Robots CompilationDr. Thomas Lairson

How Robots Work

http://science.howstuffworks.com/robot6.htm

Bipedal Robot 4/11/2016

https://www.youtube.com/watch?v=iyZE0psQsX0

The Economist

Special report: Robots

Immigrants from the future

Robots offer a unique insight into what people want from technology. That makes their progress peculiarly fascinating, says Oliver MortonMar 29th 2014 | From the print edition

Video @ http://www.economist.com/news/special-report/21599522-robots-offer-unique-insight-what-people-want-technology-makes-their

SCHAFT, A BLUE-LIMBED robot, lifts its right foot to the seventh step of the ladder, its left foot to the eighth, and stops; it sways alarmingly in the strong Florida sea breeze. Of the 17 teams competing in the DARPA Robotics Challenge (DRC), a first-of-its-kind event held at a speedway track near Miami in December 2013, only two others got their robots this high up the ladder. One of those two then took a nasty tumble.

For most of a minute SCHAFT is still, except for a flap on its chest that slowly rises and falls in a breathing motion. Then it springs into action again. Its left knee straightens, its right foot rises, its left knee bends again—not forwards, as a human knee would, but backwards—and in four swift movements it firmly plants both feet on the platform at the top of the ladder.

With this latest triumph SCHAFT has become the undisputed champion of the DRC. In the past two days it has driven a small jeep-like car over a short, twisting course, walked over ramps, steps and rubble, negotiated various doorways, cleared debris from its path, cut a hole in a wall with a power tool, connected a fire hose and shut off a series of valves. Now, as the Japanese engineers who built it celebrate below, it squats impassively on its backwards-facing haunches.

The prize for victory is not just the applause of fans, rivals and robo-curious spectators, who have come in their thousands. DARPA, the Pentagon research agency which runs the DRC, is rewarding the best teams at the event with up to $1m so that they can improve their robots and compete again in a year’s time at a more demanding second event. All told the project is costing it some $80m.

The agency made robots a priority because, like many others, it suspects that the technology may be on the cusp of scaling far greater heights than a nine-step aluminium ladder. It is expressing its support in the unusual, quasi-sporting, highly public forum of the DRC because robotics is a technology unlike any other. As machines that sense their environment, analyse it and respond accordingly, robots lend themselves to showmanship, judged as they are by their actions in the world (this special report will deal only glancingly with other machines sometimes called robots that do not have a moving physical presence, such as software “bots” or stationary bits of automation). They exert a fascination, both on their designers and their fans, that transcends the technology’s current practical uses. The engineers who made SCHAFT started their company not because they thought it would make them a fortune, says Takashi Kato, an entrepreneurial investor who helped them with it; they did it “because they would rather build robots than anything else”.

This fascination has produced robots of many shapes and sizes. Academics have tried their hand at mimicking nature, basing robots on everything from termites to pterodactyls. For robots designed to make money, form has followed function, leading to the multi-jointed, mostly cast-iron arms of the world’s 1.2m-1.5m manufacturing robots; the spindlier limbs of robots designed for surgery; the deep-pan pizza-dish form of service robots that vacuum the floors of the house-proud and gadget-friendly. But at the DRC, as in the public imagination, the robots are mostly humanoid.

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There are exceptions. RoboSimian, competing on behalf of JPL, the laboratory that runs most of NASA’s planetary missions, looks appropriately alien, with the knees, or elbows, of its four limbs articulated in ways that are distinctly non-human, and for that matter un-simian; it moves more like a body-popping spider. SCHAFT, though, has two legs and two arms, even if it lacks a recognisable head and its hips do double duty as shoulders. Hubo, a South Korean robot being used by two of the teams, and Atlas, the machine chosen by seven American teams, go the whole arms-legs-head-and-shoulders humanoid hog.

The reason for this convergence on the humanoid form is that they must function in an environment shaped to human specifications. The ladders, doors, valves and rubble of the DRC are meant to test how well robots would cope if sent into a disaster area inaccessible to humans, such as a stricken nuclear power station or chemical plant. And although such rescue operations are unusual, the constraints they impose fit with one of the main aims of current robotic research: learning how to operate flexibly in an environment designed for humans, not robots.

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Not coincidentally, such operations are typical of robots in science fiction—the land of their birth. More thoroughly than any other technology—except, perhaps, that of the spaceship—robots were imagined in print and on film long before they were created in laboratories and installed in factories (some of the cinematic imaginings serve to illustrate this special report). And to some extent robots remain science fiction to this day; they may have become real, but they continue to be shaped by expectations created by fiction and continuously nurtured by it.

There have always been stories of artificial people and magical mechanisms. They were, though, for the most part singular creations, given their being through mystical powers or masterly skills. The fiction of the 20th century introduced something new: mass production. Like the factory-made labourers to which the word was first applied in Karel Capek’s play, “R.U.R.: Rossum’s Universal Robots”, robots became both the products of industrial technology and a way of talking about that technology’s effects: of what it does to people’s futures; of how it can make them robotic in themselves; of how it still always seems to leave some space for the strange and quirky.

Fiction before fact

Capek’s factory-born robots, embodying anxieties about industrial progress, rose up to wipe out the human race, and many of their fictional successors have followed a similar course. But Isaac Asimov, a Russian-born American who did more than anyone else to steer science fiction towards the idea of robots as industrial artefacts, offered a kinder, more complex version of the conflict between the made and the makers. Before electronic computers existed, Asimov saw that robots would be programmed, and thus constrained by their programming. He also realised that humans would fail to appreciate the predictability such programming brought, and that the clash between what was programmed and what humans expected of, wanted from and feared in their robots would be a rich source of plots.

But for all that they were industrial, Asimov’s robots were also the product of a particular sensibility, background and set of concerns—those of a child of hard-working and hard-pressed Russian parents in 1930s Brooklyn. Always content to do what they are told; always consigned to work on the “dull, dirty, dangerous” jobs; often uneasily aware that they are superior in some ways to their masters; endlessly at risk of pogrom because of their masters’ resentment and fear of them: his robot stories, and those of his successors, were immigrant stories. Except that the robots are immigrants not from abroad but from the future.

Robot researchers are keenly aware of the fictional foundations of their work. Gill Pratt, an academic from the Massachusetts Institute of Technology (MIT) currently on secondment to DARPA, where he runs the DRC programme, immediately brings up Asimov when asked why he got interested in robots. Any visit to a Japanese robot laboratory soon leads to a discussion about Astro Boy, the helpful android who in the 1960s starred in Japan’s first popular animated television show, to help explain the country’s rampant robophilia. And robots that offer domestic services are routinely compared to Rosie, the robot maid in “The Jetsons”, an American television show of the same vintage. No discussion of the military use of drones will continue for long without reference to the “Terminator” films.

Yet those who work with robots also know better than anyone else that what they do, although prefigured and even shaped by fiction, still falls far short of it. Willow Garage, a robotics company founded in 2006 by Scott Hassan, one of the first people to work at Google, spent millions of dollars developing PR2, a two-armed “personal robot” designed to help with tasks at home and elsewhere. Able to navigate itself and manipulate objects of various sorts with its hands, it is about as good at what it does as any robot built so far, and dozens have been sold or donated to research laboratories around the world. Still, it is, Mr Hassan says, “dumber than a doornail”.

In “A Christmas Carol”, the first thing Charles Dickens tells the reader is that Jacob Marley is “dead as a doornail”: this fact “must be distinctly understood, or nothing wonderful can come of the story I am to relate”. Something similar applies to the doornail dumbness of robots. To see what may come of them, however wonderful, you have distinctly to understand how very little thought they are currently capable of. They are roughly as intelligent as a small bug, says Mr Pratt.

The field of artificial intelligence (AI), from which academic robotics has developed, has achieved quite a lot since it was founded in the 1960s—but nothing like the generalised intelligence, capable of seeing, understanding and planning, that those founders were after. It has shown that although computers can easily do some things people find hard (such as playing chess), they cannot fathom many things people can do

without thinking. Getting robots to walk moderately well has taken decades and many hundreds of millions of dollars, mostly spent in Japan. To get a (non-walking) PR2 simply to recognise a thing that needs picking up still takes a lot of work. In Masayuki Inaba’s laboratory at the University of Tokyo, where some of the SCHAFT team got their start, a PR2 programmed by gifted students tried to serve your correspondent a can of coffee from a fridge. It opened the fridge door and got the coffee out, but then tried to serve the can to the fridge instead.

The reason why a robot like SCHAFT can negotiate doors, climb ladders, cut holes in walls and so on is that it is getting help from humans. All the robots at the DRC were being “tele-operated”; their near-term goals were set and monitored by operators in the garages that line the speedway track’s pit lane. The robots were keeping their balance and taking their steps using on-board software and processing power; the back-room boys were interpreting what the robots saw and planning their next moves.

The robots that did best in Florida will reconvene in late 2014 or early 2015 for the finals, where the tasks will be harder and performance, everyone hopes, better. Mr Pratt explains that one purpose of the DARPA challenge is to give a sense of how much robotic progress a year of research and funding can buy. A previous competition proved wildly successful at promoting progress in a related field. The first DARPA Grand Challenge, in 2004, required teams to get cars to drive themselves over a 240km desert route. None of them made it even a 20th of the distance. Yet when the race was reconvened a year later, better software for mapping and understanding the world allowed five competitors to complete the course. In the exhibition ground at the DRC sat one of Google’s driverless cars, its ancestry directly traceable to the winning team in that second challenge. If such progress is possible on the roads, why not in the kitchen, the retirement home or the shopping mall?

The comparison is given extra bite by Google’s acquisition, in the run-up to the DRC, of eight robotics companies with products and services at various levels of development. They included the Japanese startup that made SCHAFT as well as Boston Dynamics, which has done a great deal of work for DARPA. It designed and built the Atlas robots that most of the American DRC teams were using, and has produced impressive walking and running quadruped robots for military test programmes.

Putting money on it

Google is being tight-lipped about its plans for all these robots; speculation on what lies ahead ranges from far better factory automation and door-to-door delivery robots to a mission to the moon which will allow, on the 50th anniversary of Apollo 11, one small step for robotkind. But the mere fact that a company with an impressive track record in innovation has rounded up a lot of robot engineering talent and intellectual property is a striking vote of confidence in the field’s prospects. That does not mean that robots in general will progress as quickly as driverless cars have done; progress in robotics will often be limited by the rate at which the most difficult of all the different kinds of problem encountered can be solved. But change does not have to be fast for its long-run consequences to be profound.

Nor do robots have to become fully autonomous in order to be able to do a far wider range of interesting and useful things than they do today. For the most part they are not replacements for humans; they are better seen as extensions. Humans can come together to do things they cannot do alone; in future they will increasingly come together with robots to do things they cannot otherwise do so easily, or in some cases at all.

Robotic extensions will come in a variety of forms. But just as the need to work at a range of tasks in the human world can force robots into a humanoid shape, so the need to work with humans in that world means that many of them will become, to some extent, socially humanoid. The most useful robots will be those that are best suited to working with people, at whatever level of autonomy is appropriate to the task. To fit into the social world, robots will need to take both casual and formal instructions and to meet tacit expectations: daunting tasks for doornails.

But robots could move farther into the social world than people currently expect, in part because that world may prove oddly welcoming. People will ascribe human feelings to, and invest their own feelings in, things which have only the most passing claim to them—cats, toys, comfort blankets. And, pace Asimov, the relationship need not always be one of fear and distrust. Especially when helped along by good design, people can be quite empathetic towards technology.

The ladder task at the DRC demonstrates the point. It was impressive to see SCHAFT reach the top of its ladder when so many others had only barely got off the ground. But it was remarkable to see Drexel University’s little Hubo reach the last step and then succumb to a gust of wind, losing its footing. The most arresting thing was not the slip itself—a safety harness stopped the robot’s fall before it could hit the ground—but the gasp of genuine dismay from the onlookers. Writers invented robots as a way of exploring human feelings about technology; the depth of those feelings may yet surprise their makers and users.

