - Указ Президента Российской Федерации от 7 мая 2018 №204 “О национальных целях и стратегических задачах развития Российской Федерации на период до 2024 года”.]]

– Federal Law №149 of July 27, 2006 (as amended on May 1, 2019) “On Information, Information Technologies and Information Protection” [[4 - Федерального закона от 27.07.2006 №149-ФЗ (ред. от 01.05.2019) “Об информации, информационных технологиях и о защите информации”. [Electronic resource] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://www.kremlin.ru/acts/bank/24157 (http://www.kremlin.ru/acts/bank/24157)]].

– Strategy for the Development of the Information Society in the Russian Federation for 2017—2030 [[5 - Указ Президента Российской Федерации от 09.05.2017 г. №203. О Стратегии развития информационного общества в Российской Федерации на 2017 – 2030 годы. [Electronic resource] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://kremlin.ru/acts/bank/41919 (http://kremlin.ru/acts/bank/41919)]].

– National strategy for the development of artificial intelligence for the period up to 2030 [[6 - Указ Президента Российской Федерации от 10.10.2019 г. №490. О развитии искусственного интеллекта в Российской Федерации. [Электронный ресурс] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://www.kremlin.ru/acts/bank/44731 (http://www.kremlin.ru/acts/bank/44731)]].

– AI Code of Ethics [[7 - Кодекс этики в сфере ИИ. [Электронный ресурс] // a-ai.ru URL: https://a-ai.ru/code-of-ethics/ (https://a-ai.ru/code-of-ethics/)]].

– Strategy for the development of healthcare in the Russian Federation for the period up to 2025, approved by Decree of the President of the Russian Federation of June 6, 2019 №254 [[8 - Указ Президента Российской Федерации от 06.06.2019 г. №254 “О Стратегии развития здравоохранения в Российской Федерации на период до 2025 года”. [Электронный ресурс] // kremlin.ru URL: http://www.kremlin.ru/acts/bank/44326 (http://www.kremlin.ru/acts/bank/44326)]].

– Strategy for the development of the electronic industry of the Russian Federation for the period up to 2030 [[9 - Стратегия развития электронной промышленности РФ на период до 2030 года. [Электронный ресурс] // conference.tass.ru. URL: https://conference.tass.ru/events/prezentaciya-proekta-strategii-razvitiya-elektronnoj-promyshlennosti-rf-na-period-do-2030-g- (https://conference.tass.ru/events/prezentaciya-proekta-strategii-razvitiya-elektronnoj-promyshlennosti-rf-na-period-do-2030-g-)]].

– Federal Law of July 27, 2006 №152 (as amended on April 24, 2020) “On Personal Data” [[10 - Федеральный закон от 27.07.2006 N 152-ФЗ (ред. от 24.04.2020) “О персональных данных”. [Электронный ресурс] // legalacts.ru URL: https://legalacts.ru/doc/152_FZ-o-personalnyh-dannyh/ (https://legalacts.ru/doc/152_FZ-o-personalnyh-dannyh/)]].

– National program “Digital Economy of the Russian Federation” [[11 - Национальная программа “Цифровая экономика Российской Федерации”. Министерство цифрового развития, связи и массовых коммуникаций Российской Федерации. [Электронный ресурс] // digital.gov.ru. URL: https://digital.gov.ru/ru/activity/directions/858/ (https://digital.gov.ru/ru/activity/directions/858/)]].

– State Program “Digital Economy of the Russian Federation” [[12 - Государственная Программа “Цифровая экономика Российской Федерации”. [Электронный ресурс] // static.government.ru URL: http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf (http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf)]].



1000 terms and definitions.

Is it a lot or a little?

Our experience suggests that for mutual understanding it is enough for two interlocutors to know a dozen or a maximum of two dozen definitions, but when it comes to professional activities, it may turn out that it is not enough to know even a few dozen terms.

This book contains the terms, in our opinion, the most frequently used, both in everyday work and professional activities by specialists of various professions interested in the topic of “artificial intelligence”.

In conclusion, I would like to add and inform the dear reader that we have tried very hard to make for you the necessary and useful “product” and “tool”.






35th Moscow International Book Fair



The first version of the book was presented by us at the 35th Moscow International Book Fair in 2022.



This book is a completely open and free document for distribution. If you use it in your practical work, please make a link to this book.



Many of the terms and definitions for them in this book are found on the Internet. They are repeated dozens or hundreds of times on various information resources (mainly foreign ones). Nevertheless, we set ourselves the goal of collecting and systematizing the most relevant of them in one place from a variety of sources, translating and adapting the necessary ones into Russian, and rewriting some of them based on our own experience. In view of the foregoing, we do not claim authorship or uniqueness of the terms and definitions presented.



Links to primary sources are affixed to the original terms and definitions (that is, if the definition was originally in English, then the link is indicated after this definition). If the definition was given in Russian, translated into English and adapted, then the reference is not indicated (in this edition of the book). This book was written by Russian authors and therefore the translation of terms into Russian is given in brackets.



We continue to work on improving the quality and content of the text of this book, including supplementing it with new knowledge in the subject area. We will be grateful for any feedback, suggestions and clarifications. Please send them to aleksander.chesalov@yandex.ru



Happy reading and productive work!



Yours, Alexander Chesalov, Alexander Vlaskin and Matvey Bakanach.



09/22/2022




ARTIFICIAL INTELLIGENCE GLOSSARY





“A”


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 - A/B Testing [Electronic resource] // vwo.com URL: https://vwo.com/ab-testing/ (date of the application: 28.01.2022)]].

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 Logic Programming (ALP) [Electronic resource] // engati.com URL https://www.engati.com/glossary/abductive-logic-programming (https://www.engati.com/glossary/abductive-logic-programming) (date of the application 14.02.2022)]].

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 - Abductive reasoning [Electronic resource] // MRS BLOG URL: http://msrblog.com/science/mathematic/about-abductive-reasoning.html (http://msrblog.com/science/mathematic/about-abductive-reasoning.html) (date of the application 14.02.2022)]].

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 - Abstract data type [Electronic resource] // EMBEDDED ARTISTRY URL: https://embeddedartistry.com/fieldmanual-terms/abstract-data-type/ (date of the application 14.02.2022)]].

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 - Accelerating change [Электронный ресурс] // ru.knowledgr.com (дата обращения: 14.02.2022)]].

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 - https://www.semanticscholar.org/topic/Action-language/72365 (https://www.semanticscholar.org/topic/Action-language/72365)]].

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 model learning [Электронный ресурс] // Semantic Scholar URL: https://www.semanticscholar.org/topic/Action-model-learning/1677625 (дата обращения 14.02.2022)]].

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 - Action selection [Электронный ресурс] // https://www.netinbag.com/ URL: https://www.netinbag.com/ru/internet/what-is-action-selection.html (https://www.netinbag.com/ru/internet/what-is-action-selection.html) (дата обращения: 18.02.2022)]].

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 - https://appen.com/ai-glossary/ (https://appen.com/ai-glossary/)]].

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 - Adam optimization algorithm [Электронный ресурс] // archive.org URL: https://archive.org/details/riseofexpertcomp00feig (дата обращения: 11.03.2022)]].

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 algorithm. [Электронный ресурс] // dic.academic.ru (дата обращения: 27.01.2022)]].

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 Gradient Algorithm. [Электронный ресурс] // jmlr.org. URL: https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf (дата обращения: 18.02.2022)]].

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 neuro fuzzy inference system (ANFIS) [Электронный ресурс] // hrpub.ru URL: https://www.hrpub.org/download/20190930/AEP1-18113213.pdf (дата обращения 14.02.2022)]].

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 - Affective computing [Электронный ресурс] // OpenMind URL: https://www.bbvaopenmind.com/en/technology/digital-world/what-is-affective-computing/ (дата обращения 14.02.2022)]].

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 - Agent architecture [Электронный ресурс] // dic.academic URL: https://en-academic.com/dic.nsf/enwiki/2205509 (дата обращения 28.02.2022)]].

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 - Aggregate [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)]]

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 - Aggregator [Электронный ресурс] www.techopedia.com URL: https://www.techopedia.com/definition/2502/feed-aggregator (https://www.techopedia.com/definition/2502/feed-aggregator) (дата обращения: 07.07.2022)]]

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 winter [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/AI_winter (https://en.wikipedia.org/wiki/AI_winter) (дата обращения: 07.07.2022)]].

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-complete [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/eng_rus/373471/AI (https://dic.academic.ru/dic.nsf/eng_rus/373471/AI) (дата обращения: 27.01.2022)]]

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 - AlphaGo [Электронный ресурс] // tadviser.ru URL: https://www.tadviser.ru/index.php/Продукт:AlphaGo (https://www.tadviser.ru/index.php/%D0%9F%D1%80%D0%BE%D0%B4%D1%83%D0%BA%D1%82:AlphaGo) (дата обращения: 19.02.2022)]]

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 - Ambient intelligence (AmI) [Электронный ресурс] // infosys.com URL: https://www.infosys.com/insights/ai-automation/ambient-intelligence.html#:~:text=Ambient%20intelligence%20(AmI)%20represents%20the,user's%20needs%20and%20desires%20seamlessly (https://www.infosys.com/insights/ai-automation/ambient-intelligence.html#:~:text=Ambient%20intelligence%20(AmI)%20represents%20the,user%27s%20needs%20and%20desires%20seamlessly). (дата обращения: 31.07.2022)]]

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 - Analogical Reasoning [Электронный ресурс] // studme.org URL: https://studme.org/171664/logika/rassuzhdeniya_analogii_vidy_rassuzhdeniy_analogii (дата обращения: 19.02.2022)]].

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 - Analysis of algorithms (AofA) [Электронный ресурс] // aofa.cs.purdue.edu URL: https://aofa.cs.purdue.edu/#:~:text=Analysis%20of%20Algorithms%20(AofA)%20is,%2C%20combinatorial%2C%20and%20analytic%20methods. (дата обращения: 18.02.2022)]].

Annotation (Аннотация) – A metadatum attached to a piece of data, typically provided by a human annotator [[36 - Annotation [Электронный ресурс] //appen.com URL: https://appen.com/ai-glossary/ (дата обращения 05.04.2020)]].

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.

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 - Auto Associative Memory [Электронный ресурс] www.tutorialspoint.com URL: https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern (https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern). (дата обращения: 07.07.2022)]].








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 - Autoencoder [Электронный ресурс] // neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/avtojenkoder-tipy-arhitektur-i-primenenie/ (дата обращения: 28.01.2022)]].

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 [Электронный ресурс] // vvsu.ru URL: https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt (https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt) (дата обращения: 28.01.2022)]] 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 - Automata theory [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/introduction-of-theory-of-computation/ (https://www.geeksforgeeks.org/introduction-of-theory-of-computation/) (дата обращения: 07.07.2022)]].

