Глоссариум по искусственному интеллекту: 2500 терминов. Том 2

Discriminator is a system that determines whether examples are real or fake. The subsystem within a generative adversarial network that determines whether the examples created by the generator are real or fake414.

Disparate impact – making decisions about people that impact different population subgroups disproportionately. This usually refers to situations where an algorithmic decision-making process harms or benefits some subgroups more than others415.

Disparate treatment – factoring subjects’ sensitive attributes into an algorithmic decision-making process such that different subgroups of people are treated differently416.

Dissemination of information – actions aimed at obtaining information by an indefinite circle of persons or transferring information to an indefinite circle of persons417.

Dissemination of personal data – actions aimed at disclosing personal data to an indefinite circle of persons418.

Distributed artificial intelligence (DAI) (also decentralized artificial intelligence) is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems419.

Distributed registry technologies (Blockchain) are algorithms and protocols for decentralized storage and processing of transactions structured as a sequence of linked blocks without the possibility of their subsequent change420.

Distribution series are series of absolute and relative numbers that characterize the distribution of population units according to a qualitative (attributive) or quantitative attribute. Distribution series built on a quantitative basis are called variational421.

Divisive clustering – see hierarchical clustering422,423.

Documentation generically, any information on the structure, contents, and layout of a data file. Sometimes called «technical documentation» or «a codebook». Documentation may be considered a specialized form of metadata424.

Documented information – information recorded on a material carrier by means of documentation with details that make it possible to determine such information, or, in cases established by the legislation of the Russian Federation, its material carrier425.

Downsampling – overloaded term that can mean either of the following: Reducing the amount of information in a feature in order to train a model more efficiently. For example, before training an image recognition model, downsampling high-resolution images to a lower-resolution format. Training on a disproportionately low percentage of over-represented class examples in order to improve model training on under-represented classes. For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough about the minority class. Downsampling helps balance the amount of training on the majority and minority classes426.

Driver is computer software that allows other software (the operating system) to access the hardware of a device427.

Drone – unmanned aerial vehicle (unmanned aerial system)428.

Dropout regularization is a form of regularization useful in training neural networks. Dropout regularization works by removing a random selection of a fixed number of the units in a network layer for a single gradient step. The more units dropped out, the stronger the regularization429.

Dynamic epistemic logic (DEL) is a logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple agents and studies how their knowledge changes when events occur430.

Dynamic model is a model that is trained online in a continuously updating fashion. That is, data is continuously entering the model431,432.

«E»

Eager execution is a TensorFlow programming environment in which operations run immediately. By contrast, operations called in graph execution don’t run until they are explicitly evaluated. Eager execution is an imperative interface, much like the code in most programming languages. Eager execution programs are generally far easier to debug than graph execution programs433.

Eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system434.

Early stopping is a method for regularization that involves ending model training before training loss finishes decreasing. In early stopping, you end model training when the loss on a validation dataset starts to increase, that is, when generalization performance worsens435.

Earth mover’s distance (EMD) is a measure of the relative similarity between two documents. The lower the value, the more similar the documents436.

Ebert test is a test which gauges whether a computer-based synthesized voice can tell a joke with sufficient skill to cause people to laugh. It was proposed by film critic Roger Ebert at the 2011 TED conference as a challenge to software developers to have a computerized voice master the inflections, delivery, timing, and intonations of a speaking human. The test is similar to the Turing test proposed by Alan Turing in 1950 as a way to gauge a computer’s ability to exhibit intelligent behavior by generating performance indistinguishable from a human being437.

Echo state network (ESN) is a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can (re) produce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system438.

Ecosystem of the digital economy is a partnership of organizations that ensures the constant interaction of their technological platforms, applied Internet services, analytical systems, information systems of state authorities of the Russian Federation, organizations and citizens439.

Edge computing is a subspecies of distributed computing in which information processing takes place in close proximity to the place where the data was received and will be consumed (for example, using phones and other consumer devices)440.

Electronic circuit is a product, a combination of individual electronic components, such as resistors, capacitors, diodes, transistors and integrated circuits, interconnected to perform any task or a circuit with conventional signs441,442.

Electronic Data Interchange (EDI) is a series of standards and conventions for the transfer of structured digital information between organizations, based on certain regulations and formats of transmitted messages443.

Electronic government (e-Government) is a package of technologies and a set of related organizational measures, regulatory and legal support for organizing digital interaction between public authorities of various branches of government, citizens, organizations and other economic entities444.

Electronic industry is a set of organizations that perform scientific, technological and other work in the field of development, production, maintenance of operation, as well as providing services related to electronic and microelectronic products, respectively445.

Electronic Medical Record (EMR) is electronic health record, is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different healthcare settings446.

Electronic state is a way of implementing the information aspects of state activity based on the use of IT systems, as well as a new type of state based on the use of this technology. In the Russian Federation, activities to create an «electronic state» are carried out within the framework of the federal target program «Electronic Russia»447,448.

