Глоссариум по искусственному интеллекту: 2500 терминов. Том 2. Александр Юрьевич Чесалов
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adding a new dataset and repeating iterations.
AI benchmark 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 – 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 is 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 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 – chipset market for systems with artificial intelligence (AI), see also AI chipset.
AI chipset market is the market for chipsets for artificial intelligence (AI) systems.
AI cloud services – 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 is a central processing unit for AI tasks, synonymous with AI processor.
AI engineer – AI systems engineer.
AI engineering – 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) – artificial intelligence infrastructure system hardware, AI infrastructure. Explanations in the Glossary are usually brief.
AI hardware is infrastructure hardware or artificial intelligence system, AI infrastructure.
AI industry – for example, commercial AI industry – commercial AI industry, commercial sector of the AI industry.
AI industry trends are promising directions for the development of the AI industry.
AI infrastructure (also AI-defined infrastructure, AI-enabled Infrastructure) – artificial intelligence infrastructure systems, for example, AI infrastructure research – research in the field of AI infrastructures (see also AI, AI hardware).
AI server (artificial intelligence server) – is a server with (based on) AI; a server that provides solving AI problems.
AI shopper 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 is 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 is a term from the field of AI (from terminology, AI vocabulary), for example, in AI terms – in terms of AI (in AI language) (see also AI terminology).
AI term 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 (artificial intelligence terminology) is a set of special terms related to the field of AI (see also AI term).
AI terminology is the terminology of artificial intelligence, a set of technical terms related to the field of AI.
AI TRiSM is the management of an AI model to ensure trust, fairness, efficiency, security, and data protection38.
AI vendor is a supplier of AI tools (systems, solutions).
AI vendor is a supplier of AI tools (systems, solutions).
AI winter (Winter of artificial intelligence) 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 later39,40.
AI workstation 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 workstation is 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-based management system – 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 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 systems41.
AI-complete. In the field of artificial intelligence, the most difficult problems are informally
Управление доверием, рисками и безопасностью ИИ (Концепция AI TRiSM) [Электронный ресурс] https://oprosso.ru URL: https://oprosso.ru/landing/blog/study/desyat-strategicheskikh-tekhnologicheskikh-trendov-na-2023-god/ (дата обращения 04.07.2023)
AI winter [Электронный ресурс] https://en.wikipedia.org URL: https://en.wikipedia.org/wiki/AI_winter (дата обращения: 07.07.2022)
.Commonsense knowledge (artificial intelligence). [Электронный ресурс] en.wikipedia.org URL: https://en.wikipedia.org/wiki/Commonsense_knowledge_(artificial_intelligence) (дата обращения: 30.08.2023)
Системы на основе ИИ [Электронный ресурс] https://en.unesco.org URL: https://en.unesco.org/artificial-intelligence/ethics#recommendation (дата обращения: 29.01.2022)