In the age of artificial intelligence, predicting which jobs will fall to automation is as much about what machines can do as it is about what they can't. More than half of all jobs in America -- both blue and white-collar -- are resistant to automation, according to an acclaimed study published in 2013 by two Oxford University researchers. Co-author Carl Benedikt Frey, who directs Oxford's Technology and Employment program, broke down three areas where human intelligence still beats artificial intelligence: perception and manipulation, social intelligence; and creativity. Each type has what Frey calls a "bottleneck," which slows the pace at which certain workforces can be automated. The premise is simple: Technology won't replace human workers if it can't do the job.

New technologies actually end up creating different, higher-paying jobs than the ones they replace, according to new research. Fears of "technological unemployment," a term coined by renowned economist John Maynard Keynes in 1930, and recently popularized by MIT Sloan professors Erik Brynjolfsson and Andrew McAfee, are rampant. The worry is that computer-driven automation will take over repetitive tasks across a swath of industries, from retail and financial services to manufacturing and, maybe sooner than we realize, taxicabs and trucking. It turns out, however, that new technologies--most notably AI--aren't harbingers of the so-called robot apocalypse. "Throughout history, automation commonly creates more, and better-paying, jobs than it destroys," writes The Wall Street Journal's Greg Ip. "The reason: Companies don't use automation simply to produce the same thing more cheaply. Instead, they find ways to offer entirely new, improved products. As customers flock to these new offerings, companies have to hire more people."

As digital giants swallow a growing share of shoppers' spending, thousands of stores have closed and tens of thousands of workers have lost their jobs. Belinda Duperre, who sold jewelry at Sam's Club in Fall River, Mass., was one. In early 2016, the struggling store closed. But Ms. Duperre, a lifelong resident of the once-thriving factory town an hour south of Boston, went from victim of the digital revolution to beneficiary. Inc. announced plans to hire 500 full-time workers for a new 1.2-million square foot fulfillment center on the outskirts of town.

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.

Foreign Affairs Editor Gideon Rose discusses work and life in the age of automation. He speaks on "Bloomberg Surveillance." "Sophia," an artificially intelligent (AI) human-like robot developed by Hong Kong-based humanoid robotics company Hanson Robotics, is seen in Geneva on June 7, 2017. That's what influential economist John Maynard Keynes prophesied in his famous 1930 essay "Economic Possibilities for Our Grandchildren," forecasting that in the next century technology would make us so productive we wouldn't know what to do with all our free time. This is not the future Keynes imagined.

Washington (AFP) - Are robots coming for your job? Although technology has long affected the labor force, recent advances in artificial intelligence and robotics are heightening concerns about automation replacing a growing number of occupations, including highly skilled or "knowledge-based" jobs. Just a few examples: self-driving technology may eliminate the need for taxi, Uber and truck drivers, algorithms are playing a growing role in journalism, robots are informing consumers as mall greeters, and medicine is adapting robotic surgery and artificial intelligence to detect cancer and heart conditions. Of 700 occupations in the United States, 47 percent are at "high risk" from automation, an Oxford University study concluded in 2013. A McKinsey study released this year offered a similar view, saying "about half" of activities in the world's workforce "could potentially be automated by adapting currently demonstrated technologies."

Since the 1970s, when automated teller machines arrived, the number of bank tellers in America has more than doubled. James Bessen, an economist who teaches at Boston University School of Law, points to that seeming paradox amid new concerns that automation is "stealing" human jobs. To the contrary, he says, jobs and automation often grow hand in hand. Sometimes, of course, machines really do replace humans, as in agriculture and manufacturing, says Massachusetts Institute of Technology labor economist David Autor in a succinct and illuminating TED talk, which could have served as the headline for this column. Across an entire economy, however, Dr. Autor says that's never happened.

Much ink has been spilled on the subject of how the jobs market is being impacted by artificial intelligence (AI) and robotics. The well-known study by economists Frey and Osborne published in 2013, which predicts that 47% of all currently existing jobs in the United States will come under threat over the next twenty years, is regularly brought out of the cupboard as a terrifying spectre. Other far more optimistic studies, based on longer time-frames, have delivered a riposte to this – largely unfounded – scaremongering, which has in fact been repeated many times over throughout our history. However, the potential impact of AI on general education and vocational skills training – two means of preparing people for the labour market – is still being largely disregarded. The model whereby you learn during the first half of your life and spend the remaining years applying what you have learned in the world of work has held up pretty well.

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.