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 planning and scheduling [Электронный ресурс] // researcher.watson.ibm.com URL: https://researcher.watson.ibm.com/researcher/view_group.php?id=8432 (дата обращения: 28.01.2022)]].

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 reasoning [Электронный ресурс] // techtarget.com URL: https://www.techtarget.com/searchenterpriseai/definition/automated-reasoning#:~:text=Automated%20reasoning%20is%20the%20area,inferences%20towards%20that%20goal%20automatically. (дата обращения: 18.02.2022)]].

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 - Automation bias [Электронный ресурс] // databricks.com URL: https://databricks.com/glossary/automation-bias#:~:text=Automation%20bias%20is%20an%20over,aids%20and%20decision%20support%20systems.&text=It%20is%20a%20human%20tendency,leaning%20towards%20%22automation%20bias%22. (дата обращения: 26.01.2022)]].

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 car [Электронный ресурс] // synopsys.com URL: https://www.synopsys.com/automotive/what-is-autonomous-car.html (дата обращения: 28.01.2022)]].

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 robot [Электронный ресурс] // techopedia.com URL: https://www.techopedia.com/definition/32694/autonomous-robot (дата обращения: 28.01.2022)]].

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 - Autoregressive Model [Электронный ресурс] // wiki.loginom.ru URL: https://wiki.loginom.ru/articles/autoregressive-model.html (дата обращения: 08.02.2022)]].

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 - Average precision [Электронный ресурс] // jonathan-hui.medium.com URL: https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173 (дата обращения: 28.01.2022)]].

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.




“B”


Backpropagation (Обратное распространение ошибки) – Backpropagation, also called “backward propagation of errors,” is an approach that is commonly used in the training process of the deep neural network to reduce errors.

Backpropagation through time (BPTT) (Обратное распространение во времени) – A gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.

Backward Chaining (Обратная цепочка (или обратное рассуждение)) – Backward chaining, also called goal-driven inference technique, is an inference approach that reasons backward from the goal to the conditions used to get the goal. Backward chaining inference is applied in many different fields, including game theory, automated theorem proving, and artificial intelligence [[72 - Backward Chaining [Электронный ресурс] www.educba.com URL: https://www.educba.com/backward-chaining/ (дата обращения 11.03.2022)]].

Bag-of-words model (Модель мешка слов) — A simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier [[73 - Bag-of-words model [Электронный ресурс] // machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/gentle-introduction-bag-words-model/ (дата обращения: 11.03.2022)]].

Bag-of-words model in computer vision (Модель мешка слов в компьютерном зрении) — In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.

Baldwin effect (Эффект Балдвина) – the skills acquired by organisms during their life as a result of learning, after a certain number of generations, are recorded in the genome.

Baseline (Базовый уровень) – A model used as a reference point for comparing how well another model (typically, a more complex one) is performing. For example, a logistic regression model might serve as a good baseline for a deep model. For a particular problem, the baseline helps model developers quantify the minimal expected performance that a new model must achieve for the new model to be useful.

Batch (Пакет) – The set of examples used in one gradient update of model training.

Batch Normalization (Пакетная нормализация) – A preprocessing step where the data are centered around zero, and often the standard deviation is set to unity.

Batch size (Размер партии) – The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes.

Bayes’s Theorem (Теорема Байеса) – A famous theorem used by statisticians to describe the probability of an event based on prior knowledge of conditions that might be related to an occurrence.

Bayesian classifier in machine learning (Байесовский классификатор в машинном обучении) is a family of simple probabilistic classifiers based on the use of the Bayes theorem and the “naive” assumption of the independence of the features of the objects being classified.

Bayesian Filter (Фильтрация по Байесу) is a program using Bayesian logic. It is used to evaluate the header and content of email messages and determine whether or not it constitutes spam – unsolicited email or the electronic equivalent of hard copy bulk mail or junk mail. A Bayesian filter works with probabilities of specific words appearing in the header or content of an email. Certain words indicate a high probability that the email is spam, such as Viagra and refinance [[74 - Bayesian Filter [Электронный ресурс] //certsrv.ru URL: http://certsrv.ru/eset_ss.ru/pages/bayes_filter.htm (дата обращения: 12.02.2022)]].

Bayesian Network (Байесовская сеть) – also called belief network, or probabilistic directed acyclic graphical model, is a probabilistic graphical model (a statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph [[75 - Bayesian Network [Электрчатонный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1738444 (дата обращения: 31.01.2022)]].

Bayesian optimization (Байесовская оптимизация) – A probabilistic regression model technique for optimizing computationally expensive objective functions by instead optimizing a surrogate that quantifies the uncertainty via a Bayesian learning technique. Since Bayesian optimization is itself very expensive, it is usually used to optimize expensive-to-evaluate tasks that have a small number of parameters, such as selecting hyperparameters.

Bayesian programming (Байесовское программирование) – A formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available.

Bees algorithm (Алгоритм пчелиной колонии) — A population-based search algorithm which was developed by Pham, Ghanbarzadeh and et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.

Behavior informatics (BI) (Информатика поведения) — The informatics of behaviors so as to obtain behavior intelligence and behavior insights.

Behavior tree (BT) (Дерево поведения) – A mathematical model of plan execution used in computer science, robotics, control systems and video games. They describe switchings between a finite set of tasks in a modular fashion. Their strength comes from their ability to create very complex tasks composed of simple tasks, without worrying how the simple tasks are implemented. BTs present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Its ease of human understanding make BTs less error-prone and very popular in the game developer community. BTs have shown to generalize several other control architectures [[76 - Behavior tree (BT) [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/cloud_mts/blog/306214/ (дата обращения: 31.01.2022)]].

Belief-desire-intention software model (BDI) (Модель убеждений, желаний и намерений) — A software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent’s beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library or an external planner application) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer. [[77 - Belief-desire-intention software model (BDI) [Электронный ресурс] // fccland.ru URL: https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html (https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html) (дата обращения: 31.01.2022)]]

Bellman equation (Уравнение Беллмана) – named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the “value” of a decision problem at a certain point in time in terms of the payoff from some initial choices and the “value” of the remaining decision problem that results from those initial choices. This breaks a dynamic optimization problem into a sequence of simpler subproblems, as Bellman’s “principle of optimality” prescribes [[78 - Bellman equation [Электронный ресурс] //mruanova.medium.com URL: https://mruanova.medium.com/bellman-equation-90f2f0deaa88 (дата обращения 28.02.2022)]].

Benchmark (also benchmark program, benchmarking program, benchmark test) (Бенчмарк) – test program or package for evaluating (measuring and / or comparing) various aspects of the performance of a processor, individual devices, computer, system or a specific application, software; a benchmark that allows products from different manufacturers to be compared against each other or against some standard. For example, online benchmark – online benchmark; standard benchmark – standard benchmark; benchmark time comparison – comparison of benchmark execution times. [[79 - Benchmark [Электронный ресурс] // URL: https://medium.com/@tauheedul/it-hardware-benchmarks-for-machine-learning-and-artificial-intelligence-6183ceed39b8 (дата обращения 11.03.2022)]].

Benchmarking (Бенчмаркинг) is a set of techniques that allow you to study the experience of competitors and implement best practices in your company.

BETA (БЕТА версия) A beta publication refers to a phase in online service development in which the service is coming together functionality-wise but genuine user experiences are required before the service can be finished in a user-centered way. In online service development, the aim of the beta phase is to recognize both programming issues and usability-enhancing procedures. The beta phase is particularly often used in connection with online services and it can be either freely available (open beta) or restricted to a specific target group (closed beta). [[80 - BETA [Электронный ресурс] www.sofokus.com URL: https://www.sofokus.com/glossary-of-digital-business/#ABCD (https://www.sofokus.com/glossary-of-digital-business/#ABCD) (дата обращения: 07.07.2022)]]

Bias (Погрешность) is a systematic trend that causes differences between results and facts. Error exists in the numbers of the data analysis process, including the source of the data, the estimate chosen, and how the data is analyzed. Error can seriously affect the results, for example, when studying people’s shopping habits. If the sample size is not large enough, the results may not reflect the buying habits of all people. That is, there may be discrepancies between survey results and actual results.

Biased algorithm (Алгоритмическая предвзятость) – systematic and repetitive errors in a computer system that lead to unfair results, such as one privilege persecuting groups of users over others. Also, sexist and racist algorithms.

Bidirectional (BiDi) (Двунаправленность) – A term used to describe a system that evaluates the text that both precedes and follows a target section of text. In contrast, a unidirectional system only evaluates the text that precedes a target section of text.

Bidirectional Encoder Representations from Transformers (BERT) (Представления двунаправленного кодировщика от трансформаторов) – A model architecture for text representation. A trained BERT model can act as part of a larger model for text classification or other ML tasks. BERT has the following characteristics: Uses the Transformer architecture, and therefore relies on self-attention. Uses the encoder part of the Transformer. The encoder’s job is to produce good text representations, rather than to perform a specific task like classification. Is bidirectional. Uses masking for unsupervised training.

Bidirectional language model (Двунаправленная языковая модель) – A language model that determines the probability that a given token is present at a given location in an excerpt of text based on the preceding and following text.

Big data (Большие данные) is a term for sets of digital data whose large size, rate of increase or complexity requires significant computing power for processing and special software tools for analysis and presentation in the form of human-perceptible results.

Big O notation (Запись Big O notation) – A mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau, and others, collectively called Bachmann – Landau notation or asymptotic notation [[81 - Big O notation [Электронный ресурс] // upread.ru URL: https://upread.ru/art.php?id=659 (дата обращения: 04.02.2022)]].

Bigram (Биграмм) – An N-gram in which N=2.

Binary choice regression model (Регрессионная модель бинарного выбора) is a regression model in which the dependent variable is dichotomous or binary. Dependent variable can take only two values and mean, for example, belonging to a particular group.

Binary classification (Двоичная, бинарная или дихотомическая классификация) — A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either “spam” or “not spam” is a binary classifier.

Binary format (Двоичный формат) Any file format in which information is encoded in some format other than a standard character-encoding scheme. A file written in binary format contains information that is not displayable as characters. Software capable of understanding the particular binary format method of encoding information must be used to interpret the information in a binary-formatted file. Binary formats are often used to store more information in less space than possible in a character format file. They can also be searched and analyzed more quickly by appropriate software. A file written in binary format could store the number “7” as a binary number (instead of as a character) in as little as 3 bits (i.e., 111), but would more typically use 4 bits (i.e., 0111). Binary formats are not normally portable, however. Software program files are written in binary format. Examples of numeric data files distributed in binary format include the IBM-binary versions of the Center for Research in Security Prices files and the U.S. Department of Commerce’s National Trade Data Bank on CD-ROM. The International Monetary Fund distributes International Financial Statistics in a mixed-character format and binary (packed-decimal) format. SAS and SPSS store their system files in binary format. [[82 - Binary format [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)]]

Binary number (Двоичное число) A number written using binary notation which only uses zeros and ones. Example: Decimal number 7 in binary notation is: 111. [[83 - Binary number [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)]]

Binary tree (Бинарное дерево) – A tree data structure in which each node has at most two children, which are referred to as the left child and the right child. A recursive definition using just set theory notions is that a (non-empty) binary tree is a tuple (L, S, R), where L and R are binary trees or the empty set and S is a singleton set. Some authors allow the binary tree to be the empty set as well. [[84 - Binary tree [Электронный ресурс] // habr.com URL: https://habr.com/ru/post/267855/ (https://habr.com/ru/post/267855/) (дата обращения: 31.01.2022)]]

Binning (Биннинг) is the process of combining charge from neighboring pixels in a CCD during readout. This process is performed prior to digitization in the CCD chip using dedicated serial and parallel register control. The two main benefits of binning are improved signal-to-noise ratio (SNR) and the ability to increase frame rates, albeit at the cost of reduced spatial resolution.

