Data is the new oil, it has been said for years now. If data is the new oil, then China is already the largest producer with its factories packed with labourers working hard to annotate images and data for machine learning. Machine Learning needs loads of data to perform well and the need for high quality hand annotated data has skyrocketed in the last decade. “I used to think the machines are geniuses. Now I know we’re the reason for their genius.” Hou, a young blue-collared labeler in one of these factories, told an international daily.
There is a new industrial blue collar labour demand surge in the most white collar of all industries, i.e. artificial intelligence. Young workers who are more tech savvy than the older generation have found a new employment opportunity of hand labeling data for companies working in artificial intelligence. These young employees generally work in smaller cities which host several startups whose sole aim is to supply big machine learning companies with high quality data.
Rushkoff, author of the book titled “Throwing Rocks at the Google Bus” says, “It’s harder to find a job, or everybody’s working more hours for less money. Technology just seems to put us in this always-on state where our labour and data and our time are being extracted from us.” In such an era, it seems like data labeling has emerged as the blue-collar job that can keep young people away safe from AI snatching their jobs. It seems like the younger generation might be able to feed the AI beast rather than being eaten by them.
Demand Surges For Cheap Human Labour
Case in point is Beijing-based startup Mada Code, which has a freelance employee strength of 10,000. These freelancers annotate data for various tasks such as Optical Character Recognition (OCR) and natural language processing. This firm counts Microsoft, Carnegie Mellon University and other global firms as clients.
There can be online forms of data labeling jobs where people can annotate pictures via a mobile app. And in the other form, factories ar full of data labellers sitting in front of computers doing manual annotation in shifts. AI is supposed to be the dream where humans can be truly free and many jobs can be automated. But before we reach there, the world needs a load of human annotation which can be very demanding and also monotonous. Looking at the vision of artificial intelligence and its participation in our future, the Chinese industry of data labeling is more than just irony.
Serving The Silicon Valley, At Minimum Wage
There can be no machine learning without hand labeling and hence the artificial intelligence ecosystem needs such industries in place. As Li Yuan quotes the co-founder of a Chinese data labeling company in a recent piece for the New York Times: “We’re the construction workers in the digital world. Our job is to lay one brick after another. But we play an important role in A.I. Without us, they can’t build the skyscrapers.”
Home » Human Data Labeling Factories Are The Answer To China’s Growing AI Ambitions
The condition at these factories are very different from the plush offices of Silicon Valley or Chinese tech hubs at Beijing and Shenzhen. The labourers are paid only minimum wage for and again no free food is served like many technology companies. Again there no other perks like healthcare or even pool tables. But sans these labourers, the AI revolution will be virtually impossible.
There is an aggressive push, to further push costs lower by growing to regions where labour costs are much lower. There are tasks that are much harder than others. Like human body annotation is much harder where it requires 15-40 dots per figure. The applications of labelled data are great. There are applications ranging from robots to language processing engines.
“There were no iPhone or Foxconn workers 10 years ago. I guess while some jobs are replaced, there will always be some new jobs.” Zhang, the project manager of Mada Code said.
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As a thorough data geek, most of Abhijeet's day is spent in building and writing about intelligent systems. He also has deep interests in philosophy, economics and literature.