In the past, they are all prologues. Past is prologue. —Shakespeare’s Tempest People have two understandings of this sentence. One understanding is that the past has pre-determined what will happen next. Therefore, I believe that how the development of artificial intelligence to the present day will determine our future direction in this field. So studying the past is worthwhile; The other is that the past is insignificant and most of the major things will happen next. I also agree with this understanding. In the field of artificial intelligence, even the initial stage is not even considered, we still need to deal with many problems. Early stage John McCarthy presented the term "arTIficial intelligence" at the Dartmouth conference in 1956. The proposal was completed by a number of people, in order: John McCarthy of Dartmouth College, Marvin Minsky of Harvard University, Nathaniel Rochester of IBM, and Claude Shannon of Bell Labs. In the introduction to the proposal, John McCarthy outlines some of the themes of artificial intelligence, including how to make computers use human language; how to use neural networks; how machines can improve themselves (learning or evolution); how machines can form abstract images with their own sensors. Let's "observe" the world and so on. They are really ambitious! In retrospect, there were only a handful of computers in the world, and each of them had only a few tens of kilobytes of memory. Of course, John McCarthy is not the first person to discuss machines and "smart." In fact, Alan Turing has written and published related articles before, but it is not described by the word "artificial intelligence." His most widely known work is CompuTIng Machinery and Intelligence, published in 1950. In this paper, he introduces the "ImitaTIon Game", which was later called the "Turing Test". Although the title of the paper contains "intelligence", the word in the text is only mentioned once. The term refers to human intelligence that attempts to create machines that mimic adults. He believes that machines can think like humans, and at that time predicted that this goal could be achieved in 2000. Earlier, a paper entitled "Intelligent Machinery" was written in 1948, but it was not published until 1970. Artificial intelligence was originally inspired by human performance and human intelligence. I think that in the first 60 years, this goal has attracted a large number of researchers into the field. But in fact, we have not yet achieved any research results that are close to this goal. This is not because the researchers are not working hard enough, nor that they are not good enough, but it is too difficult to achieve this goal. Developed so far As Turing inferred, early research in artificial intelligence focused on areas where there was little need for perception or action. At the time, there was some research on the game. With keyboards and printers, human actions can be easily input into or output from a computer. The process of writing games that can be used quickly led to the concept of "tree search", which is the key to many early artificial intelligence experiments, and in fact, it has become a fundamental tool in many computer sciences. People have developed a field of "building blocks" that can explore all types of smart technologies. In 1963, Larry Roberts of the Massachusetts Institute of Technology published a paper showing that all edges of a building with a smooth surface can be restored in a well-designed lighting scene. This is probably the first doctoral thesis on computer vision. Some people have studied the use of virtual robots to solve problems in a two-dimensional building block world. The robot can pick up blocks from the top of the stack or place the blocks in a virtual one-dimensional space. There are also people who study the field of natural language understanding, all of which seek answers in the complex world of three-dimensional building blocks. Over time, people's understanding of solutions to specific problems in the field of artificial intelligence has become more in-depth, and sub-fields of artificial intelligence may develop. Shortly afterwards, new research topics emerged. The subject of artificial intelligence coverage is very wide, and no one can understand it all. These sub-areas include: natural language processing, search, games, neural networks, machine inference, statistical machine learning, robotics, mobile robotics, simultaneous positioning and mapping, computer vision, and picture understanding. Strong artificial intelligence or general artificial intelligence (AGI) I think the media and those who are not in the industry have recently been confused by a name, they call it strong artificial intelligence or general artificial intelligence. Other agencies call themselves AGI institutions/companies. As far as I know, there has been little progress in this area. This will lead to misunderstandings in the media and non-industry, thinking that it is now really pushing artificial intelligence that is comparable to human intelligence. However, in fact, thousands of artificial intelligence researchers have been working in this field for 62 years, and there has not been a sudden turning point. Why write this article? I want to clarify some confusion about artificial intelligence.
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