Cars will be transformed by AI

The emergence of ChatGPT surprised the whole world, because it marked the entry of artificial intelligence into the intermediate level and the beginning of a new round of industrial revolution. Of course, it also brings new challenges to employment and business models.

With the emergence of ChatGPT, a number of companies have emerged in my country to devote themselves to big data models, but it is difficult to judge how many will succeed. There are several reasons. First, the requirements for calculation examples for big data models are too high, which cannot be achieved without sufficient high-end chip support. The resulting huge capital investment is not something that ordinary companies can afford. Secondly, the industry concentration is far higher than that of the current Internet platform. Whether it is Alibaba, Tencent, or ByteDance, whether it can survive independently in the future is a question. As for who can stay, the decision lies with the user. It is the user's use of the big data model that determines the outcome of the knockout round. The competition is about who is more in line with the user's mind, satisfaction and usage habits. Third, the birth of intermediate AI systems will make a group of people unemployed and create some new job opportunities. The national level must have a plan for the resulting impact.

What will the car of the future look like?

At the just-concluded Shanghai International Auto Show, almost all media hailed Chinese cars as leading the world. In fact, this is just a lead in the field of low-level artificial intelligence. What is not seen is the changes that medium-level artificial intelligence will bring to cars.

To put it most intuitively, cars in the future may no longer need so many screens, and there may be no need to view high-definition maps. You just need to tell the car where you want to go, and the car will automatically plan the route and choose the road and driving mode based on your driving habits. Nowadays, various application mini-programs on the screen will be merged into big data models, because behind each mini-program is a database. In front of the big data model, they are all "small data" and are the objects of integration.

As far as automobile manufacturing is concerned, big data models can provide actuarial results. For example, if a manufacturer wants to launch a new car for a certain competitive model, you only need to enter the relevant requirements, and the system will provide you with a complete industrial chain and supply chain. Chain solutions allow you to obtain the most economical manufacturing solution while ensuring quality. Customized production can even be easily completed on one production line to meet everyone's individual needs. Because this big data not only covers the entire vehicle production line, but also covers every piece of equipment from all suppliers (including equipment production capacity, quality level, equipment integrity, process compliance, integrity of tooling equipment such as molds and fixtures, etc.) , based on huge basic data, the AI ​​system can provide the boss of the OEM with a complete set of process flows. Of course, it can also make corresponding adjustments in a timely manner according to market demand.

As for the early market research, decision analysis, and later communication strategies, etc., it is even easier. However, the public relations industry is still needed, because this industry solves communication problems between people.

New Issues in Employment

Now Auto-GPT can replace people to do a lot of work, whether it is coming up with a plan, writing a program, writing a paper, making a poster, chatting with people... This means that a group of people will be unemployed from now on.

How many “coders” will be needed in the future? The basic work has been done by ChatGPT, leaving only a few senior program developers. Because big data models iterate so fast, many people’s jobs are replaced by a new generation of artificial intelligence systems before they even realize it.

Of course, because the basic data in the big data model needs to be constantly updated, a new industry has emerged, that is, database service personnel. Their main task is to update and calibrate the basic data. For example, the Galaxy L7 is on the market today. When you search, the system will tell you that this is the most modern car because it has... configurations. However, in less than a year, the Galaxy L7 will no longer be the most advanced model because of new products. Then, the basic data may need to be adjusted and updated at this time. In other words, some basic work still needs people to do it. For another example, the emergence of some new vocabulary requires people to enter the vocabulary and word definitions into the database. In addition, the data captured by the initial system is not 100% correct, and the system will also answer errors. On the one hand, the system can automatically repair this problem through continuous learning, but on the other hand, it requires active human intervention.

As far as China’s reality is concerned, computing power may not be inferior to that of the United States, but our basic data is a weakness. Without accurate data, no matter how advanced the system is, it will be in vain—garbage in, garbage out. The same is true for the automotive industry. Big data models will also be used extensively in the development of artificial intelligence systems. There will also be problems with job additions, reductions and adjustments, and it will affect the change of R&D models to become more efficient.

It is worth noting that the iteration speed of ChatGPT (big data model) is very fast, which means that the iteration speed from intermediate artificial intelligence to advanced artificial intelligence may be faster than we imagined, which will bring great changes to traditional manufacturing industry. The impact will be profound, and may even be completely reborn, and the impact and impact on human society will be more obvious than that of intermediate artificial intelligence.

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