Machine learning depends on human labeling by That's why has ethical guidelines for crowd work:
⁦Thorough, deep reporting on #ghost work, highlighting the need to see+value the human labor core to AI by via ⁦
“Porn, porn, porn, beheading, beheading, beheading,” Ms. O'Sullivan said. 🤦🏻‍♀️😂
The most interesting thing I’ve read today - the humans who work, in often dystopian conditions, training “artificial intelligence”
Tech companies rarely discuss the labor-intensive process that goes into the creation of A.I. traveled to India and New Orleans to visit the offices where hordes of people label data to help A.I. learn
This NYTimes article on the people doing the ground truth labelling/classification of medical #AI reveals how deeply flawed and unethical the work is #HealthTech #MedTech
Are there issues with how tech companies train A.I. technology?
“This is an expanding world, hidden beneath the technology,” said Mary Gray, an anthropologist at Microsoft and the co-author of the book “Ghost Work,” which explores the data labeling market. “It is hard to take humans out of the loop.”
They call it artificial intelligence. Machines can now learn some impressive skills by analyzing vast troves of data, including face recognition, language translation, and medical diagnosis. But first, humans must label that data. Many humans. Meet them
Must-read about ‘fauxtomation’: People in the tech industry tell you AI is the future, improving fast thanks to something called machine learning. But they rarely discuss the labor-intensive process that goes into its creation: A.I. is learning from humans
Humans are teaching machines how to spot polyps in a colon to pedestrians on the road, helping develop #ArtificialIntelligence that will one day lead to better diagnostics and autonomous cars. Or will it? ⁦
A.I. Is Learning From Humans. Many Humans. - The New York Times
A.I. Is Learning From Humans. Many Humans.
A.I. Is Learning From Humans. Many Humans.
In India, a different kind of "supervised" learning for medicine humans reviewing colonoscopy videos to prep for neural nets, by
A.I. Is Learning From Humans. Many Humans: Before an A.I. system can learn, someone has to label the data supplied to it. Data labelling accounted for 80 percent of the time spent building A.I. technology.
The irony of creating new, lower paid jobs to automate existing, higher paid jobs. Or a window into new opportunities for people in the developing world. Hard not to have mixed feelings.