The build-up

Good and ready

After slow beginnings, a big push in robotics now seems imminentMar 29th 2014 | From the print edition

ON THE OUTSKIRTS of Odense, a small city in southern Denmark, Enrico Krog Iversen shows off the building he has bought to serve as the new headquarters and assembly facility for Universal Robots, a company of which he is both the chief executive and a big shareholder. It is about ten times the size of the company’s current headquarters, a five-minute drive away. Universal Robots, founded in 2005 by academics from the nearby university, is growing pretty fast. In the past four years, says Mr Iversen, its sales have increased more than 40-fold. By 2017 he hopes for a turnover of DKr1 billion ($190m).

Universal Robots makes robot arms that are light and easily programmed, and hence well suited to use in small manufacturing businesses. At €22,000 ($31,000) each, plus a similar amount in set-up costs, they are also affordable. Universal’s website is stuffed with case studies to demonstrate to potential buyers that the robots’ cost can be recouped in less than a year.

The advent of robots that are cheap and safe enough to be used outside big factories is one reason for a resurgence of interest in robotics over the past few years. Rethink Robotics, a Boston-based company founded by one of the most respected researchers in the field, Rodney Brooks, has attracted a lot of media interest because it sells a particularly appealing and innovative two-armed robot, Baxter, designed for this market.

Whereas Rethink and Universal focus on smallish customers, others are planning to go big. Foxconn, a Taiwanese company that manufactures and assembles electronic kit, says it is aiming to roboticise much of its operation with hundreds of thousands of its own relatively cheap Foxbots.

Affordable does not necessarily mean simple. UBR-1 is a robot arm sitting on an autonomous body that can navigate from place to place. It was developed by Unbounded Robotics, a four-person startup which, like a number of others, was recently spun out of Scott Hassan’s Willow Garage, the hothouse that developed the

PR2 robot mentioned in the previous article. UBR-1 is a sort of pared-down, one-armed and less capable PR2, if a much more attractive piece of industrial design. But then, starting at $35,000, it is only about a tenth the price of its older sibling.

This is all a long way from the high-value industrial-robot market served by big manufacturers like Germany’s KUKA, Sweden’s ABB and Japan’s Fanuc and Yaskawa. According to the International Federation of Robotics (IFR), between 2008 and 2012 industrial-robot sales increased by 7% a year, to $8.7 billion. The business is concentrated in Japan, South Korea, Germany, China and America, and on specific industries like cars and electronics. Car companies use the lion’s share of industrial robots; in 2012 they accounted for 52% of robot installations in America. The country with the most robots per person is South Korea, which takes the technology very seriously.

Sensing progress

Cheaper robots should be able to move into fields in which today’s big beasts have shown only passing interest, such as food processing. But that is not the only reason for the buzz around robotics. In academia, robotics, which has been a slow developer compared with booming areas like biotechnology, is on a roll.

The most obvious spur to progress has been the increasing amount of computing power and sensor technology that can be bought for a given price. “Everything built out of silicon is taking off,” says Chris Atkeson, a researcher at Carnegie Mellon. Many of the benefits come in the clever things that people operating in bigger markets have found to do with better, cheaper chips, which can be useful to robot-makers.

Take the Kinect sensor developed at Microsoft for the company’s Xbox 360 game console, released in 2010. An array of microphones and cameras are artfully combined with high-powered chips and well-tailored software to sense players’ movements from a distance and apply them to the game.

Used in robotics, the Kinect sensor is a cheap, easy and fairly reliable way to provide both a sense of depth and a kind of “person-detector”, which is a great help to robots that need to map and navigate their surroundings. Most robot laboratories have some version of it, either bolted to a robot or mounted on the wall or ceiling. A shopping mall in Osaka is wired up with the sensors set up by Japan’s Advanced Telecommunications Research Institute International to tell robots where they are and how to spot the shoppers to whom they are learning to give leaflets and directions.

As well as having access to better sensors and processors from other fields, robotics has devised its own new ways of making software for them. The PR2 was a test bed for the Robot Operating System (ROS), a uniform way of passing messages between the various software routines that run a robot. ROS, now looked after by a not-for-profit spin-off from Willow Garage, the Open Source Robotics Foundation (OSRF), is free to use and easily customised, and is being taken up by more and more researchers, many of whom happily share their fixes to the software. Using an ROS navigation “stack” and a Kinect, it is now relatively easy to build a rudimentary robot capable of finding its way round a building—call it a “trundlebot”—though making it robust and reliable is a lot harder.

S.K. Gupta, a robotics researcher at the University of Maryland who is currently running the National Robotics Initiative at America’s National Science Foundation (NSF), sees ROS and the like not just as solutions to specific problems but as developments that are reshaping the field. Robotics used to be hard to do because to make even a poor robot you had to be good at a whole lot of different things: artificial intelligence, building manipulators, engineering joints and wheels, electronics and so on. As a result, academic robotics research has generally been concentrated at universities that already have a flourishing robotics programme with capabilities across the board, such as Carnegie Mellon, MIT and the University of Tokyo. Now a small team with a fresh insight in a single area—making hands, say, or machine-learning—can use ROS and reasonably cheap hardware to put together a robotic system on which to try out its ideas without being expert in any of the other areas involved. Perhaps as a consequence, the first funding round

for the initiative Mr Gupta oversees produced applications for $1 billion in grants, more than 20 times the amount eventually awarded.

Sometimes there is no need to build a physical robot at all. Many of the teams that used Boston Dynamics’ Atlas robot at the DRC had no real-world experience in getting bipedal robots to nip around without falling over. They were chosen through a preliminary competition in which the software they had developed for the trials was used to animate a virtual robot in a simulated environment called Gazebo, which had been developed by the OSRF and hosted in the cloud. Being able to try out robot software in real time this way, says DARPA’s Mr Pratt, is a big deal. He draws an analogy with the early days of integrated circuits on silicon chips in the late 1970s. At first the only way to see how well a circuit was going to work—if at all—was to build it. It was only when simulators became available that designers could ensure in advance that the circuits would work, which vastly sped development in the field.

Back in the physical world, 3D printers—almost as ubiquitous as Kinect sensors in robotics labs—are another technology that is making research faster. Anders Billesø Beck of the Danish Technological Institute says they have greatly sped up his team’s efforts to find ways for small businesses to use robots. Instead of being sent out for manufacture, items like new designs for manipulators or little gizmos to hold a part being worked on can be cheaply produced in-house overnight.

There was very little academic research in robotics before there were undergraduates who had seen “Star Wars” as kids

Like Universal Robots’ premises, the Danish Institute’s robot labs are in Odense. Both can trace their origins to research at the city’s university decades ago, as can a number of other robot companies and consultancies in the area. Such clusters take time to come into their own, which may be another reason why robotics research feels as though it is entering a new stage of development. There was very little academic research before there were undergraduates who had seen “Star Wars” as kids; those former fans have now had a professional lifetime to build academic research groups and spin off companies.

The biggest cluster, that around MIT, is home not only to Boston Dynamics, to date supported mostly by military R&D contracts, but also to iRobot, which has shown the way in developing a profitable service-robotics business aimed at consumers. The company was started in 1990 by Mr Brooks (now at Rethink Robotics), Colin Angle and Helen Greiner, all robotics researchers at MIT’s artificial-intelligence lab. They knew they wanted to make a business out of robotics, but not what that business should be. It was not until 2002 that they came up with the two products that have made the company’s reputation: the Packbot, which has helped soldiers deal with improvised-explosive attacks in Iraq and Afghanistan, and the Roomba, which cleans floors. The company has sold more than 8m of these; last year home robots, mostly Roombas, accounted for 88% of its sales.

The company not only makes a profit; it has also rewarded its original investors by going public in 2005, something which no other robot startup has done. However, in the past few years a number of them have been bought up for tidy sums. In 2012 Amazon, a retailing giant, took over Kiva Systems, a company based near Boston which makes robots for warehouses. Google’s robotic acquisitions followed in late 2013. “Great news for the industry,” says Mr Angle.

Such proof of viable exit strategies should inspire further investment, which a new French fund, Robolution, hopes to tap. Its boss, Bruno Bonnell, explains that ten years ago he was unable to persuade the company he ran, Infogrames/Atari, to get into robots. He left to become the French distributor for iRobot’s Roombas. In 2006 he sold 800; last year the figure had risen to 100,000. In early 2014 he closed the Robolution fund for early investment in robot companies at €80m, admittedly most of it put in by the French government and the European Union. Pointing to a sharp increase in American venture funding for robots—he puts it at $400m last year—he is convinced a lot of talent can be got out of European labs too.

Another potential source of money may be entrepreneurs who have done well out of other technologies at a young age and for whom the science-fiction feel of robotics is a turn-on, not a danger signal. The space business points the way. Elon Musk is shaking it up, using some of his internet-derived riches to create SpaceX, a disruptively good rocket-maker; Amazon’s Jeff Bezos has a rocket company too, and Google’s Larry Page takes a keen interest in such things. Robots offer a similar attraction; witness Mr Hassan’s creation of Willow Garage with some of the money he made from Google. Or, indeed, witness Google itself. The company’s recent acquisitions in the field are being supervised by Andy Rubin, who has long been fascinated by robots. When he developed the operating system Google uses for mobile phones, which became a great success, he named it Android.

Promise in the cloud

Google’s expertise at dealing with huge amounts of data will almost certainly play a key part in its plans. By drawing on the computing power of cloud-based systems, its robots, and others, should be able to do much more than they are currently capable of. The self-driving car demonstrates the idea; it can mesh information on its whereabouts from its sensors with maps of the world held in the cloud, with various programs using the comparison to generate instructions for the cars’ motors, steering systems and so on. Ken Goldberg of the University of California, Berkeley, suggests that a similar use of “cloud robotics”—a term coined by a Google employee, James Kuffner—could make it much easier for robots to recognise objects for what they are and act accordingly.

The cloud already houses libraries and programs that can help computers work out what an image is of, and robots to work out from the shape of an object how to pick it up. The European Union’s “RoboEarth” project imagines a cloud-based system that would contain all sorts of such information in a form that robots could use, and that would let robots learn from each other, both about the world around them and about successful ways of tackling tasks in that world.

Far better computers, on board and in the cloud; good, cheap sensors; maturing industrial-academic clusters; a broadening and speeding up of the field’s research base; expanding markets; exciting hardware; and a newly encouraging investment outlook: all of these have helped stimulate interest in robotics. The collapse of another science-fiction dream at Japan’s Fukushima Dai-ichi nuclear power plant in 2011 gave it an extra push. Mr Pratt traces the genesis of the DRC to the day after a tsunami hit Fukushima, when it became clear that the robots needed for such emergencies, widely believed to exist already, were nowhere to be found. Mr Inaba at Tokyo University suggests that some day emergency robots will become mandatory at big industrial installations, just as fire extinguishers are required in offices.

And a final reason why interest in robots has taken off is that some of the machines have been doing so themselves, in a very high-profile way. The greatly expanded role of aerial drones in warfare shows what such machines can achieve—and raises both hopes and fears about what they may do next.

Military uses

Up in the air

Drones will change war—and moreMar 29th 2014 | From the print edition

AMERICA’S DECADE OF misbegotten war in the early 21st century will be remembered for many things, but when it comes to technology, the rise of the drone will stand out. When America invaded Iraq in 2003, it had a couple of hundred; by the time it left, it had almost 10,000.

Pilotless aircraft had been around for decades. What was new was that, thanks to the Global Positioning System (GPS), they knew where they were, and thanks to better satellite and other communications links they could send back copious data. That allowed them to feed intelligence, surveillance and reconnaissance (ISR) to all levels of America’s increasingly information-hungry armed forces. A platoon of soldiers wanting to look beyond the building in front of them; an intelligence agency tracking a target; a general staff trying to understand what was going on across a broad area: today there is a drone for them all.

Throughout this entire period no drone, or indeed any other robot, was put through the full qualification process usually required for any new American weapons. They were sent into the field in various stages of unreadiness by people who saw a need for them. On the ground that need was dealing with bombs; in the air it was almost entirely ISR.

A very small number were used for launching attacks, both on the battlefield and off it, often in countries—Pakistan, Yemen, Somalia—with which America was not at war. Most of these attacks were carried out by the CIA, which is not new to killing people it has identified as enemies; its Operation Phoenix was responsible for the death of tens of thousands in South Vietnam in the 1970s. But drones allowed such tactics to be employed much farther away from any logistical support, and allowed decisions on whom to kill to be made far higher up the tree.

