What he considers another important focus – how artificial intelligence will change the value of skills – is difficult to predict, as the answer partly depends on how the new tools are designed, regulated and used.
Get customer service. Many companies have delegated the task of answering the phone to an automated decision tree, bringing in the human operator only for troubleshooting. But one Fortune 500 enterprise software company has looked at the problem differently. It built a generative AI tool to suggest agents what to say — keeping humans, and their ability to read social cues, in the loop. When researchers from Stanford and MIT compared the performance of groups that were not given the equipment found The tool significantly improved the performance of less skilled agents.
Even if a job becomes completely automated, the fare of displaced workers will depend on how companies decide to use technology in new types of work, especially the work we are doing now. Can’t even imagine, said Daron Acemoglu, a professor at MIT and author of “Power.” And Progress: Our Thousand Year Struggle Over Technology and Prosperity. These options will include whether to completely automate the work or use technology to augment human expertise.
The seemingly scary numbers predicting how many jobs AI could eliminate, even if it’s not clear how, were a “wake up call,” he said.
He believes people “can move in a better direction,” he said, but he is not optimistic. They don’t think we’re on a “pro-human” path.
All estimates of how much work AI can take over are heavily dependent on humans: researchers estimate what the technology can do. Mr. Frey and Mr. Osborne invited experts to a workshop to score the potential for businesses to automate. More recent studies rely on information such as a database tracking AI capabilities maintained by the Electronic Frontier Foundation, a nonprofit digital rights group. Or they rely on workers using platforms like CrowdFlower, where people complete small tasks for money. Workers act on factors that make them prone to automation. For example, if it’s something with a high tolerance for error, it’s a better candidate for a technology like ChatGPT to automate.
Many researchers involved in this type of analysis say the exact number doesn’t matter.
“I would describe our methodology as almost certainly inaccurate, but hopefully perceptibly correct,” said Michael Chui, an AI expert at McKinsey who co-authored a 2017 white paper suggesting that About half work, and 5 percent may be businesses. self drive.
What the data describes is, in some ways, more mundane than is often imagined: big changes are coming, and it’s worth noting.