Глоссариум по искусственному интеллекту: 2500 терминов. Том 2. Александр Юрьевич Чесалов
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
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