A small number of drones designed to fire missiles thus allowed new strategies to be adopted in the “global war on terror”—an example of how a relatively minor technological advance can have big consequences. Military drones, after all, are no cleverer than doornail-dumb earthbound robots; many are dumber still. They can do more because they operate in a much less difficult environment, with few obstacles in the way and most of the tasks achieved by pointing a camera or a missile in the right direction. The smart ones can follow commands such as “stay where you are,” “follow this flight plan” or “come home and land”; if their communications are cut off, some will return to the place where they last received a command. But humans are always involved in any tricky or lethal decisions.

That may change. Some military planners see a big future for drones—and for robots both on land and at sea—which will do a lot more than just provide ISR. The more drones that the armed forces deploy, the greater the pressure for autonomy becomes. One reason is cost: a drone that needs minimal supervision is a lot cheaper to operate than one that needs detailed attention. The other is safety. The greater your reliance on drones, the more your enemies will want to attack them. The drones’ command, control and communications network will become an important target. More autonomous drones are less vulnerable to such attack.

Not the drones you are looking for

Many people are alarmed by the idea that new forms of warfare will lead to increasingly deadly autonomous weapons, not just drones but also smart undersea systems. Various human-rights organisations have banded together in a “Campaign to Stop Killer Robots” which seeks to ban fully autonomous weapons systems. The Convention on Certain Conventional Weapons, responsible for previous bans on laser blinding weapons and some types of explosives, will begin discussing such a ban in Geneva in May.

Some military lawyers claim that most of what the campaigners hope a ban would outlaw is in fact already illegal under existing rules of war, which forbid indiscriminate attacks; and for some purposes, such as defending ships against missile attacks, autonomous systems are both necessary (because of their speed of response) and legally and morally unproblematic, since they operate in areas where no civilians, and possibly no enemy combatants either, will be affected.

Once again, responses to robots reflect broader worries about technology. The idea that technology makes war too easy and removes its reliance on soldierly virtues goes far beyond concerns about the specific roles that robots might play. It is shared by a significant number of military men, who consider killing people a serious matter. Many would be deeply uneasy about delegating it. They have no problem with robots that may save the lives of their comrades—working on bomb disposal, looking round a corner into the unknown and perhaps in future evacuating the wounded from the battlefield. But robots that might replace those comrades (or, in an enemy’s hands, threaten them directly) are seen as much more troubling.

That may be one reason why, surprisingly, the Pentagon’s robot budget is currently shrinking. In its 2014 budget request unmanned systems’ funding was down by a third on the previous year. With ever fewer soldiers in combat zones, there is less need for bomb disposal and the like; but the money for both buying drones and developing better ones is scarce, too. Some, especially in the air force, never much cared for them anyway and are happy to make them a lower priority. And other programmes—most notably the extraordinarily expensive F-35 fighter plane—have far more effective champions in the military-industrial-congressional complex than drones do. At a recent meeting on autonomous weapons, a retired American colonel suggested that for the next decade or so there is no need to worry about America getting new drone capabilities because it will not get any new drones, period.

Mark Gunzinger, another retired colonel, now at CBSA, a Washington think-tank, worries about that. By not developing newer, better drones, he says, the armed forces risk missing opportunities for big improvements in their capabilities. Take aircraft carriers, whose ability to project force is fundamental to America’s global military strategy. Close to the coast of a sophisticated adversary, they are increasingly at risk of missile attack. If they had bomb- and missile-carrying drones on board, they might be able to strike from greater distances. An experimental American drone, the X47B, has shown that it can take off from and, more impressively, land on carriers. But there is currently no programme to develop that capability to allow carriers to attack well-defended targets on land from a safe distance at sea. That would require drones to be able to overcome enemy defences, which the present generation cannot do.

A deeper worry is that potential adversaries will themselves push ahead with drones, perhaps finding entirely new ways of using them en masse. Low-cost computing power has especially benefited drones that use a number of rotor blades for their lift; co-ordinating many rotors takes quite a lot of computing, but makes take-off and landing much easier. “Quadcopters” with a range of 10-20km and a battery life of half an hour are now mass-produced and can cost less than $1,000. A recent report on “War in 20YY” by CNAS, another think-tank, points out that the ability to blacken a town’s sky with a swarm of such gadgets,

or send a wave of them across a sea, could produce completely new tactical possibilities; it might be the sort of technological development which changes how wars are won.

Drones will get cheaper still, in part because markets for them are opening up quickly. In America there is as yet no legal framework permitting their commercial use; elsewhere they are being used by journalists, for safety checks and—as in America—for fun. The Federal Aviation Administration is drawing up a regime for commercial use in the country’s air space from 2015.

Most of those uses, to begin with, will probably be in the civilian equivalent of ISR, both by government bodies such as police departments (of which America has 20,000) and by private entities. Chris Anderson, a former writer on this newspaper and now the boss of 3D Robotics, which makes drones, says they supply that fashionable commodity, big data, to fields where it is otherwise hard to come by, such as agriculture. Mr Anderson’s company, along with others, wants to enable “alpha farmers”—free-spending, tech-friendly innovators committed to coming up with the highest-quality produce—to keep an eye on their crops more or less plant by plant and hour by hour. When they have worked out what to do with such data, the rest of the industry may follow. Another use could be checking up on infrastructure such as roads, pipelines and transmission lines.

Peter Singer of the Brookings Institution in Washington, DC, who has written extensively on drone warfare, draws an analogy with military aviation to show how far drones have yet to go. At the beginning of the first world war, military aviation was largely concerned with reconnaissance. It grew quickly, taking on new roles with specialised bombers and fighters, and by 1918 there were tens of thousands of warplanes. Men like Billy Mitchell, Hugh Trenchard and Giulio Douhet started to argue that aircraft could fundamentally change warfare. The planes went on to do so, though not quite in the way that those air-power visionaries imagined.

For Mr Singer, the interesting part is what military aircraft in the first world war did not do. None of the planes was used for transmitting messages, moving cargo and people or spraying crops—the uses to which aircraft would be put in their hundreds of thousands over the next few decades. Today’s military drones are a little more diversified. Whereas most of them look and some kill, others are used for logistics or

communications. But as they become more capable and the rules are relaxed, those other capabilities will come into their own beyond the battlefield.

Business service robots

The invisible unarmed

The best robot technology is unseenMar 29th 2014 | From the print edition

THE ANNOUNCEMENT IN November 2013 by Amazon’s boss, Jeff Bezos, that he wanted to use drones for deliveries was, to many in the industry, something of a stretch. Applying drones safely on such a scale without line-of-sight control, and re-engineering the company’s logistics model accordingly, would be a big undertaking. But it generated a lot of media interest, and just in time for Christmas, too. His company’s most serious commitment to robotics to date—its acquisition in early 2012 of Kiva, a company whose robots move shelves around in warehouses—got far less attention. This is one example of a pervasive quality of robotic technology: the high visibility of its promises and the near-invisibility of its successes.

In the 1990s Danny Hillis, a computer scientist and entrepreneur, pointed out that when people talk about “technology” they really mean “everything that doesn’t work yet”; once technologies work they simply become computers, televisions, phones and the like. Robots suffer from a similar double standard. When they are seen as providing a reliable, routine solution to a problem—cleaning floors, for example—in some ways they cease being seen as robots. Conversely, as long as they continue to be seen as robots, they may seem experimental and bug-prone. One entrepreneur who recently used the word “robotics” in the name of a fledgling company is already having second thoughts, worrying that customers will expect the products to be both higher-tech and less reliable than they are.

It is quite easy to imagine a future in which “robots” remain an esoteric subject of public fascination even as more and more services are automated with techniques developed in robotics laboratories. Take Aethon,

a Pittsburgh company that makes robots for hospitals. Its Tug robots, limbless and faceless, are uncharismatic but reliable heavy-duty trundlebots designed to move hospital trolleys around. Aethon’s boss, Aldo Zini, says he has come across hospitals where porters have to move dirty-linen carts weighing 350kg; such jobs carry a significant risk of injury and have a very high turnover. They fit the “dull, dangerous and dirty” category perfectly.

Aethon’s Tugs can be summoned with a smartphone app and attached to a trolley carrying pharmaceuticals, diagnostic materials, meals or laundry. About 150 hospitals, mostly in America, already use them. In some of those hospitals they could soon be running into (not literally; they have collision-avoidance systems) other trundlebots. The Ava made by iRobot is a pedestal that can navigate around a building it has got to know, and onto which various types of “telepresence” packages can be mounted. One application has high-resolution cameras and other sensors designed for diagnostic work, allowing patients to benefit remotely from the skills of specialists in other places. Another application is to move terminals for videoconferencing equipment to where they are needed, allowing conversations to take place on the factory floor or walking down a corridor.

Suitable Technologies, another Willow Garage spin-off, is now also selling a telepresence system, Beam, based on a sort of trundlebot. The move was inspired by seeing the in-house success of a gadget put together by Willow Garage staff that let an engineer living in Indiana participate more fully in the company’s life in Palo Alto. Similarly, Paolo Pirjanian, iRobot’s chief technology officer, who lives in California, uses an Ava system that allows him to be a more or less daily presence at the company’s Massachusetts headquarters; his colleagues say he is “there” much more than he could ever be if he used only phone, e-mail, instant messaging and Skype. When he wants to move from one floor of the building to another, he simply logs out of one Ava 500 and on to one on the next floor; his abandoned chariot will trundle back to its charging point unsupervised.

This is just one of the solutions robot designers have had to find for the lift problem. Aethon’s Tugs are equipped with a wireless system for summoning them. Suitable Technologies recommends different Beams for different floors. The rather charming CoBot at Carnegie Mellon, on the other hand, relies on the kindness of strangers, standing by the lift door displaying a little sign asking passers-by please to press the appropriate button for it.

What self-navigating robots do not do when they want to travel in a lift is use their arms to press the buttons. Arms, and the software that tells a robot what to do with them, are expensive and fallible things. Away from the highly regimented world of the production line, they are worth investing in only if they are vital to the tasks that the robot has, as it were, at hand: if it needs to change something in its environment in the way a human would. And to a large extent, success in practical service robotics has revolved around choosing or designing tasks that do not require changes of that kind.

Both Mr Angle of iRobot and Mr Zini of Aethon are very keen on the word “practical”. Their companies sell systems that solve problems for a lot of people, companies and institutions in a way that would not be possible without robots; but since robots currently have many shortcomings, those systems have to be designed in a way that minimises their responsibilities as well as the need for human supervision. Mr Angle stresses that the right business plan is crucial. His company got well down the road towards developing robots for commercial floor-cleaning before realising it did not have the right model for the business.

A robot by any other name

What gets a robot seen as a robot is, to a large extent, its ability to do different things, of which the arm can be seen as an emblem; businesses which design their solutions in such a way that what the robot does, though crucial, is also highly constrained, may in effect make their robots invisible. In a decade or so trolleys moving around hospital corridors unsupervised will just be trolleys, no more meriting special attention than doors that open automatically when someone approaches them. In Japan such automation, from self-driving cars to camera autofocus, is called robotech, to differentiate it from robots proper. Growth

in robotech will allow robotics companies to make money without filling the world with things thought of as robots. They will simply be supplying non-factory automation that works.

This goes far wider than trundlebots in hospitals. For a very different form of constructive invisibility, consider Bot & Dolly, a San Francisco company that uses industrial-robot arms for art projects. The company has developed a way to mesh the software used to control robot arms with the software that visual-effects makers use to plot out what is on a movie screen frame by frame. That enables film-makers such as Alfonso Cuarón, who used Bot & Dolly to spin cameras and lights around Sandra Bullock and George Clooney in “Gravity”, to move a camera along any trajectory they choose without knowing anything about robotics. All they need to know is what they need to see at which angle. How the robot achieves that is not their problem.

Perhaps the most pervasive invisibility will be achieved by robots on the road. Self-driving cars are “hard-core robotics”, says Sebastian Thrun, who masterminded Google’s self-driving-car programme. His former colleagues at Carnegie Mellon, meanwhile, have been working with GM on automating cars. Thanks to all this work, along with a great deal more funding than any academic robotics programme would ever garner, self-driving cars are now a practical possibility.

