Abstract data type is a mathematical model for data types, where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations5.

Abstraction — the process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest6.

Accelerating change is a perceived increase in the rate of technological change throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change7.

Access to information – the ability to obtain information and use it8.

Access to information constituting a commercial secret – familiarization of certain persons with information constituting a commercial secret, with the consent of its owner or on other legal grounds, provided that this information is kept confidential9.

Accuracy – the fraction of predictions that a classification model got right. In multi-class classification, accuracy is defined as follows:

In binary classification, accuracy has the following definition:

See true positive and true negative. Contrast accuracy with precision and recall10,11.

Action in reinforcement learning, is the mechanism by which the agent transitions between states of the environment. The agent chooses the action by using a policy12.

Action language is a language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world. Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning13.

Action model learning is an area of machine learning concerned with creation and modification of software agent’s knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners14.

Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, «the action selection problem» is typically associated with intelligent agents and animats – artificial systems that exhibit complex behaviour in an agent environment15.

Activation function in the context of Artificial Neural Networks, is a function that takes in the weighted sum of all of the inputs from the previous layer and generates an output value to ignite the next layer16.

Active Learning/Active Learning Strategy is a special case of Semi-Supervised Machine Learning in which a learning agent is able to interactively query an oracle (usually, a human annotator) to obtain labels at new data points. A training approach in which the algorithm chooses some of the data it learns from. Active learning is particularly valuable when labeled examples are scarce or expensive to obtain. Instead of blindly seeking a diverse range of labeled examples, an active learning algorithm selectively seeks the particular range of examples it needs for learning17,18,19.

Adam optimization algorithm it is an extension of stochastic gradient descent which has recently gained wide acceptance for deep learning applications in computer vision and natural language processing