Some recent links showing the on-going march toward job automation:
Automating Legal Work - New York Magazine.
This is more on the the use of e-discovery software to process documents. Automation is likely to hit especially hard at the entry level (and more routine) jobs often taken by recent graduates in a variety of skilled occupations, including law and journalism:
Rapidly Improving Manufacturing Robots – Singularity Hub.
Military Robots – GMA News
Construction Robots – Construction Digital
Productivity Increases are Going to Capital — Not Labor – Paul Krugman
Falling Labor Force Participation Rate – Conversable Economist – As this post points out, demographics (aging workforce) and cyclical factors explain only part of this…
Foxconn, of course, is infamous for the number of its workers who committed suicide. Amazon has had issues of its own. At its Allentown, PA, warehouse, employees were repeatedly overwhelmed by heat and had to seek medical attention. A recent article in Mother Jones tells the story of what it’s like to work in one of these warehouses (the article does not identify the company).
Automation is not just about increasing efficiency. There’s some evidence to suggest that workers are simply being driven beyond their limits. As production speeds continue to increase, there has to come a point where the only option is to get the humans out of the loop. In many industries, automation may penetrate more rapidly than we expect simply because a threshold is reached where people can no longer keep up.
Anyone who is interested in how manufacturing jobs are evolving (and disappearing) should be sure to read Adam Davidson’s excellent article in the current issue of The Atlantic: “Making it in America.” The article is based on interviews with workers and executives at Standard Motor Products, a manufacturer and distributor of auto parts with a factory in Greenville, South Carolina.
Here’s a a quote from the article, focusing on the future prospects for an unskilled worker named Maddie:
Tony [the factory manager] points out that Maddie has a job for two reasons. First, when it comes to making fuel injectors, the company saves money and minimizes product damage by having both the precision and non-precision work done in the same place. Even if Mexican or Chinese workers could do Maddie’s job more cheaply, shipping fragile, half-finished parts to another country for processing would make no sense. Second, Maddie is cheaper than a machine. It would be easy to buy a robotic arm that could take injector bodies and caps from a tray and place them precisely in a laser welder. Yet Standard would have to invest about $100,000 on the arm and a conveyance machine to bring parts to the welder and send them on to the next station. As is common in factories, Standard invests only in machinery that will earn back its cost within two years. For Tony, it’s simple: Maddie makes less in two years than the machine would cost, so her job is safe—for now. If the robotic machines become a little cheaper, or if demand for fuel injectors goes up and Standard starts running three shifts, then investing in those robots might make sense.
“What worries people in factories is electronics, robots,” she tells me. “If you don’t know jack about computers and electronics, then you don’t have anything in this life anymore. One day, they’re not going to need people; the machines will take over. People like me, we’re not going to be around forever.”
As the article makes clear, Maddie has a job largely because she is still cheaper than installing automation equipment — assuming a two-year payback period for the equipment. But now consider that just last year the Boston Globe reported that start-up company Heartland Robotics expects to introduce a manufacturing robot that will sell for around $5,000. The cost of automating jobs like Maddie’s seems likely to fall quite dramatically in the relatively near future. At a cost of $5,000 — or even $10,000 — a robot could easily pay for itself within a matter of months.
Now here’s another quote from the article focusing on a skilled operator named Luke. Luke runs computer-controlled “Gildemeister” machines, which cut precision parts used in fuel injectors:
After six semesters studying machine tooling, including endless hours cutting metal in the school workshop, Luke, like almost everyone who graduates, got a job at a nearby factory, where he ran machines similar to the Gildemeisters. When Luke got hired at Standard, he had two years of technical schoolwork and five years of on-the-job experience, and it took one more month of training before he could be trusted alone with the Gildemeisters. All of which is to say that running an advanced, computer-controlled machine is extremely hard. Luke now works the weekend night shift, 6 p.m. to 6 a.m., Friday, Saturday, and Sunday.
When things are going well, the Gildemeisters largely run themselves, but things don’t always go well. Every five minutes or so, Luke takes a finished part to the testing station—a small table with a dozen sets of calipers and other precision testing tools—to make sure the machine is cutting “on spec,” or matching the requirements of the run. Standard’s rules call for a random part check at least once an hour. “I don’t wait the whole hour before I check another part,” Luke says. “That’s stupid. You could be running scrap for the whole hour.”
The conventional wisdom, of course, is that while Maddie’s job may well be in danger at some point, Luke has little to worry about. Demand for skilled machine operators is strong and is likely to increase in the future. But is that really the way things will play out?
