target="_blank" rel="nofollow" href="#note85" type="note">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]
“C”
Caffe – is short for Convolutional Archi- tecture for Fast Feature Embedding which is an open-source deep learning framework de- veloped in Berkeley AI Research. It supports many different deep learning architectures and GPU-based acceleration computation kernels.
Calibration layer (Калибровочный слой) – A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.
Candidate generation (Генерация кандидатов) — The initial set of recommendations chosen by a recommendation system. [92].
Candidate sampling (Выборка кандидатов) — A training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence). The idea is that the negative classes can learn from