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
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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 20212022.
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 Chesalovs 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.
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].