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[Simple explanation] What is AI (artificial intelligence)? AI you can learn from the basics

Table of contents

  • Basic knowledge of AI
  • History of AI
  • AI usage examples
  • How AI learns
  • summary

AI has become more commonplace in our lives. Although many people understand it somehow, the concept is vague and difficult to explain. In the coming era, basic knowledge of AI will be essential. Therefore, in this article, we will briefly explain the overview and history of AI from the basics. If you want to know about AI, please refer to it.

Basic knowledge of AI

First, let’s look at the definition of AI and its types.

What is AI?

AI is an abbreviation for “Artificial Intelligence,” which is a part of human intelligence that is artificially reproduced using software. However, the definition of AI is not clearly defined at this time, and academic experts have a wide range of understandings. In any case, it is good to remember that it is a computer system with functions that imitate human intelligence.

What is machine learning?

Machine learning is a method of analyzing data, and is a technology in which computers automatically learn from data to find rules and patterns behind the data. Algorithms that were previously implemented through manual programming can now be automatically constructed from large amounts of data, and are being applied in a variety of fields.

What is deep learning?

Deep learning is a machine learning method that uses neural networks. A neural network is a mathematical model based on the neural circuits (neurons) of the human brain, and is characterized by its ability to perform more complex calculations and learning.

What is big data?

Big data refers to huge groups of data that are difficult to record, store, and analyze using traditional database management systems. The widespread use of big data has made it possible to handle data that could not be collected in the past.

What is a quantum computer?

A quantum computer is a computer that can decipher complex calculations that conventional computers cannot solve by applying the phenomena of quantum mechanics to information processing technology. Quantum computers are being researched and developed as next-generation high-speed computers.

History of AI

AI has become a term that everyone has heard of at least once, but it took many years of development to get to this point. Let’s take a look at the history of AI and its roots.

The birth of AI and the first boom

The concept of AI has its roots in British mathematician Alan Turing’s book “Computing Machines and Man” published in 1950. In the same book, he posed the question, “Can machines think?” At the Dartmouth Conference in 1956, the term “artificial intelligence” was coined to refer to machines that think like humans. After this conference, AI became known among scientists.

The first AI boom was in the 1960s. The first AI boom was about using computers to perform inference and exploration, and people were amazed to see computers solving specific problems one after another, such as puzzles and games with clear rules. . However, when I realized that the rules were unclear and I couldn’t solve complex problems, I gradually lost interest.

The second boom arrived in the 1980s.

The second AI boom that occurred in the 1980s saw the rise of “expert systems.” An expert system is a program that has knowledge in a specific specialized field and can reason and judge phenomena like an expert. Expert systems seemed like a great approach, but the boom was short-lived because the AI ​​at the time couldn’t accurately handle every case.

Third boom and future

In the third boom, which has continued from the 2000s to the present, the practical level of machine learning has significantly evolved through the use of big data. The person who sparked the boom around this time was a research team from the University of Toronto in Canada. In an image recognition software competition held in 2012, the company used neural networks to win by a large margin over second place. Also, in the same year, a group of Google researchers published a paper on cat image discrimination using neural networks, which is said to be the trigger for the third boom.

AI usage examples

Currently, AI is widely used in various fields. Here we introduce familiar examples of AI being utilized.

Netflix recommendation function

In order to increase viewer engagement, Netflix, a video distribution service, has built a unique recommendation system that displays thumbnails of works tailored to the viewer’s tastes. Even if the movie or content is the same, viewers will react differently to different images. Netflix also changes the themes and actors that are emphasized depending on the user’s viewing history, and displays the most effective thumbnails, which leads to increased viewership of the content.

google search engine

AI technology is also used in the field of search. For example, Google, a well-known search engine, utilizes “semantic search,” a system that analyzes the meaning of search terms and displays the most appropriate search results. Various other technologies are also used, such as “entity search,” which displays accurate search results even from ambiguous information, and “voice search,” which supports voice searches.

NTT data image diagnosis

NTT Data is progressing with the development of image diagnostic AI that will make doctors’ diagnoses more efficient. We support accurate diagnosis by analyzing patient medical images using AI technology and showing possible areas of disease on the screen of the PACS system used for diagnosis.

How AI learns

There are several ways to learn AI. This time, we will list three learning methods that are particularly effective among AI learning methods.

buy reference books

Currently, there are many reference books related to AI in the world. You can use books like this to learn on your own. By incorporating knowledge using reference books, you can experience the benefits of “the information is comprehensive”, “the expressions are easy to understand even for beginners”, and “you can memorize important parts while writing down”. .

go to school

In order to master AI, a wide range of specialized knowledge is required, including knowledge of machine learning and deep learning, mathematics and statistics. Therefore, for those who find it difficult to study on their own or those who do not know where to start, one option is to learn from professionals at a school.
There are two types of schools: on-campus and online schools. If you want to learn more efficiently, you should use a school-based programming school, and if you want to learn at home in your spare time, you should use an online school.

Deploy tools

Another method is to introduce AI tools and systematically learn while actually operating the tools. By learning through experience, you can master AI in the shortest possible time and help improve work efficiency.


In this article, we introduced an overview of AI, basic knowledge, and familiar examples where AI is currently being used. AI itself is still in its growth stage. In the future, as “evolution of machine learning and deep learning technology” and “further improvements in computer calculation performance” progress, Japan will play a role in solving social issues facing Japan and supporting sustainable economic growth. Why not read this article to deepen your knowledge about AI and try using AI in your business?


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