I help create the automated jobs that are taking jobs.

I Help Create the Automated Technologies That Are Taking Jobs

I Help Create the Automated Technologies That Are Taking Jobs

The citizen’s guide to the future.
Jan. 26 2017 7:00 AM

I Help Create the Automated Technologies That Are Taking Jobs

And I feel guilty about it.

Manufacturing is up, in terms of production, but manufacturing employment is down.


You would not know it from the discussions about the economic issues of the nation’s industrial heartland, but manufacturing is at an all-time high in the United States according to a November 2016 Brookings Institution study. The manufacturing recovery since the Great Recession has been quite remarkable. If you will forgive me, America is already great in manufacturing again.

However, more manufacturing no longer translates necessarily into more jobs thanks to technology-driven productivity gains. So manufacturing is up, in terms of production, but manufacturing employment is down. Companies are doing well while many employees have been displaced. This transformation in manufacturing is largely not the fault of NAFTA or a weak Chinese trade policy, but of people like me: engineers and scientists working on new technologies, constantly extending the capabilities of machines, and reducing the need for humans for many tasks.


I work on the mathematics, algorithms, signal processing techniques, and machine-learning approaches for the next generation of communications systems, radars, and anticipatory analytics. Success for me is to create technical disruption and provide everyone with more tools and capabilities. I always hope that my work will improve the quality of human life. However, these “improvements” are not universal. As I view the technical future, I am admittedly conflicted about the implications. I see the wondrous technologies being integrated into our society, but I also see the lives broken by the accelerating changes.

Manufacturing jobs are only the latest to be affected by technological advancement. Think of agriculture. It once employed a majority of the population, but now it employs less than 2 percent of Americans. I witnessed some of this change personally, because my grandfather and father were dairymen. When my grandfather started his farm, he milked fewer then 100 cows. By the time I was in high school, my dad was milking between 300 and 400 cows. The technology for breeding, feeding, and milking cows had improved, as had the science of nutrition. Then a large corporate dairy started a mile away from my dad’s dairy. It had a fancy carousel barn with moving stalls on which thousands of cows were milked. The drive to increase operational efficiency continued to grow, and smaller family farms were no longer viable. The production of milk was increased, but there were fewer dairy jobs. Several years after the corporate dairy launched, my dad made the difficult but wise decision to sell the dairy while he was ahead.

Those changes happened relatively slowly, but it seems to me that employment disruption is accelerating. A large reason for this is that what used to be a room-sized super computer now fits in my pocket. Over the last two decades, I have observed a fundamental change in how we can apply advanced algorithms to sensing and controlling systems—the kinds of technology that enable more sophisticated robotic manufacturing. I can remember discussing various algorithms and believing they were well beyond what we could ever implement. Now these same algorithms are considered elementary. They are just some of the changes that have fueled the revolution in manufacturing.

Even in my own field, a constant flow of computer-aided tools is displacing humans, pushing them to other tasks. When I was young, I used to solder circuits and assemble machine code by hand. I converted logic to digital circuit design and spent hours evaluating integrals. Today, my computer is much faster and more accurate at many of these tasks. Fortunately, I am still better at being creative and setting up the problems, and it will be some time before artificial intelligence will be able to challenge humans’ ability to understand the context of situations. However, in many arenas, machines are already far more capable than humans. I regularly develop computational and analysis techniques that significantly outperform what humans can see in data. As one example, while to a human it would be uninterpretable, our algorithms can accurately classify activity using accelerator signals from a simple Fitbit-like device.


These disruptive effects will not be limited to low-skilled labor or to the edges of tech. With the coming advances—driven in part by my sensing, communications, and signal processing research—we will make the machines better drivers than humans. This will work out nicely for larger companies that can invest in the technology—they will have fleets of self-driving trucks. But just like with the dairy down the street from my father’s farm, many independent truck owners will be put out of business.

These effects will quickly encroach into areas that we may think of as protected. Machines can do many of the document-review tasks performed by entry-level lawyers. (The firms’ partners never liked having to pay them, anyway.) Recently, machine intelligence has been applied to dispute arbitration, currently to guide users, but soon it will replace human lawyers all together. We are even teaching machines to produce music and art. There are no completely safe jobs.

I am doing my part to create this disruption, and that makes me feel uneasy. I feel even worse when I hear misleading statements about the source of the problem. Blaming China and NAFTA is a convenient deflection, but denial will only make the wrenching employment dislocation for millions all the more painful. To claim that we will (or even can) return to a time without these disruptions is unfair and dangerous. We need to rework the social contract to deal with these changes, and to provide the economic and technical tools to enable constant retraining.

Given the results of the recent election, in which government was often vilified, this may be a strange time to say this. Helping these individuals is a job for government. No other entity can address this huge problem. Above all, we need government support for education—both for first-time and returning students. Many who have suffered technologically induced job loss need to learn fundamentally new skills. Those of us who benefit from technological disruption owe these individuals sufficient time and financial support to develop so they can take part in our economy—and our future.


Daniel W. Bliss is a professor at Arizona State University. He has degrees in physics and electrical engineering, and performs research on communications and sensing systems.