Last year, venture capitalists poured $5 billion into AI startups in China, which raised more money in the sector than the United States for the first time, according to consultancy AIB research. The Chinese government has made the field a priority, announcing an ambitious policy the same summer to construct an industry worth $150 billion by 2030.AI is also one of the ten key industries outlined in Made in China 2025, an economic master plan that the government is pushing to take the country from a mass manufacturing, low-value economy to a high technology, high-value one. China is now home to Sensetime, the world’s most valuable AI company, which focuses on facial and image recognition and works with local governments across the country on surveillance. It’s worth an estimated $4.5 billion, according to research firm CB Insights.But in an echo of the manufacturing factories that pushed China’s economic development in the 2000s, the country has also found itself home to a growing side industry of labor-intensive data labeling companies, which supply and process the massive amounts of data for the algorithms to consume. Aside from a few established large firms in China’s biggest cities, these companies are mainly growing in smaller cities, towns, and rural areas.
Zhou landed in the industry after graduating as a mechanic from trade school and had been searching for something to do. The possibilities were finite.
“You can’t expect people who have such high salaries to do this labor-intensive work”
Han counts more than 6,000 data labeling outfits that have registered on a Craigslist-like platform he built, where smaller outfits can find outsourcing gigs and hire new employees.Zhao Mengyao, 18, is new to the job. She started working at Zhou’s company in October. When I visit the office she is tracing over the white lines of a parking space in a parking lot. The picture is distorted, with the lines bent as if the camera had a fish-eye lens, but she mouses over them with ease. After 20 minutes, Zhao moves on to the next photo in her set. It’s another photo of a parking lot, from a different angle.
A healthcare business that helps provide more accurate diagnoses needs very detailed points to help the artificial intelligence learn the difference, say, for example, between a tumor and an eyeball in a CT scan, because it wouldn’t be able to distinguish them on its own at first, Peter Yang, the founder of data labeling company Awakening Vector, told me by phone. It needs data that points out what a tumor looks like in a picture, across many different pictures, which requires a human to click and label the photo.
“Do you think mankind will let something that’s not even alive control mankind?”