Artificial Narrow Intelligence (ANI) (Узкий искусственный интеллект) Artificial Narrow Intelligence, 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 systems [54,55].
Artificial Neural Network (ANN) (Искусственная нейронная сеть) 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 task.
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 bounded [56,57].
Artificial Superintelligence (ASI) (Искусственный сверхинтеллект) is a term referring to the time when the capability of computers will surpass humans. Artificial intelligence, which has been much used since the 1970s, refers to the ability of computers to mimic human thought. Artificial superintelligence goes a step beyond and posits a world in which a computers cognitive ability is superior to a human.
Assistive intelligence (Вспомогательный интеллект) is AI-based systems that help make decisions or perform actions.
Association (Ассоциация) is another type of unsupervised learning method that uses different rules to find relationships between variables in a given dataset. These methods are frequently used for market basket analysis and recommendation engines, along the lines of Customers Who Bought This Item Also Bought recommendations.
Association for the Advancement of Artificial Intelligence (AAAI) (Ассоциация по развитию искусственного интеллекта) An international, nonprofit, scientific society devoted to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence (AI), improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions
Association Rule Learning (Правила обучения ассоциации) A rule-based Machine Learning method for discovering interesting relations between variables in large data sets.
Asymptotic computational complexity (Асимптотическая вычислительная сложность) In computational complexity theory, asymptotic computational complexity is the usage of asymptotic analysis for the estimation of computational complexity of algorithms and computational problems, commonly associated with the usage of the big O notation [58].
Asynchronous inter-chip protocols (Асинхронные межкристальные протоколы) are protocols for data exchange in low-speed devices; instead of frames, individual characters are used to control the exchange of data.
Attention mechanism (Механизм внимания) is one of the key innovations in the field of neural machine translation. Attention allowed neural machine translation models to outperform classical machine translation systems based on phrase translation. The main bottleneck in sequence-to-sequence learning is that the entire content of the original sequence needs to be compressed into a vector of a fixed size. The attention mechanism facilitates this task by allowing the decoder to look back at the hidden states of the original sequence, which are then provided as a weighted average as additional input to the decoder.
Attributional calculus (AC) (Атрибутивное исчисление) A logic and representation system defined by Ryszard S. Michalski. It combines elements of predicate logic, propositional calculus, and multi-valued logic. Attributional calculus provides a formal language for natural induction, an inductive learning process whose results are in forms natural to people [59].
Augmented Intelligence (Дополненный (расширенный) интеллект) is the intersection of machine learning and advanced applications, where clinical knowledge and medical data converge on a single platform. The potential benefits of Augmented Intelligence are realized when it is used in the context of workflows and systems that healthcare practitioners operate and interact with. Unlike Artificial Intelligence, which tries to replicate human intelligence, Augmented Intelligence works with and amplifies human intelligence [60]
Augmented reality (AR) (Дополненная реальность) An interactive experience of a real-world environment where the objects that reside in the real-world are augmented by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory, and olfactory.
Augmented reality technologies (Технологии дополненной реальности) are visualization technologies based on adding information or visual effects to the physical world by overlaying graphic and/or sound content to improve user experience and interactive features.
Auto Associative Memory (Автоассоциативная память) is a single layer neural network in which the input training vector and the output target vectors are the same. The weights are determined so that the network stores a set of patterns. As shown in the following figure, the architecture of Auto Associative memory network has n number of input training vectors and similar n number of output target vectors [61].
Autoencoder (Автокодер) а type of Artificial Neural Network used to produce efficient representations of data in an unsupervised and non-linear manner, typically to reduce dimensionality [62].
Automata theory (Теория автоматов) The study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). [63] Automata theory (part of the theory of computation) is a theoretical branch of Computer Science and Mathematics, which mainly deals with the logic of computation with respect to simple machines, referred to as automata [64].
Automated control system (Автоматизированная система управления) a set of software and hardware designed to control technological and (or) production equipment (executive devices) and the processes they produce, as well as to control such equipment and processes.
