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The Next 10 Years Of Automation And What It Might Mean For The Job Market

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After decades of subtle developments that largely went unnoticed by much of the working world, artificial intelligence (AI) has taken center stage in the last 2-3 years as a “hot” technology.

From Google’s surge of acquisitions (DeepMind, Boston Dynamics, etc.), to increased venture capital attention, to the safety concerns of Elon Musk and Bill Gates about potentially super-intelligent AI, the field is undeniably back in the spotlight.

One of the most pressing concerns for those of us in the working world is the effect of automation on job security — in both blue-collar and white-collar work.

Though more far-out considerations are difficult to predict, many experienced computer science researchers feel reasonably comfortable speaking about AI’s influence in the coming 5-10 years.

With so much potentially unfounded speculation about how automation might influence the nature and demand for human work, I decided to ask six artificial intelligence PhDs about their informed perspectives on how AI might impact the job market in the coming decade. Their answers didn’t share much commonality in terms of industry, but they did share a common thread: The expanded or strengthened use of existing algorithms.

One wide swath of jobs that may be most easily automated are likely to be jobs that involve narrow and repetitive manipulation or assessment of data. Irfan Essa at Georgia Tech focuses his research on machine vision, a domain that has developed markedly in the last 10 years. “Many fields were AI could be applied have been in ‘aggregation mode’ for quite some time, and now we’re finally getting to a point of sense-making,” says Essa.

While identifying human faces, or categorizing web images (identifying animals, landmarks, objects) was once the arduous job of human beings, many of these tasks can now be automated by trained neural networks (Google’s Peter Norvig explains this process rather well).

Visual data is far from being the only area of narrowly focused intelligence that might be under siege. Martin Ford (author of the well-received book Rise of the Robots) mentions that in the coming 10 years, we’re likely to see more automated job displacement in white-collar jobs rather than blue-collar.

There is ongoing debate as to whether or not technological advancements inherently create more job market opportunities than they destroy.

Daniel Berleant agrees, stating the current difficulties of “mobility is undeniably a rather difficult technical problem, and computers are more likely to manipulate data better than humans than they are to take over most manual labor jobs, at least for the time being.” Despite the impressive developments in bipedal robots in the last 10 years, people with dexterous physical jobs such as moving furniture or carrying plates in a busy restaurant aren’t likely to be automated out of a job anytime soon (though stationary assembly jobs are under siege now as much as ever, with devices like Rethink Robotics’ Baxter).

Some researchers believe that the same might be said of narrow data assessment, not just data manipulation. Andras Kornai states, “IBM is moving Watson into the medical field — I expect the same thing to happen in the legal area.” Though it may be possible that machine learning will aid in the detection of cancer or other maladies in medical imaging, these technologies don’t seem likely to put doctors out of a job.

Long story short, if a large portion of your time at work involves tinkering with spreadsheets, there is likely to be software that will perform your job faster and cheaper than human labor. Marc Andreessen put this in intelligible terms in his “software eating the world” WSJ interview, and it’s worth understanding if you plan on being employed in 2025.

However, the influence of AI in the coming decade may imply an expansion beyond the “narrow” focuses that it’s best known for (i.e., analyzing images, beating silly humans at chess, etc.), and some of the AI experts I’ve interviewed seem to think that people are becoming comfortable handing over that control.

Eyal Amir is a Stanford PhD and Associate Professor at The University of Illinois at Urbana-Champaign focused on AI research. “More generally what you see as a trend is for different pieces of data coming together, and that we give the computers a little bit more autonomy,” says Amir. “We start trusting the ability of the computer to do basic tasks and to have knowledge that we don’t have.”

In a recent AI-focused interview, Amir states that he sees this increased degree of trust as a byproduct of the increased effectiveness of AI programs, such as Apple’s Siri and Facebook’s advertising algorithms (which infer data about individuals’ preferences, vocation, gender and more — based on cues and clues from Facebook’s myriad data points). The concierge services of the future may simply be no match for a souped-up Siri who can instantly bring you information and perform tasks for you (order pizza, order pick-up for dry cleaning, etc.).

