How Artificial Intelligence Will Kill Some Jobs But Create Others
- by Barb Darrow
Report says AI adoption will mostly hurt low-paid workers, but don’t believe it.
The Obama administration may be headed for the exits, but it continues to focus on the impact of artificial intelligence on the economy and the nation at large.
Artificial intelligence (AI) is an umbrella term for a group of technologies—including machine learning—that enable computers to learn new skills and capabilities based on the data they are exposed to, among other factors.
The just-released report, titled “Preparing for the Future of Artificial Intelligence,” does not downplay potential job loss due to the advent of ever-smarter computers, but still posits that the technology will open up new career opportunities for those versed in it or who have higher-level skills. It also holds that public policy, especially re-training programs, can mitigate the negative impact of increasing automation by preparing displaced workers for other jobs.
Some experts argue that it is not just lower-paying jobs that will be stressed by AI and other agents of automation. At an MIT conference a few months back, one researcher, Mary “Missy” Cummings, director of the Humans and Autonomy Lab at Duke University, noted that some plum positions are also on the endangered species list. Take commercial pilots, for example. These pilots, she explained, “touch the stick for three to seven minutes per flight and that’s on a tough day.” The rest of the time that flight is literally on autopilot. It doesn’t take a genius to see which way that wind is blowing.
The White House report also notes that the best way to attack some jobs in the future will be to pair humans with machines in situations where the human partner can compensate for weakness in the computer or vice versa.
“One example is in chess playing, where a weaker computer can often beat a stronger computer player, if the weaker computer is given a human teammate—this is true even though top computers are much stronger players than any human. Another example is in radiology. In one recent study, given images of lymph node cells, and asked to determine whether or not the cells contained cancer, an AI-based approach had a 7.5 percent error rate, where a human pathologist had a 3.5 percent error rate; a combined approach, using both AI and human input, lowered the error rate to 0.5 percent, representing an 85 percent reduction in error.”
One thing to consider: Unless people can be retrained to make a living in this new world, all that AI-fueled productivity could be for naught. If only a small percentage of the population earns enough to have disposable income, who’s going to buy all these AI-enabled goods and services?