An adaptive algorithm is an algorithm that changes its behaviour at the time it is run, based on information available and on a priori defined reward mechanism
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,
Action model learning (sometimes abbreviated action 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.
In computer science, an 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.
In futures studies and the history of technology, accelerating change is a perceived increase in the rate of technological change throughout history
In software engineering and computer science, abstraction is a technique for arranging complexity of computer systems.
In computer science, an abstract data type (ADT) 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 operations.
Abductive reasoning is a form of logical inference which starts with an observation then seeks to find the simplest and most likely explanation.
Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning.
Capsule is a nested set of neural layers. So in a regular neural network, you keep on adding more layers. In a capsule network, you would add more layers inside a single layer. Or in other words, nest a neural layer inside another.
Linear algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces, but is also concerned with properties common to all vector spaces.
Data science, also known as data-driven science, is an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD).
Supervised learning is the machine learning task of inferring a function from labeled training data.
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural network that have successfully been applied to analyzing visual imagery.
Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
Deep learning is the application of artificial neural networks to learning tasks that contain more than one hidden layer.