Any time workers are highly paid and in short supply, there is a clear incentive for innovation that will either eliminate those workers or “dumb down” the job so it can be done by less skilled people. Currently, Luke spends a lot of effort testing parts to make sure they remain in spec, and then recalibrating the machine as necessary. Is it possible that machines like the Gildemeisters will someday be able to automatically test parts and then make the required adjustments? Perhaps it might be possible to use precise computer imaging technology to analyze parts and then auto-adjust the machine to produce consistent results. Since this hasn’t happened yet, we can assume the technology probably isn’t there. But what about five or ten years from now?
In a previous post, I wrote about the new “cloud robotics” strategy being pursued by Google, Willow Garage and other companies. Cloud robotics involves migrating much of the software that controls robots or other machines into centralized servers. Could something similar be done with computer-controlled machine tools — so that perhaps dozens of machines are controlled by advanced software that incorporates artificial intelligence, eliminating the need for individual operators?
Yet another possibility involves offshoring. Jobs in areas like customer service can, of course, easily be outsourced to low-wage countries. Could skilled machine operator jobs also be offshored? If the parts are moved around robotically so that the entire job consists of analysis, programming and control, then those tasks could potentially be done from anywhere. China currently has a huge surplus of college graduates — a high percentage of whom have studied science and engineering — and for jobs of this type, English language skills wouldn’t necessarily be critical. Moving an entire factory to China results in major transportation costs and logistics issues. Moving specific jobs offshore electronically, as is currently done in the service sector, might someday prove more cost effective.
In other words, our conventional assumptions about the jobs of the future are not necessarily all that reliable. While Maddie’s job is certainly at risk, even Luke may not turn out to have the level of job security we expect over the longer run.
A final quote is from a discussion with the CEO of the company:
To keep the business of the giant auto-parts retailers, Standard has to constantly lower costs while maintaining quality. High quality is impossible without good raw materials, which Standard has to buy at market rates. The massive global conglomerates, like Bosch, might be able to command discounts when buying, say, specially formulated metals; but Standard has to pay the prevailing price, and for years now, that price has been rising. That places an even higher imperative on reducing the cost of labor. If Standard paid unskilled workers like Maddie more or hired more of them, Larry says, the company would have to charge its customers more or accept lower profits. Either way, Standard would collapse fairly soon.
The main point here is that when a company is squeezed between rising costs for commodity inputs and an inability to raise its own prices, the only significant variable it really has to work with is labor. This dynamic is not limited to manufacturing companies like Standard Parts; previously, I made a similar point about the fast food industry.
Fast Food (or beverage) workers are classified as service workers by the government, but from a technical point of view, fast food is really a kind of just-in-time manufacturing. As automation technology gets better and cheaper, and as the price of food commodities continues to rise in response to ever increasing global demand, it seems likely that the rate of job creation in fast food as well as in a variety of other service areas may be in danger of falling short of expectations.
When it comes to the jobs of the future, beware the conventional wisdom. A great many widely-held assumptions seem to be based primarily on simply projecting the continuation of existing trends, rather than on any meaningful analysis of what the industries of the future might really look like.
At the 2011 Google I/O developer’s conference, Google announced a new initiative called “cloud robotics” in conjunction with robot manufacturer Willow Garage. Willow Garage and a variety of other contributors have developed an open source (free) operating system for robots, with the unsurprising name “ROS” — or Robot Operating System. ROS is being positioned as the MS-DOS (or MS Windows) of robotics.
With ROS and a package called “rosjava“, software developers will be able to write code in the Java programming language and control robots in a standardized way — much in the same way that programmers writing applications for Windows or the Mac can access and control computer hardware.
Google’s approach also offers compatibility with Android. Robots will be able to take advantage of the “cloud-based” (in other words, online) features used in Android phones, as well as new cloud-based capabilities specifically for robots. In essence this means that much of the intelligence that powers the robots of the future may reside on huge server farms, rather than in the robot itself. While that may sound a little “Skynet-esque,” it’s a strategy that could offer huge benefits for building advanced robots.
One of the most important cloud-based robotic capabilities is certain to be object recognition. In my book, The Lights in the Tunnel, I have a section where I talk about the difficulty of building a general-purpose housekeeping robot largely because of the object recognition challenge:
A housekeeping robot would need to be able to recognize hundreds or even thousands of objects that belong in the average home and know where they belong. In addition, it would need to figure out what to do with an almost infinite variety of new objects that might be brought in from outside.