They are not yet a business (a Google car would still cost far too much to put into production, even if the regulatory framework were to allow its widespread use), and it is not clear how they will turn into one. But although there are technical and business challenges still to be overcome, the idea that within a decade or so cars will be increasingly able to drive themselves is now widely accepted as plausible, even if the details are hazy. And it is a safe bet that the more capable, acceptable and thus widespread self-driving cars become, the less they will be seen as robots and the more as just cars.

Labour markets

A mighty contest

Job destruction by robots could outweigh creationMar 29th 2014 | From the print edition

“OUR ROBOTS PUT people to work,” says the rejected slogan still on the whiteboard in Rodney Brooks’s office. It was meant to convey the belief that led Mr Brooks to start Rethink Robotics: that robots in small manufacturing businesses can create new jobs, or at least bring old ones back from China, thus helping to launch an American manufacturing renaissance. But the message could also be read another way: robot overlords forcing human helots into back-breaking labour. Better left unsaid.

Small and medium-sized companies are between 20 and 200 times less likely to use robots than large ones in similar sectors, according to a study carried out by Metra Martech, a consultancy, for the IFR. They could thus become an important market if someone were to offer them the right robots, which would open up new sectors of the economy to the productivity gains that can come with automation. Such robots would still do routine tasks but would be able to switch from one set of tasks to another as required, perhaps every few weeks, perhaps a couple of times a day. They would therefore be heavily dependent on their human fellow workers to set them up and get them going.

Rethink reckons it has the right robot for the job in Baxter, a two-armed quasi-humanoid (it cannot move itself) that can be easily adapted to a number of packaging and assembly tasks. It embodies a lot of innovative technology. The joints of its arms use a relatively new gizmo called a series elastic actuator which gives the robot’s software control over the amount of force that they exert at any given time, rather than just their location in space (an innovation pioneered by Mr Pratt, now running robotics at DARPA). This makes Baxter very safe to be around; if it meets unexpected resistance from, say, the head of a human worker, it will stop before any harm is done.

Baxter also has a splendidly intuitive programming interface. By grasping Baxter’s wrists, an operator can easily take it through new movements; the robot’s “face”—a screen with animated eyes which show what Baxter is “paying attention to”, among other things—helps gauge the success of the programming. Mike Fair of Rethink says it took him just a couple of hours to teach Baxter to make a cup of coffee using a kitchen coffee-maker, and he did not have to touch a computer keyboard. That all this cleverness could be put into a machine that sells for just $25,000 has amazed many of Mr Brooks’s former colleagues in academia.

However, Baxter has not taken the market by storm, perhaps in part because it started off rather imprecise in its movements (a software upgrade, Mr Brooks says, has improved precision a lot). Being designed for a market that almost by definition barely knows it wants such a thing has not made life any easier. Rethink laid off some workers last December and is trying harder to sell Baxter to robotics researchers. One former colleague of Mr Brooks’s sees this as a potentially dangerous splitting of the company’s attention: if Baxter is to succeed as a practical robot, the company should concentrate on the robot’s industrial users.

More broadly, though, the idea that robots are no longer the preserve of manufacturers with capital budgets in the tens of millions of dollars is taking root, alongside the idea that such robots offer industrial countries a way of keeping, or winning back, jobs that would otherwise be carried out in places where labour is a lot cheaper. Universal Robots, which has less nifty technology than Rethink but perhaps a more down-to-earth approach to the market, is taken seriously in Denmark in part because Danish workers are among the most expensive in the world and will keep their jobs, or find new ones, only by becoming ever more productive. Integrating ever more user-friendly robots into the human teams on the factory and workshop floor could offer such productivity gains, especially when the strengths of the robots are used to the full—for example, when robot precision is used to guide work carried out by human hands.

Is this time different?

Robot-makers see their wares as a way of creating employment, both by allowing companies to make existing products more efficiently and by enabling them to manufacture new things that could not be made in any other way, such as ever more precisely engineered electronics and cars, not to mention films like “Gravity”. Others fear that their net effect will be to destroy a lot of jobs, and indeed that they may already be doing so. Nick Bloom, an economics professor at Stanford, has seen a big change of heart about such

technological unemployment in his discipline recently. The received wisdom used to be that although new technologies put some workers out of jobs, the extra wealth they generated increased consumption and thus created jobs elsewhere. Now many economists are taking the short- to medium-term risk to jobs far more seriously, and some think the potential scale of change may be huge. Mr Thrun draws a parallel with employment in agriculture, which accounted for almost all jobs in the pre-modern era but has since shrunk to just 2% of the workforce. The advent of robots will have a similar effect, he predicts, but over a much shorter period. Even so, he is sure that human ingenuity will generate new jobs, just as it created vast new industries to counteract the decline in agricultural employment.

Technological dislocation may create great problems for moderately skilled workers in the coming decades

Erik Brynjolfsson and Andrew McAfee, both at MIT, also have high hopes for the long-term effect of robots and similar technologies. But in a recent book, “The Second Machine Age”, they argue that technological dislocation may create great problems for moderately skilled workers in the coming decades. They reckon that innovation has speeded up a lot in the past few years and will continue at this pace, for three reasons: the exponential growth in computing power; the progressive digitisation of things that people work with, from maps to legal texts to spreadsheets; and the opportunities for innovators to combine an ever-growing stock of things, ideas and processes into ever more new products and services.

Between them, these trends might continue to “hollow out” labour markets in developed countries and, soon enough, developing ones, as more and more jobs requiring medium levels of skill are automated away. This helps explain, the authors argue, why the benefits of economic growth increasingly accrue to a small group of highly paid people, citing in evidence the lack of growth in America’s median wage and the decline in workforce participation. A paper by Jeffrey Sachs and Lawrence Kotlikoff highlights the worrying possibility that this shift could be self-perpetuating: if automation absorbs jobs previously reserved for young people, who have not yet had time to build up skills, it will stop them from acquiring those skills, and its destructive effects will reverberate down the years.

There is some cause for scepticism. If new technology is eating jobs, one might expect it to show up more clearly in productivity figures, which are not changing much—though it is possible that those figures fail to pick up the benefits of, say, the easier availability of a much wider range of entertainment through the internet. The rise in the number of women in the workforce and the effects of globalisation have also had an effect on the working prospects of American men.

That said, even if it turned out that technology was not the problem, the fact that people worry about technology and that they project their technological worries onto robots means that robots would be blamed. In truth, a noticeable robot presence in a workplace may be a good indicator that human employment, too, is flourishing there; it shows that the process is worth investing in. Even in a heavily robotised modern car factory such as the one which builds Tesla’s electric cars—perhaps the most advanced such workplace in the world—there are still a lot of human workers to be seen.

“Invisible” robots, such as Aethon’s Tugs, look like more pernicious job eaters, ready to take over much of the work that hospital porters do today. Mr Thrun offers Kiva’s warehouse robots as an example of a similar labour-replacing system. And software will take over a lot of the tasks carried out by humans sitting in front of screens. In a recent study of the susceptibility of jobs to computerisation carried out by Carl Benedikt Frey and Michael Osborne at Oxford University, many of the job categories at greatest risk involved hardly any manual labour at all.

Given the doornail dumbness of machines, how can they take over so many moderately skilled jobs? One of the answers is that if you have enough doornails and enough data, there are ways of simulating smartness that are proving good enough to solve an ever greater range of problems, and that problems restricted to the world of data are much more tractable than those that require manipulating things in the real world.

Andrew Ng of Stanford is a pioneer and advocate of this sort of “machine learning”, a product of the trends towards ever cheaper computing power and ever more widespread digitisation that Mr Brynjolfsson and Mr McAfee describe. Working with Google, Mr Ng came up with a system that, using 16,000 processors to look at a significant fraction of a video on YouTube, came to “recognise” cats with no prior knowledge that there was such a thing. Google uses related approaches to tackle a number of more practical problems, such as machine translation and voice recognition. It would be surprising if it did not apply the same sort of thinking to its new acquisitions in robotics, whether they are used in manufacturing, in services or for that matter in agriculture or construction.

To work, perchance to play

Managing changing tasks in a changing world means that many workplaces will still need humans, but as workplaces become more efficient the number of people employed will shrink in the long run. William Nordhaus, a Yale economist, has shown that even though the world has become much better lit in an ever-widening variety of ways over the past few centuries, the number of people who provide the ever better lighting has declined. There is, in the end, only so much light that people can consume. Many other human needs, too, can probably be satisfied with less labour in the future, though that will take time.

Whether this job attrition will be too quick to allow for the creation of new jobs in other sectors of the economy (if, indeed, there are sectors that can continue to grow without limit) is impossible to say, not least because it depends on how well society as a whole adapts through continuing education and other investments. It is even conceivable that the fruits of greater productivity will be distributed so as to allow people to work less and spend more time doing other things. After all, the humour in the double meaning of the message that “Our robots put people to work” depends on understanding that people do not necessarily want to work, if they have better things to do.

Domestic service robots

Seal of approval

A robot around the house doesn’t just have to be handy. It has to be likeable tooMar 29th 2014 | From the print edition

WHEN TAKANORI SHIBATA began working on robots in the early 1990s, he had something practical in mind, perhaps to help the elderly with their daily chores. But he soon realised that robots were not really able to do anything useful, so he decided to make a robot that did not even try—but that could nevertheless deliver real benefits.

The result of his labours, Paro, has been in development since 1998. It is 57cm long and looks like a baby harp seal. Thanks to an array of sub-skin sensors, it responds amiably to stroking; and though it cannot walk, it can turn its head at the sound of a human voice and tell one voice from another. It is a comforting and gentle presence in your arms, on your lap or on a table top, where it gives the impression of following a conversation. The best thing about it is that it seems to be helping in the care of people with dementia and other health problems.

You could see Paro as a very well-designed $5,000 pet that will never turn on the person holding it, and will never be hurt if its master flies into a rage. It is as happy in one lap as the next, needs no house-training, can be easily washed and will not die. This makes it a much more practical proposition to have in a nursing home or hospital than a live pet. It is used in such homes in Japan, in parts of Europe and in America. As well as simply making people happy—no mean goal—it can act as a source of reassurance and calm. People with Alzheimer’s often suffer from “sundowning”—a distressed urge to wander that comes on towards the end of the afternoon. Mr Shibata has found that a seal in the arms tends to reduce such wandering, which means fewer falls. Experience in Italy, Denmark and America indicates that care homes equipped with Paro need less medication for their residents. Larger trials now under way in Australia should establish whether this and other benefits can be provided simply by a soft toy, or whether Paro’s ability to interact with the world makes a clinical difference.

If Paro proves to be more useful than a plush animal, there is a huge market for it. Akifumi Kitashima, who works on Japan’s robotics strategy at the Ministry for the Economy, Trade and Industry, points out that in 2025 Japan will have 10.7m more elderly people than it did in 2005. Though Japan is ageing particularly quickly, a lot of the rest of the world is on a similar course. Some will long remain spry; most will eventually need care.

Looking after old people in homes might become easier with robots, be they mood enhancers like Paro or something more practical that can help careworkers lift and reposition their charges (Mr Kitashima says 70% of carers have bad backs). Yoshiyuki Sankai, perhaps Japan’s best-known robotics entrepreneur, has set up a company called Cyberdyne to make wearable systems that help people walk and lift things by adding artificial strength to their limbs.

Robots may also make it possible for old people to stay independent in their own homes for longer. Mr Angle says this is iRobot’s “long-term guiding star”, towards which the Roomba is a small step. Mr Gupta at the NSF thinks that general-purpose home-help robots would be a big advance which, given a push, could be achieved in a couple of decades (though that, he stresses, is his own view, not the foundation’s). Mr Thrun reckons it could be done more quickly. Mr Ng points out that if you get a graduate student to teleoperate a PR2 robot, it can already do more or less everything a home-help robot might be required to do, so all that is needed is better software and more processing power, both of which are becoming ever more easily available.

Cloud robotics can probably provide much of the required software. Mr Pratt says that if there were dramatic performance improvements in the finals of the DRC, he would expect them to come from the cloud. But specific robot hardware will need upgrading, too. No robot hand yet comes close to the utility of the human hand. Tasks that require feedback in terms of force and fit—like putting a plug into a socket—remain particularly hard for robots, and there are a lot of such tasks around a house. General technological progress will not help; the only way to find a solution to this sort of problem is to work specifically on it.

