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Different branches of Artificial Intelligence || The Article Point.

Artificial intelligence:

AI is a branch of computer science that emphasizes the creation of intelligent machines which work and react like humans. From games to chatbots, AI is used to bring an element of humanity to computer programs.

The term was coined in 1956 by John McCarthy, who defined it as the science of making machines intelligent, and today people associate it with self-driving cars and technology that can learn on its own. The AI industry is booming, but not all of its applications are for entertainment.

From self-driving cars to speech synthesis and even digital assistants, AI is making our lives easier, and there are many different branches that are also growing in popularity. For further study of artificial intelligence read what is artificial intelligence and its scope. and pros and cons of artificial intelligence.

Branches of Artificial intelligence:

There are six major branches of artificial intelligence (AI). Their introduction and other details are written below.

Machine Learning:

branches of artificial intelligence, machine learning

Machine learning is an area of computer science that gives computers the ability to learn without being explicitly programmed.

In this branches of artificial intelligence, you’ll see how machine learning algorithms help computers learn from data, and how these algorithms are used in software applications. But first, let’s start with a brief history of machine learning.

Machine Learning is the science of getting computers to act without being explicitly programmed. Machine learning has grown by leaps and bounds over the past few years.

The field has produced some tangible benefits, but there are also some misconceptions floating around. Even if you have a handle on the basics of machine learning, you might not know where to start. This guide helps you figure out where machine learning is used and where it can be applied.

Robotics:

branches of artificial intelligence, robotics

Robotics is also one of the branches of artificial intelligence that deals with the design, manufacture, and use of robots, as well as computer systems for their control, sensory feedback, and information processing.

These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition.

Many of today’s robots are inspired by nature contributing to the field of bio-inspired robotics. These robots have also created a new field of research in soft robotics.

Deep learning:

Deep Learning is a special branches of artificial intelligence that is based on neural networks. A neural network is a statistical model that is made of up multiple layers of simple functions that process information.

The data is then used to adjust the functions, which makes the model more accurate as it is trained. Deep learning is a subset of machine learning, but is a more recent application of neural networks. It is “deep” because it uses many layers of these function-based networks.

Deep learning is a sub-field of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Deep learning algorithms are loosely modeled on the human brain, in that they process data in multiple stages, with some stages performing non-linear transformations on the output of previous stages.

Natural Language Processing:

Natural Language Processing (NLP) is also one of the branches of artificial intelligence. The main goal is to enable computers to communicate and understand natural language.

NLP-systems can be used for various applications, such as search engines and information extraction. Natural language processing is a very broad concept and usually is divided into three categories: computational linguistics, cognitive science and computer science.

Natural Language Processing (NLP) is a field of computer science, which deals with all the things that a computer can do with natural human language. These things include the ability to process text, speech, numerical inputs and outputs, and even recognize objects as well as to generate natural language text.

Fuzzy logic:

The term “fuzzy logic” was coined by Lotfi Zadeh in 1965. Zadeh defined fuzzy logic as a “many valued logic,” which is an opposite of the traditional binary logic, which only accepts values of ‘true’ or ‘false’. Fuzzy logic’s association with fuzzy set theory was first described by Lotfi Zadeh in 1965.

Zadeh, however, did not use the term “fuzzy logic”. In the late 1970s, Zadeh’s interest was focused on fuzzy theory and its applications to approximate reasoning for solving engineering problems.

Zadeh later proposed that a fuzzy set could be represented by a membership function, which assigns to each element of a universe a degree of membership.

Fuzzy logic is a method of reasoning that is used to analyze vague and imprecise information. It is a form of multiple-valued logic that represents truth as a value between 0 and 1 (instead of the traditional truth values of ‘0’ and ‘1’, or ‘FALSE’ and ‘TRUE’). In a fuzzy logic system, truth values are known as “membership grades” or “fuzzy sets”.

Expert systems:

Expert Systems also in the branches of artificial intelligence (AI) system which mimic the expert decision-making ability of a human being. An expert system uses a set of rules (typically represented in a production system or a decision tree) to solve a problem.

These rules are based on expert knowledge about the problem domain (such as medical diagnosis, financial analysis, etc.). An expert system is typically composed of an inference engine, a database and a knowledge base (KB).

The inference engine processes the information in the KB, which is composed of facts and rules. It processes the information to draw conclusions and make decisions. In some expert systems, the inference engine is hard-coded into the expert system, while in others, it is a separate, user-programmable module. The expert system can be used to solve a variety of problems, ranging from simple decision-making to more complex problems such as diagnosis and process management.

An expert system is a computer program designed to simulate the decision-making ability of a human expert. The computer program is constructed using knowledge encoded in “if-then” rules and heuristics.

The earliest expert systems were developed in the 1970s, primarily on mainframes and minicomputers. The earliest expert systems were designed primarily for applications requiring knowledge of medical diagnosis (such as MYCIN), complex technical analysis (such as Auto Solve, which solved systems of linear equations), and geological prediction (such as GEOS).

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What is Artificial Intelligence? its scope, areas and working

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