Глоссариум по искусственному интеллекту: 2500 терминов. Том 2

Biometrics is a people recognition system, one or more physical or behavioral traits170,171.

Black box is 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 interpret172,173.

Blackboard system is 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 problem174.

BLEU (Bilingual Evaluation Understudy) is a text quality evaluation algorithm 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 translation175.

Blockchain is algorithms and protocols for decentralized storage and processing of transactions structured as a sequence of linked blocks without the possibility of their subsequent change176.

Boltzmann machine (also stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks177.

Boolean neural network 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) 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 unsatisfiable178.

Boosting is 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 ones179.

Bounding Box 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) is 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»180.

Brain—computer interface (BCI), 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 literature181.

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

Branching factor 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 calculated182,183.

Broadband 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 waves184.

Brute-force search (also exhaustive search or generate and test) is 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 statement185.

Bucketing – converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range186.

Byte – 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 format187.

«C»

CAFFE is short for Convolutional Architecture 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 kernels188,189.

Calibration layer is a post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels190.

Candidate generation — the initial set of recommendations chosen by a recommendation system191.

Candidate sampling is 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 less frequent negative reinforcement as long as positive classes always get proper positive reinforcement, and this is indeed observed empirically. The motivation for candidate sampling is a computational efficiency win from not computing predictions for all negatives192.

Canonical Formats in information technology, canonicalization is the process of making something conform] with some specification… and is in an approved format. Canonicalization may sometimes mean generating canonical data from noncanonical data. Canonical formats are widely supported and considered to be optimal for long-term preservation193.

Capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization194,195.

Case-Based Reasoning (CBR) is a way to solve a new problem by using solutions to similar problems. It has been formalized to a process consisting of case retrieve, solution reuse, solution revise, and case retention196.

Categorical data — features having a discrete set of possible values. For example, consider a categorical feature named house style, which has a discrete set of three possible values: Tudor, ranch, colonial. By representing house style as categorical data, the model can learn the separate impacts of Tudor, ranch, and colonial on house price. Sometimes, values in the discrete set are mutually exclusive, and only one value can be applied to a given example. For example, a car maker categorical feature would probably permit only a single value (Toyota) per example. Other times, more than one value may be applicable. A single car could be painted more than one different color, so a car color categorical feature would likely permit a single example to have multiple values (for example, red and white). Categorical features are sometimes called discrete features. Contrast with numerical data197.

Center for Technological Competence is an organization that owns the results, tools for conducting fundamental research and platform solutions available to market participants to create applied solutions (products) on their basis. The Technology Competence Center can be a separate organization or be part of an application technology holding company198.

Central Processing Unit (CPU) is a von Neumann cyclic processor designed to execute complex computer programs199.

Centralized control is a process in which control signals are generated in a single control center and transmitted from it to numerous control objects200.

Centroid – the center of a cluster as determined by a k-means or k-median algorithm. For instance, if k is 3, then the k-means or k-median algorithm finds 3 centroids201.

Centroid-based clustering is a category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms202.

Character format is any file format in which information is encoded as characters using only a standard character-encoding scheme. A file written in «character format» contains only those bytes that are prescribed in the encoding scheme as corresponding to the characters in the scheme (e.g., alphabetic and numeric characters, punctuation marks, and spaces)203.

Сhatbot is a software application designed to simulate human conversation with users via text or speech. Also referred to as virtual agents, interactive agents, digital assistants, or conversational AI, chatbots are often integrated into applications, websites, or messaging platforms to provide support to users without the use of live human agents. Chatbots originally started out by offering users simple menus of choices, and then evolved to react to particular keywords. «But humans are very inventive in their use of language,» says Forrester’s McKeon-White. Someone looking for a password reset might say they’ve forgotten their access code, or are having problems getting into their account. «There are a lot of different ways to say the same thing,» he says. This is where AI comes in. Natural language processing is a subset of machine learning that enables a system to understand the meaning of written or even spoken language, even where there is a lot of variation in the phrasing. To succeed, a chatbot that relies on AI or machine learning needs first to be trained using a data set. In general, the bigger the training data set, and the narrower the domain, the more accurate and helpful a chatbot will be204.

Checkpoint — data that captures the state of the variables of a model at a particular time. Checkpoints enable exporting model weights, as well as performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). Note that the graph itself is not included in a checkpoint205.

Chip is an electronic microcircuit of arbitrary complexity, made on a semiconductor substrate and placed in a non-separable case or without it, if included in the micro assembly206,207.

Class — one of a set of enumerated target values for a label. For example, in a binary classification model that detects spam, the two classes are spam and not spam. In a multi-class classification model that identifies dog breeds, the classes would be poodle, beagle, pug, and so on208.

Classification model is a type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian209.

Classification threshold is a scalar-value criterion that is applied to a model’s predicted score in order to separate the positive class from the negative class. Used when mapping logistic regression results to binary classification210.

Classification. Classification problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees and random forest are all common types of classification algorithms211.

Сloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent «brain» in the cloud. The «brain» consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.212.

Clinical Decision Support (CDS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support, that is, assistance with clinical decision- making tasks213.