Other algorithms in use today include those used to judge the credit scores of consumers and businesses. Andras Kornai, a Stanford PhD and professor at the Budapest Institute of Technology with experience in designing credit algorithms, states, “It is no longer a local friendly banker who makes these decisions around credit, and that trend isn’t likely to slow down.” It’s likely that other efficient algorithmic use isn’t going to slow down either, and because there wasn’t much backlash in AI taking over loan and insurance decisions, it seems quite likely that it’ll handle more complex financial issues in the coming decade.

Kornai also refers explicitly to the use of algorithms in specific medical diagnostics, or even in legal proceedings, and believes that slow and steady traction in these domains is somewhat inevitable, and may invariably box out human expertise from tasks such as x-ray assessments or certain kinds of legal research.

Nearly all the researchers I’ve spoken to about automation and the job market have brought up the topic of self-driving cars.

Speech-recognition algorithms of tomorrow may create their own economic shakeups. Daniel Roth received his PhD from Harvard in 1995. He now teaches at University of Illinois and has been working in the domain of natural language processing for nearly 20 years: “In ten years, I can see us being able to communicate with computers in a truly natural way…. I will be able to consult a machine in really thinking through a world problem… a physician will be able to consult a computer to navigate research articles.”

Roth mentions that many millions of medical research articles will be published in the coming decade, and that having a machine that can understand natural commands to sift through this massive swath of information would be of extreme value (i.e., “Find me all the articles published within the last three years in any language that study the impact of air pollution on osteoporosis in men.”). The same natural language algorithms might comb legal files or compliance documents, potentially shaving hours of tedious work from a professional’s day, but also potentially leaving some entry-level positions (such as paralegals) out of a job.

Though the AI researchers I spoke with didn’t tend to converge on similar industries when it came to making predictions, nearly all the researchers I’ve spoken to about automation and the job market have brought up the topic of self-driving cars. To Amir’s point — there seem to be few more visceral ways of “giving up control” than letting the machine take the wheel, and 10-15 years seems to be enough time for many AI experts to suspect that we’ll see consumers buying cars that drive them, not the other way around.

Berleant mentions there has been a steady progression to automatic transmissions, anti-lock breaks, automatic locks and cars that can park themselves. He states, “I believe it’s reasonable to suppose that such completely autonomous cars will be commonplace in ten years.” If even one-tenth of the cars on the road in 10 years are self-driving, the impact on the economy as a whole could be relatively drastic.

Among other sectors, the immediate impact on the job market for motor vehicle operation would be hit the hardest. “There are a million cab drivers in the United States alone — that might be a million people without a job” says Kornai. In addition to direct unemployment for folks in truck driving or taxi driving positions, there also could be a drastic decrease in demand for car ownership if cars can be ubiquitously accessed for transportation with the push of a button on an app.

Car manufacturers might be fighting over a much smaller market of individuals who still wish for a car of their own — or they would battle over who’s autonomous fleets are employed in the most cities. Manufacturing demand for vehicles seems destined to decline sharply under these circumstances.

One of the most pressing concerns for those of us in the working world is the effect of automation on job security.

The incumbents to driverless cars are likely to fight just as fiercely as those currently railing against Uber, and Kornai and others foresee a reasonably gradual shift to autonomous vehicles, and this may cushion the shock of a drastic economic shift.

We might see a way around these legal concerns with a gradual “trust transition” from man to machine, rather than an overt jump from 100 percent human driver to 0 percent human driver. Either way, a lot of very smart AI folks seem to think that the next decade is the one when driverless will kick in.

Like many double-edged effects of technological change and automation, driverless cars may have tremendous upsides, as well. “There’s so much release of human potential if you don’t have to be behind the wheel for an hour per day or more,” says Berleant. This isn’t to say that truck drivers are all going to become tremendously efficient with all the freed up time they have in their hands-free commute to their next job, but it’s a potential example of the silver lining of automation and the job market.

There is (and for the foreseeable future, will continue to be) ongoing debate as to whether or not technological advancements inherently create more job market opportunities than they destroy. The most ignorant arguments are black-and-white, and it’s clear from interviewing subject-matter experts that there is no consensus on the future outcomes, economically or technologically.

What does seem clear is that there are important current automation and AI trends with existing algorithms and technologies that are likely to only have a greater job-market influence in the coming decade, and they are worth keeping an eye on. Maybe machine vision can help us with that.

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