Bioconservatism (Биоконсерватизм) (a portmanteau of biology and conservatism) is a stance of hesitancy and skepticism regarding radical technological advances, especially those that seek to modify or enhance the human condition. Bioconservatism is characterized by a belief that technological trends in today’s society risk compromising human dignity, and by opposition to movements and technologies including transhumanism, human genetic modification, “strong” artificial intelligence, and the technological singularity. Many bioconservatives also oppose the use of technologies such as life extension and preimplantation genetic screening [[85 - Bioconservatism [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Bioconservatism (https://en.wikipedia.org/wiki/Bioconservatism) (дата обращения: 07.07.2022)],[86 - .Bioconservatism [Электронный ресурс] www.wise-geek.com URL: https://www.wise-geek.com/what-is-bioconservatism.htm (https://www.wise-geek.com/what-is-bioconservatism.htm) (дата обращения: 07.07.2022)]].

Biometrics (Биометрия) is a people recognition system, one or more physical or behavioral traits.

Black box (Чёрный ящик) – A description of some deep learning system. They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret.

Blackboard system (Системы, использующие принцип классной доски) – An artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the “blackboard”, is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem.

BLEU (Bilingual Evaluation Understudy) (Алгоритм BLEU) – A score between 0.0 and 1.0, inclusive, indicating the quality of a translation between two human languages (for example, between English and Russian). A BLEU score of 1.0 indicates a perfect translation; a BLEU score of 0.0 indicates a terrible translation.

Blockchain (Блокчейн) is algorithms and protocols for decentralized storage and processing of transactions structured as a sequence of linked blocks without the possibility of their subsequent change.

Boltzmann machine (Also stochastic Hopfield network with hidden units) (Машина Больцмана) – A type of stochastic recurrent neural network and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks [[87 - Boltzmann machine [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1828062 (дата обращения: 04.02.2022)]].

Boolean neural network (Булевая нейронная сеть) – is an artificial neural network approach which only consists of Boolean neurons (and, or, not). Such an approach reduces the use of memory space and computation time. It can be implemented to the programmable circuits such as FPGA (Field-Programmable Gate Array or Integrated circuit).

Boolean satisfiability problem (Also propositional satisfiability problem; abbreviated SATISFIABILITY or SAT) (Проблема булевой выполнимости) – is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called satisfiable. On the other hand, if no such assignment exists, the function expressed by the formula is FALSE for all possible variable assignments and the formula is unsatisfiable. [[88 - Boolean satisfiability problem A. de Carvalho M.C. Fairhurst D.L. Bisset, An integrated Boolean neural network for pattern classification. Pattern Recognition Letters Volume 15, Issue 8, August 1994, Pages 807—813 (дата обращения: 10.02.2022)]].

Boosting (Бустинг) – A Machine Learning ensemble meta-algorithm for primarily reducing bias and variance in supervised learning, and a family of Machine Learning algorithms that convert weak learners to strong ones.

Bounding Box (Ограничивающая рамка) – Commonly used in image or video tagging, this is an imaginary box drawn on visual information. The contents of the box are labeled to help a model recognize it as a distinct type of object.

Brain technology (Also self-learning know-how system) (Мозговая технология) – A technology that employs the latest findings in neuroscience. The term was first introduced by the Artificial Intelligence Laboratory in Zurich, Switzerland, in the context of the ROBOY project. Brain Technology can be employed in robots, know-how management systems and any other application with self-learning capabilities. In particular, Brain Technology applications allow the visualization of the underlying learning architecture often coined as “know-how maps”.

Brain – computer interface (BCI, Интерфейс мозг-компьютер), sometimes called a brain – machine interface (BMI), is a direct communication pathway between the brain’s electrical activity and an external device, most commonly a computer or robotic limb. Research on brain – computer interface began in the 1970s by Jacques Vidal at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The Vidal’s 1973 paper marks the first appearance of the expression brain – computer interface in scientific literature [[89 - Brain – computer interface [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface (https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface) (дата обращения: 07.07.2022)]].

Brain-inspired computing (Мозгоподобные вычисления) – calculations on brain-like structures, brain-like calculations using the principles of the brain (see also neurocomputing, neuromorphic engineering).

Branching factor (коэффициент ветвления дерева) – In computing, tree data structures, and game theory, the number of children at each node, the outdegree. If this value is not uniform, an average branching factor can be calculated.

Broadband (Широкополосный доступ) refers to various high-capacity transmission technologies that transmit data, voice, and video across long distances and at high speeds. Common mediums of transmission include coaxial cables, fiber optic cables, and radio waves. [[90 - Broadband [Электронный ресурс] www.investopedia.com URL: https://www.investopedia.com/terms/b/broadband.asp (https://www.investopedia.com/terms/b/broadband.asp) (дата обращения: 07.07.2022)]]

Brute-force search (Also exhaustive search or generate and test) (Полный перебор) – A very general problem-solving technique and algorithmic paradigm that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement.

Bucketing (Разделение на сегменты) – Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range.

Byte (Байт) Eight bits. A byte is simply a chunk of 8 ones and zeros. For example: 01000001 is a byte. A computer often works with groups of bits rather than individual bits and the smallest group of bits that a computer usually works with is a byte. A byte is equal to one column in a file written in character format. [[91 - Byte [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)]]




“C”


Caffe – is short for Convolutional Archi- tecture for Fast Feature Embedding which is an open-source deep learning framework de- veloped in Berkeley AI Research. It supports many different deep learning architectures and GPU-based acceleration computation kernels.

Calibration layer (Калибровочный слой) – A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.

Candidate generation (Генерация кандидатов) — The initial set of recommendations chosen by a recommendation system. [[92 - Candidate generation [Электронный ресурс] // developers.google.com URL: https://developers.google.com/machine-learning/recommendation/overview/candidate-generation (дата обращения: 10.01.2022)]].

Candidate sampling (Выборка кандидатов) — A training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence). The idea is that the negative classes can learn from less frequent negative reinforcement as long as positive classes always get proper positive reinforcement, and this is indeed observed empirically. The motivation for candidate sampling is a computational efficiency win from not computing predictions for all negatives.

Canonical Formats (Канонические форматы) In information technology, canonicalization is the process of making something [conform] with some specification… and is in an approved format. Canonicalization may sometimes mean generating canonical data from noncanonical data. Canonical formats are widely supported and considered to be optimal for long-term preservation. [[93 - Canonical Formats [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]

Capsule neural network (CapsNet) (Капсульная нейронная сеть) – A machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. [[94 - Capsule neural network [Электронный ресурс] // ru.what-this.com URL: https://ru.what-this.com/7202531/1/kapsulnaya-neyronnaya-set.html (https://ru.what-this.com/7202531/1/kapsulnaya-neyronnaya-set.html) (дата обращения: 07.02.2022)]] The approach is an attempt to more closely mimic biological neural organization [[95 - Capsule neural network [Электронный ресурс] // neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/kapsulnaja-nejronnaja-set-capsnet/ (https://neurohive.io/ru/osnovy-data-science/kapsulnaja-nejronnaja-set-capsnet/) (дата обращения: 08.02.2022)]]

Case-Based Reasoning (CBR) (Рассуждения по прецедентам) – is a way to solve a new problem by using solutions to similar problems. It has been formalized to a process consisting of case retrieve, solution reuse, solution revise, and case retention [[96 - Case-Based Reasoning [Электронный ресурс] www.telusinternational.com URL: https://www.telusinternational.com/articles/50-beginner-ai-terms-you-should-know (дата обращения 15.01.2022)]].

Categorical data (Категориальные данные) — Features having a discrete set of possible values. For example, consider a categorical feature named house style, which has a discrete set of three possible values: Tudor, ranch, colonial. By representing house style as categorical data, the model can learn the separate impacts of Tudor, ranch, and colonial on house price. Sometimes, values in the discrete set are mutually exclusive, and only one value can be applied to a given example. For example, a car maker categorical feature would probably permit only a single value (Toyota) per example. Other times, more than one value may be applicable. A single car could be painted more than one different color, so a car color categorical feature would likely permit a single example to have multiple values (for example, red and white). Categorical features are sometimes called discrete features. Contrast with numerical data [[97 - Categorical data [Электронный ресурс] // machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/understanding-feature-engineering-part-2-categorical-data-f54324193e63/ (дата обращения: 03.03.2022)]].

Center for Technological Competence (Центр технологических компетенций) is an organization that owns the results, tools for conducting fundamental research and platform solutions available to market participants to create applied solutions (products) on their basis. The Technology Competence Center can be a separate organization or be part of an application technology holding company.

Central Processing Units (CPU) (Центральный процессор) is a von Neumann cyclic processor designed to execute complex computer programs.

Centralized control (Централизованное управление) is a process in which control signals are generated in a single control center and transmitted from it to numerous control objects.

Centroid (Центроид) – The center of a cluster as determined by a k-means or k-median algorithm. For instance, if k is 3, then the k-means or k-median algorithm finds 3 centroids.

Centroid-based clustering (Кластеризация на основе центроида) – A category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms.

Character format (Формат символов)

Any file format in which information is encoded as characters using only a standard character-encoding scheme. A file written in “character format” contains only those bytes that are prescribed in the encoding scheme as corresponding to the characters in the scheme (e.g., alphabetic and numeric characters, punctuation marks, and spaces). [[98 - Character format [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]

Chatbot (Чат-бот) is a software application designed to simulate human conversation with users via text or speech. Also referred to as virtual agents, interactive agents, digital assistants, or conversational AI, chatbots are often integrated into applications, websites, or messaging platforms to provide support to users without the use of live human agents. Chatbots originally started out by offering users simple menus of choices, and then evolved to react to particular keywords. “But humans are very inventive in their use of language,” says Forrester’s McKeon-White. Someone looking for a password reset might say they’ve forgotten their access code, or are having problems getting into their account. “There are a lot of different ways to say the same thing,” he says. This is where AI comes in. Natural language processing is a subset of machine learning that enables a system to understand the meaning of written or even spoken language, even where there is a lot of variation in the phrasing. To succeed, a chatbot that relies on AI or machine learning needs first to be trained using a data set. In general, the bigger the training data set, and the narrower the domain, the more accurate and helpful a chatbot will be [[99 - Сhatbot [Электронный ресурс] www.cio.com URL: https://www.cio.com/article/189347/what-is-a-chatbot-simulating-human-conversation-for-service.html (https://www.cio.com/article/189347/what-is-a-chatbot-simulating-human-conversation-for-service.html) (дата обращения: 07.07.2022)]].