Clipping is a technique for handling outliers. Specifically, reducing feature values that are greater than a set maximum value down to that maximum value. Also, increasing feature values that are less than a specific minimum value up to that minimum value. For example, suppose that only a few feature values fall outside the range 40—60. In this case, you could do the following: Clip all values over 60 to be exactly 60. Clip all values under 40 to be exactly 40. In addition to bringing input values within a designated range, clipping can also used to force gradient values within a designated range during training214.

Closed dictionary in speech recognition systems, a dictionary with a limited number of words, to which the recognition system is configured and which cannot be replenished by the user215.

Cloud computing is an information technology model for providing ubiquitous and convenient access using the Internet to a common set of configurable computing resources («cloud»), data storage devices, applications and services that can be quickly provided and released from the load with minimal operating costs or with little or no involvement of the provider216.

Cloud is a general metaphor that is used to refer to the Internet. Initially, the Internet was seen as a distributed network and then with the invention of the World Wide Web as a tangle of interlinked media. As the Internet continued to grow in both size and the range of activities it encompassed, it came to be known as «the cloud.» The use of the word cloud may be an attempt to capture both the size and nebulous nature of the Internet217.

Cloud TPU is a specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform218.

Cluster analysis is a type of unsupervised learning used for exploratory data analysis to find hidden patterns or groupings in the data; clusters are modeled with a similarity measure defined by metrics such as Euclidean or probability distance.

Clustering is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, etc219.

Co-adaptation is when neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network’s behavior as a whole. When the patterns that cause co-adaption are not present in validation data, then co-adaptation causes overfitting. Dropout regularization reduces co-adaptation because dropout ensures neurons cannot rely solely on specific other neurons220.

COBWEB is an incremental system for hierarchical conceptual clustering. COBWEB was invented by Professor Douglas H. Fisher, currently at Vanderbilt University. COBWEB incrementally organizes observations into a classification tree. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object221.

Code is a one-to-one mapping of a finite ordered set of symbols belonging to some finite alphabet222.

Codec is a codec is the means by which sound and video files are compressed for storage and transmission purposes. There are various forms of compression: ’lossy’ and ’lossless’, but most codecs perform lossless compression because of the much larger data reduction ratios that occur with lossy compression. Most codecs are software, although in some areas codecs are hardware components of image and sound systems. Codecs are necessary for playback, since they uncompress or decompress the moving image and sound files and allow them to be rendered223.

Cognitive architecture – the Institute of Creative Technologies defines cognitive architecture as: «hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments»224.

Cognitive computing is used to refer to the systems that simulate the human brain to help with the decision- making. It uses self-learning algorithms that perform tasks such as natural language processing, image analysis, reasoning, and human—computer interaction. Examples of cognitive systems are IBM’s Watson and Google DeepMind225.

Cognitive Maps are structured representations of decision depicted in graphical format (variations of cognitive maps are cause maps, influence diagrams, or belief nets). Basic cognitive maps include nodes connected by arcs, where the nodes represent constructs (or states) and the arcs represent relationships. Cognitive maps have been used to understand decision situations, to analyze complex cause-effect representations and to support communication226.

Cognitive science – the interdisciplinary scientific study of the mind and its processes227.

Cohort is a sample in study (conducted to evaluate a machine learning algorithm, for example) where it is followed prospectively or retrospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants’ exposure characteristics (risk factors) are associated with it.

Cold-Start is a potential issue arising from the fact that a system cannot infer anything for users or items for which it has not gathered a sufficient amount of information yet228.

Collaborative filtering – making predictions about the interests of one user based on the interests of many other users. Collaborative filtering is often used in recommendation systems229.

Combinatorial optimization in operations research, applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects230.

Committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response. The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare ensembles of classifiers231.

Commoditization is the process of transforming a product from an elite to a generally available (comparatively cheap commodity of mass consumption)232.

Common Data Element (CDE) is a tool to support data management for clinical research233.

Commonsense reasoning is a branch of artificial intelligence concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day234.

Compiler is a program that translates text written in a programming language into a set of machine codes. AI framework compilers collect the computational data of the frameworks and try to optimize the code of each of them, regardless of the hardware of the accelerator. The compiler contains programs and blocks with which the framework performs several tasks. The computer memory resource allocator, for example, allocates power individually for each accelerator235.

Composite AI is a combined application of various artificial intelligence methods (deep machine learning, computer vision, natural language processing, contextual analysis, knowledge graphs, data visualization, forecasting methods, etc.) to increase the efficiency of model training in order to achieve a synergistic effect from their use and the best results of the work of artificial intelligence systems. One of the ideas that is laid down in the creation of composite artificial intelligence is to obtain a sane artificial intelligence that will be able to understand the essence of the problems and solve a wide range of problems, offering optimal solutions.236,237,238.

Compression is a method of reducing the size of computer files. There are several compression programs available, such as gzip and WinZip239.

Computation is any type of arithmetic or non-arithmetic calculation that follows a well-defined model (e.g., an algorithm)240.

Computational chemistry is a discipline using mathematical methods for the calculation of molecular properties or for the simulation of molecular behaviour. It also includes, e.g., synthesis planning, database searching, combinatorial library manipulation.241,242,243.

Computational complexity theory – focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm244.

Computational creativity (also artificial creativity, mechanical creativity, creative computing, or creative computation) is a multidisciplinary endeavour that includes the fields of artificial intelligence, cognitive psychology, philosophy, and the arts245.