Automated planning and scheduling (Also simply AI planning.) (Планирование ИИ) A branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory [65].
Automated processing of personal data (Автоматизированная обработка персональных данных) processing of personal data using computer technology.
Automated reasoning (Автоматизированное мышление) An area of computer science and mathematical logic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science, and even philosophy [66].
Automated system (Автоматизированная система) is an organizational and technical system that guarantees the development of solutions based on the automation of information processes in various fields of activity.
Automation (Автоматизация) is a technology by which a process or procedure is performed with minimal human intervention.
Automation bias (Предвзятость автоматизации) When a human decision maker favors recommendations made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors [67].
Autonomic computing (Автономные вычисления) is the ability of a system to adaptively self-manage its own resources for high-level computing functions without user input.
Autonomous (Автономность) A machine is described as autonomous if it can perform its task or tasks without needing human intervention.
Autonomous artificial intelligence (Автономный искусственный интеллект) is a biologically inspired system that tries to reproduce the structure of the brain, the principles of its operation with all the properties that follow from this.
Autonomous artificial intelligence systems (Системы автономного искусственного интеллекта) simulate the work and structure of the brain (thinking, creativity, emotions, will, freedom of choice and decision-making, search for new knowledge and making optimal decisions, memory, etc.). Such systems are also called adaptive artificial intelligence or neuromorphic artificial intelligence.
Autonomous car (Also self-driving car, robot car, and driverless car.) (Автономный автомобиль) A vehicle that is capable of sensing its environment and moving with little or no human input [68].
Autonomous robot (Автономный робот) A robot that performs behaviors or tasks with a high degree of autonomy. Autonomous robotics is usually considered to be a subfield of artificial intelligence, robotics, and information engineering [69].
Autonomous vehicle (Автономное транспортное средство) is a mode of transport based on an autonomous driving system. The control of an autonomous vehicle is fully automated and carried out without a driver using optical sensors, radar and computer algorithms.
Autoregressive Model (Авторегрессионная модель) An autoregressive model is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. In statistics and signal processing, an autoregressive model is a representation of a type of random process. It is used to describe certain time-varying processes in nature, economics, etc. [70].
Auxiliary intelligence (Дополнительный интеллект) systems based on artificial intelligence that complement human decisions and are able to learn in the process of interacting with people and the environment.
Average precision (Средняя точность) A metric for summarizing the performance of a ranked sequence of results. Average precision is calculated by taking the average of the precision values for each relevant result (each result in the ranked list where the recall increases relative to the previous result) [71].
Ayasdi (Платформа Ayasdi) is an enterprise scale machine intelligence platform that delivers the automation that is needed to gain competitive advantage from the companys big and complex data. Ayasdi supports large numbers of business analysts, data scientists, endusers, developers and operational systems across the organization, simultaneously creating, validating, using and deploying sophisticated analyses and mathematical models at scale.
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Backpropagation (Обратное распространение ошибки) Backpropagation, also called backward propagation of errors, is an approach that is commonly used in the training process of the deep neural network to reduce errors.
Backpropagation through time (BPTT) (Обратное распространение во времени) A gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.
Backward Chaining (Обратная цепочка (или обратное рассуждение)) Backward chaining, also called goal-driven inference technique, is an inference approach that reasons backward from the goal to the conditions used to get the goal. Backward chaining inference is applied in many different fields, including game theory, automated theorem proving, and artificial intelligence [72].
Bag-of-words model (Модель мешка слов) A simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier [73].
Bag-of-words model in computer vision (Модель мешка слов в компьютерном зрении) In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.
Baldwin effect (Эффект Балдвина) the skills acquired by organisms during their life as a result of learning, after a certain number of generations, are recorded in the genome.
Baseline (Базовый уровень) A model used as a reference point for comparing how well another model (typically, a more complex one) is performing. For example, a logistic regression model might serve as a good baseline for a deep model. For a particular problem, the baseline helps model developers quantify the minimal expected performance that a new model must achieve for the new model to be useful.