Checkpoint (Контрольная точка) — Data that captures the state of the variables of a model at a particular time. Checkpoints enable exporting model weights, as well as performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). Note that the graph itself is not included in a checkpoint.

Chip (Чип) – an electronic microcircuit of arbitrary complexity, made on a semiconductor substrate and placed in a non-separable case or without it, if included in the micro assembly.

Class (Класс) — One of a set of enumerated target values for a label. For example, in a binary classification model that detects spam, the two classes are spam and not spam. In a multi-class classification model that identifies dog breeds, the classes would be poodle, beagle, pug, and so on.

Classification (Классификация). Classification problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees and random forest are all common types of classification algorithms.

Classification model (Модель классификации) — A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian.

Classification threshold (Порог классификации) — A scalar-value criterion that is applied to a model’s predicted score in order to separate the positive class from the negative class. Used when mapping logistic regression results to binary classification.

Clinical Decision Support (CDS) (Поддержка принятия клинических решений) – A clinical decision support system is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support, that is, assistance with clinical decision- making tasks [[100 - Clinical Decision Support (CDS) [Электронный ресурс] www.quora.com URL: https://www.quora.com/What-are-clinical-decision-support-systems-What-benefits-do-they-provide (дата обращения 28.02.2022)]].

Clipping (Отсечение) – A technique for handling outliers. Specifically, reducing feature values that are greater than a set maximum value down to that maximum value. Also, increasing feature values that are less than a specific minimum value up to that minimum value. For example, suppose that only a few feature values fall outside the range 40—60. In this case, you could do the following: Clip all values over 60 to be exactly 60. Clip all values under 40 to be exactly 40. In addition to bringing input values within a designated range, clipping can also used to force gradient values within a designated range during training.

Closed dictionary (Закрытый словарь) – In speech recognition systems, a dictionary with a limited number of words, to which the recognition system is configured and which cannot be replenished by the user

Cloud (Облако) – The cloud is a general metaphor that is used to refer to the Internet. Initially, the Internet was seen as a distributed network and then with the invention of the World Wide Web as a tangle of interlinked media. As the Internet continued to grow in both size and the range of activities it encompassed, it came to be known as “the cloud.” The use of the word cloud may be an attempt to capture both the size and nebulous nature of the Internet [[101 - Cloud [Электронный ресурс] // dropbox.com URL: https://www.dropbox.com/ru/business/resources/what-is-the-cloud (дата обращения: 09.02.2022)]].

Cloud computing (Облачные вычисления) is an information technology model for providing ubiquitous and convenient access using the Internet to a common set of configurable computing resources (“cloud”), data storage devices, applications and services that can be quickly provided and released from the load with minimal operating costs or with little or no involvement of the provider.

Cloud robotics (Облачная робототехника) – A field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent “brain” in the cloud. The “brain” consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc. [[102 - Сloud robotics [Электронный ресурс] // digitrode.ru URL: http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html (http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html) (дата обращения: 09.02.2022)]]

Cloud TPU (Облачный процессор) – A specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform [[103 - Cloud TPU [Электронный ресурс] github.com URL: https://github.com/tensorflow/tpu (https://github.com/tensorflow/tpu) (дата обращения: 25.02.2022)]]

Cluster analysis (Кластерный анализ) – A type of unsupervised learning used for exploratory data analysis to find hidden patterns or groupings in the data; clusters are modeled with a similarity measure defined by metrics such as Euclidean or probability distance.

Clustering (Кластеризация) is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, etc.

Co-adaptation (Коадаптация) – When neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network’s behavior as a whole. When the patterns that cause co-adaption are not present in validation data, then co-adaptation causes overfitting. Dropout regularization reduces co-adaptation because dropout ensures neurons cannot rely solely on specific other neurons.

Cobweb (Метод COBWEB) – An incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University. COBWEB incrementally organizes observations into a classification tree. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object.

Code (Код) is a one-to-one mapping of a finite ordered set of symbols belonging to some finite alphabet.

Codec (Кодек) “A codec is the means by which sound and video files are compressed for storage and transmission purposes. There are various forms of compression: ‘lossy’ and ‘lossless’, but most codecs perform lossless compression because of the much larger data reduction ratios that occur [with lossy compression]. Most codecs are software, although in some areas codecs are hardware components of image and sound systems. Codecs are necessary for playback, since they uncompress [or decompress] the moving image and sound files and allow them to be rendered.” [[104 - Codec [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]

Cognitive architecture (Когнитивная архитектура) – The Institute of Creative Technologies defines cognitive architecture as: “hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments”

Cognitive computing (Когнитивные вычисления) — is used to refer to the systems that simulate the human brain to help with the decision- making. It uses self-learning algorithms that perform tasks such as natural language processing, image analysis, reasoning, and human – computer interaction. Examples of cognitive systems are IBM’s Watson and Google DeepMind [[105 - Cognitive computing [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/ibm/blog/276855/ (https://habr.com/ru/company/ibm/blog/276855/) (дата обращения: 31.01.2022)]]

Cognitive Maps (Когнитивные карты) Cognitive maps are structured representations of decision depicted in graphical format (variations of cognitive maps are cause maps, influence diagrams, or belief nets). Basic cognitive maps include nodes connected by arcs, where the nodes represent constructs (or states) and the arcs represent relationships. Cognitive maps have been used to understand decision situations, to analyze complex cause-effect representations and to support communication. [[106 - Cognitive Maps [Электронный ресурс] www.igi-global.com URL: https://www.igi-global.com/dictionary/qplan/34624 (https://www.igi-global.com/dictionary/qplan/34624) (дата обращения: 07.07.2022)]]

Cognitive science (Когнитивистика, когнитивная наука) – The interdisciplinary scientific study of the mind and its processes. [[107 - Cognitive science Когнитивная наука и интеллектуальные технологии: Реф. сб. АН СССР. – М.: Ин-т науч. информ. по обществ. наукам, 1991. (дата обращения: 04.02.2022)]]

Cohort (Когорта) – A sample in study (conducted to evaluate a machine learning algorithm, for example) where it is followed prospectively or retrospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants’ exposure characteristics (risk factors) are associated with it.

Cold-Start (Холодный запуск) – A potential issue arising from the fact that a system cannot infer anything for users or items for which it has not gathered a sufficient amount of information yet.

Collaborative filtering (Коллаборативная фильтрация) – Making predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems.

Combinatorial optimization (Комбинаторная оптимизация) – In Operations Research, applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects [[108 - Combinatorial optimization [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/107404 (дата обращения: 11.02.2022)]].

Committee machine (Комитетная машина) – A type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response. The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare ensembles of classifiers.

Commoditization (Коммодитизация) is the process of transforming a product from an elite to a generally available (comparatively cheap commodity of mass consumption)

Common Data Element (CDE) (Общий элемент данных) – Common Data Element is a tool to support data management for clinical research [[109 - Common Data Element (CDE) [Электронный ресурс] // URL: https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html (https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html) (дата обращения 30.04.2022)]].

Commonsense knowledge (Здравый смысл) — In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as “Lemons are sour”, that all humans are expected to know. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy [[110 - Commonsense knowledge [Электронный ресурс] // wikiaro.ru URL: https://wikiaro.ru/wiki/Commonsense_reasoning (дата обращения: 09.02.2022)]].

Commonsense reasoning (Рассуждения на основе здравого смысла) – A branch of artificial intelligence concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day [[111 - Commonsense reasoning [Электронный ресурс] // sciencedirect.com URL: https://www.sciencedirect.com/topics/computer-science/answer-set-programming#:~:text=Answer%20set%20programming%20is%20an,is%20required%20in%20commonsense%20reasoning. (дата обращения: 09.03.2022)]].

Compiler (Компилятор) is a program that translates text written in a programming language into a set of machine codes. AI framework compilers collect the computational data of the frameworks and try to optimize the code of each of them, regardless of the hardware of the accelerator. The compiler contains programs and blocks with which the framework performs several tasks. The computer memory resource allocator, for example, allocates power individually for each accelerator.

Composite AI (Композитный искусственный интеллект) is the combined application of various AI techniques to improve learning efficiency, expand the level of knowledge representation and, ultimately, to more effectively solve a wider range of business problems.

Compression (Компрессия) A method of reducing the size of computer files. There are several compression programs available, such as gzip and WinZip. [[112 - Compression [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)]]

Computation (Вычисление) is any type of arithmetic or non-arithmetic calculation that follows a well-defined model (e.g., an algorithm) [[113 - Computation [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Computation (https://en.wikipedia.org/wiki/Computation) (дата обращения: 07.07.2022)]].

Computational chemistry (Вычислительная химия) – A branch of chemistry in which mathematical methods are used to calculate molecular properties, model the behavior of molecules, plan synthesis, search databases, and process combinatorial libraries.

Computational complexity theory (Теория сложности вычислений) – Focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm [[114 - Computational complexity theory [Электронный ресурс] // math-cs.spbu.ru URL: https://math-cs.spbu.ru/courses/teoriya-slozhnosti-vychislenij/ (дата обращения: 09.02.2022)]].

Computational creativity (Also artificial creativity, mechanical creativity, creative computing, or creative computation) (Креативные вычисления) – A multidisciplinary endeavour that includes the fields of artificial intelligence, cognitive psychology, philosophy, and the arts. [[115 - Computational creativity [Электронный ресурс] // hoster.bmstu.ru URL: http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf (http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf) (дата обращения: 14.02.2022)]]

Computational cybernetics (Вычислительная кибернетика) — is the integration of cybernetics and computational intelligence techniques.

Computational efficiency of an agent or a trained model (Вычислительная эффективность агента или обученной модели) is the number of computational resources required by the agent to solve a problem at the inference stage.

Computational efficiency of an intelligent system (Вычислительная эффективность интеллектуальной системы) is the amount of computing resources required to train an intelligent system with a certain level of performance on a given volume of tasks.

Computational Graphics Processing Unit (computational GPU; cGPU) (Графический процессор-вычислитель) – graphic processor-computer, GPU-computer, multi-core GPU used in hybrid supercomputers to perform parallel mathematical calculations; for example, one of the first GPUs in this category contains more than 3 billion transistors – 512 CUDA cores and up to 6 GB of memory. [[116 - Computational Graphics Processing Unit [Электронный ресурс] www.boston.co.uk URL: https://www.boston.co.uk/info/nvidia-kepler/what-is-gpu-computing.aspx (дата обращения 14.03.2022)]].

