A little cruel story. The story of humans losing their jobs to robots. Let's take an example of China. In just two years, 2 million workers had to hand over jobs to robots. The same goes for America. It is predicted that 36 million jobs will be replaced by artificial intelligence-based automation systems in the future. This accounts for a quarter of all jobs in the United States. There are also other predictions. That 800 million jobs, or a fifth of the world's workforce, will disappear by 2030. Then, what jobs will disappear? The simpler the work, the lower the wage, the more likely it is to be replaced by automation or artificial intelligence. For example, the office management sector is 4% likely to be replaced, while the simple packaging or machine management level is 100% likely to be replaced. Let's go in a bit more. Jobs for low-educated and uneducated workers disappear before highly educated people. Small cities rather than large cities and small businesses will disappear before large cities and large workplaces will disappear. Jobs for men than women, blacks than whites, and foreigners disappear faster than their own citizens. Let's watch a bit more. This is the pie of all jobs that exist. If you divide them according to the nature of the jobs, it will be roughly divided into three equal parts if they remain. Among them, the most likely area to be automated, 70% of the population working here will lose their jobs, and the second most likely area will also lose nearly 70% of their jobs. On the other hand, in groups that are less likely to be automated, only less than 30% of the population will lose their jobs. Then, where will all of them go? Only if all of them can enter a zone with a low possibility of automation will the result be a happy ending. But such a fairytale-like happy ending doesn't come easily from the job pie. Finally, the most extreme gloomy prediction comes out. As artificial intelligence and automation systems develop more and more, the remaining 99.9% of people, except for the very few upper classes, will survive Precarriat. Prekariat, I'm not sure about that either. A combined term of precario, which means unstable, and proletia, which means worker. It refers to the class in which human labor is mostly replaced by AI, engaged in simple labor in the form of temporary contracts or freelancers, and barely living at low wages. He's the first person who insisted on this. And this person. 99.99% made a gloomy prediction that they would live as Precariats. -Platform, the platform we know was like this. We sit here as we know it and wait for the train. It's quite exciting to go somewhere by train. But now that era of romance is over. The space where the platform and computer system are operated. This place is also definitely a platform. Because people are gathering. Instead, it is a computer program that controls and manages the people gathered here. It is also a program in which the program calls a train and determines who will and will not be picked up. It is also a program to decide where to carry people and how many people. - Since most people are working on the platform, that will soon be the standard. -The train that will load them now arrives here. It was the computer program's algorithm that led them here. We call them platform workers. 2030! Hello, how are you doing? Everyone, I'm a magician with 49 jobs. I'll get started today as well. A lot of people came in. 52,000 people are already watching. Thank you. I gain strength. That's right. My job? There are more than 50 jobs I've actually been through so far, but why 49 jobs? When I first started this channel, when I heard that there were 49, it felt so far away. So, let's work hard until I fill all 49 of these, that was my goal. Then we'll get started now. Everyone, this is what I did last month. What does this look like? You're good at guessing. That's right, it's a snow removal robot. It's a robot that cleans up the snow. Due to the abnormal climate, heavy snow has continued every winter for three years, and there are many areas where snow has accumulated more than 1m this year. As you all know, humans cannot keep up with the efficiency of robots. Now these three robots solve the amount of snow removal cars had to clean up in the past. That's right. The snow removal robot is similar to a regular road cleaning robot. Basically, it's an unmanned robot. But they have to be much smarter than that. When the snow is covered, you don't know what's inside. It's a little different from regular cleaning robots because you have to figure it out. Then where do people work? I like all of these robots. There's one flaw. They have bad eyesight. For example, let's say there's a huge snowball. There is a very expensive sports car hidden in it. However, no matter how much a see-through sensor is attached, there is a limit to accurately distinguishing objects in the eyes. So the moment this robot arm tries to clear the snow, the car may just have a scratch. Everyone, this is what I've done over the past two months. Determining the strength of a robot arm. There is a program that analyzes the scene viewed by this robot sensor step by step. Since I'm a person, I can judge something right away. For example, let's say it's a road with shopping malls. If there's a huge iron structure detected there, you can say it's almost 100% a car. But these robots interpret it as the same road regardless of whether it is a commercial road or an urban road. That's why we make judgments. This isn't hard. It's enough for a person to have the natural ability to follow. But I don't think this job will last that long either. The robot's judgment function will be improved immediately. However, due to cost issues, we are still using this model. I thought about whether human jobs would come out in that brief moment. Wouldn't human jobs come out in a short time? Then let's start talking at that point. It's job pie again. Numerous people who have lost their jobs due to automation and artificial intelligence are wandering outside the labor market. Can they all get into a pie with low automation potential? There's only one way. Expanding the area of areas with low automation potential in the proportion of all jobs. In other words, it grows the pie in here.
