With Artificial intelligence services becoming less than a vague marketing buzzword and a strict ideology, it is becoming increasingly challenging to understand all the AI terms out there. So to eliminate the new AI zone.

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24 Arti cial Intelligence Terms You Need to Know
venkat vajradhar
Oct 21, 2019 · 3 min read
With Artificial intelligence services becoming less than a vague marketing buzzword
and a strict ideology, it is becoming increasingly challenging to understand all the AI
terms out there. So to eliminate the new AI zone.
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.

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C
Chatbots: A chat robot (abbreviated chatbots) designed to simulate a conversation
with human users by communicating via text chats, voice commands, or both. These
are the most commonly used interface for computer programs that have AI capabilities.
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.
To Know More: How Artificial Intelligence is Driving Mobile App
Personalization?
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.
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.

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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.
To Know More: AI APPLICATIONS INVARIOUS FIELDS
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

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the attached hardware, although the software is also usable.
N
Natural Language Processing: A program must understand its ability to detect
human communication.
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