There is a dirty little secret about artificial intelligence.
From makeup artists in the United States of America, drawing boxes, cars, cars, cars, cars, cars, cars, cars, buses, trains, cars, cars, figure out that you want the lights on. Many such technologies would not work without massive quantities of this human-labeled data.
These repetitive tasks pay pennies apiece. But in bulk, this work can be offered a decent wage in many parts of the world – even in the United States. This burgeoning but largely unseen cottage industry is the foundation of a technology that could change humanity forever: AI that will drive us around, execute verbal commands without flaw, and, possibly, one day think on its own.
This human input industry has long been nurtured by the search engines Google and Bing, who for more than a decade have used people to rate the accuracy of their results. Since 2005, Amazon's Mechanical Turk service, which matches freelance workers with temporary online jobs, has also made crowd-sourced data entry available to researchers worldwide.
More recently, investors have poured tens of millions of dollars into startups like Mighty AI and CrowdFlower, which is developing software that makes it easier to label photos and other data, even on smartphones.
Venture capitalist S. Somasegar says he sees “billions of dollars of opportunity” in servicing the needs of machine learning algorithms. His firm, Madrona Venture Group, invested in Mighty AI. Humans will be in the loop “for a long, long, long time to come,” he says.
Accurate labeling could make the difference between a self-driving car, distinguishing between the sky and the side of a truck – a distinction Tesla's Model S failed in the first known fatality involving self-driving systems in 2016.
“Mighty AI CEO Daryn Nakhuda .
Khrisna earns a living by adding word tags to clothing pictures on websites such as eBay and Amazon. – AP
Marjorie Aguilar, a 31-year-old freelance makeup artist in Maracaibo, Venezuela, spends four to six hours. She earns about 50 cents (RM1.90) an hour, but in a crisis-wracked country with runaway inflation, just a few hours. “It does not sound like a lot of money.”
“It does not sound like a lot of money, but for me it's pretty decent, “she says. “You can imagine how important it is for me to get paid in US dollars.”
Aria Khrisna, a 36-year-old father of three in Tegal, Indonesia, says. as eBay and Amazon pays him about US $ 100 (RM390) a month, approximately half his income.
And for the 25-year-old Shamima Khatoon, her job annotating cars, lane markers and traffic lights at an all-female outpost of data-labeling company iMerit in Metiabruz, India, represents the only chance she has to work outside her home in her conservative Muslim community.
“It's a good way to increase your skills and support your family,” she says.  Major automakers like Toyota, Nissan and Ford, ride-hailing companies like Uber and other tech giants like Alphabet Inc's Waymo are paying reams of labellers, often through third-party vendors.
The benefits of greater accuracy can be immediate.  At InterContinental Hotels Group, every call that its digital assistant Amelia can take from a human saves US $ 5 (RM19) to US $ 10 (RM39), says information technology director Scot Whigham.
When Amelia fails, the program listens while a call is rerouted to one of about 60 service desk workers. It learns from their response and tricks the technique is on the next call, freeing up human employees to do other things. “We've transformed those jobs,” Whigham says.
One of the Interaction's employees is directing the computer on how to respond to a customer. – AP
When a computer can not be made out of a customer call to the Hyatt Hotels chain, an audio snippet is sent to an AI-powered call center in Franklin, Massachusetts. There, while the customer waits on the phone, one of the roomful of headphone-wearing, “intent analysts”, transcribes everything from misheard numbers to profanities and quickly directs the computer how to respond.
That information feeds back into the system. “Next time through, we've got a better chance of being successful,” says Robert Nagle, Interactions' chief technology officer.
Researchers have tried to find workarounds for human-labeled data, but the results are often inadequate.  In a project that used Google Street View images of parked cars to estimate the demographic makeup of neighborhoods, then-Stanford researcher Timnit Gebru tried to train her  But the product shots did not look anything like the ones in the street, and the program could not recognize them. In the end, she says, she spent US $ 35,000 (RM136,000) to hire auto dealer. “
The need for human labellers is” enormous “and” dynamic, “says Robin Bordoli, CEO of labeling technology company CrowdFlower. “You can not trust the algorithm 100%.”
At the moment, figuring out how to get computers to learn without so-called “ground truth” data provided by humans remains an open research question.
Trevor Darrell , a machine learning expert at the University of California, Berkeley, says he expects it will be five to ten years before the computer algorithms can learn to perform without the need for human labeling.
His group alone spends hundreds of thousands of dollars a year paying people to annotate images. “Right now, if you're selling a product and you want perfection, it would be negligent not to invest the money in that kind of annotation,” he says.
Several companies like Waymo and game-maker Unity Technologies are developing simulated worlds to train their algorithms in controlled scenarios
CloudSight, for instance, offers website and app developers a handy tool for uploading a photo and getting a few words back describing it. The retailer Kohl's uses the service for a “Snap and Shop” visual search feature on its app.
But it's not just a fancy computer program. If the algorithm does not have a good answer, one of its 800 employees in places like India, Southeast Asia or Africa type in the answer in real time.
“We want to be the ones that can label any image without any human involvement, “says Ian Parnes, CloudSight's head of business development. “How long that will take is anyone's guess.” – AP