Computational humor (Вычислительный юмор) — A branch of computational linguistics and artificial intelligence which uses computers in humor research.

Computational intelligence (CI) (Вычислительный интеллект) — Usually refers to the ability of a computer to learn a specific task from data or experimental observation [].

Computational intelligence (CI) (Вычислительный интеллект) — Usually refers to the ability of a computer to learn a specific task from data or experimental observation.

Computational learning theory (COLT) (Теория вычислительного обучения) – In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms [[117 - Computational learning theory (COLT) [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/topic/Computational-learning-theory/164025 (дата обращения 28.02.2022)]].

Computational linguistics (Компьютерная лингвистика) – An interdisciplinary field concerned with the statistical or rule-based modeling of natural language from a computational perspective, as well as the study of appropriate computational approaches to linguistic questions.

Computational mathematics (Вычислительная математика) — is the mathematical research in areas of science where computing plays an essential role.

Computational neuroscience (Also theoretical neuroscience or mathematical neuroscience) (Вычислительная нейробиология) – is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system.

Computational number theory (Also algorithmic number theory) (Вычислительная теория чисел) – The study of computational methods for researching and solving problems in number theory and arithmetic geometry, including algorithms for checking primality and numerical factorization, finding solutions to Diophantine equations, and explicit methods in arithmetic geometry. Computational number theory has applications to cryptography, including RSA, elliptic curve cryptography, and post-quantum cryptography, and is used to investigate the hypothesis and open problem of number theory, including the Riemann hypothesis, the Birch and Swinnerton-Dyer hypothesis, the ABC hypothesis, the modularity hypothesis, the Sato- Tate and explicit aspects of the Langlands program.

Computational problem (Вычислительная задача) – In theoretical computer science, a computational problem is a mathematical object representing a collection of questions that computers might be able to solve [[118 - Computational problem [Электронный ресурс] //cs.stackexchange.com URL: https://cs.stackexchange.com/questions/47757/computational-problem-definition (дата обращения 12.03.2022)]].

Computational statistics (Also statistical computing) (Вычислительная статистика) – Computational science is the application of computer science and software engineering principles to solving scientific problems. It involves the use of computing hardware, networking, algorithms, programming, databases and other domain-specific knowledge to design simulations of physical phenomena to run on computers. Computational science crosses disciplines and can even involve the humanities.

Computer engineering (Компьютерный инжиниринг) – technologies for digital modeling and design of objects and production processes throughout the life cycle.

Computer incident (Компьютерный инцидент) is a fact of violation and (or) cessation of the operation of a critical information infrastructure object, a telecommunication network used to organize the interaction of such objects, and (or) a violation of the security of information processed by such an object, including as a result of a computer attack.

Computer science (Информатика) – The theory, experimentation, and engineering that form the basis for the design and use of computers. It involves the study of algorithms that process, store, and communicate digital information. A computer scientist specializes in the theory of computation and the design of computational systems. Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to practical disciplines (including the design and implementation of hardware and software). Computer science is generally considered an area of academic research and distinct from computer programming [[119 - Computer science [Электронный ресурс] //view.officeapps.live.com URL: https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.lib.unn.ru%2Fstudents%2Fsrc%2FZibtceva4.doc&wdOrigin=BROWSELINK (https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.lib.unn.ru%2Fstudents%2Fsrc%2FZibtceva4.doc&wdOrigin=BROWSELINK) (дата обращения: 07.07.2022)]].

Computer simulation (Компьютерное моделирование) is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering [[120 - Computer simulation. [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Computer_simulation (https://en.wikipedia.org/wiki/Computer_simulation) (дата обращения: 07.07.2022)]].

Computer vision (Компьютерное зрение) is scientific discipline, field of technology and the direction of artificial intelligence (AI), which deals with computer processing, recognition, analysis and classification of dynamic images of reality. It is widely used in video surveillance systems, in robotics and in modern industry to improve product quality and production efficiency, comply with legal requirements, etc. In computer vision, the following areas are distinguished: face recognition (face recognition), image recognition (image recognition), augmented reality (augmented reality (AR) and optical character recognition (OCR). Synonyms – artificial vision, machine vision

Computer vision processing (CVP, Обработка компьютерного зрения) is the processing of images (signals) in a computer vision system, in computer vision systems – about algorithms (computer vision processing algorithms), processors (computer vision processing unit, CVPU), convolutional neural networks (convolutional neural network), which are used for image processing and implementation of visual functions in robotics, real-time systems, smart video surveillance systems, etc.

Computer-Aided Detection/Diagnosis (CAD) (Компьютерная диагностика) – Computer-aided detection (CAD), or computer-aided diagnosis (CADx), uses computer programs to assist radiologists in the interpretation of medical images. CAD systems process digital images for typical appearances and highlight suspicious regions in order to support a decision taken by a professional.

Computer-automated design (CAutoD) (Компьютерно-автоматизированное проектирование) – Design automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and computer-automated designare concerned with a broader range of applications, such as automotive engineering, civil engineering, composite material design, control engineering, dynamic system identification and optimization, financial systems, industrial equipment, mechatronic systems, steel construction, structural optimisation, and the invention of novel systems. More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically inspired machine learning, including heuristic search techniques such as evolutionary computation, and swarm intelligence algorithms.

Computing modules (Вычислительные модули) are plug-in specialized computers designed to solve narrowly focused tasks, such as accelerating the work of artificial neural networks algorithms, computer vision, voice recognition, machine learning and other artificial intelligence methods, built on the basis of a neural processor – a specialized class of microprocessors and coprocessors (processor, memory, data transfer).

Computing system (Вычислительная система) is a software and hardware complex intended for solving problems and processing data (including calculations) or several interconnected complexes that form a single infrastructure.

Computing units (Вычислительные блоки) are blocks that work like a filter that transforms packets according to certain rules. The instruction set of the calculator can be limited, which guarantees a simple internal structure and a sufficiently high speed of operation.

Concept drift (Дрейф концепций) – In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes [].

Confidentiality of information (Конфиденциальность информации) – a mandatory requirement for a person who has access to certain information not to transfer such information to third parties without the consent of its owner.

Confirmation Bias (Предвзятость подтверждения) – the tendency to search for, interpret, favor, and recall information in a way that confirms one’s own beliefs or hypotheses while giving disproportionately less attention to information that contradicts it.

Confusion matrix (Матрица неточностей) — is a situational analysis table that summarizes the prediction results of a classification model in machine learning. The records in the dataset are summarized in a matrix according to the real category and the classification score made by the classification model.

Connectionism (Коннекционизм) – An approach in the fields of cognitive science, that hopes to explain mental phenomena using artificial neural networks.

Consistent heuristic (Последовательная (непротиворечивая) эвристика) – In the study of path-finding problems in artificial intelligence, a heuristic function is said to be consistent, or monotone, if its estimate is always less than or equal to the estimated distance from any neighboring vertex to the goal, plus the cost of reaching that neighbor

Constrained conditional model (CCM) (Условная модель с ограничениями) – A machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative сonstraints [].

Constraint logic programming (Логическое программирование ограничений) – A form of constraint programming, in which logic programming is extended to include concepts from constraint satisfaction. A constraint logic program is a logic program that contains constraints in the body of clauses. [[121 - Сonstraint logic programming [Электронный ресурс] www.definitions.net URL: https://www.definitions.net/definition/Constraint (https://www.definitions.net/definition/Constraint) (дата обращения 28.02.2022)]].

Constraint programming (Ограниченное программирование) – A programming paradigm wherein relations between variables are stated in the form of constraints. Constraints differ from the common primitives of imperative programming languages in that they do not specify a step or sequence of steps to execute, but rather the properties of a solution to be found [[122 - Сonstraint programming [Электронный ресурс] www.definitions.net URL: https://www.definitions.net/definition/Constraint (дата обращения 28.02.2022)]].

Constructed language (Also conlang) (Искусственные языки) – A language whose phonology, grammar, and vocabulary are consciously devised, instead of having developed naturally. Constructed languages may also be referred to as artificial, planned, or invented languages.

Consumer artificial intelligence (Бытовой искусственный интеллект) is specialized artificial intelligence programs embedded in consumer devices and processes.

Continuous feature (Непрерывная функция) – A floating-point feature with an infinite range of possible values. Contrast with discrete feature.

Contributor (Сотрудник) – A human worker providing annotations on the Appen data annotation platform.

Control theory (Теория управления) – In control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner without delay or overshoot and ensuring control stability [[123 - Сontrol theory [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/paper/Modern-control-systems-theory-Leondes-青木/c0fb8d86dec3dc0d09c207fa9888369328b766a9 (дата обращения 06.04.2022)]].

Convenience sampling (Удобная выборка) – Using a dataset not gathered scientifically in order to run quick experiments. Later on, it’s essential to switch to a scientifically gathered dataset.

Convergence (Конвергенция) – Informally, often refers to a state reached during training in which training loss and validation loss change very little or not at all with each iteration after a certain number of iterations. In other words, a model reaches convergence when additional training on the current data will not improve the model. In deep learning, loss values sometimes stay constant or nearly so for many iterations before finally descending, temporarily producing a false sense of convergence. See also early stopping.

Convex function (Выпуклая функция) – is a function where the area above the graph of the function is a convex set. The prototype of a convex function is U-shaped. A strictly convex function has exactly one local minimum point. Classical U-shaped functions are strictly convex functions. However, some convex functions (such as straight lines) do not have a U-shape. Many common loss functions are convex: L2 loss, Log Loss, L1 regularization, L2 regularization. Many variants of gradient descent are guaranteed to find a point close to the minimum of a strictly convex function. Similarly, many variants of stochastic gradient descent have a high probability (though not a guarantee) of finding a point close to the minimum of a strictly convex function. The sum of two convex functions (e.g., L2 loss + L1 regularization) is a convex function. Deep models are never convex functions. Notably, algorithms designed for convex optimization tend to find reasonably good solutions in deep networks anyway, even if those solutions do not guarantee a global minimum.

Convex optimization (Выпуклая оптимизация) – The process of using mathematical techniques such as gradient descent to find the minimum of a convex function. A great deal of research in machine learning has focused on formulating various problems as convex optimization problems and in solving those problems more efficiently. For complete details, see Boyd and Vandenberghe, Convex Optimization [[124 - Convex optimization [Электронный ресурс] //en.mimi.hu URL: https://en.mimi.hu/artificial_intelligence/convex_optimization.html (дата обращения 22.02.2022)]].

Convex set (Выпуклое множество) – A subset of Euclidean space such that a line drawn between any two points in the subset remains completely within the subset. For instance, the following two shapes are convex sets.

