First, humans have been underestimated. It turns out that we (well, many of us) are really amazing at what we do, and for the foreseeable future we are likely to prove indispensable across a range of industries, especially column-writing. Computers, meanwhile, have been overestimated. Though machines can look indomitable in demonstrations, in the real world A.I. has turned out to be a poorer replacement for humans than its boosters have prophesied.
What’s more, the entire project of pitting A.I. against people is beginning to look pretty silly, because the likeliest outcome is what has pretty much always happened when humans acquire new technologies — the technology augments our capabilities rather than replaces us. Is “this time different,” as many Cassandras took to warning over the past few years? It’s looking like not. Decades from now I suspect we’ll have seen that artificial intelligence and people are like peanut butter and jelly: better together.
It was a recent paper by Michael Handel, a sociologist at the Bureau of Labor Statistics, that helped me clarify the picture. Handel has been studying the relationship between technology and jobs for decades, and he’s been skeptical of the claim that technology is advancing faster than human workers can adapt to the changes. In the recent analysis, he examined long-term employment trends across more than two dozen job categories that technologists have warned were particularly vulnerable to automation. Among these were financial advisers, translators, lawyers, doctors, fast-food workers, retail workers, truck drivers, journalists and, poetically, computer programmers.
His upshot: Humans are pretty handily winning the job market. Job categories that a few years ago were said to be doomed by A.I. are doing just fine. The data show “little support” for “the idea of a general acceleration of job loss or a structural break with trends pre-dating the A.I. revolution,” Handel writes.
Consider radiologists, high-paid medical doctors who undergo years of specialty training to diagnose diseases through imaging procedures like X-rays and MRIs. As a matter of technology, what radiologists do looks highly susceptible to automation. Machine learning systems have made computers very good at this sort of task; if you feed a computer enough chest X-rays showing diseases, for instance, it can learn to diagnose those conditions — often faster and with accuracy rivaling or exceeding that of human doctors.
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