Artificial Intelligence Glossarium: 1000 terms

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 - Auto Associative Memory [Электронный ресурс] www.tutorialspoint.com URL: https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern (https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_associate_memory.htm#:~:text=These%20kinds%20of%20neural%20networks,with%20the%20given%20input%20pattern). (дата обращения: 07.07.2022)]].

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 - Autoencoder [Электронный ресурс] // neurohive.io URL: https://neurohive.io/ru/osnovy-data-science/avtojenkoder-tipy-arhitektur-i-primenenie/ (дата обращения: 28.01.2022)]].

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 [Электронный ресурс] // vvsu.ru URL: https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt (https://www.vvsu.ru/files/529128A0-237E-434E-8B31-4553FB108EF2.ppt) (дата обращения: 28.01.2022)]] 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 - Automata theory [Электронный ресурс] www.geeksforgeeks.org URL: https://www.geeksforgeeks.org/introduction-of-theory-of-computation/ (https://www.geeksforgeeks.org/introduction-of-theory-of-computation/) (дата обращения: 07.07.2022)]].

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 planning and scheduling [Электронный ресурс] // researcher.watson.ibm.com URL: https://researcher.watson.ibm.com/researcher/view_group.php?id=8432 (дата обращения: 28.01.2022)]].

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 reasoning [Электронный ресурс] // techtarget.com URL: https://www.techtarget.com/searchenterpriseai/definition/automated-reasoning#:~:text=Automated%20reasoning%20is%20the%20area,inferences%20towards%20that%20goal%20automatically. (дата обращения: 18.02.2022)]].

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 - Automation bias [Электронный ресурс] // databricks.com URL: https://databricks.com/glossary/automation-bias#:~:text=Automation%20bias%20is%20an%20over,aids%20and%20decision%20support%20systems.&text=It%20is%20a%20human%20tendency,leaning%20towards%20%22automation%20bias%22. (дата обращения: 26.01.2022)]].

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 car [Электронный ресурс] // synopsys.com URL: https://www.synopsys.com/automotive/what-is-autonomous-car.html (дата обращения: 28.01.2022)]].

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 robot [Электронный ресурс] // techopedia.com URL: https://www.techopedia.com/definition/32694/autonomous-robot (дата обращения: 28.01.2022)]].

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 - Autoregressive Model [Электронный ресурс] // wiki.loginom.ru URL: https://wiki.loginom.ru/articles/autoregressive-model.html (дата обращения: 08.02.2022)]].

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 - Average precision [Электронный ресурс] // jonathan-hui.medium.com URL: https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173 (дата обращения: 28.01.2022)]].

Ayasdi (Платформа Ayasdi) is an enterprise scale machine intelligence platform that delivers the automation that is needed to gain competitive advantage from the company’s 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.

“B”

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 - Backward Chaining [Электронный ресурс] www.educba.com URL: https://www.educba.com/backward-chaining/ (дата обращения 11.03.2022)]].

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 [Электронный ресурс] // machinelearningmastery.ru URL: https://www.machinelearningmastery.ru/gentle-introduction-bag-words-model/ (дата обращения: 11.03.2022)]].

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.

Batch (Пакет) – The set of examples used in one gradient update of model training.

Batch Normalization (Пакетная нормализация) – A preprocessing step where the data are centered around zero, and often the standard deviation is set to unity.

Batch size (Размер партии) – The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes.

Bayes’s Theorem (Теорема Байеса) – A famous theorem used by statisticians to describe the probability of an event based on prior knowledge of conditions that might be related to an occurrence.

Bayesian classifier in machine learning (Байесовский классификатор в машинном обучении) is a family of simple probabilistic classifiers based on the use of the Bayes theorem and the “naive” assumption of the independence of the features of the objects being classified.

Bayesian Filter (Фильтрация по Байесу) is a program using Bayesian logic. It is used to evaluate the header and content of email messages and determine whether or not it constitutes spam – unsolicited email or the electronic equivalent of hard copy bulk mail or junk mail. A Bayesian filter works with probabilities of specific words appearing in the header or content of an email. Certain words indicate a high probability that the email is spam, such as Viagra and refinance [[74 - Bayesian Filter [Электронный ресурс] //certsrv.ru URL: http://certsrv.ru/eset_ss.ru/pages/bayes_filter.htm (дата обращения: 12.02.2022)]].

Bayesian Network (Байесовская сеть) – also called belief network, or probabilistic directed acyclic graphical model, is a probabilistic graphical model (a statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph [[75 - Bayesian Network [Электрчатонный ресурс] // dic.academic.ru URL: https://dic.academic.ru/dic.nsf/ruwiki/1738444 (дата обращения: 31.01.2022)]].

