A
Algorithms: a set of rules or instructions given to help AI, neural network, or other machines learn on their own; Classification, clustering, recommendation, and regression are the four most popular types.
Artificial intelligence: the ability of decision-making and decision making machines to simulate human intelligence and behavior.
Artificial Neural Network (ANN): A learning paradigm has been created to act as a human brain that solves tasks that are difficult for traditional computer systems to solve.
Autonomic Computing: The ability of the system for adaptive self-management of its own resources for high-level computing functions without user input.
C
Classification: Classification algorithms allow machines to assign a category to a data point based on training data.
Cluster Analysis: A type of unsupervised practice used for exploratory data analysis to find hidden patterns or group them into data; Groups are made up of a measure of similarity defined by metrics such as Euclidean or likelihood distance.
Clustering: Clustering algorithms allow machines to group clusters of data points or objects with similar properties.
Cognitive Computing: A computerized model that simulates the way the human brain thinks. Self-learning involves data mining, natural language processing, and pattern recognition.
Convolutional neural networks (CNN): A kind of neural network that detects and understands images.
D
Data Mining: Examining data sets and finding mine samples from those data that are more useful.
Data Science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into the phenomenon through structured or unstructured data.
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Decision Tree: A tree and branch-based model used to map decisions and their consequences, similar to a flow chart.
Deep learning: the ability of machines to automatically simulate human thinking through artificial neural networks with information cascading layers.
F
Fluent: A type of situation that can change over time.
G
Game AI: A pattern of AI-specific to gaming that uses an algorithm to compensate randomness. It is the human-like intelligence in non-player characters and the computational behavior used to generate the player’s reaction-based actions.
Genetic algorithm: An evolutionary algorithm based upon these principles of genetics and natural selection, used to find optimal or near-optimal solutions to difficult problems that would otherwise take decades to solve.
H
Heuristic Search Methods: Support that reduces the search for the right solutions to a problem by eliminating the wrong choices.
K
Knowledge engineering: focuses on building knowledge-based systems, including all scientific, technical, and social aspects of it.
L
Logic Programming: A type of programming example where computation is performed based on a knowledge repository of facts and rules; LISP and Prolog are two logic programming languages used for AI programming.
M
Machine Intelligence: An umbrella term that includes machine learning, deep learning, and classical learning algorithms.
Machine learning: One aspect of AI that focuses on algorithms, which allows machines to learn and change when exposed to new data without being programmed.
Machine Awareness: The ability of a system to receive and interpret data from the external world in the same way that humans use our senses. This is usually done with the attached hardware, although the software is also usable.
N
Natural Language Processing: A program must understand its ability to detect human communication.
Credit: BecomingHuman By: venkat vajradhar
