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. 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.The benefits of greater accuracy can be immediate.Venture capitalist S.Several companies like Alphabet’s Waymo and game-maker Unity Technologies are developing simulated worlds to train their algorithms in controlled scenarios where every object comes pre-defined. Major automakers like Toyota.

Nissan and Ford, ride-hailing companies like Uber and other tech giants like Alphabet Inc.Major automakers like Toyota, Nissan and Ford, ride-hailing companies like Uber and other tech giants like Alphabet Inc. “You can’t trust the algorithm 100 per cent. “Soma” Somasegar says he sees “billions of dollars of opportunity” in servicing the needs of machine learning algorithms. But in bulk, this work can offer a decent wage in many parts of the world — even in the US.Trevor Darrell, a machine learning expert at the University of California Berkeley, says he expects it will be five to 10 years before computer algorithms can learn to perform without the need for human labelling. rapier loom There’s a dirty little secret about artificial intelligence: It’s powered by hundreds of thousands of real people.But the product shots didn’t look anything like the car images in Street View, and the program couldn’t recognise them.There, while the customer waits on the phone, one of a roomful of headphone-wearing “intent analysts”transcribe everything from misheard numbers to profanities and quickly directs the computer how to respond. “You can imagine how important it is for me getting paid in US dollars.Accurate labelling 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. “Next time through, we’ve got a better chance of being successful,” says Robert Nagle, Interactions’ chief technology officer.“It’s a good platform to increase your skills and support your family,” she says.

Marjorie Aguilar, a freelance makeup artist in Maracaibo, Venezuela, spends four to six hours a day drawing boxes around traffic objects to help train self-driving systems for Mighty AI.This human input industry has long been nurtured by search engines Google and Bing, who for more than a decade have used people to rate the accuracy of their results. It learns from their response and tries the technique out on the next call, freeing up human employees to do other things.From makeup artists in Venezuela to women in conservative parts of India, people around the world are doing the digital equivalent of needlework — drawing boxes around cars in street photos, tagging images, and transcribing snatches of speech that computers can’t quite make out.“We’ve transformed those jobs,” Whigham says.That information feeds back into the system.In a project that used Google Street View images of parked cars to estimate the demographic makeup of neighbourhoods, then-Stanford researcher Timnit Gebru tried to train her AI by scraping Craigslist photos of cars for sale that were labelled by their owners.And for 25-year-old Shamima Khatoon, her job annotating cars, lane markers and traffic lights at an all-female outpost of data-labelling company iMerit in Metiabruz, India, represents the only chance she has to work outside the home in her conservative Muslim community.”