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The Future of Work Part 4: The Kinds Of Jobs That Are At Risk

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Recent improvements in hardware, a massive increase in the number of processors available, and new math tools have increased concerns that computers may soon replace millions of workers. The shorthand for this is Artificial Intelligence, although the term seems like hyperbole considering the kinds of things computers can do at present. The Obama White House issued a paper on this issue, Artificial Intelligence, Automation and the Economy, which can be found here. It cites two studies of the impact of AI on automation over then next 10 years or so. One, by the OECD, estimates about 9% of US jobs may be lost to automation. The other is a more interesting 2013 paper by two professors at Oxford, Carl Benedikt Frey and Michael A. Osborne, estimating that as many as 49% of US jobs could be lost or seriously affected over 10 or so years.

The Frey-Osborne Paper is here. Frey is a professor in a public policy college, and Osborne is in the engineering college; they aren’t economists. Perhaps for that reason, the introductory sections are instructive on the history of technological change and some of its effects on society. The technical approach of the Frey-Osborne Paper is to identify the bottlenecks that make it difficult to automate the tasks needed in a specific job. They use machine learning to identify patterns in the skills needed by specific jobs.

The authors identify three main bottlenecks to automation:

1. Tasks requiring perception and manipulation. P. 24
2. Tasks requiring creative intelligence. P. 25
3. Tasks requiring social intelligence. P. 26

The O-NET database of jobs is managed by the US Department of Labor. The current version contains detailed descriptions of job tasks for 903 occupations. Here are the top eight tasks of 21 listed for forest firefighter, one of the bright future jobs according to O-NET,:

Rescue fire victims, and administer emergency medical aid.

Establish water supplies, connect hoses, and direct water onto fires.

Patrol burned areas after fires to locate and eliminate hot spots that may restart fires.

Inform and educate the public about fire prevention.

Participate in physical training to maintain high levels of physical fitness.

Orient self in relation to fire, using compass and map, and collect supplies and equipment dropped by parachute.

Fell trees, cut and clear brush, and dig trenches to create firelines, using axes, chainsaws or shovels.

Maintain knowledge of current firefighting practices by participating in drills and by attending seminars, conventions, and conferences.

Frey and Osborne describe their methodology as follows:

First, together with a group of [machine learning] researchers, we subjectively hand-labelled 70 occupations, assigning 1 if automatable, and 0 if not. For our subjective assessments, we draw upon a workshop held at the Oxford University Engineering Sciences Department, examining the automatability of a wide range of tasks. Our label assignments were based on eyeballing the O-NET tasks and job description of each occupation.

They identified nine variables related to the three bottlenecks and assigned levels of difficulty of the variables in carrying out each task, high, medium, or low. Then they verified their data, and used it as training data in a machine learning program. The paper gives a description of the way they prepared and ran the rest of the O-NET data through the trained machine to estimate the likelihood that each job would be automated over the next 10 years or so. They produced a chart showing the likely effects of AI on categories of jobs. The following chart shows the results of their work.

The authors say that large numbers of transportation and logistics workers, office workers and administrative support workers are at risk. They also think many service workers are at risk as robots become more efficient. They think people whose jobs require great manual dexterity and perception, or high levels of creativity, or strong social intelligence are reasonably safe in the near term. They assert that low-skill workers will have to move to jobs in the service sector that require these skills, and will have to sharpen their own through training and education.

There have been several articles on this issue lately. This one by Reuters says that investors think the future is in automation. Since the election shares in companies working in that area are up dramatically as is an ETF in the sector. Reuters says that this means that investors think that Trump’s assertion he will increase jobs in the manufacturing sector will not happen. Instead, as the cost of advanced technology drops labor becomes expendable. Any increase in manufacturing will have little effect on overall unemployment, as displaced workers move to other jobs with the same employers doing “value-added” tasks.

Matthew Yglesias goes a step farther in this 2015 post at Vox. He says the big problem in job growth in the US is the lack of increase in productivity due to inadequate automation. He thinks rising productivity is essential to higher wages, or more likely a reduction in the time spent working. Yglesias lays out the case for not worrying. He ignores, as all economists do, the possibility that the returns from work might be shared more equitably between capital and labor. His relentless optimism contrasts with the lived experience of millions of Americans, the real lives that gave us Trumpism.

I wonder what Yglesias makes of this article in the Guardian discussing the efforts of the billionaire Ray Dalio to create software to manage the day-to-day operations of the world’s largest hedge fund in accordance with “… a set of principles laid out by Dalio about the company vision.” The article provides a more pessimistic view of the future even for management work.

