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Is Your Job Predictable? Immigrants Aren’t Coming for Your Job, *Robots* Are

Is Your Job Predictable? Immigrants Aren’t Coming for Your Job, Robots Are

Last updated on: March 29, 2018 at 10:06 am by Carl Gregg

Is Your Job Predictable? Immigrants Aren’t Coming for Your Job, *Robots* Are 2018-03-29T10:06:10-04:00 Carl Gregg

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There is a lot of xenophobic fearmongering these days about immigration. But as the saying goes, “Immigrants aren’t coming for your jobs, robots are.” Robots, equipped with increasingly sophisticated Artificial Intelligence, are rapidly becoming competent in a widening array of fields.

Martin Ford, in his important book Rise of the Robots: Technology and the Threat of a Jobless Future (2015), invites his readers to reflect on a thought experiment in which robots of the near future are reimagined as an alien invasion. Unlike in the movies, these robot aliens “have not come to conquer us, or to extract our resources, or even to meet our leader. The aliens, it turns out, have come to work.” And not only do they want to work, they are also good at it. Each of these alien robots are “highly intelligent and capable of learning language, solving problems, and even exhibiting creativity.” And once they master a skill, they work tirelessly, need little repairs or time-off, and can be replicated at increasingly cheap rates (194-196).

If robots do eventually take over most—or essentially all—of the jobs currently done by humans, then what are we humans going to do for money? One option is some sort of government-sponsored “jobs program” in which jobs that robots could do much more cheaply and efficiently are instead reserved for humans. This possibility was famously skewered in the 1960s by the late Milton Friedman (1912-2006), a Nobel Prize-winning economist who has been called the “second-most popular economist of the twentieth century.” (If you’re curious, number one was John Maynard Keynes.”):

While consulting with the government of a developing Asian nation, Friedman was taken to a large-scale public works project, where he was surprised to see large numbers of workers wielding shovels, but very few bulldozers, tractors, or other heavy earth-moving equipment…. Friedman asked, “So then, why not give the workers spoons instead of shovels?” (ix)

After all, spoons are also a technology that makes work less labor-intensive for humans. If the only reason to not use a bulldozer is a belief that workers must do some sort of labor to give their lives meaning and justify paying them, why not just let them use their hands? For that matter, why not make them “do the Hokey-Pokey and turn themselves around” for eight hours before punching the clock, if a robot can come in behind them and do their job many times faster?

The economy is more complicated than that, of course. As we heard at the end of the spoken meditation, “A worker is also a consumer…. When a worker is replaced by a machine, that machine does not go out and consume.” So if machines take all our jobs, who is going to have enough spare wages or wealth to buy stuff to power the economy? One response worth considering could be to restructure our economy. As the saying goes, “Never let a crisis go to waste.” A crisis can be an opportunity to do things differently (xviii).

And history tells us that similar crises have happened in the pas, although these crises certainly not always resulted in more equitable job access for all:

The mechanization of agriculture vaporized millions of jobs and drove crowds of unemployed farmhands into cities in search of factory work. Later, automation and globalization pushed workers out of the manufacturing sector and into new service jobs…. What’s more, those new jobs were often better than earlier counterparts, requiring upgraded skills and offering better wages. (ix-x)

Describing jobs as “better” is admittedly a subjective evaluation, and there were also many workers displaced and sometimes unable to access one of these new jobs. In light of this history, we need to have a serious reckoning about how hard we should fight to protect the jobs that robots might take away. Would more leisure time be so terrible? To quote another economist, this time the late John Kenneth Galbraith (1908 – 2006): “Leisure is a peculiar thing. Leisure is [often seen as] very good for the rich, quite good for Harvard professors—and very bad for the poor” (48).

As to how much any one of us should be concerned about the “rise of the robots,” there was an article in The New York Times earlier this month that summed up the current situation: “Most Americans See Artificial Intelligence as a Threat to Jobs (Just Not Theirs).” So—almost all of us continue to regularly use early stage Artificial Intelligence devices (navigation apps, streaming services, smartphone personal assistances, and other smart home devices) that are collecting all the data about human behavior that will make the next generation of AI possible. And the possibilities keep growing—or should I say metastasizing?!—exponentially from there.