Even more important will be interfaces to tell the robots what to do. Take-me-by-the-wrist Baxter, stroke-me Paro and the film-enabling industrial arms of Bot & Dolly, all very different from each other, show that interfaces can matter just as much as any other technological advance. Tobias Kinnebrew, of Bot & Dolly, thinks that new interfaces could open up markets and applications of robotics in all sorts of fields, and might do so surprisingly quickly.

Needing help with something can engender affection. People do not resent Paro’s need to be stroked; it is one of the things they like about it

Voice would be an obvious choice, but it has its drawbacks: give a robot a voice, says Mr Hassan, and the user will think it is smart. An interface that allows the robot to be dumb and the user not to care might be preferable. Indeed, small errors can be endearing, and needing help with something can engender affection. People do not resent Paro’s need to be stroked; it is one of the things they like about it. CoBot’s need for

help with the lifts at Carnegie Mellon makes people warm to it, though being pestered for help by random robots in offices and shopping malls would probably not work so well. But if the interface is properly designed, teaching a robot home help to do the job better might make it more welcome.

It may also be a good idea to let the robots turn for help to people other than those they are working for. As Mr Goldberg at Berkeley points out, the cloud does not just contain computers; it provides access to a lot of humans, too. One of the things that make Aethon’s Tugs a success in hospitals is that the company’s headquarters has a small but always staffed help desk which deals with queries from robots. If one gets stuck or lost, a remote operator can look through its eyes, check its logs and sort things out before the hospital concerned even becomes aware that anything is wrong. If similar support could be provided for robot home helps, the occasional mistake might not matter.

If the robot can call on a help desk, it can communicate with other people too, perhaps providing a way for friends and relatives to stay in touch. Some home-automation products already allow a degree of monitoring, notes Oz Chambers of Carnegie Mellon’s Quality of Life Technology Centre, but what they offer leaves much to be desired. It makes the adult offspring feel greater responsibility—which they often cannot exercise—rather than giving them reassurance. The elderly, for their part, can feel snooped upon. A robot with a defined presence in the house might make a better intermediary.

What matters, as iRobot and other practically minded companies have learned, is not so much having robots but having a business model that does a job, be it washing the dishes, checking that medication is being taken or providing telepresence. Producing something reliable and likeable that can be sold in large numbers and does not get its makers sued may prove a lot more difficult than simply developing the required robotic skills, but not impossible. To be sure, robots will not spread as quickly or relentlessly as mobile phones have done. Over a decade they may not achieve much. Over a century, though, they could turn everyday life upside down.

Regulation

That thou art mindful of him

Robots are as good, or as bad, as the people who make themMar 29th 2014 | From the print edition

ISAAC ASIMOV WAS wrong to think that the laws of robotics would be hard-wired into every robot brain. He was right, though, to think that robots would need regulation, and that such regulation would cause heated debate on the role that they might play.

In many of the areas touched on by this special report, laws and regulations will be crucial to the way that markets for robots develop. The uptake of industrial robots has always been constrained by health-and-safety considerations. Autonomy for lethal military robots remains a serious concern, soon to be discussed at the Convention on Certain Conventional Weapons in Geneva. The spread of civilian drones will depend on freeing up airspace, along with bandwidth for their control. The advent of self-driving cars opens up all sorts of legal and regulatory issues.

As Mr Gupta of the NSF points out, manufacturers’ technical ability to produce robots that can help in the home might easily outrun their capacity to deal with the resulting liability issues, especially if the robots operate in the homes of elderly people with cognitive difficulties. Sweet little Paro, rated as a consumer product in some places, is regulated in others.

Denmark’s Council of Ethics—an advisory rather than a regulatory body—has looked at how acceptable it is for robots to be designed to fool people into thinking that they have feelings. The council decided that such robots were not a problem in themselves, but that carers responsible for people who might be easily fooled had to be vigilant in safeguarding the dignity of their charges. That seems to be a good general principle.

Robots have no will of their own; they are machines designed for an end, and it is that end which regulators need to concentrate on. In “Das Kapital”, Karl Marx argued that fetishising money and commodities as “figures endowed with a life of their own, which enter into relations both with each other and with the human race” blinds people to the social relationships built into the world of trade and economics. Subsequent generations have noted that technology is often similarly fetishised. Robots could serve as the nec plus ultra of this fetishisation, forms of capital seemingly so “endowed with a life of their own” that their nature as unfeeling mechanisms built for particular purposes is hard to lay bare. But that is what lawmakers, along with those seeking to make money out of practical robots, must learn to do.

In a weapons system, the precise level of autonomy is probably less important than the discrimination and care with which the person responsible for the system handles it. If a civilian drone is used as a tool by a Peeping Tom, it is the peeping, not the drone, that should be penalised.

Means and ends

Keeping the ends rather the means in mind, though, will be hard—and in everyday life perhaps unnecessary. The effort it takes not to anthropomorphise robots will often not be worth making. Most of the people who buy iRobot’s Roombas, says Colin Angle, tell the sales staff that they are just buying a machine to clean the floor; it may be a nifty machine, but they wouldn’t go as far as, say, giving it a pet name. Yet some 80% of owners go on to do just that.

It may be that, in time, such charms in robots wear off and they become part of the scenery. But as quickly as that happens, robots with even richer repertoires of behaviour will arrive to engage humans anew.

Robots will get better at seeing things, manipulating things and moving things around, just as they have got better at walking. When Japanese engineers first started working seriously on walking robots in the 1980s, there weren’t any. Now Honda’s ASIMO can walk quite well, as can other humanoid robots. AIST’s Mr Kajita reckons that within 20 years they may do it as well as people. Others expect it to take longer; no one rules it out.

And they will continue to get better beyond that. Man may be the measure of many things, but not of the ultimate capabilities of robots—or, to put fetishisation aside, the ultimate capabilities of humans working together, with and through robots, to enhance their abilities. Clearly there will be limits to the things robots can do. But such limits are not yet in sight.

The future, like “technology” and “robots”, is by its nature an ill-defined residue of hope or fear left behind when the seamlessly working, unconsciously accepted and apparently inevitable parts of the world are taken out of the picture. Sometimes, in some ways, it seems to be already present: there are robots doing the bidding of scientists on the surface of Mars right now. Sometimes it feels permanently deferred, with dreams of progress borne back ceaselessly into the past. But for all its strangeness and contradiction, we already know its natives. They are coming to work and play among us in ever greater numbers.

NYT

Time to Talk Robots

Emma Roller JAN. 5, 2016

Manufacturing — that is, the business of making stuff — has changed significantly over the past half-century. Perhaps you’ve noticed. While America’s share of industry has constricted, with fewer people needed to perform the same amount of work as in the past, it’s not quite time to start eulogizing.

But the way the presidential candidates have been talking about reviving manufacturing jobs has not been very enlightening, and in some cases they have been willfully obtuse. Their statements are meant to appeal to disaffected workers, but they both oversimplify the problems and ignore the real source of trouble.

Donald J. Trump, the Republican front-runner, has promised to bring manufacturing jobs back to American workers from abroad. “They can’t get jobs, because there are no jobs, because China has our jobs and Mexico has our jobs,” Mr. Trump said in his campaign announcement speech in June. (He neglected to mention that his own line of neckties is fabricated in China.)

Mike Huckabee, the former Arkansas governor, seems to regularly misconstrue the state of American manufacturing — more than any other candidate. At the undercard debate in Milwaukee on Nov. 10, Mr. Huckabee explained that the United States has lost five million manufacturing jobs since 2000: “The reason they don’t have jobs is because their jobs are in Mexico, they’re in China, they’re in Indonesia,” he said,

referring to American workers. While that is certainly true for some of the jobs lost, outsourcing is not the main driver of domestic job loss.

Photo

Marco Rubio, left, touring an equipment manufacturing facility in Iowa. Credit Charlie Neibergall/Associated Press

Republicans aren’t the only ones obsessing over reclaiming these factory jobs. Last month, Hillary Clinton mentioned factory closings when she released her own plan to restore manufacturing jobs through a network of tax credits and federal funding for research. Senator Bernie Sanders, meanwhile, in criticizing the Trans-Pacific Partnership, has argued that such international trade deals are to blame for the loss of manufacturing jobs in this country.

The problem with this sort of rhetoric is that a lot of the manufacturing jobs the United States lost over the past 50 years didn’t go overseas; they simply disappeared with the advent of new technology.

James Sherk, a research fellow in labor economics at the Heritage Foundation, said the trend in machines taking over factory work that was previously done by humans has been going on since the 1950s. But for presidential candidates, it’s a lot easier to blame other countries rather than robots.

“It’s those basically rote, repetitive tasks where you’re fixing the same thing,” he said. “It’s very hard to imagine any of those positions coming back. Basically, a robot is a lot more affordable than a human employee.”

The skills needed to work on a factory floor today are quite different than they were 20, 10 or even five years ago. Don’t blame stingy companies or over-regulation by the government; blame the rapid progress of technology.

Mark Muro, the policy director of the Brookings Institution’s Metropolitan Policy Program, said candidates should recognize that because of advances in technology, manufacturing simply does not employ as many people as it once did. Then again, that level of honesty doesn’t make for as much of a feel-good message.

“My fear is that the Republicans to date may not fully understand what modern advanced manufacturing is,” he said. “It’s not necessarily thousands of people pouring into the plant as in the old days.”

Instead of talking down to blue-collar workers, candidates should admit that trying to restore manufacturing to what it once was in this country is not an attainable, or even a desirable, goal. This is not to say the

government should not work to bring jobs back to the United States, or that manufacturing as an industry is not valuable to the American economy. But many of the jobs politicians want to restore aren’t on the table anymore.

Unfortunately, when talking about how they would increase manufacturing, most of the candidates have not reached beyond platitudes and into the realm of reality. And some of the points they have made have been baffling. At the November Republican presidential debate in Milwaukee, Senator Marco Rubio stressed manufacturing’s role in the economy, while espousing a curious notion of how the new economy works. “If you raise the minimum wage, you’re going to make people more expensive than a machine,” he said. “That means all this automation that’s replacing jobs and people right now is only going to be accelerated.” (He went on to wax poetic about welders versus philosophers.)

It sounded as if Mr. Rubio believed employers should suddenly realize that machine labor is cheaper than human labor — though that’s been the case for a long time. Machines are cheaper than people, marginal wage increase or not. Yet Mr. Rubio has a point: Automation has fundamentally changed the manufacturing industry. We don’t, however, need to respond to the shrinking manufacturing job market by becoming all-out technophobes.

A few examples of solutions currently underway: In Delaware, high school students can participate in a program aimed at preparing them for modern manufacturing jobs. Some academic hubs like Cambridge, Mass., and Raleigh-Durham, N.C., are set up as innovation districts, where companies and research institutions can commingle with smaller start-ups and job seekers.

There’s no easy answer on how best to foster innovation, though. Even the most enticing tax incentives are unlikely to make companies fire their robots and hire back assembly-line workers. Some of the burden lies with American companies to bring production back home — which actually could be less of a burden than they might think, according to some economists.

The manufacturing sector drives 69 percent of all business-related research and development in the country. The findings of that research and development — like innovations in 3-D printing and the so-called Internet of things — ripple across industries. Employing more American workers also gives them more buying power, which is good for the economy as a whole.

Americans are world-class consumers. Since the recession, our appetite for buying stuff has grown. In the last quarter of 2014, consumer spending in the United States rose 4.3 percent — the fastest rate of growth since 2006 — though spending has slowed since then. And the University of Michigan’s index of consumer sentiment rated consumer confidence in 2015 at 92.9, the highest rating posted since 2004. Still, it’s unclear whether Americans would be willing to shell out more money for a product made by their neighbors than one made halfway across the world.

“We have yet to prove that American consumers are willing to pay a premium for products sourced in the U.S.,” said Willy C. Shih, a professor at Harvard Business School and a co-author of the book “Producing Prosperity: Why America Needs a Manufacturing Renaissance.” “The bottom line is, what product do they put in their shopping basket in the store?”

