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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).