Eli5 environment is a Python environment that is used to debug and visualize machine learning models. By default, it supports several machine learning frameworks – Scikit-learn, XGBoost, LightGBM, CatBoost, lightning, Keras and so on. Eli5 also provides LIME and Permutation Importance models to test machine learning pipelines as black boxes449.

ELIZA effect is a term used to discuss progressive artificial intelligence. It is the idea that people may falsely attach meanings of symbols or words that they ascribe to artificial intelligence in technologies450.

Embedding (Word Embedding) is one instance of some mathematical structure contained within another instance, such as a group that is a subgroup451.

Embedding space – the d-dimensional vector space that features from a higher-dimensional vector space are mapped to. Ideally, the embedding space contains a structure that yields meaningful mathematical results; for example, in an ideal embedding space, addition and subtraction of embeddings can solve word analogy tasks. The dot product of two embeddings is a measure of their similarity452.

Embeddings is a categorical feature represented as a continuous-valued feature. Typically, an embedding is a translation of a high-dimensional vector into a low-dimensional space453.

Embodied agent (also interface agent) is an intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, embodiment454.

Embodied cognitive science is an interdisciplinary field of research, the aim of which is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies: 1) the modeling of psychological and biological systems in a holistic manner that considers the mind and body as a single entity, 2) the formation of a common set of general principles of intelligent behavior, and 3) the experimental use of robotic agents in controlled environments455.

Empirical risk minimization (ERM) – choosing the function that minimizes loss on the training set. Contrast with structural risk minimization456,457.

Encoder in general, is any system that converts from a raw, sparse, or external representation into a more processed, denser, or more internal representation. Encoders are often a component of a larger model, where they are frequently paired with a decoder. Some Transformers pair encoders with decoders, though other Transformers use only the encoder or only the decoder. Some systems use the encoder’s output as the input to a classification or regression network. In sequence-to-sequence tasks, an encoder takes an input sequence and returns an internal state (a vector). Then, the decoder uses that internal state to predict the next sequence. Refer to Transformer for the definition of an encoder in the Transformer architecture458.

Encryption is the reversible transformation of information in order to hide from unauthorized persons, while providing, at the same time, authorized users access to it459,460.

End-to-end digital technologies is a set of technologies that are part of the digital economy: big data, neurotechnologies and artificial intelligence, distributed registry systems, quantum technologies, new production technologies, industrial Internet, robotics and sensor components, wireless communication technologies, virtual and augmented reality technologies461.

Energy Efficiency – from both economic and environmental points of view, it is important to minimize the energy costs of both training and running an agent or model.

Ensemble averaging in machine learning, particularly in the creation of artificial neural networks, is the process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model462.

Ensemble is a merger of the predictions of multiple models. You can create an ensemble via one or more of the following: different initializations; different hyperparameters; different overall structure. Deep and wide models are a kind of ensemble463.

Enterprise Imaging has been defined as «a set of strategies, initiatives and workflows implemented across a health- care enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all clinical imaging and multimedia content to enhance the electronic health record» by members of the HIMSSSIIM Enterprise Imaging Workgroup464.

Entity annotation – the process of labeling unstructured sentences with information so that a machine can read them. This could involve labeling all people, organizations and locations in a document, for example465.

Entity extraction is an umbrella term referring to the process of adding structure to data so that a machine can read it. Entity extraction may be done by humans or by a machine learning model466.

Entropy — the average amount of information conveyed by a stochastic source of data467.

Environment in reinforcement learning, the world that contains the agent and allows the agent to observe that world’s state. For example, the represented world can be a game like chess, or a physical world like a maze. When the agent applies an action to the environment, then the environment transitions between states468.

Episode in reinforcement learning, is each of the repeated attempts by the agent to learn an environment469.

Epoch in the context of training Deep Learning models, is one pass of the full training data set470,471.

Epsilon greedy policy in reinforcement learning, is a policy that either follows a random policy with epsilon probability or a greedy policy otherwise. For example, if epsilon is 0.9, then the policy follows a random policy 90% of the time and a greedy policy 10% of the time472.

Equality of opportunity is a fairness metric that checks whether, for a preferred label (one that confers an advantage or benefit to a person) and a given attribute, a classifier predicts that preferred label equally well for all values of that attribute. In other words, equality of opportunity measures whether the people who should qualify for an opportunity are equally likely to do so regardless of their group membership. For example, suppose Glubbdubdrib University admits both Lilliputians and Brobdingnagians to a rigorous mathematics program. Lilliputians’ secondary schools offer a robust curriculum of math classes, and the vast majority of students are qualified for the university program. Brobdingnagians’ secondary schools don’t offer math classes at all, and as a result, far fewer of their students are qualified. Equality of opportunity is satisfied for the preferred label of «admitted» with respect to nationality (Lilliputian or Brobdingnagian) if qualified students are equally likely to be admitted irrespective of whether they’re a Lilliputian or a Brobdingnagian473.

Equalized odds is a fairness metric that checks if, for any particular label and attribute, a classifier predicts that label equally well for all values of that attribute474.

