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
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