Convolution (Свертка) — The process of filtering. A filter (or equivalently: a kernel or a template) is shifted over an input image. The pixels of the output image are the summed product of the values in the filter pixels and the corresponding values in the underlying image [[125 - Convolution [Электронный ресурс] // spec-zone.ru/ URL: https://spec-zone.ru/RU/OSX/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html (https://spec-zone.ru/RU/OSX/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html) (дата обращения: 09.02.2022)]]

Convolutional filter (Сверточный фильтр) – One of the two actors in a convolutional operation. (The other actor is a slice of an input matrix.) A convolutional filter is a matrix having the same rank as the input matrix, but a smaller shape.

Convolutional layer (Сверточный слой) – A layer of a deep neural network in which a convolutional filter passes along an input matrix.

Convolutional neural network (CNN) (Распознавательная нейронная сеть) is a type of neural network that identifies and interprets images

Convolutional neural network (Сверточная нейронная сеть) – In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. Сonvolutional neural network is a class of artificial neural network most commonly used to analyze visual images. They are also known as Invariant or Spatial Invariant Artificial Neural Networks (SIANN) based on an architecture with a common weight of convolution kernels or filters that slide over input features and provide equivalent translation responses known as feature maps.

Convolutional operation (Сверточная операция) – The following two-step mathematical operation: Element-wise multiplication of the convolutional filter and a slice of an input matrix. (The slice of the input matrix has the same rank and size as the convolutional filter.) Summation of all the values in the resulting product matrix.

Corelet programming environment (Среда программирования Corelet) is a scalable environment that allows programmers to set the functional behavior of a neural network by adjusting its parameters and communication characteristics.

Corpus (Корпус) – A large dataset of written or spoken material that can be used to train a machine to perform linguistic tasks.

Correlation (Корреляция) is a statistical relationship between two or more random variables.

Correlation analysis (Корреляционный анализ) is a statistical data processing method that measures the strength of the relationship between two or more variables. Thus, it determines whether there is a connection between the phenomena and how strong the connection between these phenomena is.

Cost (Стоимость) – synonym for loss. A measure of how far a model’s predictions are from its label. Or, to put it more pessimistically, a measure of how bad a model is. To determine this value, the model must define a loss function. For example, linear regression models typically use the standard error for the loss function, while logistic regression models use the log loss.

Co-training (Совместное обучение)

Co-training essentially amplifies independent signals into a stronger signal. For instance, consider a classification model that categorizes individual used cars as either Good or Bad. One set of predictive features might focus on aggregate characteristics such as the year, make, and model of the car; another set of predictive features might focus on the previous owner’s driving record and the car’s maintenance history. The seminal paper on co-training is Combining Labeled and Unlabeled Data with Co-Training by Blum and Mitchell [[126 - Co-training [Электронный ресурс] www.v7labs.com URL: https://www.v7labs.com/blog/semi-supervised-learning-guide (дата обращения 06.03.2022)]].

Counterfactual fairness (Контрфактическая справедливость) – A fairness metric that checks whether a classifier produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more sensitive attributes. Evaluating a classifier for counterfactual fairness is one method for surfacing potential sources of bias in a model. See “When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness” for a more detailed discussion of counterfactual fairness.

Coverage bias (Систематическая предвзятость охвата) this bias means that the study sample is not representative and that the data set in the array has zero chance of being included in the sample.

Crash blossom (Двусмыссленная фраза) – A sentence or phrase with an ambiguous meaning. Crash blossoms present a significant problem in natural language understanding. For example, the headline Red Tape Holds Up Skyscraper is a crash blossom because an NLU model could interpret the headline literally or figuratively.

Critic (Критик) – Synonym for Deep Q-Network.

Critical information infrastructure (Критическая информационная инфраструктура) – objects of critical information infrastructure, as well as telecommunication networks used to organize the interaction of such objects.

Critical information infrastructure of the Russian Federation (Критическая информационная инфраструктура Российской Федерации) – a set of critical information infrastructure objects, as well as telecommunication networks used to organize the interaction of critical information infrastructure objects with each other.

Cross-entropy (Кросс-энтропия) – A generalization of Log Loss to multi-class classification problems. Cross-entropy quantifies the difference between two probability distributions. See also perplexity.

Crossover (Also recombination) (Кроссовер) – In genetic algorithms and evolutionary computation, a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and analogous to the crossover that happens during sexual reproduction in biological organisms. Solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions are typically mutated before being added to the population [[127 - Crossover [Электронный ресурс] //brainly.in URL: https://brainly.in/question/5802477 (дата обращения 28.02.2022)]].

Cross-Validation (k-fold Cross-Validation, Leave-p-out Cross-Validation) (Перекрёстная проверка) – A collection of processes designed to evaluate how the results of a predictive model will generalize to new data sets. k-fold Cross-Validation; Leave-p-out Cross-Validation.

Cryogenic freezing (cryonics, human cryopreservation) is a technology of preserving in a state of deep cooling (using liquid nitrogen) the head or body of a person after his death with the intention to revive them in the future.

Cyber-physical systems (Киберфизические системы) are intelligent networked systems with built-in sensors, processors and drives that are designed to interact with the physical environment and support the operation of computer information systems in real time; cloud computing is an information technology model for providing ubiquitous and convenient access using the information and telecommunications network “Internet” to a common set of configurable computing resources (“cloud”), data storage devices, applications and services that can be promptly provided and relieved from the load with minimal operating costs or almost without the participation of the provider.

Cyber-physical systems (Киберфизические системы) are intelligent networked systems with built-in sensors, processors and drives that are designed to interact with the physical environment and support the operation of computer information systems in real time; cloud computing is an information technology model for providing ubiquitous and convenient access using the information and telecommunications network “Internet” to a common set of configurable computing resources (“cloud”), data storage devices, applications and services that can be promptly provided and relieved from the load with minimal operating costs or almost without the participation of the provider.




“D”


Darkforest (Программа Darkforest) – A computer program, based on deep learning techniques using a convolutional neural network. Its updated version Darkforest2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them. With the update, the system is known as Darkforest3.

Dartmouth workshop (Дартмутский семинар) – The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many (though not all) to be the seminal event for artificial intelligence as a field.

Data (Данные) – Data is a collection of qualitative and quantitative variables. It contains the information that is represented numerically and needs to be analyzed.

Data analysis (Анализ данных) – Obtaining an understanding of data by considering samples, measurement, and visualization. Data analysis can be particularly useful when a dataset is first received, before one builds the first model. It is also crucial in understanding experiments and debugging problems with the system [[128 - Data analysis [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1727524 (дата обращения: 16.02.2022)]].

Data analytics (Аналитика данных)

Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. [[129 - Data analytics [Электронный ресурс] www.investopedia.com URL:https://www.investopedia.com/terms/d/data-analytics.asp (https://www.investopedia.com/terms/d/data-analytics.asp) (дата обращения: 07.07.2022)]]

Data augmentation (Увеличение данных в анализе данных) – Data augmentation in data analysis are techniques used to increase the amount of data. It helps reduce overfitting when training a machine learning [[130 - Data augmentation [Электронный ресурс] // ibm.com URL: https://www.ibm.com/docs/ru/oala/1.3.5?topic=SSPFMY_1.3.5/com.ibm.scala.doc/config/iwa_cnf_scldc_scl_dc_ovw.html (дата обращения: 18.02.2022)]].

Data Cleaning (Очистка данных) – Data Cleaning is the process of identifying, correcting, or removing inaccurate or corrupt data records.

Data Curation (Курирование данных) – Data Curation includes the processes related to the organization and management of data which is collected from various sources [[131 - Data Curation [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/data-curation-lifecycle/ (дата обращения 22.02.2022)]].

Data entry (Ввод данных) The process of converting verbal or written responses to electronic form. [[132 - Data entry [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D) (дата обращения: 07.07.2022)]]

Data fusion (Слияние данных) — The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source [].

Data Integration (Интеграция данных) – involves the combination of data residing in different resources and then the supply in a unified view to the users. Data integration is in high demand for both commercial and scientific domains in which they need to merge the data and research results from different repositories [].

Data Lake (Озеро данных) – A type of data repository that stores data in its natural format and relies on various schemata and structure to index the data.

Data markup (Разметка данных) is the stage of processing structured and unstructured data, during which data (including text documents, photo and video images) are assigned identifiers that reflect the type of data (data classification), and (or) data is interpreted to solve a specific problem, in including using machine learning methods (National Strategy for the Development of Artificial Intelligence for the period up to 2030).

Data Mining (Интеллектуальный анализ данных) – is the process of data analysis and information extraction from large amounts of datasets with machine learning, statistical approaches. and many others. [[133 - Data Mining [Электронный ресурс] // bigdataschool.ru URL: https://www.teradata.ru/Glossary/What-is-Data-Mining (https://www.teradata.ru/Glossary/What-is-Data-Mining) (дата обращения: 17.02.2022)]]

Data parallelism (Параллелизм данных) – A way of scaling training or inference that replicates an entire model onto multiple devices and then passes a subset of the input data to each device. Data parallelism can enable training and inference on very large batch sizes; however, data parallelism requires that the model be small enough to fit on all devices. See also model parallelism.

Data protection (Защита данных) is the process of protecting data and involves the relationship between the collection and dissemination of data and technology, the public perception and expectation of privacy and the political and legal underpinnings surrounding that data. It aims to strike a balance between individual privacy rights while still allowing data to be used for business purposes. [[134 - Data protection [Электронный ресурс] www. techopedia.com URL: https://www.techopedia.com/definition/29406/data-protection (https://www.techopedia.com/definition/29406/data-protection) (дата обращения: 07.07.2022)]]

Data Refinement (Уточнение данных) – Data refinement is used to convert an abstract data model in terms of sets for example into implementable data structures such as arrays [[135 - Data Refinement [Электронный ресурс] www.atscale.com URL: https://www.atscale.com/blog/what-is-data-extraction/ (дата обращения 12.01.2022)]].

Data Science (Наука о данных) — A broad grouping of mathematics, statistics, probability, computing, data visualization to extract knowledge from a heterogeneous set of data (images, sound, text, genomic data, social network links, physical measurements, etc.). The methods and tools derived from artificial intelligence are part of this family.

Data set (Набор данных) – a set of data that has undergone preliminary preparation (processing) in accordance with the requirements of the legislation of the Russian Federation on information, information technology and information protection and is necessary for the development of software based on artificial intelligence (National strategy for the development of artificial intelligence for the period up to 2030).

Data Streaming Accelerator (DSA) (Ускоритель потоковой передачи данных) – is an accelerator, that is, a device that performs a specific task, which in this case is the transfer of data in less time than the CPU would do. What makes DSA special is that it is designed for one of the characteristics that Compute Express Link brings with it over PCI Express 5.0, which is to provide consistent access to RAM for all peripherals connected to a PCI Express port, i.e., they use the same memory addresses.