No candidate wants to be painted as anti-manufacturing, which may account for this lack of honesty about where all the manufacturing jobs have gone. When the moderator Sandra Smith dared to suggest during the Nov. 10 undercard debate that the United States is moving away from “a manufacturing economy to a services-based, technological economy,” Mr. Huckabee quickly shot her premise down. “I don’t know why we have to move away from manufacturing,” Mr. Huckabee replied. The audience applauded.

Mr. Huckabee is right; the United States doesn’t “have” to move away from manufacturing. It’s just that advancing economies like this one inevitably sacrifice some labor for innovation, just as the United States and other developed countries did over the past century when it came to agriculture.

If we’re going to move away from anything, let’s leave behind stock stump speeches on factories. They’re not going to get anyone a job — especially not in the White House.

Emma Roller, a former reporter for National Journal, is a contributing opinion writer.

Robots Video @ http://nyti.ms/1QExM5z

Smart Robots Make Strides, but There’s No Need to Flee Just YetBy CLYDE HABERMANMARCH 6, 2016

U.S. & Politics Retro Report By RETRO REPORT 11:40 The Terminator and the Washing Machine Continue reading the main story Video

The Terminator and the Washing Machine

What the legendary matches between supercomputer Deep Blue and chess grandmaster Garry Kasparov reveal about today’s artificial intelligence and machine learning fears.

By RETRO REPORT on Publish Date March 6, 2016. Watch in Times Video »

It may not strike everyone as the loftiest ambition: creating machines that are smarter than people. Not setting the bar terribly high, is it? So the more cynical might say. All the same, an array of scientists and futurists are convinced that the advent of devices with superhuman intelligence looms in the not-distant future. The prospect fills some of our planet’s brainiest specimens with dread.

They include certified smart men like Bill Gates of Microsoft, the physicist Stephen Hawking and Elon Musk, head of SpaceX. Messrs. Hawking and Musk have been especially grim. “The development of full artificial intelligence could spell the end of the human race,” Mr. Hawking told the BBC in 2014. At about the same time, Mr. Musk worried that “with artificial intelligence, we are summoning the demon,” a fiend that he feared would become “our biggest existential threat.”

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When people of their caliber speak, it seems reasonable to listen. And so, alarms about a computer-spawned apocalypse are a backdrop to the latest installment in the Retro Report series, video documentaries that explore major news events of the past and their continuing effects.

Men of science are not alone in the hand-wringing over the possibility of machines running wild. Asked what they feared most, Americans interviewed by researchers at Chapman University in Southern California ranked the consequences of modern technology near the top. Even death did not rattle them as much; it was way down on their list of worries, at No. 43.

While not discounting that doomsayers may prove someday to be right, Retro Report offers more reassuring views from computer specialists who sense that the end is not nigh — if only, they say, because machines are not nearly as clever, or necessarily as pernicious, as the fretters believe.

Jitters over humanity’s falling victim to various creations are as old Mary Shelley’s Frankenstein monster and the Golem of Jewish tradition. Hostile robots have been on the scene since at least the 1920s with the play “R.U.R.,” by the Czech writer Karel Capek. The initials stood for “Rossum’s Universal Robots.” Indeed, this work introduced “robot” into the language. Since then, run-amok machines have been a science-fiction staple in books and films like “Colossus: The Forbin Project,” “I, Robot,” “2001: A Space Odyssey,” “Transcendence,” “Ex Machina” and the seemingly inexhaustible supply of “Terminator” movies. One sure bet about those films is that, like the Terminator itself, they’ll be back.

On occasion, machines are cast as a benign presence, as in the 2013 film “Her,” in which a man finds intimacy with an operating system that is guided by artificial intelligence (not to mention made alluring by the voice of Scarlett Johansson). In Japan, some people have closely bonded with robot dogs, to the point of holding funerals for automated pooches that cease to function.

More typically, though, the machines — robots, cyborgs, androids, clones — are depicted as threats to human survival. As Retro Report recalls, fear of them in real life grew in 1997 when a chess-playing IBM computer, Deep Blue, defeated the world champion, Garry Kasparov. Apprehension deepened for some in 2011 when two stars of the quiz show “Jeopardy!” were soundly defeated by a new IBM gizmo. (What is Watson?) This week, artificial intelligence will again challenge the human brain as Google’s DeepMind competes in South Korea against a champion in Go, the Chinese board game with trillions of possible moves.

Arguably, there is no reason to lose sleep over those souped-up gadgets. Sure, Watson and its brethren are good at games and other sorts of data processing. But contemplating a takeover of the world’s nuclear arsenals? Not a chance. Nonetheless, some experts foresee a time, not far off, when artificial intelligence, A.I., will match and then exceed human intelligence, at ever-accelerating and frightening speeds.

“Shortly after, the human era will be ended,” Vernor Vinge, a computer scientist and science fiction author, wrote in 1993. That moment, he predicted, would come “within 30 years.” In other words, check your calendars — a mere seven years remain until the arrival of this “technological singularity,” as it was called.

Another A.I. expert, Raymond Kurzweil, has pinpointed 2045 as the due date. Still another student of the subject, James Barrat, also says that once the machines blow past us, man’s reign is through.

“We humans steer the future not because we’re the strongest beings on the planet, or the fastest, but because we are the smartest,” Mr. Barrat has said. “So when there is something smarter than us on the planet, it will rule over us on the planet.”

A -- perhaps the -- central problem with high-level automata is that we expect them to do what they are advertised to do even in fog-of-war...

Horrific scenarios abound. Superintelligent computers will cause global financial systems to collapse. They will wage war on humans with killer robots far more lethal than today’s drones. They will control nuclear weaponry — think Skynet in the “Terminator” series — to dominate humankind or, worse, wipe it out.

In these grim predictions, the machines always seem to be anthropomorphic: Their instincts are essentially the same as those of humans at their worst; just as people have run roughshod over lower life forms, artificial intelligence networks will abuse their supremacy. Possibilities for them to do good — figuring out how to regenerate human cells, for instance, or creating immunities against disease, or gobbling up carbon dioxide in the atmosphere — tend to get short shrift.

For some analysts, any worry about human survival is theoretical and certainly less immediate than more prosaic, yet vital, concerns. Technological advances have enhanced the ability of governments to spy on their citizens. How to shape policy is now reflected in the struggle between Apple and the Obama administration over access to the iPhone of one of the terrorists in the mass shooting in San Bernardino, Calif., in December.

Economic issues are unavoidable as well. Lawrence H. Summers, president emeritus at Harvard University and a former Treasury secretary, noted that unemployment is disproportionately higher among those whose duties “in various ways have been mechanized.” There are “important consequences for the way the economy is organized and for how fair the economy is,” Mr. Summers said in an interview with Retro Report.

There is, too, a question of how smart robots truly are and whether they can develop superintelligence at the blinding speed envisioned by the more pessimistic forecasters. “Things that are easy for humans are hard for computers,” Guruduth S. Banavar, the director of cognitive computing research at IBM, told Retro Report, “and things that are easy for computers are hard for humans.” Yes, a computer can multiply two numbers of 1,000 digits each in a matter of seconds. But it cannot hold a candle to a toddler when it comes to recognizing faces or performing a task as simple as climbing steps.

Perhaps it is human nature to assume the worst with something new. “Humans often converge around massive technological shifts — around any change, really — with a flurry of anxieties,” Adrienne LaFrance, who covers technology for The Atlantic magazine, wrote a year ago.

But it is too soon for hyperventilating, Fei-Fei Li, a professor of computer science at Stanford University, told Retro Report. With A.I., she said, “we are closer to a washing machine than a Terminator.”

CIO

Opinion

The future of jobs in a machine world

Visitors to the Hilton Hotel in McLean, Va. meet “Connie,”a robot concierge Credit: IBM

Between 30 percent and 50 percent of today's jobs will be automated by machines over the next 20 to 30 years, so which jobs are the safest?

By Matthew Griffin

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CIO | Mar 16, 2016 7:05 AM PT

Leadership and Management Careers

Over the past year there has been much speculation and tens of thousands of column inches devoted to trying to determine just how many jobs and what type of jobs are going to be replaced by new generation technology.

Today we’re used to, dare we say complacent towards Blue collar worker’s jobs being automated and as the joke goes the factory of the future might only need a Human and a dog to keep it running – a dog to make sure no one tampers with the machines and a Human to feed the dog. Fifty years from now however we might find that that job statistic was overly optimistic.

Researchers who have been looking at the impact that technology will have on the workforce seem to agree that in the next twenty to thirty years between 30% and 50% of the global workforce will be at great risk and for every job that’s handed over to the Machines there will be at least two that are no longer advertised.

The human in the loop

When people talk about the impact technology will have on the jobs market the debates are predicated on how it’s technology that will take our jobs. I want to point this out as a misnomer because it’s not technology that will come after your job it will be company executives guided by market forces and

economics that decide that new age machines and systems can do your job better and cheaper than you and it’s they, not an AI that will instruct HR to E-Mail you your P45.

This might seem like a quaint point to make but it’s important to remember that it’s todays and tomorrows executives who have the final say on whether or not they make a tier or a section of their workforce redundant. The social responsibility of this “Human in the Loop” is all too easily over looked and as a society we mustn’t forget that we have options and a duty of care to each other - just because we can make people redundant, sometimes ruining lives and families it doesn’t necessarily mean we should.

The law of accelerating returns

Technology is advancing now faster than it ever has before. At current rates, as advanced technologies are combined to create even more advanced technologies over the next 100 years we won’t see 100 years’ worth of technological progress we’ll see 20,000. Think back to the technologies and platforms we had available to us 500, 200, 100, 10 years ago and just 5 years ago and you’ll be able to see the acceleration for yourself. The consequence of all of this is that the length of time between each wave of industrial and societal disruption is getting progressively shorter and the upshot is that if technology doesn’t put your job at risk today then it’s likely it will tomorrow.

The squeezed middle

Today there are a collection of technologies that are stirring the debate. However, unlike the disruptions of yesteryear where technologies replaced simple repetitive Blue Collar job functions near the bottom of the Skills and Complexity Pyramid they’re now starting to replace White Collar knowledge workers near the top. The result is an increasingly nervy global workforce and for the first time ever a squeezed middle who are becoming increasingly worried about their lack of specialisms and skills.

The technologies that will have the greatest impact and influence on the job markets can be divided into two groups. “Individual Emerging Technologies” such as Artificial Intelligence, Machine Vision and hardware and software based Robots and “Aggregated Emerging Technologies” that combine different technologies together to create platforms that include Autonomous Vehicles, Avatars, Cloud, Connected Home, the Internet of Everything, Smarter Cities, Wearables and Telehealth.

Some of the world’s best self learning Artificial and Cognitive computer systems are already replacing advisors, artists, commentators, consultants, doctors, investigators, journalists, musicians, paralegals, teachers, translators and even the data scientists who created the original Algorithmic Models. Machine Vision systems are replacing quality inspectors, security analysts and security guards. Hardware Robots have already replaced many of the Blue Collar factory and warehouse jobs and now they’re replacing bar staff, maintenance workers, porters, soldiers, waiters and surgeons while their new, modern day software only counterparts are replacing administrative staff, customer service clerks and FX traders.

In the AET space Autonomous Vehicles – from cars and trucks to aircraft and half a million ton cargo ships are reducing the need for drivers, operating crews, pilots and even traffic wardens. Avatars are replacing actors, bank tellers, call centre agents, teachers and support staff. Cloud reduced the need for change managers, enterprise architects and operations staff while the Internet of Everything is reducing the need for engineers, facilities managers and maintenance workers. Smarter Cities will reduce the need for police, street cleaners and a myriad of other public servants while Wearables and Telehealth are both reducing the demand for secondary care workers, doctors and personal trainers.

The lists could go on and on.

Today we’re sitting on the beach mouths agape watching the tidal wave raise worrying about our futures but while it looks like the balance of power is only ever going to shift into the machines favour there is hope from a number of directions.

Safe harbous

Technology is going to keep improving at an exponential rate so where do we Humans flee to for work - which jobs are going to be safe, or at least safer harbors for the next twenty years?

Jobs that hard to specify and that require deep expertise in either one or ideally a mix of the following disciplines will be the hardest to automate: Dexterity, Emotional Intelligence, Negotiation, Originality, Perception, Persuasion and Social Intelligence.