I don’t have an opinion about these forecasts or the reasoning behind them. Yglesias says people will work less, but doesn’t explain how workers who have no bargaining power will be able to increase their income enough to have free time. Dalio must think that he is so wise that his AI automaton will replicate his success forever, and that his competitors won’t take advantage of the rigidity of his principles.

Suppose that the investors described by Reuters are right, that manufacturing increases but without increased employment in the sector. What will all those Trump voters do next? Change their minds about what they want from the economy and the government that fosters it, and live happily ever after?

I think both Yglesias and Dalio are so steeped in neoliberal economics with its model of human beings as Homo Economicus that they assume these changes will come about smoothly. Nothing else will change; there are no dynamic tipping points. No large number of human beings will raise hell. There will be no feedback effects. The displaced of all ages will just retrain to some other job and/or resign themselves to their reduced lives. They won’t resist, or riot, or insist on government protection, or demand a completely new system. Investment bankers will blandly accept the judgment of computers as to their value and will not insist on being treated like superstars even if the machine says they are just gas giants.

Yglesias and Dalio are wrong. That is precisely what history says won’t happen.

The Future of Work Part 1: John Maynard Keynes

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As the global depression spiraled towards its depths in 1930, John Maynard Keynes wrote a cheerful article on the future of work: Economic Possibilities for our Grandchildren. He argued that it wouldn’t be too long before capital accumulation and technological change would come near to solving the economic problem of material subsistence, of producing enough goods and services to provide everyone with the necessities of life and largely relieving them of the burden of work.

The paper begins with a very brief description of the problems of the time:

We are suffering, not from the rheumatics of old age, but from the growing-pains of over-rapid changes, from the painfulness of readjustment between one economic period and another. The increase of technical efficiency has been taking place faster than we can deal with the problem of labour absorption; the improvement in the standard of life has been a little too quick; the banking and monetary system of the world has been preventing the rate of interest from falling as fast as equilibrium requires.

This statement anticipates the views of Karl Polanyi in The Great Transformation, and of Hannah Arendt in The Origins of Totalitarianism. They argue persuasively that massive technological changes led to changes in social structures which were profoundly upsetting to large numbers of people. Polanyi says that a decent society would take steps to relieve people of these stresses, perhaps by forcing a slower pace of change, or perhaps by legislation to protect the masses. Arendt claims that for a while, imperialism offered a solution by absorbing some of the excess workers. Both believed that the stresses of constant change and displacement of workers played an important role in the rise of fascism.

Keynes then points out the history of growth in world output. From the earliest time of which we have records, he says, to the early 1700s, there was little or no change in the standard of life of the average man. There were periods of increase and decrease, but the average was well under .5%, and never more than 1% in any period. The things available at the end of that period are not much different from those available at the beginning. He argues that growth began to accelerate when capital began to accumulate, around 1700.

It’s interesting to note that this sketch of economic history accords nicely with that provided by Thomas Piketty in Capital In The Twenty-First Century. This is Piketty’s Table 2.5. Compare this with Figure 2.4, The growth rate of world per capita output since Antiquity until 2100.

Keynes argues that since 1700 there has been a great improvement in the lives of most people, and there is every reason to think that will continue. Certainly there was the then current problem of technological unemployment, with technology displacing people faster than the it was creating new jobs. But he says it is reasonable to think that in 100 years, by 2030, people will be 8 times better off, absent war and other factors. He says there are two kinds of needs, those that are absolute, and those with the sole function of making us feel superior to others. The latter may be insatiable, he says, but the former aren’t, and we are getting closer to satisfying them. In so doing, we are getting close to solving the ancient economic problem: the struggle for subsistence.

That problem is indeed ancient. It shows up in Genesis, 3:17. Adam and Eve have eaten the fruit of the Tree of Knowledge of Good and Evil, and the Almighty punishes Adam with these words:

To Adam he said, “Because you listened to your wife and ate fruit from the tree about which I commanded you, ‘You must not eat from it,’ “Cursed is the ground because of you; through painful toil you will eat food from it all the days of your life.

To be relieved of this ancient curse should be a wonderful thing. Keynes doesn’t think it will be an easy transition though. The struggle for subsistence is replaced by a new problem: how to use the new freedom, how to use the new-found leisure. He thinks people will have to have some work, at least at first, to give us time as a species to learn to enjoy leisure. He thinks that those driven to make tons of money will be seen once again in moral terms: as committing the sin of Avarice. They will be ignored or controlled in the interests of the rest of us.

