Artificial Intelligence Glossarium: 1000 terms

“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].

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.

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 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 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].

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]

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].

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].

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]

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.

Bidirectional language model (Двунаправленная языковая модель) – A language model that determines the probability that a given token is present at a given location in an excerpt of text based on the preceding and following text.

Big data (Большие данные) is a term for sets of digital data whose large size, rate of increase or complexity requires significant computing power for processing and special software tools for analysis and presentation in the form of human-perceptible results.

Big O notation (Запись Big O notation) – A mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau, and others, collectively called Bachmann – Landau notation or asymptotic notation [⁸¹].

Bigram (Биграмм) – An N-gram in which N=2.

Binary choice regression model (Регрессионная модель бинарного выбора) is a regression model in which the dependent variable is dichotomous or binary. Dependent variable can take only two values and mean, for example, belonging to a particular group.

Binary classification (Двоичная, бинарная или дихотомическая классификация) — A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either “spam” or “not spam” is a binary classifier.

Binary format (Двоичный формат) Any file format in which information is encoded in some format other than a standard character-encoding scheme. A file written in binary format contains information that is not displayable as characters. Software capable of understanding the particular binary format method of encoding information must be used to interpret the information in a binary-formatted file. Binary formats are often used to store more information in less space than possible in a character format file. They can also be searched and analyzed more quickly by appropriate software. A file written in binary format could store the number “7” as a binary number (instead of as a character) in as little as 3 bits (i.e., 111), but would more typically use 4 bits (i.e., 0111). Binary formats are not normally portable, however. Software program files are written in binary format. Examples of numeric data files distributed in binary format include the IBM-binary versions of the Center for Research in Security Prices files and the U.S. Department of Commerce’s National Trade Data Bank on CD-ROM. The International Monetary Fund distributes International Financial Statistics in a mixed-character format and binary (packed-decimal) format. SAS and SPSS store their system files in binary format. [⁸²]

Binary number (Двоичное число) A number written using binary notation which only uses zeros and ones. Example: Decimal number 7 in binary notation is: 111. [⁸³]

Binary tree (Бинарное дерево) – A tree data structure in which each node has at most two children, which are referred to as the left child and the right child. A recursive definition using just set theory notions is that a (non-empty) binary tree is a tuple (L, S, R), where L and R are binary trees or the empty set and S is a singleton set. Some authors allow the binary tree to be the empty set as well. [⁸⁴]

Binning (Биннинг) is the process of combining charge from neighboring pixels in a CCD during readout. This process is performed prior to digitization in the CCD chip using dedicated serial and parallel register control. The two main benefits of binning are improved signal-to-noise ratio (SNR) and the ability to increase frame rates, albeit at the cost of reduced spatial resolution.

Bioconservatism (Биоконсерватизм) (a portmanteau of biology and conservatism) is a stance of hesitancy and skepticism regarding radical technological advances, especially those that seek to modify or enhance the human condition. Bioconservatism is characterized by a belief that technological trends in today’s society risk compromising human dignity, and by opposition to movements and technologies including transhumanism, human genetic modification, “strong” artificial intelligence, and the technological singularity. Many bioconservatives also oppose the use of technologies such as life extension and preimplantation genetic screening [⁸⁵,⁸⁶].

Biometrics (Биометрия) is a people recognition system, one or more physical or behavioral traits.

Black box (Чёрный ящик) – A description of some deep learning system. They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret.

Blackboard system (Системы, использующие принцип классной доски) – An artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the “blackboard”, is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem.

BLEU (Bilingual Evaluation Understudy) (Алгоритм BLEU) – A score between 0.0 and 1.0, inclusive, indicating the quality of a translation between two human languages (for example, between English and Russian). A BLEU score of 1.0 indicates a perfect translation; a BLEU score of 0.0 indicates a terrible translation.

Blockchain (Блокчейн) is algorithms and protocols for decentralized storage and processing of transactions structured as a sequence of linked blocks without the possibility of their subsequent change.

Boltzmann machine (Also stochastic Hopfield network with hidden units) (Машина Больцмана) – A type of stochastic recurrent neural network and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks [⁸⁷].

Boolean neural network (Булевая нейронная сеть) – is an artificial neural network approach which only consists of Boolean neurons (and, or, not). Such an approach reduces the use of memory space and computation time. It can be implemented to the programmable circuits such as FPGA (Field-Programmable Gate Array or Integrated circuit).

Boolean satisfiability problem (Also propositional satisfiability problem; abbreviated SATISFIABILITY or SAT) (Проблема булевой выполнимости) – is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called satisfiable. On the other hand, if no such assignment exists, the function expressed by the formula is FALSE for all possible variable assignments and the formula is unsatisfiable. [⁸⁸].

Boosting (Бустинг) – A Machine Learning ensemble meta-algorithm for primarily reducing bias and variance in supervised learning, and a family of Machine Learning algorithms that convert weak learners to strong ones.

Bounding Box (Ограничивающая рамка) – Commonly used in image or video tagging, this is an imaginary box drawn on visual information. The contents of the box are labeled to help a model recognize it as a distinct type of object.

Brain technology (Also self-learning know-how system) (Мозговая технология) – A technology that employs the latest findings in neuroscience. The term was first introduced by the Artificial Intelligence Laboratory in Zurich, Switzerland, in the context of the ROBOY project. Brain Technology can be employed in robots, know-how management systems and any other application with self-learning capabilities. In particular, Brain Technology applications allow the visualization of the underlying learning architecture often coined as “know-how maps”.

Brain – computer interface (BCI, Интерфейс мозг-компьютер), sometimes called a brain – machine interface (BMI), is a direct communication pathway between the brain’s electrical activity and an external device, most commonly a computer or robotic limb. Research on brain – computer interface began in the 1970s by Jacques Vidal at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The Vidal’s 1973 paper marks the first appearance of the expression brain – computer interface in scientific literature [⁸⁹].

Brain-inspired computing (Мозгоподобные вычисления) – calculations on brain-like structures, brain-like calculations using the principles of the brain (see also neurocomputing, neuromorphic engineering).

Branching factor (коэффициент ветвления дерева) – In computing, tree data structures, and game theory, the number of children at each node, the outdegree. If this value is not uniform, an average branching factor can be calculated.

Broadband (Широкополосный доступ) refers to various high-capacity transmission technologies that transmit data, voice, and video across long distances and at high speeds. Common mediums of transmission include coaxial cables, fiber optic cables, and radio waves. [⁹⁰]

Brute-force search (Also exhaustive search or generate and test) (Полный перебор) – A very general problem-solving technique and algorithmic paradigm that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement.

Bucketing (Разделение на сегменты) – Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range.

Byte (Байт) Eight bits. A byte is simply a chunk of 8 ones and zeros. For example: 01000001 is a byte. A computer often works with groups of bits rather than individual bits and the smallest group of bits that a computer usually works with is a byte. A byte is equal to one column in a file written in character format. [⁹¹]