How did Artificial intelligence come to fruition?

Without getting lost in science fiction, how did artificial intelligence develop? learn about the development of artificial intelligence and its objectives.

Over 60 years ago, man established that he could benefit from the assistance of machine logic in his development. Learn about the history of artificial intelligence, some of its early uses, and how far it has come in recent decades in the sections that follow. It was born out of the need to speed up known processes so that people could concentrate on other abstract ideas.

What defines AI?

The field of study known as artificial intelligence, or AI, was established in the early 1960s. It is a collection of theories, concepts, and methods — such as mathematical logic, statistics, probabilities, computational neurobiology, and computer science — with the goal of imitating human cognitive abilities.

Begun at the level of WWII, its improvements are firmly connected to those of figuring and have driven PCs to perform progressively complex undertakings, which beforehand must be finished by an individual.

The beginnings (1940–1966) It is impossible to separate the development of the computational process from the origin of artificial intelligence. We can't forget to mention Alan Turing, the great computer pioneer who simply built the machine that helped the allies win the war more quickly.

When it comes to this computer genius, many experts now believe that the Turing test is actually a useful chatbox tool rather than an accurate measure of artificial intelligence. Instead, the Turing test was a strong inspiration for the idea that would lead to the development of artificial intelligence.

The Second World War accelerated technological development and the desire to understand how to approximate the functions of machines and living things in the time period between 1940 and 1960.

John McCarthy of MIT (Massachusetts Institute of Technology) is credited with coining the term "AI," which can be defined as the creation of computer programs that perform human-like tasks at a higher level of abstraction. level, including: critical thinking, memory organization, and perceptual learning.

Artificial intelligence slowed down around the year 1960 as a result of the time's technical limitations, like a lack of computer memory.

The second era (from 1972 to 1997) saw an increase in memory partially address the technical limitations of computers. The extraordinary thing that resuscitated innovation was workmanship and film, people who had contact with the ideas in their childhood released their imagination.

In point of fact, microprocessors made the concept feasible once more in the technical realm. Despite this, the fact of the matter is that very little has evolved in a way that can be felt, and even though scientists had a lot of information, they were only able to study evolutions.

A first large step was taken at Stanford College in 1972 with MYCIN (a framework gaining practical experience in the finding of blood issues and physician endorsed drugs). An "inference engine," which was programmed to be a logical mirror of human reasoning, served as the foundation of this system. The engine responded with a high level of expertise when entering the data.

In 1997, the Dark Blue PC beat chess ace Garry Kasparov , regardless of this, the IBM PC was a specialist in a restricted universe, not fit for displaying and working out a whole world.

The current era of artificial intelligence (2010–present) was sparked by two primary events. The first is having access to a lot of data. The second factor was the discovery of the extremely high efficiency with which computer graphics card processors accelerate learning algorithm computation.

Outstanding Feats: 

In 2012, Google X, the company's research lab, was able to train an artificial intelligence to recognize cats in a video;

In 2016, Fan Hui, the European champion, and Lee Sedol, the world champion, both came from AlphaGO, a Google AI-powered game of Go. There are way more variations in GO than in Deep Blue's chess.

How could that be? through expert systems' complete paradigm shift. The method is now inductive. In short, it is no longer about coding rules for expert systems; rather, it is about letting computers find them through correlation and classification using a lot of data and their own responses.

The vast majority of research teams suddenly turned to this technology, which provided significant advantages.

Text recognition has also made significant progress thanks to this type of learning, but text comprehension systems still have a long way to go. That is because AI is still unable to fully contextualize and analyze our intentions with particular writing styles.

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