Designing computer software capable of recognizing objects in a very complex and variable field of view and then controlling a robot arm to correctly manipulate those objects is extraordinarily difficult. The task is made even more challenging by the fact that the objects could be in many possible orientations or configurations. Consider the simple case of a pair of sunglasses sitting on a table. The sunglasses might be closed with the lenses facing down, or with the lenses up. Or perhaps the glasses are open with the lenses oriented vertically. Or maybe one side of the glasses is open and the other closed. And, of course, the glasses could be rotated in any direction. And perhaps they are touching or somehow entangled with other objects.
Building and programming a robot that is able to recognize the sunglasses in any possible configuration and then pick them up, fold them and put them back in their case is so difficult that we can probably conclude that the housekeeper’s job is relatively safe for the time being.
Cloud robotics is likely to be a powerful tool in ultimately solving that challenge. Android phones already have a feature called “Google Goggles” that allows users to take photos of an object and then have the system identify it. As this feature gets better and faster, it’s easy to see how it could have a dramatic impact on advances in robotics. A robot in your home or in a commercial setting could take advantage of a database comprising the visual information entered by tens of millions of mobile device users all over the world. That will go a long way toward ultimately making object recognition and manipulation practical and affordable.
In general, there are some important advantages to the cloud-based approach:
- As in the object recognition example, robots will be able to take advantage of of a wide range of online data resources.
- Migrating more intelligence into the cloud will make robots more affordable, and it will be possible to upgrade their capability remotely — without any need for expensive hardware modifications. Repair and maintenance might also be significantly easier and largely dealt with remotely.
- As noted in the video below, it will be possible to train one robot, and then have an unlimited number of other robots instantly acquire that knowledge via the cloud. As I wrote previously, I think that machine learning is likely to be be highly disruptive to the job market at some point in the future in part because of this ability to rapidly scale what machines learn across entire organizations — potentially threatening huge numbers of jobs.
The last point cannot be emphasized enough. I think that many economists and others who dismiss the potential for robots and automation to dramatically impact the job market have not fully assimilated the implications of machine learning. Human workers need to be trained individually, and that is a very expensive, time-consuming and error-prone process. Machines are different: train just one and all the others acquire the knowledge. And as each machine improves, all the others benefit immediately.
Imagine that a company like FedEx or UPS could train ONE worker and then have its entire workforce instantly acquire those skills with perfect proficiency and consistency. That is the promise of machine learning when “workers” are no longer human. And, of course, machine learning will not be limited to just robots performing manipulative tasks — software applications employed in knowledge-based tasks are also going to get much smarter.
The bottom line is that nearly any type of work that is on some level routine in nature — regardless of the skill level or educational requirements — is likely to someday be impacted by these technologies. The only real question is how soon it will happen.
The video below is a presentation from Google’s I/O conference on Cloud Robotics. It is fairly long and very technical, but if you have a strong interest or would like to see what some actual robot programming code looks like, check it out:
Honda has just released a new version of its ASIMO robot, which is now fully autonomous (as opposed to remote-controlled).
ASIMO can navigate complex environments along with people, recognize and distinguish faces and voices — even when people are speaking simultaneously. And it can do a lot of other stuff.
Details are here. Check out the video below:
As I wrote previously, a lot of people have jobs that are safe from automation not because they are especially advanced or creative, but because they involve skills such as dexterity and hand-eye coordination that are currently beyond the capability of machines. Things are changing.
And, as I noted here on the subject of personal robots:
The thing is that for a robot to autonomously run around the house doing a variety of tasks requires a very sophisticated level of technology. If that technology is developed and becomes affordable then it will certainly make its way into a variety of commercial applications—in fact, it may well be deployed there first.
It seems to me that if we have affordable personal robots that are actually capable of doing anything useful, then that technology implies that millions of jobs will be at risk in areas like:
- stocking shelves in supermarkets and other retail stores
- moving materials in stores and warehouses
- providing security in a variety of settings
This is the second version of ASIMO. What will the 5th version look like? What about v. 10?
Singularity Hub also has the story.
A few links from around the web on the impact that technology is having on the job market and economy:
Ford’s basic thesis (laid out in his book “The Lights in the Tunnel”) is that machines are now getting so advanced that they’re going to be better than human beings at doing everything and thus there will be no jobs. He also thinks this is a very bad thing, a view which I think is terribly strange.
In fact, I’ve never said there will be “no jobs.” I’ve only said that technology may ultimately eliminate the bulk of routine jobs. But that will nonetheless result in major problems. History suggests that the 25% unemployment the United States experienced during the Great Depression is probably pretty close to the limit of what a democratic society can withstand.
