What is AI (artificial intelligence)? Easy-to-understand and easy explanation from the meaning and definition of words to machine learning and deep learning
AI is like a "computer with intelligence like a human being", and its major feature is "learning by itself".
Nowadays, AI has become very familiar, but I think there are still many people who "somehow understand" without knowing its essential meaning and mechanism.
In this article, we will thoroughly explain the meaning and definition of the word "AI", the history of research so far, learning methods such as machine learning and deep learning, and the effects that will be brought to the future. We will give a lecture on basic knowledge about AI.
What is AI (artificial intelligence)?
First of all, what is "AI"? Let's review the meaning and definition of words.
The birth of the word AI (artificial intelligence)
The word AI was first used in 1956. It was proposed by Professor John McCarthy, a computer scientist and cognitive scientist, at the Dartmouth Conference held at Dartmouth College in the United States.
What does "AI" stand for?
AI is an abbreviation for Artificial Intelligence. Artificial means "artificial" and Intelligence means "intelligence / intelligence".
Antonym of AI (artificial intelligence)
The antonym of AI is Natuar Intelligence. The abbreviation is NI. The Japanese translation of Natuar Intelligence is the word "natural intelligence," which refers to the intelligence created by nature, such as humans and animals.
Definition of AI (artificial intelligence)
The Japanese Society for Artificial Intelligence translates the words of Professor John McCarthy, the creator of the word AI, into "intelligent machines, especially the science and technology for creating intelligent computer programs."
It is not a mistake to think of "what is AI" as "
a reproduction of NI (natural intelligence) on a computer" and "a computer with human-like intelligence", but AI is clear. The current situation is that it is not defined.
History of AI (artificial intelligence) research
Currently, AI is being accepted by human society as a familiar presence, such as voice assistants represented by Apple's "Siri", eye robot cleaning robot "Roomba", and Softbank's emotion recognition humanoid robot "Pepper".
The progress of AI technology in recent years has been remarkable, but AI research has been active since the latter half of the 1950s. AI has gradually evolved through repeated booms and winter eras (stagnation periods). Here, I will briefly introduce the history of such AI research.
First AI boom (late 1950s-1960s)
The first AI boom broke out in the late 1950s and 1960s, when the Dartmouth Conference, which coined the word "AI," was held.
Behind the boom was the ability to "reason" and "search" by computer, and to derive answers to specific problems. However, AI at that time was limited to problems with clear rules and definitions.
The AI boom gradually slowed down as it became clear that it was not possible to solve problems in which various factors occurring in the real world were intricately intertwined. In the 1970s, the winter era (stagnation period) was reached.
Second AI boom (1980s-1990s)
The second AI boom arrived in the 1980s and 90s. The major factor is that "knowledge representation" has become possible with the birth of the "expert system".
The expert system is an artificial intelligence that composes knowledge with a group of rules such as "If it is ○, do ×. In other cases, do △". There is no mechanism to learn by yourself, but it works by predicting the situation as much as the expert can think in advance and preparing coping methods and judgments.
The more rules there are, the better the accuracy, but all the necessary information must be understood by humans, and the actual use is limited to information in a specific area. Just. Since the amount of knowledge that can be utilized was limited in this way, AI entered the winter era again from around 1995.
Third AI boom (2000s-present)
The third AI boom began in the 2000s and is still in the midst of 2019 when this article was written.
The reason for the boom was the progress of practical application of "machine learning" in which AI itself acquires knowledge from a large amount of data (big data). Furthermore, in 2006, deep learning (deep learning) in which AI learns the elements (features) that define knowledge by itself was advocated, which spurred the boom.