Thanks to rapid advances in the fields of artificial intelligence (AI) and robotics, smart machines that would have once been relegated to works of science-fiction are now a part of our reality.
Currently, opinions on the subject are as varied as the types of AIs in development. In January, MIT Technology Review compiled a list of 19 studies focused on automation and the future of work. No two reached identical conclusions.
In 2017, research and advisory company Gartner released a study predicting automation would destroy 1.8 million jobs worldwide by 2020. That same year, another research and advisory company, Forrester, released their own report on automation and the workforce. According to their calculations, the U.S. alone will lose 13.8 million jobs to automation in 2018.
The numbers vary even more wildly the farther out you look. By 2030, futurist Thomas Frey predicts humans will lose 2 billion jobs to robots, while researchers from consulting firm McKinsey predict a comparatively paltry 400 to 800 million in losses.
Beyond the numbers, experts also disagree on the professions that will become automated, as well as where in the world will bear the brunt of the job losses.
In an attempt to get to the bottom of the automation mystery, Futurism asked several experts to tell us who they believe will be most likely to suffer as a result of automation. Here’s what they had to say.
Edward D. Hess, professor of business administration and Batten Executive-in-Residence at the University of Virginia:
Automation is going to dramatically impact service and professional workers. To find work, one must be good at doing what the technology won’t be able to do well.
For the near term, those skills are: (1) higher order thinking (critical, innovative, imaginative) that is not linear; (2) the delivery of customized services that require high emotional intelligence to other humans; and (3) trade skills that call for real-time iterative problem diagnosis and solving and/or complex human dexterity.
Jobs that have a high risk of being automated are jobs that involve repetitive tasks and linear tasks that are easy to code: “if this, then do this.”
High-risk fields are retail, fast food, agriculture, customer service, accounting, marketing, management consulting, investment management, finance, higher education, insurance, and architecture. Specific jobs include security guards, long-haul truck drivers, manual laborers, construction workers, paralegals, CPAs, radiologists, and administrative workers.
Technology is going to continue to advance, and in reality, all of us are going to have become life-long learners, constantly upgrading our skills. The most important skills to have will be knowing how to be highly efficient at iterative learning — “unlearning and relearning” — and develop high emotional and social intelligence.
Jobs requiring high emotional engagement in the customization and delivery of services to other human beings will be the most safe. Those include psychological counselors, social workers, elementary school teachers, physical therapists, personal trainers, trial lawyers, and estate planners. Other jobs that will be in high demand are in computer and data science.
What will become human beings’ unique skill? Emotional and social intelligence.
The short answer is people who have boring, routine, repetitive, and physically arduous jobs.
The long answer is that labor-saving process innovation and “classical” productivity increase may make some workers redundant as they are replaced by robots and machines that can do their jobs better and cheaper.
This could get a lot worse if AI will also replace workers who are trained and skilled in medium human-capital intensity jobs, such as drivers, legal assistants, bank tellers, etc. So far, the evidence for that is very weak, but it could change, depending on what happens to demand and output as prices fall and quality improves. What counts is demand elasticities with regards to price and with regards to product quality (including user-friendliness).
However, product innovation (unlike process innovation) is likely to create new jobs that were never imagined. Who in 1914 would have suspected that their great-grandchildren would be video game designers or cybersecurity specialists or GPS programmers or veterinary psychiatrists?
The dynamic is likely to be that machines pick up more and more routine jobs (including mental ones) that humans used to do. At the same time, new tasks and functions will be preserved and created that only humans can perform because they require instinct, intuition, human contact, tacit knowledge, fingerspitzengefühl, or some kind of je ne sais quoi that cannot be mechanized.
Bob Doyle, director of communications for the Association for Advancing Automation:
I would argue that the question should be phrased as the following: “Who is actually going to thrive because of automation?” And the answer is everyone who embraces automation.
Automation is the competitive advantage used by companies around the world, and for good reason. Companies automate heavy-lifting, repetitive, low-value processes in order to achieve higher output and product quality so that they can be more competitive in global markets.
That gives them the resources to innovate, to improve business processes, and to continue to meet consumer demands. That lets those companies continue to hire human workers for the jobs they’re best-suited for: insight-driven, decision-based, and creative processes. You can say that another word for “automation” is “progress.”
The inability to compete is the real threat to jobs, not automation.
Between 2010 and 2016, there were almost 137,000 robots deployed in manufacturing facilities in the U.S. During that time, manufacturing jobs increased by 894,000 (U.S. Bureau of Labor Statistics) and the unemployment rate declined by 5.1 percent.
