In 2013, researchers at the University of Oxford released some startling numbers about the future of work. They estimate that 47% of all jobs in the United States are “at risk” of automation “for an unspecified number of years, perhaps 10 or 20 years.” But ten years later, the country’s unemployment rate is at a record low. The tsunami of dire headlines at the time, such as “Half of the world’s jobs are about to disappear because of the rich and robots,” seems utterly irrelevant. But the study’s authors say they didn’t mean to imply that the end was really near. Instead, they were trying to explain what technology could do. Think tanks, corporate research groups, and economists will publish paper after paper pinpointing exactly how much work is being “affected” or “exposed” to technology. It was the first attempt at what became a long-running thought experiment. In other words, if the cost of tools is not a factor, and the only goal is to automate human labor as much as possible, how much work can technology replace? Carl Benedict, researcher at the University of Oxford・When Frey and Michael A. Osborne were doing research, IBM Watson, a question-answering system using artificial intelligence, had just won Jeopardy! and shocked the world. A test version of a self-driving car has circumnavigated the road for the first time. There is now a new wave of research due to the rise of tools that use generative AI. Goldman Sachs estimated in March that the technology behind popular AI tools like DALL-E and ChatGPT could automate the equivalent of 300 million full-time jobs. Researchers at Open AI and the University of Pennsylvania, who developed these tools, found that 80% of U.S. employees could influence at least 10% of their tasks. . “There’s a lot of uncertainty,” says David Autor, an economics professor at the Massachusetts Institute of Technology who has studied technological change and labor markets for more than two decades. “And people want to provide those answers.” But what exactly does it mean, say, that 300 million full-time jobs could be affected by AI? “Being affected can mean getting better, getting worse, disappearing, or doubling down,” Autor said. One complicating factor is that technology tends to automate tasks rather than entire operations. For example, in 2016, AI pioneer Jeffrey Hinton considered a new “deep learning” technology that could read medical images. He concluded, “If you’re working as a radiologist, you’re like a coyote who’s already over the edge of a cliff but hasn’t looked down yet.” He figured it would take him five years, maybe he ten years, before the algorithms “work better” than humans. What he probably overlooked was that reading images is just one of many jobs performed by radiologists (30 of them, according to the US government). He also does things like “meetings with medical professionals” and “providing counseling.” Today, some in the field worry about an impending shortage of radiologists. And Hinton has since become a vocal and public critic of the same technology he helped develop. Frey and Osborne calculated the 47% figure by asking technical experts to rate the likelihood that entire occupations such as “telemarketer” and “accountant” would be automated. bottom. But three years after the paper was published, a group of researchers at the Mannheim, Germany-based ZEW Center for European Economic Research published a similar study evaluating tasks such as “describe a product or service.” , the response rate was found to be only 9%. 60 occupations could be automated in 21 countries. “People love numbers,” says Melanie Arntz, lead author of the ZEW paper. “People always think that numbers must be certain in some way because they are numbers. But numbers can actually be very misleading.” essentially created a tool, not a complete job replacement. You are now a miner who can use an excavator instead of a shovel. Or nurses who have access to better information to diagnose their patients. You may need to pay a higher hourly rate because you can get more work done. In other scenarios, technology complements the workforce rather than replaces it. Alternatively, you can change from a job that requires special skills to a job that does not require special skills. It’s unlikely to work for you. In any case, Autor said technological developments throughout history have tended to affect the distribution of wages and wealth primarily, not the number of available jobs. “With this kind of exercise, you risk focusing on a single tree that stands out too much and missing the forest,” he said of research investigating how much human work could be replaced by AI. How artificial intelligence will change the value of skills, which he already sees as one key focus, is difficult to predict. Because the answer depends in part on how new tools are designed, regulated and used. Consider customer service. Many companies leave the task of answering the phone to an automated decision tree and deploy human operators only for troubleshooting. But one of his Fortune 500 enterprise software companies took a different approach to the problem. It created a generative AI tool that suggested what to say to agents, keeping them informed of their ability to read human and social cues. When researchers from Stanford University and the Massachusetts Institute of Technology compared the performance of a group given the tool to those who weren’t given it, they found that the tool significantly improved the performance of less-skilled agents. . Even if jobs were fully automated, what would happen to the lives of unemployed workers would depend on how companies harness technology for new kinds of work, especially those we can’t yet imagine. said Daron Acemoglu, a professor at the Massachusetts Institute of Technology and author of Power. And progress: our millennial struggle for technology and prosperity. ” These choices include whether to fully automate the work or use technology to augment human expertise. He said the seemingly frightening numbers predicting how many jobs could be cut by AI are “alarm bells,” even if it’s not clear how. He believes people can “steer in a better direction,” but he is not optimistic, he said. He doesn’t think we’re on the “pro-human” path. All estimates of how much work AI can take over rely heavily on humans. So it’s the researchers who make assumptions about what the technology can do. Frey and Osborne invited experts to a workshop and scored the likelihood of the occupation being automated. More recent research has relied on information such as a database that tracks AI capabilities created by the Electronic Frontier Foundation, a non-profit digital rights organization. Alternatively, it relies on workers using platforms like Cloudflower where people complete small tasks to earn money. Workers score tasks based on factors that are amenable to automation. For example, if your tolerance for error is high, a technology like ChatGPT is a good candidate to automate. Many researchers involved in this kind of analysis say the exact numbers don’t matter. Michael Chui, an AI expert at McKinsey and author of the 2017 white paper, said, “I would say that our methodology is arguably exactly wrong, but we are headed in the right direction.” Approximately half of jobs and 5% of occupations will be automated. What the data describes is, in some ways, more mundane than commonly assumed. That said, big changes are coming, and they’re worth keeping an eye on. This article was originally published in The New York Times.By Sarah Kessler, circa 2023, The New York Times