Indeed, Kevin Kelly (1952-), the founding Executive Editor of Wired magazine, has outlined the Seven Stages of Denial related to having your job replaced by a robot. A little time passes between each stage, during which the robot’s AI rapidly improves its performance capabilities:

A robot/computer cannot possibly do the tasks I do. [Time passes]
OK, it can do a lot of those tasks, but it can’t do everything I do. [Time passes]
Ok, it can do everything I do, except it needs me when it breaks down, which is often. [Time passes]
OK, it operates flawlessly on routine stuff, but I need to train it for new tasks. [Time passes]
OK, Ok, it can have my old boring job, because it’s obvious that was not a job that humans were meant to do. [Time passes]
Wow, now that robots are doing my old job, my new job is much more interesting and pays more! [Time passes]
I am so glad a robot/computer cannot possibility do what I do now. (59-60)

At this point you’ve slipped into denial again, so go back to stage one and repeat until enough time passes that a robot can now do your new job too!

One of the biggest misconceptions is that robots are only coming for so-called “blue collar” jobs. But it is not only “routine” and “repetitive” jobs that are in the crosshairs of AI-empowered robots. Increasingly sophisticated algorithms have shown us that the key word for determining whether your job is under threat is whether it is predictable:

Could another person learn to do your job by studying a detailed record of everything you’ve done in the past? Or could someone become proficient by repeating the tasks you’ve already completed, in the way that a student might take a practice test to prepare for an exam? (xiv-xv)

To put myself in the spotlight, what ministerial algorithms might be created by constantly surveilling what all the religious professionals across the world are doing? I suspect we ministers might someday find ourselves among those people experiencing the Seven Stages of Denial about being replaced by a robot.

To give a more high-end example of what I’m talking about, radiologists (physicians who specialize in the interpretation of medical images) are among the highest paid doctors, with an average annual salary of around $340,000. Today, to become a radiologist requires approximately thirteen years of training beyond high school; but each year, computers are getting better at analyzing images. I can already see the appeal of getting a second opinion from a computer that has in its memory bank every interpretation every previous radiologist has ever made.” And the day is likely coming soon when we have only robot radiologists (xv).

The day is also likely coming soon when you will enter a fast food restaurant that is almost 100% automated. You will enter you order through an interface and your food will be prepared precisely to your specifications by a robot. A few years ago, for instance, a company named Momentum Machines designed a machine “capable of producing about 360 hamburgers per hour, that also toasts the bun and then slices and adds fresh ingredients like tomatoes, onion, and pickles only after the order is placed” (12). The company estimates that their machine will pay for itself in less than a year based on not having to pay for human labor (13). There may well not even be a human manager physically present. Rather, there may be a centralized facility remotely monitoring the individual restaurants (15). Heck, it may be that you skip going to the restaurant altogether, order through a smartphone app, and have a drone deliver the food to wherever you are.

To give a few more examples, StatsMonkey is a program being developed to “automate sports reporting by transforming objective data about a particular game into a compelling narrative” (84). Even more shocking are the advances that machines are making in composing music that is emotionally moving to humans—based on inputting data on the history of music and the human response to it. Dishearteningly to we mere mortals, these complex pieces of music take machines mere minutes to compose (111). Similar software is being developed to allow machines to generate new works of art. Using a “database of paintings that had been labeled by humans with adjectives like ‘dark,’ ‘sad,’ or ‘inspiring,’ the software can itself discern “whether or not it is achieving its objectives as it paints” (112-113).

I will continue this topic tomorrow in a post on “Is Artificial Intelligence ‘Progress’ or Programming Human Obsolesce?“

The Rev. Dr. Carl Gregg is a certified spiritual director, a D.Min. graduate of San Francisco Theological Seminary, and the minister of the Unitarian Universalist Congregation of Frederick, Maryland. Follow him on Facebook (facebook.com/carlgregg) and Twitter (@carlgregg).

Learn more about Unitarian Universalism: http://www.uua.org/beliefs/principles

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