Глоссариум по искусственному интеллекту: 2500 терминов. Том 2

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 known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem – making computers as intelligent as people, or strong AI. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm42.

AI-enabled – using AI and equipped with AI, for example, AI-enabled tools – tools with AI (see also AI-enabled device).

AI-enabled device is a device supported by an artificial intelligence (AI) system, such as an intelligent robot.

AI-enabled device is a device supported by an artificial intelligence (AI) system, such as an intelligent robot (see also AI-enabled healthcare device)43.

AI-enabled healthcare device is an AI-enabled device for healthcare (medical care), see also AI-enabled device.

AI-enabled healthcare device is an AI-enabled healthcare device44.

AI-enabled is hardware or software that uses AI-enabled AI, such as AI-enabled tools.

AI-optimized – optimized for AI tasks; AI-optimized chip, for example, an AI-optimized chip is a chip optimized for AI tasks (see also artificial intelligence).

AI-optimized 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.

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.45

Algorithm – 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 problems46.

Algorithmic Assessment 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 making47.

AlphaGo is the first computer program that defeated a professional player on the board game Go in October 2015. Later in October 2017, AlphaGo’s 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 universe48.

Physical media is the physical material that is used to store or transmit information in a data transmission49.

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 user’s 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 use50.

Analogical Reasoning – solving problems by using analogies, by comparing to past experiences51.

Analysis of algorithms (AofA) – 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 algorithm’s input to the number of steps it takes (its time complexity) or the number of storage locations it uses (its space complexity)52.

Annotation is a metadatum attached to a piece of data, typically provided by a human annotator53.

Anokhin’s theory of functional systems is 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 process54.

Anomaly detection – 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 suspicious55,56.

Anonymization – the process in which data is de-identified as part of a mechanism to submit data for machine learning57.

Answer set programming (ASP) is 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 search58.

Antivirus software 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 more59.

Anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends60.

API-AS-a-service (AaaS) combines the API economy and software renting and provides application programming interfaces as a service61.

Application programming interface (API) is 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 library62.

Application security 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 attacks63.

Application-specific integrated circuit (ASIC) is a specialized integrated circuit for solving a specific problem64.

Approximate string matching (also fuzzy string searching) – 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 approximately65.

Approximation error – the discrepancy between an exact value and some approximation to it66.

Architectural description group (Architectural view) is a representation of the system as a whole in terms of a related set of interests67,68.

Architectural frameworks 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 evolution69.

Architecture of a computer 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 software70.

Architecture of a computing system is the configuration, composition and principles of interaction (including data exchange) of the elements of a computing system71.

Architecture of a system 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 evolution72.

Archival Information Collection (AIC) is information whose content is an aggregation of other archive information packages. The digital preservation function preserves the capability to regenerate the DIPs (Dissemination Information Packages) as needed over time73.

Archival Storage is a source for data that is not needed for an organization’s 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 requirements74.

Area under curve (AUC) – 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 classifier’s accuracy75.

Area Under the ROC curve 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 positive76.

Argumentation framework is 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 arguments77.

Artifact 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 assessments78.

Artificial General Intelligence (AGI) as opposed to narrow intelligence, also known as complete, strong, super intelligence, Human Level Machine Intelligence, indicates the ability of a machine that can successfully perform any tasks in an intellectual way as the human being. Artificial superintelligence is a term referring to the time when the capability of computers will surpass humans79,80.

Artificial Intelligence (AI) – (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 195681.

Artificial Intelligence Automation Platforms – 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, Infrrd82.

Artificial intelligence engine (also AI engine, AIE) 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»83,84,85.

Artificial Intelligence Markup Language (AIML) is an XML dialect for creating natural language software agents86.

Artificial Intelligence of the Commonsense knowledge is one of the areas of development of artificial intelligence, which is engaged in modeling the ability of a person to analyze various life situations and be guided in his actions by common sense87.

Artificial Intelligence Open Library is a set of algorithms designed to develop technological solutions based on artificial intelligence, described using programming languages and posted on the Internet88.

Artificial intelligence system (AIS) 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 – 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 intelligence89.

Artificial life (Alife, A-Life) 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. 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 phenomena90.

Artificial Narrow Intelligence (ANI), also known as weak or applied intelligence, represents most of the current artificial intelligent systems which usually focus on a specific task. Narrow AIs are mostly much better than humans at the task they were made for: for example, look at face recognition, chess computers, calculus, and translation. The definition of artificial narrow intelligence is in contrast to that of strong AI or artificial general intelligence, which aims at providing a system with consciousness or the ability to solve any problems. Virtual assistants and AlphaGo are examples of artificial narrow intelligence systems91.

Artificial Neural Network (ANN) is a computational model in machine learning, which is inspired by the biological structures and functions of the mammalian brain. Such a model consists of multiple units called artificial neurons which build connections between each other to pass information. The advantage of such a model is that it progressively «learns» the tasks from the given data without specific programing for a single task92.

Artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. The difference between an artificial neuron and a biological neuron is shown in the figure. Artificial neurons are the elementary units of an artificial neural network. An artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials on nerve dendrites) and sums them to produce an output signal (or activation, representing the action potential of the neuron that is transmitted down its axon). Typically, each input is weighted separately, and the sum is passed through a non-linear function known as an activation function or transfer function. Transfer functions are usually sigmoid, but they can also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable, and bounded93,94.