Bayesian optimization (Байесовская оптимизация) – A probabilistic regression model technique for optimizing computationally expensive objective functions by instead optimizing a surrogate that quantifies the uncertainty via a Bayesian learning technique. Since Bayesian optimization is itself very expensive, it is usually used to optimize expensive-to-evaluate tasks that have a small number of parameters, such as selecting hyperparameters.

Bayesian programming (Байесовское программирование) – A formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available.

Bees algorithm (Алгоритм пчелиной колонии) — A population-based search algorithm which was developed by Pham, Ghanbarzadeh and et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.

Behavior informatics (BI) (Информатика поведения) — The informatics of behaviors so as to obtain behavior intelligence and behavior insights.

Behavior tree (BT) (Дерево поведения) – A mathematical model of plan execution used in computer science, robotics, control systems and video games. They describe switchings between a finite set of tasks in a modular fashion. Their strength comes from their ability to create very complex tasks composed of simple tasks, without worrying how the simple tasks are implemented. BTs present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Its ease of human understanding make BTs less error-prone and very popular in the game developer community. BTs have shown to generalize several other control architectures [[76 - Behavior tree (BT) [Электронный ресурс] // habr.com URL: https://habr.com/ru/company/cloud_mts/blog/306214/ (дата обращения: 31.01.2022)]].

Belief-desire-intention software model (BDI) (Модель убеждений, желаний и намерений) — A software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent’s beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library or an external planner application) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer. [[77 - Belief-desire-intention software model (BDI) [Электронный ресурс] // fccland.ru URL: https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html (https://fccland.ru/stati/22848-model-ubezhdeniy-zhelaniy-i-namereniy.html) (дата обращения: 31.01.2022)]]

Bellman equation (Уравнение Беллмана) – named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the “value” of a decision problem at a certain point in time in terms of the payoff from some initial choices and the “value” of the remaining decision problem that results from those initial choices. This breaks a dynamic optimization problem into a sequence of simpler subproblems, as Bellman’s “principle of optimality” prescribes [[78 - Bellman equation [Электронный ресурс] //mruanova.medium.com URL: https://mruanova.medium.com/bellman-equation-90f2f0deaa88 (дата обращения 28.02.2022)]].

Benchmark (also benchmark program, benchmarking program, benchmark test) (Бенчмарк) – test program or package for evaluating (measuring and / or comparing) various aspects of the performance of a processor, individual devices, computer, system or a specific application, software; a benchmark that allows products from different manufacturers to be compared against each other or against some standard. For example, online benchmark – online benchmark; standard benchmark – standard benchmark; benchmark time comparison – comparison of benchmark execution times. [[79 - Benchmark [Электронный ресурс] // URL: https://medium.com/@tauheedul/it-hardware-benchmarks-for-machine-learning-and-artificial-intelligence-6183ceed39b8 (дата обращения 11.03.2022)]].

Benchmarking (Бенчмаркинг) is a set of techniques that allow you to study the experience of competitors and implement best practices in your company.

BETA (БЕТА версия) A beta publication refers to a phase in online service development in which the service is coming together functionality-wise but genuine user experiences are required before the service can be finished in a user-centered way. In online service development, the aim of the beta phase is to recognize both programming issues and usability-enhancing procedures. The beta phase is particularly often used in connection with online services and it can be either freely available (open beta) or restricted to a specific target group (closed beta). [[80 - BETA [Электронный ресурс] www.sofokus.com URL: https://www.sofokus.com/glossary-of-digital-business/#ABCD (https://www.sofokus.com/glossary-of-digital-business/#ABCD) (дата обращения: 07.07.2022)]]

Bias (Погрешность) is a systematic trend that causes differences between results and facts. Error exists in the numbers of the data analysis process, including the source of the data, the estimate chosen, and how the data is analyzed. Error can seriously affect the results, for example, when studying people’s shopping habits. If the sample size is not large enough, the results may not reflect the buying habits of all people. That is, there may be discrepancies between survey results and actual results.

Biased algorithm (Алгоритмическая предвзятость) – systematic and repetitive errors in a computer system that lead to unfair results, such as one privilege persecuting groups of users over others. Also, sexist and racist algorithms.

Bidirectional (BiDi) (Двунаправленность) – A term used to describe a system that evaluates the text that both precedes and follows a target section of text. In contrast, a unidirectional system only evaluates the text that precedes a target section of text.

Bidirectional Encoder Representations from Transformers (BERT) (Представления двунаправленного кодировщика от трансформаторов) – A model architecture for text representation. A trained BERT model can act as part of a larger model for text classification or other ML tasks. BERT has the following characteristics: Uses the Transformer architecture, and therefore relies on self-attention. Uses the encoder part of the Transformer. The encoder’s job is to produce good text representations, rather than to perform a specific task like classification. Is bidirectional. Uses masking for unsupervised training.