Ergatic system is a scheme of production, one of the elements of which is a person or a group of people and a technical device through which a person carries out his activities. The main features of such systems are socio-psychological aspects. Along with the disadvantages (the presence of the «human factor»), ergatic systems have a number of advantages, such as fuzzy logic, evolution, decision-making in non-standard situations475.

Error backpropagation – the process of adjusting the weights in a neural network by minimizing the error at the output. It involves a large number of iteration cycles with the training data476.

Error-driven learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to minimize some error feedback. It is a type of reinforcement learning477.

Ethical use of artificial intelligence is a systematic normative understanding of the ethical aspects of AI based on an evolving complex, comprehensive and multicultural system of interrelated values, principles and procedures that can guide societies in matters of responsible consideration of the known and unknown consequences of the use of AI technologies for people, communities, the natural environment environment and ecosystems, as well as serve as a basis for decision-making regarding the use or non-use of AI-based technologies478.

Ethics of Artificial Intelligence is the ethics of technology specific to robots and other artificial intelligence beings, which is divided into robot ethics and machine ethics. The former one is about the concern with the moral behavior of humans as they design, construct, use, and treat artificially intelligent beings, and the latter one is about the moral behavior of artificial moral agents479.

Evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators480.

Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character481.

Evolving classification function (ECF) – evolving classifier functions or evolving classifiers are used for classifying and clustering in the field of machine learning and artificial intelligence, typically employed for data stream mining tasks in dynamic and changing environments482.

Example – one row of a dataset. An example contains one or more features and possibly a label. See also labeled example and unlabeled example483.

Executable – executable code, an executable file, or an executable program, sometimes simply referred to as an executable or binary, causes a computer «to perform indicated tasks according to encoded instructions», as opposed to a data file that must be interpreted (parsed) by a program to be meaningful484.

Existential risk – the hypothesis that substantial progress in artificial general intelligence (AGI) could someday result in human extinction or some other unrecoverable global catastrophe485.

Experience replay in reinforcement learning, a DQN technique used to reduce temporal correlations in training data. The agent stores state transitions in a replay buffer, and then samples transitions from the replay buffer to create training data486.

Experimenter’s bias it is the tester’s tendency to seek and interpret information, or give preference to one or another information, that is consistent with his point of view, belief or hypothesis. A kind of cognitive distortion and bias in inductive thinking487.

Expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if—then rules rather than through conventional procedural code488,489.

Expert systems are systems that use industry knowledge (from medicine, chemistry, law) combined with sets of rules that describe how to apply the knowledge490.

Explainable artificial intelligence (XAI) is a key term in AI design and in the tech community as a whole. It refers to efforts to make sure that artificial intelligence programs are transparent in their purposes and how they work. Explainable AI is a common goal and objective for engineers and others trying to move forward with artificial intelligence progress491

Конец ознакомительного фрагмента.

Текст предоставлен ООО «Литрес».

Прочитайте эту книгу целиком, купив полную легальную версию на Литрес.

Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.

Примечания

1

.Чесалов А. Ю. Глоссариум по искусственному интеллекту и информационным технологиям.-М.: Ridero. 2021.-304c. [Электронный ресурс] // Ridero.ru. URL: https://ridero.ru/books/glossarium_po_informacionnym_tekhnologiyam_i_iskusstvennomu_intellektu/

2

A/B Testing [Электронный ресурс] https://vwo.com URL: https://vwo.com/ab-testing/ (дата обращения: 28.01.2022)

3

Abductive Logic Programming (ALP) [Электронный ресурс] https://engati.com URL: https://www.engati.com/glossary/abductive-logic-programming (дата обращения 14.02.2022)

4

Abductive reasoning [Электронный ресурс] https://msrblog.com URL: http://msrblog.com/science/mathematic/about-abductive-reasoning.html (дата обращения 14.02.2022)

5

Abstract data type [Электронный ресурс] https://embeddedartistry.com URL: https://embeddedartistry.com/fieldmanual-terms/abstract-data-type/ (дата обращения 14.02.2022)

6

Abstraction [Электронный ресурс] https://riskfirst.org URL: https://riskfirst.org/thinking/Glossary

7

Accelerating change [Электронный ресурс] https://ru.knowledgr.com (дата обращения: 14.02.2022)

8

Доступ к информации [Электронный ресурс] https://ru.wikipedia.org/wiki/Доступность_информации (дата обращения: 09.04.2023)

9

Доступ к информации, составляющей коммерческую тайну [Электронный ресурс] http://www.fsk-ees.ru URL: http://www.fsk-ees.ru/upload/docs/Polozhenie-comtayna.pdf стр. 1 (дата обращения: 09.04.2023)

10

Accuracy [Электронный ресурс] https://www.primeclasses.in URL: https://www.primeclasses.in/glossary/data-science-course/machine-learning/accuracy (дата обращения 04.07.2023)

11

Accuracy [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#accuracy (дата обращения 04.07.2023)

12

Action [Электронный ресурс] https://developers.google.com URL: https://developers.google.com/machine-learning/glossary#action (дата обращения: 09.04.2023)

13