Charles Babbage, sometimes referred to as the "father of computing".
An automated online assistant providing customer service on a web page, an example of an application where natural language processing is a major component.
The probabilities of rolling several numbers using two dice.
Ada Lovelace published the first algorithm intended for processing on a computer.
Gerolamo Cardano (16th century)
Christiaan Huygens published one of the first books on probability (17th century)
Carl Friedrich Gauss

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

- Natural language processing

These concepts have been given an axiomatic mathematical formalization in probability theory, which is used widely in areas of study such as statistics, mathematics, science, finance, gambling, artificial intelligence, machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events.

- Probability

Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

- Computer science

Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature (complex-valued embeddings, and neural networks in general have also been proposed, for e.g. speech ).

- Natural language processing

The cache language model and other statistical language models that are used in natural language processing are also examples of applications of probability theory.

- Probability

Information theory, closely related to probability and statistics, is related to the quantification of information.

- Computer science
Charles Babbage, sometimes referred to as the "father of computing".

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Silver didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligence

Artificial intelligence

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Intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

Intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

Silver didrachma from Crete depicting Talos, an ancient mythical automaton with artificial intelligence
An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.
A parse tree represents the syntactic structure of a sentence according to some formal grammar.
Feature detection (pictured: edge detection) helps AI compose informative abstract structures out of raw data.
Kismet, a robot with rudimentary social skills
A particle swarm seeking the global minimum
Expectation-maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption.
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
Representing images on multiple layers of abstraction in deep learning
For this project the AI had to learn the typical patterns in the colors and brushstrokes of Renaissance painter Raphael. The portrait shows the face of the actress Ornella Muti, "painted" by AI in the style of Raphael.
AI Patent families for functional application categories and sub categories. Computer vision represents 49 percent of patent families related to a functional application in 2016.
The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots"

The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.

To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques—including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics.

AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.