Artificial Intelligence Glossarium: 1000 terms - Chesalov Alexander 3 стр.


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, AlphaGos team released its new version named AlphaGo Zero which is stronger than any previous human-champion defeating versions. Go is played on 19 by 19 board which allows for 10171 possible layouts (chess 1050 configurations). It is estimated that there are 1080 atoms in the universe [32]

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

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

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

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

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

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

Anokhins theory of functional systems (Теория функциональных систем Анохина)  a functional system consists of a certain number of nodal mechanisms, each of which takes its place and has a certain specific purpose. The first of these is afferent synthesis, in which four obligatory components are distinguished: dominant motivation, situational and triggering afferentation, and memory. The interaction of these components leads to the decision-making process.

Anomaly detection (Выявление аномалий)  The process of identifying outliers. For example, if the mean for a certain feature is 100 with a standard deviation of 10, then anomaly detection should flag a value of 200 as suspicious.

Anonymization (Анонимизация)  The process in which data is de-identified as part of a mechanism to submit data for machine learning.

Answer set programming (ASP) (Программирование набора ответов)  A form of declarative programming oriented towards difficult (primarily NP-hard) search problems. It is based on the stable model (answer set) semantics of logic programming. In ASP, search problems are reduced to computing stable models, and answer set solvers  programs for generating stable models  are used to perform search.

Antivirus software (Антивирусное программное обеспечение) is a program or set of programs that are designed to prevent, search for, detect, and remove software viruses, and other malicious software like worms, trojans, adware, and more. [37]

Anytime algorithm (Алгоритм любого времени)  An algorithm that can return a valid solution to a problem even if it is interrupted before it ends [38]

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

Application programming interface (API) (Интерфейс прикладного программирования)  A set of subroutine definitions, communication protocols, and tools for building software. In general terms, it is a set of clearly defined methods of communication among various components. A good API makes it easier to develop a computer program by providing all the building blocks, which are then put together by the programmer. An API may be for a web-based system, operating system, database system, computer hardware, or software library [40].

Application security (Безопасность приложений) is the process of making apps more secure by finding, fixing, and enhancing the security of apps. Much of this happens during the development phase, but it includes tools and methods to protect apps once they are deployed. This is becoming more important as hackers increasingly target applications with their attacks [41]

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

Approximate string matching (Also fuzzy string searching.) (Нечеткое соответствие строк или приблизительное соответствие строк)  The technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately.

Approximation error (Ошибка аппроксимации)  The discrepancy between an exact value and some approximation to it.

Architectural description group (Architectural view, Архитектурная группа описаний) is a representation of the system as a whole in terms of a related set of interests.

Architectural frameworks (Архитектурный фреймворк) are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution [43].

Architecture of a computer (Архитектура вычислительной машины) is a conceptual structure of a computer that determines the processing of information and includes methods for converting information into data and the principles of interaction between hardware and software.

Architecture of a computing system (Архитектура вычислительной системы) is the configuration, composition and principles of interaction (including data exchange) of the elements of a computing system.

Architecture of a system (Архитектура системы) is the fundamental organization of a system, embodied in its elements, their relationships with each other and with the environment, as well as the principles that guide its design and evolution.

Archival Information Collection (AIC) (Архивный пакет информации (AIC))

An Archival Information Package whose Content Information is an aggregation of other Archival Information Packages The digital preservation function preserves the capability to regenerate the DIPs (Dissemination Information Packages) as needed over time. [44]

Archival Storage (Архивное хранилище) Archival Storage is a source for data that is not needed for an organizations everyday operations, but may have to be accessed occasionally. By utilizing an archival storage, organizations can leverage to secondary sources, while still maintaining the protection of the data. Utilizing archival storage sources reduces primary storage costs required and allows an organization to maintain data that may be required for regulatory or other requirements. [45]

Area under curve (AUC) (Площадь под кривой)  The area under a curve between two points is calculated by performing the definite integral. In the context of a receiver operating characteristic for a binary classifier, the AUC represents the classifiers accuracy [46].

Area Under the ROC curve (Площадь под кривой ROC)  is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive.

Argumentation framework (Структура аргументации или система аргументации)  A way to deal with contentious information and draw conclusions from it. In an abstract argumentation framework, entry-level information is a set of abstract arguments that, for instance, represent data or a proposition. Conflicts between arguments are represented by a binary relation on the set of arguments. []

Artifact (Артефакт) is one of many kinds of tangible by-products produced during the development of software. Some artifacts (e.g., use cases, class diagrams, and other Unified Modeling Language (UML) models, requirements and design documents) help describe the function, architecture, and design of software. Other artifacts are concerned with the process of development itself  such as project plans, business cases, and risk assessments. [47]

Artificial General Intelligence (AGI) (Общий Искусственный Интеллект)  is a hypothetical type of AI that is completely analogous to the human mind and has self-awareness that can solve problems, learn and plan for the future.

Artificial Intelligence (AI) (Искусственный интеллект)  (machine intelligence) refers to systems that display intelligent behavior by analyzing their environment and taking actions  with some degree of autonomy  to achieve specific goals. AI-based systems can be purely software-based, acting in the virtual world (e.g., voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g., advanced robots, autonomous cars, drones, or Internet of Things applications). The term AI was first coined by John McCarthy in 1956. [48]

Artificial Intelligence Automation Platforms (Платформы автоматизации искусственного интеллекта)  Platforms that enable the automation and scaling of production-ready AI. Artificial Intelligence Platforms involves the use of machines to perform the tasks that are performed by human beings. The platforms simulate the cognitive function that human minds perform such as problem-solving, learning, reasoning, social intelligence as well as general intelligence. Top Artificial Intelligence Platforms: Google AI Platform, TensorFlow, Microsoft Azure, Rainbird, Infosys Nia, Wipro HOLMES, Dialogflow, Premonition, Ayasdi, MindMeld, Meya, KAI, Vital A.I, Wit, Receptiviti, Watson Studio, Lumiata, Infrrd. [49].

Artificial intelligence engine (also AI engine, AIE) (Движок искусственного интеллекта) is an artificial intelligence engine, a hardware and software solution for increasing the speed and efficiency of artificial intelligence system tools.

Artificial Intelligence for IT Operations (AIOps) is an emerging IT practice that applies artificial intelligence to IT operations to help organizations intelligently manage infrastructure, networks, and applications for performance, resilience, capacity, uptime, and, in some cases, security. By shifting traditional, threshold-based alerts and manual processes to systems that take advantage of AI and machine learning, AIOps enables organizations to better monitor IT assets and anticipate negative incidents and impacts before they take hold. AIOps is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics covering operational tasks include automation, performance monitoring and event correlations, among others. Gartner define an AIOps Platform thus: An AIOps platform combines big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies. [50,51].

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

Artificial Intelligence Open Library (Открытая библиотека искусственного интеллекта) is a set of algorithms designed to develop technological solutions based on artificial intelligence, described using programming languages and posted on the Internet.

Artificial intelligence system (AIS, Система искусственного интеллекта) is a programmed or digital mathematical model (implemented using computer computing systems) of human intellectual capabilities, the main purpose of which is to search, analyze and synthesize large amounts of data from the world around us in order to obtain new knowledge about it and solve them. basis of various vital tasks. The discipline Artificial Intelligence Systems includes consideration of the main issues of modern theory and practice of building intelligent systems.

Artificial intelligence technologies (Технологии искусственного интеллекта)  technologies based on the use of artificial intelligence, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support and advanced methods of artificial intelligence.

Artificial life (Alife, A-Life, Искусственная жизнь) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. [2] In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena [53].

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