It’s going to be a cold cold world - full of wise cracking artistic Chiropractors… Urgh.

Entrepreneurship on the rise

If you’ve read that list and you’re down in the mouth then there’s still hope. Today it’s easier than ever before to create your own business and the power is shifting from the corporations to the individual.

Over the past five years there has been a tenfold increase in the number of registered start ups from 10 million to over 100 million and technology has acted as a force multiplier for Entrepreneurs helping give them faster, simpler access to funding, expertise, resources, advanced software and hardware prototyping technologies and mass markets.

Consider the tale of Brian Acton. He spent $200 building an app, spent $0 on marketing, scaled it via the social networks and sold it for $19 Billion. Yes WhatsApp’s great but there are another 218 Brian Actons – forty times more than at any other point in Human history who’ve created multi billion dollar companies from next to nothing in just a few years and those companies have created over $1.5 Trillion in new value and turned established industries on their heads.

Someone Uber me a cab!

Future Human

If however Entrepreneurship isn’t for you and you think the future will look increasingly bleak then consider this. Do you really think that in the next 50 to 100 years you will only remain “Human”? During the course of this Century, as we all head towards an event called the “Singularity” we are going to be challenged to revise our definition of what it is to be Human.

Today we access information via our smart devices but in the future we will use Brain to Computer interfaces to plug directly into the web. At the same time new Genetic technologies like CRISPR, a technology that is so powerful it’s described as “God Like” will help us re-write our own genetic code and enhance our own natural capabilities.

Machines might be scary but Human Machine hybrids of the future will be scarier and let’s stay away from the term “Cyborg” it’s too 1980’s...

Conclusion

If you think the machines will inevitably win the race for jobs then you’re only looking at the short term but whatever happens the last thing you should do, in this age of individual empowerment is roll over and accept the inevitable.

This story, "The future of jobs in a machine world" was originally published by CIO.

MIT Technology Review

Videos @ https://www.technologyreview.com/s/601094/apples-recycling-robot-may-help-build-iphones-too/#/set/id/601095/

RoboticsApple’s Recycling Robot May Help Build iPhones, Too

Few people noticed, but Apple did announce a remarkable and transformative new technology at its event on Monday: its first robot.

by Will Knight March 22, 2016

Apple now makes robots. What’s more, the company’s new recycling robot, called Liam, may be evidence of a push to automate the production of the iPhone.

At Apple’s slightly humdrum event on Monday, the company showed a video of Liam carefully pulling iPhones apart for recycling. The cutesy clip showed the robot unscrewing and removing the device’s case and pulling apart different electronic chips with suction cups before tossing an iPhone shell into a bin.

What's most interesting about Liam is not its ability to pull phones apart, though—Apple has been automating the process of recycling damaged phones for some time. Rather, it is a glimpse into what an automated assembly process might look like.

Automation is rapidly moving into areas of manufacturing in China that have traditionally relied on low-cost manual skills because wages are rising so quickly—12 percent per year since 2001—and also because it offers an edge over competitors.

I visited several manufacturers in China recently to learn more about this trend, and I saw how rapidly they are adding robots to production lines. The shift seems inexorable, and it’s likely to shape the evolution of the Chinese economy, as well as the global manufacturing picture.

The technologies seen in the Liam video are becoming especially common at various stages of manufacturing. For example, the clip Apple showed included a camera capturing exactly how an opened iPhone was held in a custom robot arm so that another component could swoop in and remove screws.

Apple gave Mashable a sneak peak at Liam before Monday’s reveal, and apparently there are 29 different robot arms working together to unscrew, detach, drill, and manipulate old iPhones.

Foxconn, which built its reputation on managing hundreds of thousands of workers, is already at the forefront of the automation revolution. The company has replaced tens of thousands of workers with robots already, and it recently began selling the robots it is developing as part of this push. Perhaps Liam is evidence that Apple is doing its part to automate manufacturing of its most iconic product.

SCMP

Foxconn’s Foxbot army close to hitting the Chinese market, on track to meet 30 per cent automation targetPUBLISHED : Wednesday, 01 July, 2015, 8:01amUPDATED : Wednesday, 01 July, 2015, 8:33am

26 Nov 2015

Foxconn, the world’s biggest contract electronics maker, has been developing industrial robots as it targets 30 per cent automation at its Chinese factories by 2020, it said this week. 

The Taiwanese company, which lists Apple among its clients, is also now on the verge of marketing its Foxbots to other manufacturers on the Chinese mainland, it told the South China Morning Post.

According to senior management, re-purposed robotic solutions and automated assembly will be Foxconn’s next focus as it bids to slim its massive workforce and build new sales channels.

Previous media reports that claimed it was eyeing 70 per cent automation within three years are erroneous, the company said, pointing to comments issued by Foxconn CEO Terry Gou at last week’s annual general meeting.

The company, which has been keeping quiet about its robotics projects, opened its robot testing lab in Shenzhen, Guangdong province, to reporters from the Post last weekend.

Its Chinese factories, including those in Shenzhen, are already filled with over 50,000 fully operational industrial robots, as well as hundreds of thousands of other pieces of automated equipment, said Day Chia-Peng, general manager of Foxconn’s automation technology development committee.

He said the company plans to add at least another 10,000 robots a year to its facilities in China.

READ MORE:   China’s Alibaba, Foxconn invest US$236 million in SoftBank’s robotics business

But this may fail to satisfy chairman and founder Gou, who said in 2011 that he expected to have one million robots operating at its Chinese plants by last year. 

Instead, the company still employs over one million workers in China. 

“It was kind of a dream, in terms of numbers. We hoped it would work out, but the reality proved different,” Day said.

Foxconn began developing its own industrial robots in 2007. It now has 1,600 employees at two factories in Shenzhen and Jincheng, in Shanxi province, who churn out 10,000 Foxbots a year, Day said. 

The machines are capable of performing more than 20 manufacturing tasks, including pressing, printing, polishing, packaging and testing.

Using them to replace staff in so-called “3D” jobs – positions that are deemed dirty, dangerous and dull – is the company’s top priority, Day said. 

The company was motivated to focus on this area due to safety concerns and manpower shortages in recent years, he said.

READ MORE:   US$154 billion rise of the robots planned for Pearl River Delta manufacturing

Foxbots should hit the market soon, and the first customer is likely to be another Chinese manufacturer, he added, without giving a name or deadline.

Early this year, Gou said Foxconn would continue to deepen its investment in robotics. Day’s team will focus on researching ways to further automate assembly lines that require the most number of bodies, he said.

As such, it is keeping an eye out for any breakthroughs in automated assembly in the electronics manufacturing industry.

Day said robotics was not an easy field to venture into.

“We couldn't help but make a lot of blunders,” he said, using the difficulty of replicating people’s hand-eye coordination as an example. “It’s hard work.”

He said the company was focusing more on flexible reprogramming so the robots could be reused, or reformatted, further down the road. This is especially important as product cycles in the electronics industry keep shortening.

“Whatever robotic solution is delivered for our industry, it will have to be able to be re-purposed every few months,” he said.

The actual robot is just one part of the solution package. Grippers, sensors, vision systems, parts feeders and software are “at least as important – if not more so,” Day added.

However, he urged local authorities in China to proceed with caution rather than overheat investment in the sector, or look to robots as a cure-all solution for the nation’s labour shortages. He also stressed that they were not a tool to counter the economic slowdown China now faces after decades of unchecked growth.

READ MORE: Building work starts on first all-robot manufacturing plant in China’s Dongguan

Authorities in Guangdong said early this year they would spend 943 billion yuan (US$152.07 billion) on replacing human labour with robots by 2018. 

Cities in the province are handing out annual subsidies of between 200 million and 500 million yuan to makers of robots and manufacturers who install robots on assembly lines.

Robots are also set to take over factories in China’s Pearl River Delta area, in the south of the country, as manufacturers there step up their tech investments to take advantage of new government incentives.

China is already bristling with low-end industrial robotics that can perform routine tasks like pressing and polishing, due to all the industrial parks set up in nearby cities, Day said.

Some 200,000 intelligent robots were operating in the country at the end of 2014, the IFR said. 

IDG Connect

Business Process Automation

Office 2021: Why robots won’t end drudgery or steal our jobs

0

Posted by Kathryn Cave on March 29 2016

When it comes to robots and automation, there are two core extremes of view. The first does a gleeful happy-dance about how all our beautiful mechanical friends will liberate us from the grinding tedium of repetitive drudgery. The second warns bleakly that machines will enslave humanity and lead to mass unending unemployment. And possibly a slow and painful death.

Yet even this divisive two-part picture is skewed. As Jonathan Wilkins, marketing director of European Automation points out: “History has shown that when economic times are good, machines are celebrated as wonders of progress that will improve our lives. But when times are tough, they become objects of fear.”

So, is there any kind of sensible middle way? Well, a new book, Service Automation: Robots and the Future of Work 2016, by two academics – LSE Professor Leslie Willcocks and Dr. Mary C. Lacity of University of Missouri-St. Louis – strives to provide some balance. This contains new research, a number of case studies along with insight into managing the automation process.  

Through this Willcocks and Lacity suggest that a lot of jobs may change their structure rather than being lost altogether. The next five years will be a time of transition, rather than depletion. And many companies will look to redeploy workers rather than lay them off. They also point out that the process of automation itself will require a lot of human management. 

Overall they take the personal view that although there will, of course, be significant redundancies because of automation there will also be parallel new developments.

“When companies automate, you can expect more jobs, not fewer,” says Wilkins of European Automation. “Take Apple, for example. The multibillion dollar company automates rigorously and yet constantly provides new possibilities for jobs. It produces software that does things humans used to do and at the same time employs more engineers, designers and staff who package, market and sell new products.”

Interestingly, this view of the future is supported by historic data from the last century. Back in August, three Deloitte analysts published a new paper “Technology and people: The great job-creating machine” [PDF]. This examined statistics from UK censuses since 1871 along with Labour Force Surveys (LFS) since 1992.

The piece, which was shortlisted for the Society of Business Economists’ Rybczynski Prize, cleverly used the data to show how technology didn’t destroy jobs but instead changed the nature of employment. History shows a “dynamic process” in which “technologies become obsolete and are supplanted” over time, it explained. Take for example telephone and telegraph operators. These rose by a factor of 40 in the 100 years to 1971. But since then have receded to be replaced by other roles.  

In the automated scheme of events there is less use for manual labour but a greater need for knowledge-intense sectors – like health and education. Also, as everyday essentials become cheaper, there is an increased demand for service roles like bar staff and hairdressers. Deloitte showed in 1871 there was one hairdresser or barber for every 1,793 citizens. Now there is one for every 287.

The report argued UK employment has more doubled in the last century and a half. And as machines take on more repetitive and laborious tasks they still seem “no closer to eliminating the need for human labour than at any time in the last 150 years”.

To take a step back though, one point that gets argued over and over by businesses keen to push automation is that when basic drudge work is taken care of it frees employees up for more “sophisticated tasks”. The trouble is, this can be hard to quantify in a large enterprise.

One area where there are never enough people though, is healthcare. And in Kenya, IBM Research Africa is looking to use AI as a way to counteract a dearth of local trained doctors. While, in Scotland Genfour supplied a process automation system to the NHS which freed up existing nurses from admin so they could be deployed back onto frontline activities.

Then there are the jobs that would never have existed. At the high end in Forbes’ 10 most promising roles for 2016 Data Scientist is listed first. While also security takes a front seat – the importance of expert knowledge in this arena and a CISO to translate it to the people that hold the purse strings is becoming increasingly crucial. At the less skilled end there are an awful lot of people needed to man the coffee shops that tech startups live in as well as all the other 24/7 service roles thrown up by flexible professional working.  

The thing is, you only have to look at the emphasis placed on the global skills gap to see that lack of jobs is not really the issue - finding the right people to fill them is. And while personally, I do think it is a myth that machines will save humanity from boring work. There will always be tedious things that humans do better or cheaper than machines. The types of work available at every level of the spectrum are clearly changing and will continue to do so.

Professor Leslie Willcocks and Dr Mary Lacity believe that this will be very apparent in five years’ time when work teams will consist of both robots and humans collaborating together. This makes sense as any good team combines different strengths and human and artificial intelligence together make a good pairing.

