AI large model data indicates that the monthly income of "migrant workers" does not exceed 5,000 yuan, and the unit price drops to 4 cents

AI Large models have been a hot topic in the field of artificial intelligence in recent years. They can achieve a variety of amazing functions, such as generating realistic text and images, or having smooth conversations with humans. However, behind these large models, there is a group of unknown data annotators who work hard every day to add labels to raw data and provide the massive data required for AI technology

## The job of #data annotators is not easy. They need to face tedious tasks, low income, long-term instability and the risk of being replaced at any time. They are the cornerstone of the development of artificial intelligence technology, but they receive little attention and respect.

According to "Tech Planet", data annotators use the most primitive piece-rate system to calculate their wages.

Most practitioners per day The monthly income is only 5,000 yuan. Some of them are college graduates, some are mothers, and some are career changers. They process pictures, text, voice and other data in cubicles in third- and fourth-tier cities, providing services to major Internet companies and car companies.

This site has noticed that the data annotation industry has also experienced ups and downs. In 2017, when expectations for AI technology were booming, data annotators could earn high incomes, with a 2D pull box earning 50 cents. However, as competition in the industry intensifies and technology development does not go smoothly, the unit price of

data annotation is getting lower and lower, and now the lowest is only 4 cents.

Data annotation companies are also facing tremendous pressure. They need to have a certain scale and capital reserves to obtain orders from the source, and they have to bear problems such as long payment cycles, high employee turnover, and unstable quality and cycle. Haitian Ruisheng is currently the first main board-listed company in the data annotation industry. Its profit margin last year was just over 10%, and it fell into losses in the first half of this year.

What worries data annotators even more is that they may soon be replaced by the AI ​​they helped create. Some companies at home and abroad are developing tools that can automatically label data, using mainstream large models on the market to label data sets. These tools claim to improve annotation efficiency and reduce costs, and achieve an accuracy that is close to or even exceeds that of manual labor.

Of course, not all data annotations can be replaced by AI. Some data annotations that require professional knowledge and logical analysis capabilities still require manual participation, such as in medical, financial, autonomous driving and other fields. However, this also means that the industry threshold will continue to increase. For data annotators, more learning and effort may be required to survive in this industry

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