As it turns out, this wasn’t one of Keynes’ better predictions. It isn’t clear that there is such a thing as a minimum absolute needs, for example, and technology has not yet removed the need for all work. Still, the goal of solving the economic problem seems sensible, and his discussion of the problems of a possible transition seems accurate.

People want to work, and they want everyone else to work too. There have been a number of reported interviews with Trump voters, many of who claim that this has become a give-away society. People complain that it pays better to be out of work than in work because of all the free stuff you get, health care (Medicare), free phones, food stamps, SSDI, free housing and so on, so they voted for Trump thinking he’d fix it so that only the deserving poor would get that free stuff. They think people don’t want to work, which feels like projection, and if they have to work, everyone should. Work has a number of social benefits, including a sense of purpose, responsibility, and pride. How are these to be handled in Keynes’ Eden?

The pace of technological change has picked up. It not only affects blue-collar workers, it’s starting to hit on doctors, lawyers and even translators. Here’s an article on improvements in translation based on neural network machine learning from the New York Times Magazine; and here’s a report from the White House on the impact of artificial intelligence on jobs. And here’s an article in the NYT’s Upshot column discussing the White House Report, and a rebuttal from Dean Baker.

These problems are crucial to the future of democracy. They concern the nature of our institutions and our social structures, as well as questions about our nature as human beings. I’ll take these up in more detail in future posts in this series.

Update: Here’s a link to the Keynes paper discussed in this post.

Monday Morning: Swivel, Heads

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Somebody out there knows what this tune means in my household. For our purposes this Monday morning, it’s a reminder to take a look around — all the way around. Something might be gaining on you.

Let’s look…

Android users: Be more vigilant about apps from Google Play
Better check your data usage and outbound traffic. Seems +300 “porn clicker” apps worked their way around Google Play’s app checking process. The apps rack up traffic, fraudulently earning advertising income; they persist because of users’ negligence in vetting and monitoring downloaded apps (because Pr0N!) and weakness in Google’s vetting. If this stuff gets on your Android device, what else is on it?

IRS’ data breach bigger than first reported
This may also depend on when first reporting occurred. The number of taxpayers affected is now ~700,000 according to the IRS this past Friday, which is considerably larger than the ~464,000 estimated in January this year. But the number of taxpayers affected has grown steadily since May 15th last year and earlier.

Did we miss the ‘push for exotic new weapons’?
Nope. Those of us paying attention haven’t missed the Defense Department’s long-running efforts developing new tools and weapons based on robotics and artificial intelligence. If anything, folks paying attention notice how little the investment in DARPA has yielded in payoff, noting non-defense development moving faster, further, cheaper — a la SuitX’s $40K exoskeleton, versus decades-plus investment by DARPA in exoskeleton vaporware. But apparently last Tuesday’s op-ed by David Ignatius in WaPo on the development of “new exotic weapons” that may be deployed against China and Russia spawned fresh discussion to draw our attention to this work. THAT is the new development — not the weapons, but the chatter, beginning with the Pentagon and eager beaver reporter-repeaters. This bit here, emphasis mine:

Pentagon officials have started talking openly about using the latest tools of artificial intelligence and machine learning to create robot weapons, “human-machine teams” and enhanced, super-powered soldiers. It may sound like science fiction, but Pentagon officials say they have concluded that such high-tech systems are the best way to combat rapid improvements by the Russian and Chinese militaries.

Breathless, much? Come the feck on. We’ve been waiting decades for these tools and weapons after throwing billions of dollars down this dark rathole called DARPA, and we’ve yet to see anything commercially viable in the way of an exoskeleton in the field. And don’t point to SKYNET and ask us to marvel at machine learning, because the targeting failure rate is so high, it’s proven humans behind it aren’t learning more and faster than the machines are.

Speaking of faster development outside DARPA: Disney deploying anti-drones?
The Star Wars franchise represents huge bank — multiple billions — to its owner Disney. Control of intellectual property during production is paramount, to ensure fan interest remains high until the next film is released. It’s rumored Disney has taken measures to reduce IP poaching by fan drones, possibly including anti-drones managed by a security firm protecting the current production location in Croatia. I give this rumor more weight than the Pentagon’s buzz about exoskeletons on the battlefield.

Lickety-split quickies

That’s a wrap — keep your eyes peeled. To quote Ferris Bueller, “Life moves pretty fast. If you don’t stop and look around once in a while, you could miss it.”