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notes


Примечания





1


Чесалов А. Ю. Цифровая трансформация. -М.: Ridero. 2020.-302c. URL: https://ridero.ru/books/cifrovaya_transformaciya_2/ (https://ridero.ru/books/cifrovaya_transformaciya_2/)




2


Чесалов А. Ю. Цифровая экосистема Института омбудсмена: концепция, технологии, практика. -М.: Ridero. 2020.-320c.




3


Указ Президента Российской Федерации от 7 мая 2018 №204 “О национальных целях и стратегических задачах развития Российской Федерации на период до 2024 года”.




4


Федерального закона от 27.07.2006 №149-ФЗ (ред. от 01.05.2019) “Об информации, информационных технологиях и о защите информации”. [Electronic resource] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://www.kremlin.ru/acts/bank/24157 (http://www.kremlin.ru/acts/bank/24157)




5


Указ Президента Российской Федерации от 09.05.2017 г. №203. О Стратегии развития информационного общества в Российской Федерации на 2017 – 2030 годы. [Electronic resource] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://kremlin.ru/acts/bank/41919 (http://kremlin.ru/acts/bank/41919)




6


Указ Президента Российской Федерации от 10.10.2019 г. №490. О развитии искусственного интеллекта в Российской Федерации. [Электронный ресурс] // www.kremlin.ru (http://www.kremlin.ru/). URL: http://www.kremlin.ru/acts/bank/44731 (http://www.kremlin.ru/acts/bank/44731)




7


Кодекс этики в сфере ИИ. [Электронный ресурс] // a-ai.ru URL: https://a-ai.ru/code-of-ethics/ (https://a-ai.ru/code-of-ethics/)




8


Указ Президента Российской Федерации от 06.06.2019 г. №254 “О Стратегии развития здравоохранения в Российской Федерации на период до 2025 года”. [Электронный ресурс] // kremlin.ru URL: http://www.kremlin.ru/acts/bank/44326 (http://www.kremlin.ru/acts/bank/44326)




9


Стратегия развития электронной промышленности РФ на период до 2030 года. [Электронный ресурс] // conference.tass.ru. URL: https://conference.tass.ru/events/prezentaciya-proekta-strategii-razvitiya-elektronnoj-promyshlennosti-rf-na-period-do-2030-g- (https://conference.tass.ru/events/prezentaciya-proekta-strategii-razvitiya-elektronnoj-promyshlennosti-rf-na-period-do-2030-g-)




10


Федеральный закон от 27.07.2006 N 152-ФЗ (ред. от 24.04.2020) “О персональных данных”. [Электронный ресурс] // legalacts.ru URL: https://legalacts.ru/doc/152_FZ-o-personalnyh-dannyh/ (https://legalacts.ru/doc/152_FZ-o-personalnyh-dannyh/)




11


Национальная программа “Цифровая экономика Российской Федерации”. Министерство цифрового развития, связи и массовых коммуникаций Российской Федерации. [Электронный ресурс] // digital.gov.ru. URL: https://digital.gov.ru/ru/activity/directions/858/ (https://digital.gov.ru/ru/activity/directions/858/)




12


Государственная Программа “Цифровая экономика Российской Федерации”. [Электронный ресурс] // static.government.ru URL: http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf (http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf)




13


A/B Testing [Electronic resource] // vwo.com URL: https://vwo.com/ab-testing/ (date of the application: 28.01.2022)




14


Abductive Logic Programming (ALP) [Electronic resource] // engati.com URL https://www.engati.com/glossary/abductive-logic-programming (https://www.engati.com/glossary/abductive-logic-programming) (date of the application 14.02.2022)




15


Abductive reasoning [Electronic resource] // MRS BLOG URL: http://msrblog.com/science/mathematic/about-abductive-reasoning.html (http://msrblog.com/science/mathematic/about-abductive-reasoning.html) (date of the application 14.02.2022)




16


Abstract data type [Electronic resource] // EMBEDDED ARTISTRY URL: https://embeddedartistry.com/fieldmanual-terms/abstract-data-type/ (date of the application 14.02.2022)




17


Accelerating change [Электронный ресурс] // ru.knowledgr.com (дата обращения: 14.02.2022)




18


https://www.semanticscholar.org/topic/Action-language/72365 (https://www.semanticscholar.org/topic/Action-language/72365)




19


Action model learning [Электронный ресурс] // Semantic Scholar URL: https://www.semanticscholar.org/topic/Action-model-learning/1677625 (дата обращения 14.02.2022)




20


Action selection [Электронный ресурс] // https://www.netinbag.com/ URL: https://www.netinbag.com/ru/internet/what-is-action-selection.html (https://www.netinbag.com/ru/internet/what-is-action-selection.html) (дата обращения: 18.02.2022)




21


https://appen.com/ai-glossary/ (https://appen.com/ai-glossary/)




22


Adam optimization algorithm [Электронный ресурс] // archive.org URL: https://archive.org/details/riseofexpertcomp00feig (дата обращения: 11.03.2022)




23


Adaptive algorithm. [Электронный ресурс] // dic.academic.ru (дата обращения: 27.01.2022)




24


Adaptive Gradient Algorithm. [Электронный ресурс] // jmlr.org. URL: https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf (дата обращения: 18.02.2022)




25


Adaptive neuro fuzzy inference system (ANFIS) [Электронный ресурс] // hrpub.ru URL: https://www.hrpub.org/download/20190930/AEP1-18113213.pdf (дата обращения 14.02.2022)




26


Affective computing [Электронный ресурс] // OpenMind URL: https://www.bbvaopenmind.com/en/technology/digital-world/what-is-affective-computing/ (дата обращения 14.02.2022)




27


Agent architecture [Электронный ресурс] // dic.academic URL: https://en-academic.com/dic.nsf/enwiki/2205509 (дата обращения 28.02.2022)




28


Aggregate [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)




29


Aggregator [Электронный ресурс] www.techopedia.com URL: https://www.techopedia.com/definition/2502/feed-aggregator (https://www.techopedia.com/definition/2502/feed-aggregator) (дата обращения: 07.07.2022)




30


AI winter [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/AI_winter (https://en.wikipedia.org/wiki/AI_winter) (дата обращения: 07.07.2022)




31


AI-complete [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/eng_rus/373471/AI (https://dic.academic.ru/dic.nsf/eng_rus/373471/AI) (дата обращения: 27.01.2022)




32


AlphaGo [Электронный ресурс] // tadviser.ru URL: https://www.tadviser.ru/index.php/Продукт:AlphaGo (https://www.tadviser.ru/index.php/%D0%9F%D1%80%D0%BE%D0%B4%D1%83%D0%BA%D1%82:AlphaGo) (дата обращения: 19.02.2022)




33


Ambient intelligence (AmI) [Электронный ресурс] // infosys.com URL: https://www.infosys.com/insights/ai-automation/ambient-intelligence.html#:~:text=Ambient%20intelligence%20(AmI)%20represents%20the,user's%20needs%20and%20desires%20seamlessly (https://www.infosys.com/insights/ai-automation/ambient-intelligence.html#:~:text=Ambient%20intelligence%20(AmI)%20represents%20the,user%27s%20needs%20and%20desires%20seamlessly). (дата обращения: 31.07.2022)




34


Analogical Reasoning [Электронный ресурс] // studme.org URL: https://studme.org/171664/logika/rassuzhdeniya_analogii_vidy_rassuzhdeniy_analogii (дата обращения: 19.02.2022)




35


Analysis of algorithms (AofA) [Электронный ресурс] // aofa.cs.purdue.edu URL: https://aofa.cs.purdue.edu/#:~:text=Analysis%20of%20Algorithms%20(AofA)%20is,%2C%20combinatorial%2C%20and%20analytic%20methods. (дата обращения: 18.02.2022)




36


Annotation [Электронный ресурс] //appen.com URL: https://appen.com/ai-glossary/ (дата обращения 05.04.2020)




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)




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)




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)




40


Application programming interface (API) [Электронный ресурс] // ibm.com URL: https://www.ibm.com/cloud/learn/api (дата обращения: 19.02.2022)




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)




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)




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)




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)




45


Archival Storage [Электронный ресурс] www.komprise.com URL: https://www.komprise.com/glossary_terms/archival-storage/(дата обращения: 07.07.2022)




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)




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)




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)




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)




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)




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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)




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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)




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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)




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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)




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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)




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)




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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)




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Asymptotic computational complexity [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/eng_rus/429332/asymptotic (дата обращения: 27.01.2022)




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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.




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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)




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Auto Associative Memory [Электронный ресурс] www.tutorialspoint.com URL: https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern (https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern). (дата обращения: 07.07.2022)




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Autoencoder [Электронный ресурс] // neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/avtojenkoder-tipy-arhitektur-i-primenenie/ (дата обращения: 28.01.2022)




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Automata theory [Электронный ресурс] // vvsu.ru URL: https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt (https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt) (дата обращения: 28.01.2022)




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Automata theory [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/introduction-of-theory-of-computation/ (https://www.geeksforgeeks.org/introduction-of-theory-of-computation/) (дата обращения: 07.07.2022)




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Automated planning and scheduling [Электронный ресурс] // researcher.watson.ibm.com URL: https://researcher.watson.ibm.com/researcher/view_group.php?id=8432 (дата обращения: 28.01.2022)




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Automated reasoning [Электронный ресурс] // techtarget.com URL: https://www.techtarget.com/searchenterpriseai/definition/automated-reasoning#:~:text=Automated%20reasoning%20is%20the%20area,inferences%20towards%20that%20goal%20automatically. (дата обращения: 18.02.2022)




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Automation bias [Электронный ресурс] // databricks.com URL: https://databricks.com/glossary/automation-bias#:~:text=Automation%20bias%20is%20an%20over,aids%20and%20decision%20support%20systems.&text=It%20is%20a%20human%20tendency,leaning%20towards%20%22automation%20bias%22. (дата обращения: 26.01.2022)




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Autonomous car [Электронный ресурс] // synopsys.com URL: https://www.synopsys.com/automotive/what-is-autonomous-car.html (дата обращения: 28.01.2022)




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Autonomous robot [Электронный ресурс] // techopedia.com URL: https://www.techopedia.com/definition/32694/autonomous-robot (дата обращения: 28.01.2022)




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Autoregressive Model [Электронный ресурс] // wiki.loginom.ru URL: https://wiki.loginom.ru/articles/autoregressive-model.html (дата обращения: 08.02.2022)




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Average precision [Электронный ресурс] // jonathan-hui.medium.com URL: https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173 (дата обращения: 28.01.2022)




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Backward Chaining [Электронный ресурс] www.educba.com URL: https://www.educba.com/backward-chaining/ (дата обращения 11.03.2022)




73


Bag-of-words model [Электронный ресурс] // machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/gentle-introduction-bag-words-model/ (дата обращения: 11.03.2022)




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Bayesian Filter [Электронный ресурс] //certsrv.ru URL: http://certsrv.ru/eset_ss.ru/pages/bayes_filter.htm (дата обращения: 12.02.2022)




