Behavior informatics (BI) the informatics of behaviors so as to obtain behavior intelligence and behavior insights146.
Behavior tree (BT) is 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 makes BTs less error-prone and very popular in the game developer community. BTs have shown to generalize several other control architectures147.
Belief-desire-intention software model (BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agents 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 programmer148.
Bellman equation 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 Bellmans «principle of optimality» prescribes149.
Benchmark (also benchmark program, benchmarking program, benchmark test) 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 times150.
Benchmarking is a set of techniques that allow you to study the experience of competitors and implement best practices in your company151.
BETA 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)152.
Bias 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 peoples 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 results153.
Biased algorithm 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 algorithms154,155.
Bidirectional (BiDi) is 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 text156.
Bidirectional Encoder Representations from Transformers (BERT) is 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 encoders job is to produce good text representations, rather than to perform a specific task like classification. Is bidirectional. Uses masking for unsupervised training157,158.
Bidirectional language model is 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 text159.
Big data 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 results160.
Big O notation is 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 BachmannLandau notation or asymptotic notation161.
Bigram an N-grams in which N=2162.
Binary choice regression model 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 group163.
Binary classification is 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 classifier164.
Binary format is 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 Commerces 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 format165.
Binary number is a number written using binary notation which only uses zeros and ones. Example: Decimal number 7 in binary notation is: 111166.
Binary tree is 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 well167.
Binning 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 (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 todays 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 screening168,169.
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.
Braincomputer interface (BCI), sometimes called a brainmachine interface (BMI), is a direct communication pathway between the brains electrical activity and an external device, most commonly a computer or robotic limb. Research on braincomputer 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 Vidals 1973 paper marks the first appearance of the expression braincomputer 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 problems 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.
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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.