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.
24 artificial intelligence terms you need to know venkat vajradhar - medium
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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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