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], The Digital Ecosystem of the Ombudsman Institute: Concept, Technologies, Practice [2], 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]
Federal Law 149 of July 27, 2006 (as amended on May 1, 2019) On Information, Information Technologies and Information Protection [4].
Strategy for the Development of the Information Society in the Russian Federation for 20172030 [5].
National strategy for the development of artificial intelligence for the period up to 2030 [6].
AI Code of Ethics [7].
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].
Strategy for the development of the electronic industry of the Russian Federation for the period up to 2030 [9].
Federal Law of July 27, 2006 152 (as amended on April 24, 2020) On Personal Data [10].
National program Digital Economy of the Russian Federation [11].
State Program Digital Economy of the Russian Federation [12].
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].
Abductive logic programming (ALP) (Абдуктивное логическое программирование) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some predicates to be incompletely defined, declared as adducible predicates [14].
Abductive reasoning (Also abduction) (Абдукция) A form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. abductive inference, or retroduction [15].
Abstract data type (Абстрактный тип данных) A mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations [16].
Abstraction (Абстракция) The process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest.
Accelerating change (Ускорение изменений) A perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change [17].
Access to information (Доступ к информации) the ability to obtain information and use it.
Access to information constituting a commercial secret (Доступ к информации, составляющей коммерческую тайну) familiarization of certain persons with information constituting a commercial secret, with the consent of its owner or on other legal grounds, provided that this information is kept confidential.
Accuracy (Точность) The fraction of predictions that a classification model got right.
Action (Действие) In reinforcement learning, the mechanism by which the agent transitions between states of the environment. The agent chooses the action by using a policy.
Action language (Язык действий) A language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world. Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning [18].
Action model learning (Обучение модели действий) An area of machine learning concerned with creation and modification of software agents 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].