These companies (along with their employees) are competing and thriving today because of automation. We should remember that technological advances have always changed the nature of jobs. We believe this time is no different. We must be sure that we’re preparing the workforce to fill these jobs that are being created, especially in advanced manufacturing. The future of automation in bright!
The basic premise of automation is to allow machines to follow a set procedure –wait for it – automatically, to save on human capital and reduce human errors.
So far so good.
The complexity of what can be automated, however, is still up to humans, and is limited only by the imagination. Well, your imagination and your ability and standardize formatting your inputs, integrate into various systems, and create rules.
But beyond the ability to program your machines with the criteria listed above, you also need to consider the context of how they’re being used.
Think of a tennis ball machine vs. a tennis pro—no matter how good the pro is, it will never be as accurate as the machine. Additionally, the machine is basically a one-time fixed cost, while you’d have to pay the tennis pro every week, reschedule appointments when he gets sick, etc. And this doesn’t even account for the fact you’d have to listen to him drone on about how he “Could have qualified for the Australian Open in ‘95 if the darn line judge wasn’t completely blind…”
Obviously, the machine can’t coach form or any of the other human things a tennis pro can, but as far as shooting a pre-calibrated serve over and over and over again, with very little variance, there’s no better way to do that than with a machine. Automation fits the context of somebody who needs to work on returning tennis serves very well.
Outside the country club, and especially industries like manufacturing and technology, automation is a huge topic. What it means for the bottom line, and what it means for the future of work are questions we are all wrestling with. This is because, as we’ve mentioned in other blog posts, many jobs (such as writing) that we never thought would get automated, are being taken over by machines with artificial intelligence.
So should you stop reading this post, go buy a bunch of robots, fire all your employees, and automate your entire business? Probably not.
Context is huge for automation, and today we hope to provide some with examples from the good, the bad, and the ugly of automation.
The Good
There are many benefits of automation, and these benefits will likely increase as we continue to refine AI. There are several buckets of the good things in automation.
High productivity through efficiency. As mentioned before, humans can’t keep up with the pace of a ‘well-oiled machine’ because it’s not their nature. Once a machine is dialed into a process, you can see resource savings and higher productivity as a result of precision. You do need a process to support automation, however.
Better quality jobs. Especially in the manufacturing industry, automation has been improving the quality of life for workers since the 1800’s. Tedious, tiring, or downright dangerous jobs have been eliminated with machines. In the Information Age, automation has allowed machines and algorithms to take over things like number crunching and forecasting, which frees up employees to pursue creativity and innovation in other areas of their work. For instance, if HelloSign can use workflows to get a signed document into revenue processing faster, that leaves more time for a salesperson to do their job—instead of paperwork.
Not necessarily fewer jobs. Robots aren’t necessarily taking jobs away from humans, at least in terms of volume, and it’s actually very difficult to track this statistic. This is because when a job function is automated, it often creates a new, different job for the human to do. MIT professor Daron Acemoglu uses the example of ATMs. Yes, they took over the job of a teller, but the banking industry has not reported a significant change to the amount of people it employs as a result of the invention.
Automation, while effective in some situations, isn’t all roses…
The Bad
Misuse of automation, for lack of a better word, is bad. Automating something doesn’t create a process, and if automation doesn’t fit the context of what you’re working on, it can actually be a detriment.
In the book The Goal, by Eliyahu M. Goldratt—which covers the theory of constraints in manufacturing but is a must read for any industry—the protagonist, Alex Rogo, is on his way to speak at a conference about robotics in manufacturing when he bumps into a mysterious consultant, Jonah, at the airport. When Alex tells Jonah how the robots have increased productivity 36% (in a single department), Jonah is not impressed:
“Check your numbers if you’d like, but if your inventories haven’t gone down… and your employee expense was not reduced… and if your company isn’t selling more products—which obviously it can’t, if you’re not shipping more of them—then you can’t tell me these robots increased your plant’s productivity.”
While Alex, a plant manager at a manufacturing company, was bragging about the productivity increase his robots provided, he hadn’t even realized that they weren’t helping at all in the overall context of reaching his goal—running a profitable manufacturing plant.
Automation can’t create process for you, and if you engage in automation before you’re ready, you’ll waste a lot of time and resources.
The Ugly
The way automation goes from “bad” to “ugly” is not through misuse; it’s through neglect. Automation is inevitable (it’s actually already here) because it’s an economic advantage, but for those who choose to ignore it or fight it, the future is bleak.
