MOAR Links: July 3, 2018

Very wise words from close to where the rubber meets the road about how the Rise of the Robots is likely to work out for the labor market over the next generation or so: Shane Greenstein: Adjusting to Autonomous Trucking: "Let’s come into contact with a grounded sense of the future.... Humans have invented tools for repetitive tasks, and some of those tools are becoming less expensive and more reliable...

...Were the investments pay off quickly, firms will make use of tools. Experiments are looking for such payoffs right now. However, even with such payoffs, adjustments tend to take time and will slow adoption and change. Autonomous trucking provides a good example.... What will happen to the 3.5 million truck drivers in the US who are focused on keeping their jobs?... Total employment is not at risk in the next decade, but it would not be surprising if a few job titles and assignments change....

There is plenty of motivation for automating long-haul trucking. Of all trucking jobs, long haul is the most difficult to fill and staff. The work can be uncomfortable. It also creates enormous value.... Trucking has already taken advantage of many advances in electronics. Most trucks contain on-board computers, GPS links, and numerous systems to monitor performance.... Judging from recent prototypes, humans are not disappearing anytime soon… Take the use of autopilot in commercial airlines today; software-enhanced navigation merely changes what the pilots do and when they apply their expertise.... The hard work today focuses on other high-value propositions, such as reducing safety issues from things like inattentive driving. A little automation can go a long way for that purpose—it can stop vehicles sooner, issue warnings to drivers, and relay information to dispatchers for use by others in a fleet. The prototypes also continue trends that began with the introduction of electronics into trucking long ago. Partial automation can enable longer continuous vehicle operation, better fuel consumption, and reduced maintenance expenses.

So what limits progress? Like many applications in machine learning, there are too many “edge cases” that the software cannot yet satisfactorily handle—such as road construction, a vehicle stopped at the side of the road, unexpected detours, pedestrians unexpectedly on the side of the highway, a dead animal carcass in the road, and so on. AI researchers know this problem well. Routine work is not as routine as it seems. Humans are pretty good at handling millions of variants of the little unexpected aspects of road work, police stops, bad weather, poor drivers, and break-downs. The statistics of edge cases are quite demanding. Software can be trained to handle much of this, perhaps 99 percent of the issues in a typical drive. But 99 percent is not anywhere near good enough. If, say, 1 percent is still left for humans, that translates into more than half a minute every hour in which a human needs to intervene.... Partial or conditional automation is the most ambitious goal for the next several years. Full automation is a long way off....

We should expect that business processes will adjust and adopt new routines. The timing for fueling, maintenance, docking, and inspection will change. New procedures for monitoring daily, weekly, and monthly targets will be put in place. Will that eliminate work? No, but it will shift who does the work, what they do, and how they are trained to do it. The new timing will require new principles for organizing teams. The new teams will require new principles for responsibility.... Will that change work at the organizational level? Yes, sure, and even if we cannot precisely forecast how, it is clear where the type of work will change.... And here is the kicker. The reduction in cost might generate more demand for services, which might lead to more employment of truckers. It is hard to forecast the totality of all this change...


#shouldread

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