75


Bayesian Network [Электрчатонный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1738444 (дата обращения: 31.01.2022)




76


Behavior tree (BT) [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/cloud_mts/blog/306214/ (дата обращения: 31.01.2022)




77


Belief-desire-intention software model (BDI) [Электронный ресурс] // fccland.ru URL: https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html (https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html) (дата обращения: 31.01.2022)




78


Bellman equation [Электронный ресурс] //mruanova.medium.com URL: https://mruanova.medium.com/bellman-equation-90f2f0deaa88 (дата обращения 28.02.2022)




79


Benchmark [Электронный ресурс] // URL: https://medium.com/@tauheedul/it-hardware-benchmarks-for-machine-learning-and-artificial-intelligence-6183ceed39b8 (дата обращения 11.03.2022)




80


BETA [Электронный ресурс] www.sofokus.com URL: https://www.sofokus.com/glossary-of-digital-business/#ABCD (https://www.sofokus.com/glossary-of-digital-business/#ABCD) (дата обращения: 07.07.2022)




81


Big O notation [Электронный ресурс] // upread.ru URL: https://upread.ru/art.php?id=659 (дата обращения: 04.02.2022)




82


Binary format [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)




83


Binary number [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)




84


Binary tree [Электронный ресурс] // habr.com URL: https://habr.com/ru/post/267855/ (https://habr.com/ru/post/267855/) (дата обращения: 31.01.2022)




85


Bioconservatism [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Bioconservatism (https://en.wikipedia.org/wiki/Bioconservatism) (дата обращения: 07.07.2022)




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.Bioconservatism [Электронный ресурс] www.wise-geek.com URL: https://www.wise-geek.com/what-is-bioconservatism.htm (https://www.wise-geek.com/what-is-bioconservatism.htm) (дата обращения: 07.07.2022)




87


Boltzmann machine [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1828062 (дата обращения: 04.02.2022)




88


Boolean satisfiability problem A. de Carvalho M.C. Fairhurst D.L. Bisset, An integrated Boolean neural network for pattern classification. Pattern Recognition Letters Volume 15, Issue 8, August 1994, Pages 807—813 (дата обращения: 10.02.2022)




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Brain – computer interface [Электронный ресурс] //en.wikipedia.org URL: https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface (https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface) (дата обращения: 07.07.2022)




90


Broadband [Электронный ресурс] www.investopedia.com URL: https://www.investopedia.com/terms/b/broadband.asp (https://www.investopedia.com/terms/b/broadband.asp) (дата обращения: 07.07.2022)




91


Byte [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#B) (дата обращения: 07.07.2022)




92


Candidate generation [Электронный ресурс] // developers.google.com URL: https://developers.google.com/machine-learning/recommendation/overview/candidate-generation (дата обращения: 10.01.2022)




93


Canonical Formats [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)




94


Capsule neural network [Электронный ресурс] // ru.what-this.com URL: https://ru.what-this.com/7202531/1/kapsulnaya-neyronnaya-set.html (https://ru.what-this.com/7202531/1/kapsulnaya-neyronnaya-set.html) (дата обращения: 07.02.2022)




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Capsule neural network [Электронный ресурс] // neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/kapsulnaja-nejronnaja-set-capsnet/ (https://neurohive.io/ru/osnovy-data-science/kapsulnaja-nejronnaja-set-capsnet/) (дата обращения: 08.02.2022)




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Case-Based Reasoning [Электронный ресурс] www.telusinternational.com URL: https://www.telusinternational.com/articles/50-beginner-ai-terms-you-should-know (дата обращения 15.01.2022)




97


Categorical data [Электронный ресурс] // machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/understanding-feature-engineering-part-2-categorical-data-f54324193e63/ (дата обращения: 03.03.2022)




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Character format [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)




99


Сhatbot [Электронный ресурс] www.cio.com URL: https://www.cio.com/article/189347/what-is-a-chatbot-simulating-human-conversation-for-service.html (https://www.cio.com/article/189347/what-is-a-chatbot-simulating-human-conversation-for-service.html) (дата обращения: 07.07.2022)




100


Clinical Decision Support (CDS) [Электронный ресурс] www.quora.com URL: https://www.quora.com/What-are-clinical-decision-support-systems-What-benefits-do-they-provide (дата обращения 28.02.2022)




101


Cloud [Электронный ресурс] // dropbox.com URL: https://www.dropbox.com/ru/business/resources/what-is-the-cloud (дата обращения: 09.02.2022)




102


Сloud robotics [Электронный ресурс] // digitrode.ru URL: http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html (http://digitrode.ru/articles/2683-chto-takoe-oblachnaya-robototehnika.html) (дата обращения: 09.02.2022)




103


Cloud TPU [Электронный ресурс] github.com URL: https://github.com/tensorflow/tpu (https://github.com/tensorflow/tpu) (дата обращения: 25.02.2022)




104


Codec [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)




105


Cognitive computing [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/ibm/blog/276855/ (https://habr.com/ru/company/ibm/blog/276855/) (дата обращения: 31.01.2022)




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Cognitive Maps [Электронный ресурс] www.igi-global.com URL: https://www.igi-global.com/dictionary/qplan/34624 (https://www.igi-global.com/dictionary/qplan/34624) (дата обращения: 07.07.2022)




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Cognitive science Когнитивная наука и интеллектуальные технологии: Реф. сб. АН СССР. – М.: Ин-т науч. информ. по обществ. наукам, 1991. (дата обращения: 04.02.2022)




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Combinatorial optimization [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/107404 (дата обращения: 11.02.2022)




109


Common Data Element (CDE) [Электронный ресурс] // URL: https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html (https://techdocs.broadcom.com/us/en/ca-mainframe-software/traditional-management/ca-mics-resource-management/14-1/installing/system-modification/ca-mics-facilities/ca-mics-component-generator-mcg/generator-definition-statements/common-data-element-definition-statements.html) (дата обращения 30.04.2022)




110


Commonsense knowledge [Электронный ресурс] // wikiaro.ru URL: https://wikiaro.ru/wiki/Commonsense_reasoning (дата обращения: 09.02.2022)




111


Commonsense reasoning [Электронный ресурс] // sciencedirect.com URL: https://www.sciencedirect.com/topics/computer-science/answer-set-programming#:~:text=Answer%20set%20programming%20is%20an,is%20required%20in%20commonsense%20reasoning. (дата обращения: 09.03.2022)




112


Compression [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#C) (дата обращения: 07.07.2022)




113


Computation [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Computation (https://en.wikipedia.org/wiki/Computation) (дата обращения: 07.07.2022)




114


Computational complexity theory [Электронный ресурс] // math-cs.spbu.ru URL: https://math-cs.spbu.ru/courses/teoriya-slozhnosti-vychislenij/ (дата обращения: 09.02.2022)




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Computational creativity [Электронный ресурс] // hoster.bmstu.ru URL: http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf (http://hoster.bmstu.ru/~amas/cources/mv/lect__slides.pdf) (дата обращения: 14.02.2022)




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Computational Graphics Processing Unit [Электронный ресурс] www.boston.co.uk URL: https://www.boston.co.uk/info/nvidia-kepler/what-is-gpu-computing.aspx (дата обращения 14.03.2022)




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Computational learning theory (COLT) [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/topic/Computational-learning-theory/164025 (дата обращения 28.02.2022)




118


Computational problem [Электронный ресурс] //cs.stackexchange.com URL: https://cs.stackexchange.com/questions/47757/computational-problem-definition (дата обращения 12.03.2022)




119


Computer science [Электронный ресурс] //view.officeapps.live.com URL: https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.lib.unn.ru%2Fstudents%2Fsrc%2FZibtceva4.doc&wdOrigin=BROWSELINK (https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Fwww.lib.unn.ru%2Fstudents%2Fsrc%2FZibtceva4.doc&wdOrigin=BROWSELINK) (дата обращения: 07.07.2022)




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Computer simulation. [Электронный ресурс] //en.wikipedia.org. URL: https://en.wikipedia.org/wiki/Computer_simulation (https://en.wikipedia.org/wiki/Computer_simulation) (дата обращения: 07.07.2022)




121


Сonstraint logic programming [Электронный ресурс] www.definitions.net URL: https://www.definitions.net/definition/Constraint (https://www.definitions.net/definition/Constraint) (дата обращения 28.02.2022)




122


Сonstraint programming [Электронный ресурс] www.definitions.net URL: https://www.definitions.net/definition/Constraint (дата обращения 28.02.2022)




123


Сontrol theory [Электронный ресурс] www.semanticscholar.org URL: https://www.semanticscholar.org/paper/Modern-control-systems-theory-Leondes-青木/c0fb8d86dec3dc0d09c207fa9888369328b766a9 (дата обращения 06.04.2022)




124


Convex optimization [Электронный ресурс] //en.mimi.hu URL: https://en.mimi.hu/artificial_intelligence/convex_optimization.html (дата обращения 22.02.2022)




125


Convolution [Электронный ресурс] // spec-zone.ru/ URL: https://spec-zone.ru/RU/OSX/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html (https://spec-zone.ru/RU/OSX/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html) (дата обращения: 09.02.2022)




126


Co-training [Электронный ресурс] www.v7labs.com URL: https://www.v7labs.com/blog/semi-supervised-learning-guide (дата обращения 06.03.2022)




127


Crossover [Электронный ресурс] //brainly.in URL: https://brainly.in/question/5802477 (дата обращения 28.02.2022)




128


Data analysis [Электронный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1727524 (дата обращения: 16.02.2022)




129


Data analytics [Электронный ресурс] www.investopedia.com URL:https://www.investopedia.com/terms/d/data-analytics.asp (https://www.investopedia.com/terms/d/data-analytics.asp) (дата обращения: 07.07.2022)




130


Data augmentation [Электронный ресурс] // ibm.com URL: https://www.ibm.com/docs/ru/oala/1.3.5?topic=SSPFMY_1.3.5/com.ibm.scala.doc/config/iwa_cnf_scldc_scl_dc_ovw.html (дата обращения: 18.02.2022)




131


Data Curation [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/data-curation-lifecycle/ (дата обращения 22.02.2022)




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Data entry [Электронный ресурс] www.umich.edu URL: https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D (https://www.icpsr.umich.edu/web/ICPSR/cms/2042#D) (дата обращения: 07.07.2022)




133


Data Mining [Электронный ресурс] // bigdataschool.ru URL: https://www.teradata.ru/Glossary/What-is-Data-Mining (https://www.teradata.ru/Glossary/What-is-Data-Mining) (дата обращения: 17.02.2022)




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Data protection [Электронный ресурс] www. techopedia.com URL: https://www.techopedia.com/definition/29406/data-protection (https://www.techopedia.com/definition/29406/data-protection) (дата обращения: 07.07.2022)




135


Data Refinement [Электронный ресурс] www.atscale.com URL: https://www.atscale.com/blog/what-is-data-extraction/ (дата обращения 12.01.2022)



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