For example, let’s say you tried some form of automation at work, whether an army of robots or some slick software, and you didn’t get the results you wanted—you got stuck in The Bad category, and you abandoned your pursuit of automation. There’s a good chance your competitors didn’t, or they’re willing to stick it out and figure some way to make automation work. As they begin to produce products cheaper and better, it won’t be the robots that take jobs away, it will be other companies.
Or even worse, some companies, or industries as a whole, are rejecting the idea of retraining in hopes that the old way of doing something prevails. But it won’t, and this prevents them from finding a more lucrative job in the future after learning new skills.
The ugly side of automation is ignoring the potential.
Simplifying Work Through Automation
At HelloSign, we don’t believe in a dystopian future of robots running the world—we believe the power of automation can be a part of your daily work routine that makes life easier! From electronic document signatures with robust workflows to automating other areas of your sales process, HelloSign has a suite of digital tools to help make work go a little smoother.
“The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.” — Stephen Hawking.
There’s a rising chorus of concern about how quickly robots are taking away human jobs.
“What to do about mass unemployment? This is going to be a massive social challenge. There will be fewer and fewer jobs that a robot cannot do better [than a human]. These are not things that I wish will happen. These are simply things that I think probably will happen.” — Elon Musk
And today Bill Gates proposed that governments start taxing robot workersthe same way we tax human workers:
“You cross the threshold of job-replacement of certain activities all sort of at once. So, you know, warehouse work, driving, room cleanup, there’s quite a few things that are meaningful job categories that, certainly in the next 20 years [will go away].” — Bill Gates
Jobs are vanishing much faster than anyone ever imagined.
In 2013, policy makers largely ignored two Oxford economists who suggested that 45% of all US jobs could be automated away within the next 20 years. But today that sounds all but inevitable.
Transportation and warehousing employ 5 million Americans
Those self-driving cars you keep hearing about are about to replace a lot of human workers.
As self driving cars become legal in more states, we’ll see a rapid automation of all of these driving jobs. If a one-time $30,000 truck retrofit can replace a $40,000 per year human trucker, there will soon be a million truckers out of work.
And it’s not just the drivers being replaced. Soon entire warehouses will be fully automated.
I strongly recommend you invest 3 minutes in watching this video. It shows how a fleet of small robots can replace a huge number of human warehouse workers.
8 million Americans work as retail salespeople and cashiers.
Amazon is testing a type of store with virtually no employees. You just walk in, grab what you want, and walk out.
US restaurants employ 14 million people.
Japan has been automating aspects of its restaurants for decades — taking orders, serving food, washing dishes, and even food preparation itself.
Automation is accelerating. The software powering these robots becomes more powerful every day. We can’t stop it. But we can adapt to it.
Bill Gates recommends we tax robotic workers so that we can recapture some of the money displaced workers would have paid as income tax.
Elon Musk recommends we adopt universal basic income and give everyone a certain amount of money each year so we can keep the economy going even as millions of workers are displaced by automation.
And I recommend we take some of the taxpayer money we’re using to subsidize industries that are now mostly automated, and instead invest it in training workers for emerging engineering jobs.
The answer to the automation challenge may involve some combination of these three approaches. But we need to take action now, before we face the worst unemployment disaster since the Great Depression.
Talk with your friends and family about automation. We can’t ignore it just because it’s scary and unpredictable. We need a public discourse on this so we can decide as a country what to do about it — before the corporations and their bottom lines decide for us.
Contact your representatives and ask them what they’re doing about automation and unemployment. Tell them we need a robot tax, universal basic income, or more money invested into technology education — whichever of these best aligns with your political views.
If we act now, we can still rise to the automation challenge and save millions of Americans from hardship.
The time for machines to take over most of humanity’s work is rapidly approaching. The world is woefully unprepared to deal with the implications that automation will have over the coming decades. Universal basic income is just beginning to be discussed, and automation has the potential to displace much of the world’s workforce. Many decisions have to be made, and quickly, if we hope to keep pace with innovation.
On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words.
Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it’s vital we understand this new language, and what it’s increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it.
What actually ended up happening when they faced off? Lee went on to lose all but one of their match’s five games. An AI named AlphaGo is now a better Go player than any human and has been granted the “divine” rank of 9 dan. In other words, its level of play borders on godlike. Go has officially fallen to machines, just as Jeopardy did before it to Watson, and chess before that to Deep Blue.
“AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic.”
So, what is Go? Very simply, think of Go as Super Ultra Mega Chess. This may still sound like a small accomplishment, another feather in the cap of machines as they continue to prove themselves superior in the fun games we play, but it is no small accomplishment, and what’s happening is no game.
AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic. Advances in technology are now so visibly exponential in nature that we can expect to see a lot more milestones being crossed long before we would otherwise expect. These exponential advances, most notably in forms of artificial intelligence limited to specific tasks, we are entirely unprepared for as long as we continue to insist upon employment as our primary source of income.
This may all sound like exaggeration, so let’s take a few decade steps back, and look at what computer technology has been actively doing to human employment so far:
Let the above chart sink in. Do not be fooled into thinking this conversation about the automation of labor is set in the future. It’s already here. Computer technology is already eating jobs and has been since 1990.
ROUTINE WORK
All work can be divided into four types: routine and nonroutine, cognitive and manual. Routine work is the same stuff day in and day out, while nonroutine work varies. Within these two varieties, is the work that requires mostly our brains (cognitive) and the work that requires mostly our bodies (manual). Where once all four types saw growth, the stuff that is routine stagnated back in 1990. This happened because routine labor is easiest for technology to shoulder. Rules can be written for work that doesn’t change, and that work can be better handled by machines.
Distressingly, it’s exactly routine work that once formed the basis of the American middle class. It’s routine manual work that Henry Ford transformed by paying people middle class wages to perform, and it’s routine cognitive work that once filled US office spaces. Such jobs are now increasingly unavailable, leaving only two kinds of jobs with rosy outlooks: jobs that require so little thought, we pay people little to do them, and jobs that require so much thought, we pay people well to do them.
If we can now imagine our economy as a plane with four engines, where it can still fly on only two of them as long as they both keep roaring, we can avoid concerning ourselves with crashing. But what happens when our two remaining engines also fail? That’s what the advancing fields of robotics and AI represent to those final two engines, because for the first time, we are successfully teaching machines to learn.
NEURAL NETWORKS
I’m a writer at heart, but my educational background happens to be in psychology and physics. I’m fascinated by both of them so my undergraduate focus ended up being in the physics of the human brain, otherwise known as cognitive neuroscience. I think once you start to look into how the human brain works, how our mass of interconnected neurons somehow results in what we describe as the mind, everything changes. At least it did for me.
As a quick primer in the way our brains function, they’re a giant network of interconnected cells. Some of these connections are short, and some are long. Some cells are only connected to one other, and some are connected to many. Electrical signals then pass through these connections, at various rates, and subsequent neural firings happen in turn. It’s all kind of like falling dominoes, but far faster, larger, and more complex. The result amazingly is us, and what we’ve been learning about how we work, we’ve now begun applying to the way machines work.
One of these applications is the creation of deep neural networks – kind of like pared-down virtual brains. They provide an avenue to machine learning that’s made incredible leaps that were previously thought to be much further down the road, if even possible at all. How? It’s not just the obvious growing capability of our computers and our expanding knowledge in the neurosciences, but the vastly growing expanse of our collective data, aka big data.
Imagine programming a computer to recognize a chair. You’d need to enter a ton of instructions, and the result would still be a program detecting chairs that aren’t, and not detecting chairs that are. So how did we learn to detect chairs? Our parents pointed at a chair and said, “chair.” Then we thought we had that whole chair thing all figured out, so we pointed at a table and said “chair”, which is when our parents told us that was “table.” This is called reinforcement learning. The label “chair” gets connected to every chair we see, such that certain neural pathways are weighted and others aren’t. For “chair” to fire in our brains, what we perceive has to be close enough to our previous chair encounters. Essentially, our lives are big data filtered through our brains.
DEEP LEARNING
The power of deep learning is that it’s a way of using massive amounts of data to get machines to operate more like we do without giving them explicit instructions. Instead of describing “chairness” to a computer, we instead just plug it into the Internet and feed it millions of pictures of chairs. It can then have a general idea of “chairness.” Next we test it with even more images. Where it’s wrong, we correct it, which further improves its “chairness” detection. Repetition of this process results in a computer that knows what a chair is when it sees it, for the most part as well as we can. The important difference though is that unlike us, it can then sort through millions of images within a matter of seconds.
Go is famously a more complex game than chess, with its larger board, longer games, and many more pieces. Google’s DeepMind artificial intelligence team likes to say that there are more possible Go boards than atoms in the known universe, but that vastly understates the computational problem. There are about 10¹⁷⁰ board positions in Go, and only 10⁸⁰ atoms in the universe. That means that if there were as many parallel universes as there are atoms in our universe (!), then the totalnumber of atoms in all those universes combined would be close to the possibilities on a single Go board.