I also have never said that advancing technology, or job automation, is “a bad thing.” In fact, I agree that it is a great thing. I just believe we need to reform our economic system so it will be a great thing for everyone — and not just a tiny elite. Suggesting such reforms was really the whole point of my book, “The Lights in the Tunnel.” We cannot escape the fact that a great many people are best equipped to do routine things and will have great difficulty moving to non-routine/creative areas, even if those jobs are available. If we assume a bell curve distribution, then by definition, 50% of the workforce is average or below average in terms of capability. At the same time, technology is also encroaching on even the high skill jobs held by people with above average capability. Ultimately these trends will demand a response.
Later Worstall says this:
Machines are about to get so good that humans just won’t have anything to do at all. We won’t need to sow, weed or reap, sew or in fact anything. Not only will food and clothes be made by machine, the machines that make the machines that make the food and clothes (and houses and cars and computer games and….) will be made by machines.
Humans will therefore have no jobs, no jobs at all. Ford thinks this is appalling as therefore human beings will have no incomes. I think it sounds like a rather wondrous world actually, even without humans having any incomes.
The problem is that in the world as it exists today, you do not get to consume without an income. I think Tim is saying that all those machines will make production so efficient — and prices so low — that even people with very low incomes will still be able to consume. There are a couple of problems here. First if your income is ZERO, then it doesn’t matter how low prices are: you are out of luck. And a great many people will be in that situation unless we dramatically improve our social safety net.
The second issue is that efficient machines will not drive down many of the fixed costs that take up most household budgets. Powerful robots are not going to lower the principal on your mortgage. Nor are they likely to drive down food prices much, as agriculture is already highly mechanized. We can dream that technology will dramatically lower health care costs, and maybe it will happen someday — but probably not until after you lose your job.
In general, if prices fall in one area — say food production or computers — that has historically been a good thing. But if wages and prices fall across the board then that is DEFLATION. And a big problem with deflation is that while wages, prices and asset values may fall — debts do not. In time, people will default or debt service will leave them with little discretionary income to spend on other things — creating the risk of a deflationary spiral. I’ve written more about this in a previous post.
Marshall Brain has started posting new items to his Robotic Nation Evidence Blog.
Occupy Silicon Valley?
CBS MoneyWatch: Is Silicon Valley fueling unemployment?
I recently appeared on the TV program “Ideas in Action” to discuss the impact of robots and automation on the future job market and economy. The show will air on PBS stations at various times beginning this week.
WordPress won’t let me embed the video, but you can watch the show here.
I thought it was a very interesting discussion, and at a full half hour, was the most in-depth treatment I’ve seen so far on this issue.
Libertarian economist Arnold Kling (and Robin Hanson fan) also comments over at EconLog.
I was in Washington last week for an event entitled “Will Robots Steal Your Job?” sponsored by the New America Foundation and Slate Magazine.
Slate also published a series of articles on the subject by Farhad Manjoo.
There were two panel discussions. Here is the list of participants from the event page at the New America Foundation:
Panel 1 – Can Robots Do Nuance?
Founder and CEO, Automated Insights, Inc.
Dr. Sarah Kramer
Primary care physician, University of Washington Medicine
President, Nutonian, Inc.
Slate Video: Robots on the Silver Screen
Panel 2 – Can the Economy Survive a Robot Uprising?
Dr. Tyler Cowen
Holbert C. Harris Chair of Economics, George Mason University
Co-author, Marginal Revolution blog
Author, The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future
Policy Director, Economic Growth Program
New America Foundation
A video of the discussion is here. One of the most interesting questions Farhad asked me was one that I have seen raised quite a few times: Why do I worry that machines might outpace workers when we all know that people in the future will be enhanced by “transhumanist” technology (brain implants, etc.)? That’s at 1:01:20.
I was asked to write an op-ed for the Washington Post on how Watson (the computer that won on Jeopardy!) could impact medicine. I think it could eventually be quite transformative.
Mark Lewis, a computer science professor at Trinity University, thinks I might be too conservative in my projections for Watson’s future impact on medicine.
My feeling has been that in areas like medicine and self-driving cars/trucks the technology may run ahead of social acceptance. Also there are some powerful groups that might lobby hard to slow progress (AMA, Teamsters, etc.).
How long will it be, for example, before society would trust a machine to independently prescribe drugs? (Possible future Kindle bestseller: How to Get Watson to Give You Vicoden: The Insider’s Guide). But, then again, which would be harder: gaming a smart computer to get a prescription, or just finding a doctor that will prescribe on demand?
I’ve heard from a number of people who, like Mark, think things are likely to progress faster than we might expect in these areas. Let us know what you think in comments.