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About 1.4 million industrial robots are in use around the world today. Adoption rates have remained surprisingly low in industries that long have been at the forefront of automation. Fewer than 8 percent of tasks in the U.S. transportation-equipment industry are automated, for example, compared with a potential of 53 percent. The global installed base for robots has been growing by around 2 to 3 percent annually for around a decade—roughly in line with growth in manufacturing output.
The use of industrial robots is also highly concentrated. Nearly three-quarters of all robots operate in just four industrial groupings: computers and electronic products; electrical equipment, appliances, and components; transportation equipment; and machinery. What’s more, 80 percent of the robots sold each year are deployed in just five countries: China, Germany, Japan, South Korea, and the U.S. And because robotics systems have historically been so expensive to own and operate, they are found mainly in large factories owned by corporations with big capital budgets.
A number of economic and technical barriers to wider adoption are beginning to fall, however. As a result, a dramatic takeoff in advanced robotics is imminent. Annual shipments of robots will leap from around 200,000 units in 2014 to more than 500,000 by 2025 according to our baseline projection, and to more than 700,000 in a more aggressive scenario.
Three major trends are speeding global industries toward an inflection point at which advanced industrial robots will become much more commonplace. That point will be characterized by greater cost-effectiveness for robots when compared with human labor, technological advances that are wiping out barriers to adoption in key sectors, and the arrival of systems that smaller manufacturers can afford and easily use.
The economics of advanced robotics are improving rapidly. The prices of robotics hardware and software, which account for only one-quarter of that total cost, are around 40 percent lower than they were a decade ago. The cost of systems engineering—which includes installing, programming, and integrating a robotics system into a factory—has declined even more. Ten years ago, the average systems-engineering costs of a spot-welding robot amounted to $81,000. Those costs are now down to around $46,000, on average, and are likely to keep dropping for the rest of the decade. The costs of peripheral equipment—such as sensors, displays, and expensive safety structures that protect workers and that together typically cost more than the robots themselves—are plunging as well.
At the same time that costs have been declining, the performance of robotics systems has been improving by around 5 percent per year. Taken together, the changes in price and performance for spot welding, for example, have been translating into an annual 8 percent improvement in the cost of robotics.
The technical capabilities of most industrial robots today are still quite limited. Traditional robots are rigid: they are fixed in the same location and can handle only objects that are of a uniform size, oriented in a predictable way, and moving at a determined speed. Most can process images and detect features on objects, but they lack the logic capabilities to make decisions about those objects.
Thanks to leaps in technology advanced robotics systems can now perform tasks and work in environments that are far less structured. Advanced robots are more “intelligent” as well. They can apply logic to make decisions about objects, judge quality, and receive and provide feedback to other parts of a production system through information technology.
The steady improvements in cost, performance, and functionality of robotics systems are driving another force in the next manufacturing revolution: the wider adoption of robots by small and midsize manufacturers. Until recently, such systems have been prohibitively expensive and overly complex for enterprises with limited capital budgets and engineering resources. The new generation of innovative systems is putting robots within the financial reach of small enterprises
As economic and technical barriers continue to fall, robots are becoming accessible for more companies. The production efficiencies will spread beyond individual factories through entire supply chains, industries, and national economies.
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Industrial robots used to be big, unwieldy, and dangerous, but new “human-safe” robots are now commonplace on automotive lines, working right next to people. Robots often need to be explicitly told how to be helpful or when to stay out of the way — things human teammates seem to learn intuitively. A good human apprentice is a keen observer, inferring unspoken rules and customs, watching how others work, and then generalizing this knowledge for new situations.
Recent research indicates that we are at an inflection point in how robots observe and process data, and therefore how they work with people.
Roboticists are starting to reverse-engineer the human mind by translating the cognitive models that humans use intuitively into computational models that machines can use. With this approach, robots and humans working in pairs have been able to accomplish complex tasks as well or better than human teams.
The implications are vast. Imagine a robot that participates as a team member in planning an emergency response deployment. The robot listens to the human team’s conversation to automatically learn the game plan. Such a robot would not have to wait until after the meeting to be told what to do — it could immediately take initiative to accomplish tasks that help the team achieve its goals.
Our most recent studies show that robots can learn the complex decision-making strategies of experts performing real-world tasks in defense and health care. The key was to design the structure of our model to make very efficient use of each observation of the human expert. Each decision an expert makes provides a great deal of information, revealing how that particular option was prioritized over other options. We designed a model to leverage this logical structure by transforming the observed data into pairwise rankings on options. This approach substantially improved the machine’s ability to efficiently learn a high-quality model of the human expert’s decision-making strategy.
Robots of the future won’t need to sit on the sidelines or wait to be told what to do. Robots will truly be at our service, ready, willing, and able to learn from watching us. They will work shoulder-to-shoulder on assembly lines, in hospitals, and on the front lines of emergency response. The awkward robots of the past will be replaced by valued members of the team.
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Gross domestic product – the estimate of the total value of goods and services a country produces – is up for review.
Nobel Prize winning economist Joseph Stiglitz, IMF head Christine Lagarde and MIT professor Erik Brynjolfsson all said at Davos that GDP is a poor indicator of progress, and argued for a change to the way we measure economic and social development.
“We have to go back to GDP, the calculation of productivity, the value of things – in order to assess, and probably change, the way we look at the economy,” said Lagarde.
In order to keep up with the changes brought on by the Fourth Industrial Revolution, many are arguing that we need to find a new measure to assess the health of our economies and – more importantly – the people living in them.
World Economic Forum’s chief economist Jennifer Blanke argues, “it’s easy to forget that GDP was not initially intended for this purpose, it merely provides a measure of the final goods and services produced in an economy over a given period, without any attention to what is produced, how it’s produced or who is producing it.” Blanke mentions three key questions that GDP overlooks: is growth fair, is it green, and is it improving our lives?
This last question is one that would resonate with Richard Easterlin, professor of economics at the University of Southern California, who has been writing about the link between happiness and income for 40 years. “In rich countries – rich or poor, democratic or autocratic – happiness for most is success in doing things of everyday life. That might be making a living, raising a family, maintaining good health, and working in an interesting and secure job.”
Inclusive growth, environmental outcomes and well-being are not the only missing parts of the puzzle. Another controversial – but sadly, as explored in our Women and Work series, unsurprising – omission is the women whose unpaid efforts are overlooked by economic policy.
If GDP counted women, argues economist Diane Coyle in this piece, then GDP would look very different. In a 2011 study, the OECD found that so-called “home production” would add between 20% and 50% to the GDP of its member countries.
Inequality, happiness, sustainable development – all are inextricably linked to whatever the world’s leading economists and policy-makers decide to do next. This matters to all of us, and we hope this is reflected in this series.
As Joseph Stiglitz said in Davos: “What we measure informs what we do. And if we’re measuring the wrong thing, we’re going to do the wrong thing.”
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