Such confounding complexity makes impossible any brute-force approach to scan every possible move to determine the next best move. But deep neural networks get around that barrier in the same way our own minds do, by learning to estimate what feels like the best move. We do this through observation and practice, and so did AlphaGo, by analyzing millions of professional games and playing itself millions of times. So the answer to when the game of Go would fall to machines wasn’t even close to ten years. The correct answer ended up being, “Any time now.”
NONROUTINE AUTOMATION
Any time now. That’s the new go-to response in the 21st century for any question involving something new machines can do better than humans, and we need to try to wrap our heads around it.
We need to recognize what it means for exponential technological change to be entering the labor market space for nonroutine jobs for the first time ever. Machines that can learn mean nothing humans do as a job is uniquely safe anymore. From hamburgers to healthcare, machines can be created to successfully perform such tasks with no need or less need for humans, and at lower costs than humans.
Amelia is just one AI out there currently being beta-tested in companies right now. Created by IPsoft over the past 16 years, she’s learned how to perform the work of call center employees. She can learn in seconds what takes us months, and she can do it in 20 languages. Because she’s able to learn, she’s able to do more over time. In one company putting her through the paces, she successfully handled one of every ten calls in the first week, and by the end of the second month, she could resolve six of ten calls. Because of this, it’s been estimated that she can put 250 million people out of a job, worldwide.
Viv is an AI coming soon from the creators of Siri who’ll be our own personal assistant. She’ll perform tasks online for us, and even function as a Facebook News Feed on steroids by suggesting we consume the media she’ll know we’ll like best. In doing all of this for us, we’ll see far fewer ads, and that means the entire advertising industry — that industry the entire Internet is built upon — stands to be hugely disrupted.
Fortunately, people are beginning toaskthesequestions, and there’s an answer that’s building up momentum. The idea is to put machines to work for us, but empower ourselves to seek out the forms of remaining work we as humans find most valuable, by simply providing everyone a monthly paycheck independent of work. This paycheck would be granted to all citizens unconditionally, and its name is universal basic income. By adopting UBI, aside from immunizing against the negative effects of automation, we’d also be decreasing the risks inherent in entrepreneurship, and the sizes of bureaucracies necessary to boost incomes. It’s for these reasons, it has cross-partisan support, and is even now in the beginning stages of possible implementation in countries like Switzerland, Finland, the Netherlands, and Canada.
And now even the White House, in a stunning report to Congress, has put the probability at 83 percent that a worker making less than $20 an hour in 2010 will eventually lose their job to a machine. Even workers making as much as $40 an hour face odds of 31 percent. To ignore odds like these is tantamount to our now laughable “duck and cover” strategies for avoiding nuclear blasts during the Cold War.
All of this is why it’s those most knowledgeable in the AI field who are now actively sounding the alarm for basic income. During a panel discussion at the end of 2015 at Singularity University, prominent data scientist Jeremy Howard asked “Do you want half of people to starve because they literally can’t add economic value, or not?” before going on to suggest, ”If the answer is not, then the smartest way to distribute the wealth is by implementing a universal basic income.”
AI pioneer Chris Eliasmith, director of the Centre for Theoretical Neuroscience, warned about the immediate impacts of AI on society in an interview with Futurism, “AI is already having a big impact on our economies… My suspicion is that more countries will have to follow Finland’s lead in exploring basic income guarantees for people.”
Moshe Vardi expressed the same sentiment after speaking at the 2016 annual meeting of the American Association for the Advancement of Science about the emergence of intelligent machines, “we need to rethink the very basic structure of our economic system… we may have to consider instituting a basic income guarantee.”
Even Baidu’s chief scientist and founder of Google’s “Google Brain” deep learning project, Andrew Ng, during an onstage interview at this year’s Deep Learning Summit, expressed the shared notion that basic income must be “seriously considered” by governments, citing “a high chance that AI will create massive labor displacement.”
When those building the tools begin warning about the implications of their use, shouldn’t those wishing to use those tools listen with the utmost attention, especially when it’s the very livelihoods of millions of people at stake? If not then, what about when Nobel prize winning economists begin agreeing with them in increasing numbers?
No nation is yet ready for the changes ahead. High labor force non-participation leads to social instability, and a lack of consumers within consumer economies leads to economic instability. So let’s ask ourselves, what’s the purpose of the technologies we’re creating? What’s the purpose of a car that can drive for us, or artificial intelligence that can shoulder 60% of our workload? Is it to allow us to work more hours for even less pay? Or is it to enable us to choose how we work, and to decline any pay/hours we deem insufficient because we’re already earning the incomes that machines aren’t?
What’s the big lesson to learn, in a century when machines can learn?
I offer it’s that jobs are for machines, and life is for people.