Tesla’s layoffs hit Autopilot team as AI develops
As part of Tesla's plans to cut 10% of salaried staff
By Matt McFarland, CNN Business
Tesla is increasingly turning to machines rather than humans as it attempts to develop autonomous vehicles.
As part of Tesla’s plans to cut 10% of salaried staff, the company has laid off a significant number of its data annotation specialists. These specialists do grunt work that is critical to empowering artificial intelligence systems to handle complex tasks like driving safely down a city street.
The layoffs were first reported by Bloomberg Tuesday and confirmed by CNN Business.
Data annotation specialists use software tools to manually label objects in video clips collected from Tesla vehicles. The specialists label common road objects like lane lines, stop signs, traffic cones, curbs and traffic signals. The labeled data is fed into the artificial intelligence system so that it learns to accurately perceive its environment. The more correctly labeled data an AI system has, the better it can be.
Tesla has developed an automated way to do some of this labeling work in recent years, which allows the automaker to streamline its labor force.
“Without the auto labeling I think we would not be able to solve the self-driving problem,” Tesla CEO Elon Musk said at the company’s AI Day in August 2021.
Tesla executives have suggested that automating data labeling has already accelerated its work on self-driving vehicles.
Ashok Elluswamy, director of Autopilot software, said at the AI Day event that Tesla was able to collect 10,000 video clips from its cars and automatically label them in a week. Clips are generally 45 to 60-second video segments, as well as related GPS and odometer data.
“This would have taken several months with humans labeling every single clip,” Elluswamy said. He also described plans for producing a million auto labeled clips to “really crush this problem.”
There’s no clear cut answer to whether manual, human labeling or automated data labeling is more accurate, according to Raj Rajkumar, a Carnegie Mellon University professor who studies autonomous vehicles. Companies like Tesla may keep some humans involved to catch the flaws of the automated labeling, he said.
“If you do the economics of fewer humans, it’s a financial win,” Rajkumar said.
About five years ago, Tesla relied on an outside business to label its self-driving data, but moved efforts in house in recent years, Andrej Karpathy, who leads AI at Tesla, said last year. Data labelers have worked in San Mateo, California and Buffalo, New York. He described this as critical to improving the quality of Tesla’s data. The company built a team of more than 1,000 people, he said at AI Day in 2021.
The job cuts and auto labeling do not eliminate the need for human labor. In fact, Tesla continues to publicly post some job openings for data annotation workers.
“For us it’s much more becoming a story of, ‘How do humans and computers collaborate to actually create these vector space data sets?'” Karpathy said at AI Day.
Karpathy said that Tesla wanted the auto labeling to be extremely accurate, which may have impacted how fast Tesla has turned to it.
Artificial intelligence experts say there will be less need for human annotators in the future as techniques are developed that don’t require the costly work.
“The future of data annotation is less of it,” Pedro Domingos, a computer science professor at the University of Washington, told CNN Business. He cited the example of AI systems for language that learn from masses of text data.
Adella Petrescu, a former Autopilot data annotation supervisor at Tesla, posted on social media Tuesday that she had been laid off. Petrescu said she was promoted twice in one year and never had performance issues.
“For the past [two] years I have worked 50 [plus] hours every week, many weekends, and too many weeks with 16 hours days and all for one purpose that I truly believed in, I still do — [a] better future for our next generations,” Petrescu said.
Tesla did not immediately respond to a request for comment and generally does not engage with the professional news media.
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