In fact, this is already happening to a limited extent in contact centres where simple queries are tackled by virtual agents and more complex queries escalated to human beings. While Afiniti offers a really unique AI solution that uses big data and a sophisticated algorithm to couple human agents with human callers for the best quality outcome.

“The fact remains that automation still has its limits,” says Gajen Kandiah, Executive Vice President of Business Process Services at Cognizant. “There are some things that robots just cannot do like medical management, underwriting, case reviews, speak or comprehend colloquial slang, understand people’s emotions and think on their feet.”

This is hard to argue with. And perhaps helps to add a bit of perspective to all the silly polarised opinions on robots and automation. Because for hundreds of years, man has been predicting brilliant or terrible outcomes for employment via technology. And while some of it inevitably comes true, most tasks actually take bit of boredom, a bit of drudgery, a bit of miscommunication and a lot of human effort to run properly. So, why should the adding robots to the team make a jot of difference?

The Economist

Mobile services

Bots, the next frontier

The market for apps is maturing. Now one for text-based services, or chatbots, looks poised to take offApr 9th 2016 | From the print edition

App exterminators

“YOU are a developer and you’ve just spent two weeks writing this amazing app. What is your dream? Your dream is to get it in front of every iPhone user.” That was how Steve Jobs, then Apple’s boss, introduced an online shop for smartphone apps eight years ago. At first few paid it much heed, but it launched one of the fastest-growing software markets ever. Since then over 100 billion apps have been downloaded, generating $40 billion in revenues for developers and billions more in subscriptions and other fees.

At a conference on April 12th in San Francisco, Mark Zuckerberg, Facebook’s boss, is expected to make a similar announcement. He will probably unveil an online shop and coding tools for “chatbots”. These are text-based services which let users complete tasks such as checking news, organising meetings, ordering food or booking a flight by sending short messages. Bots are usually powered by artificial intelligence (hence the name, as in “robot”), but may also rely on humans. Many in the technology industry hope that Facebook’s event will mark the beginning of another fast-growing, multi-billion-dollar software economy. Are bots the new apps?

The timing looks right, because smartphone software is in flux. Download numbers are still growing, but the app economy is clearly maturing. “The dream of the independent developer building a business in the app store is over,” suggests Activate, a consultancy. The 20 most successful developers grab nearly half of all revenues on Apple’s app store. Building apps and promoting them is getting more costly. Meanwhile, users’ enthusiasm is waning, as they find downloading apps and navigating between them a hassle. A quarter of all downloaded apps are abandoned after a single use.

Only instant messaging bucks the trend. Over 2.5 billion people have at least one messaging app installed, with Facebook Messenger and WhatsApp, which is also owned by Facebook, leading the pack (see chart). Within a couple of years, says Activate, that will reach 3.6 billion, about half of humanity. Many teenagers now spend more time on smartphones sending instant messages than perusing social networks. WhatsApp users average nearly 200 minutes each week using the service.

Talking out of your bot

As services based on artificial intelligence improve, they need a way to talk to real people. Chatbots are one option. At a conference on March 30th Microsoft showed off several prototypes. It will be a while before anyone trusts such services, however. A few days earlier one of Microsoft’s bots, “Tay”, designed to impersonate a millennial, started parroting racist language it had learned from users on Twitter. “Tay” had to be sent to her digital room.

As a result of these various developments, a new software ecosystem has started to emerge. Text-based services have been around since the dawn of internet time, but the birth of the bot economy can be dated to June last year, when Telegram, a messaging app with Russian origins and more than 100m users, launched a bot platform and a “bot store”. It now counts thousands of bots, such as news alerts from media organisations, or feeds that link to football videos or porn.

A few dozens startups exist. Some provide tools: Chatfuel is a web-based offering that lets users build bots for Telegram. Others offer specialised services: Digit allows users to interact with their bank accounts and

find ways to save money; Pana is an online travel agency that takes text messages and turns them into bookings. MeeKan sets up meetings for users of Slack, a popular corporate-messaging service (now valued at nearly $4 billion).

Then there are firms which want to be the foundation for other services. Assist aims to be the equivalent of Google’s search box—to find bots. Another firm, Operator, hopes to become the Amazon of bot-commerce: when a shopper requests, for example, certain sports shoes, its system can contact a salesperson in a nearby shop or get one of its own “experts” to handle the order. Robin Chan, Operator’s boss, talks of creating a virtuous cycle of more buyers attracting more businesses, which will in turn draw in more buyers.

The app economy grew quickly only after Apple and then Google became enthusiastic champions. The bot economy will also need industry leaders, and Microsoft and Facebook look eager to play the role. Most smartphones are powered by operating systems controlled either by Apple or Google. The bot market, by contrast, is unconquered territory. At its conference, Microsoft also introduced tools to create clever new services. Facebook is expected to open its messaging platform to all sorts of bots (users can already chat with a selected few, including one impersonating Miss Piggy of the Muppets) and launch an online shop which will list the services.

Given the drawbacks of apps, there should be plenty of demand for bots, says Michael Vakulenko of VisionMobile, a market-research firm. Much like web pages, they live on servers, not a user’s device, meaning they are easier to create and update. This is likely to make them attractive to businesses which have shied away from developing their own apps, such as restaurants and shops.

Users should find bots smoother to use, which explains another of their monikers: “invisible apps”. Installation takes seconds; switching between bots does not involve tapping on another app icon; and talking to bots may be more appealing than dealing with a customer-support agent of a bank or airline, for example.

No guarantee exists, however, that the bot economy will be as successful as the app one, which has created 3.3m jobs just in America and Europe, according to the Progressive Policy Institute, a think-tank. The economics for developers are not obviously attractive: if bots are easier to develop, that means more competition. Consumers could, again, be overwhelmed by the cornucopia of services and ways of interacting with them. And designing good text-based interfaces can be tricky. After launching the first version for Slack, Matty Mariansky, a co-founder of MeeKan, was surprised by the many different ways users tried to communicate with his bot. He has since hired dedicated script writers, who have come up with more than 2,000 sentences to handle a meeting request.

The popularity of messaging apps suggests people will happily talk to bots. But much will depend on “killer bots”—hugely popular services that work best in the form of bots. Toby Coppel of Mosaic Ventures, a venture-capital firm, sees health care as a promising market. Bots could deal with routine ailments and send difficult ones to a doctor. Ted Livingston, the founder of Kik, another messaging app, which launched a “bot shop” on April 5th, expects “instant interaction” to dominate. He predicts businesses won’t just have phone numbers and web pages, but bots too. Restaurants could take orders via instant message—as some do already in China.

As with apps, bots will need much experimentation to find their place. That will, in turn, depend on how well providers manage their platforms. Telegram lets developers do pretty much what they want (although it has shut down chat channels related to Islamic State). Microsoft has promised to be as open as possible. Developers and investors have their doubts about Facebook, given its chequered history: it made life difficult for developers of applications for its website.

There will still be an app for that

Microsoft, Facebook and others will also have to deal with Apple and Google, both of which are laggards in messaging and bots. They could try to get ahead, for instance by attracting developers with their widely used payment systems. Or they might try something entirely new, says Benedict Evans of Andreessen Horowitz, another venture-capital firm. One possibility would be to allow bots to show up on a smartphone’s notification panel.

Still, there will soon be “a bot for that”, to paraphrase Apple’s iconic slogan which suggests that an app exists for everything. Yet bots, unlike the Daleks of Dr Who fame (pictured), won’t try to take over the world. They will be happy to co-exist on people’s smartphones with websites, apps and other things yet to be invented. The mobile world will keep changing, but will always be a mixed affair.

Yahoo Finance

Here’s how Facebook will finally convince you to use MessengerFacebook packs a slew of new goodies into Messenger

FILE - In this March 25, 2015, file photo, CEO Mark Zuckerberg gestures while delivering the keynote address at the Facebook F8 Developer Conference in San Francisco. Zuckerberg said Tuesday, Sept. 15, Facebook may finally be getting a button that lets you quickly express something beyond a "like." (AP Photo/Eric Risberg, File)

At its annual F8 conference on Tuesday, Facebook CEO Mark Zuckerberg brought out the bots.

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Sponsored

No, not robots in the traditional sense, but virtual chat bots that live inside Facebook Messenger, which the social networking giant launched as a separate app in 2014. The company today announced Messenger Platform, which lets customers communicate with businesses live, in the moment, via bots. It simultaneously opened up a new API for developers to build their own bots for Messenger.

“We think you should just be able to message a business in the same way you’d message a friend,” Zuckerberg said. “You shouldn’t have to install a new app.”

That statement is a little ironic considering that Facebook (FB) forces users to download its Messenger app to read a message. The two functions were originally in the same place, but after it launched a separate Messenger app in 2014, you now can’t even see, on the main Facebook mobile app, who sent you a

message—it tells you that you’ve received one, then demands you download the separate Messenger app if you haven’t. That has annoyed some users, even though 900 million of them have downloaded Messenger. A CNET article in 2014 summarized the resentment many users felt: “Uh, no. Why should I install a second app just so I can trade the occasional message with a Facebook friend?” Many tech sites ran stories about how to get around it and send messages without downloading Messenger.

The bot bonanza is Facebook’s way of sweetening the pot and convincing the last remaining holdouts to use Messenger regularly. It had been quietly pushing this effort in more subtle ways before today: last month, Facebook rolled out a hidden basketball game only playable within Messenger. (It also has a Chess game.) Facebook’s VP of Messenger, David Marcus, referenced it at F8 on Tuesday, saying, “For the first time ever, we recently allowed ourselves to build little delightful surprises inside Messenger-- like allowing you to gift-wrap your messages for Valentine’s Day, or our little March Madness basketball game.”

The live chat bots are not unrelated to the new Facebook Live video plans that the company announced just last week. One is video, the other is text, but both services are all about live— whether that means streaming, watching, and sharing a video, or speaking to, ordering from, and connecting with companies and services. Facebook wants to be the go-to place for basically every task, whether social or commercial, that you need to accomplish right now. (Yes, “right now” was originally the territory of Twitter and Snapchat, and yes, both should consider themselves warned.)

When Zuckerberg introduced the new bot services, his first example was CNN, which he said can now “send you a daily digest of stories right into Messenger.” Then he mentioned ordering flowers through the Messenger app from 1-800-Flowers. “Now, to order from 1-800-Flowers, you never have to call 1-800-Flowers again.” In other words, if users are willing to turn to Messenger for a wide range of new services, it could disrupt everything from retail companies’ own apps and web pages and news apps from publishers.

It can even disrupt… healthcare. One of Facebook’s “launch partners” on Messenger Platform, the company confirmed to Yahoo Finance, is HealthTap, a Palo Alto, Calif.-based health-tech startup founded in 2010. HealthTap has raised $35 million in funding and has about 100 employees. And at the launch of Facebook’s new bot-laden Messenger service, it is the first healthcare-related service.

Rel

By communicating with HealthTap on Facebook Messenger, users can have a medical question answered instantly, by an automated bot, or in a matter of minutes, by a real doctor. “Text messaging is the number one mode of communication all over the world now,” HealthTap CEO Ron Gutman tells Yahoo Finance. “That’s the big deal of what we are doing, is giving people immediate service. I mean, think about it: health care is the one area where we’ve gotten used to waiting rooms and delays. We expect to wait. But on HealthTap, it’s instant gratification.”

One could quibble with Gutman’s claim about waiting. You still wait at the DMV, or at Trader Joe’s, or at McDonald’s. But his point is clear, and maybe a little scary: Most people now want to get helped, whatever the context, right away, this second.

Gutman says HealthTap is especially useful to two groups: millennials and moms. “We have college students asking questions about acne, sexual health, things important to them,” he says. “And then we have lots of moms asking about pregnancy, or their kids getting sick. And it’s easy for them because moms are busy, she has just one hand or even one finger free, and with one finger she can tap and ask a question and get a quick answer. This is the beautiful thing about the platform—it’s for busy people, like us. We don’t want to go to a doctor, waste a lot of time, we often just have a quick question we want to ask.”

HealthTap is not for booking an appointment; it’s closer to a simple online search for a simple medical problem. (If you need to go to the hospital, one hopes, you won’t try using Facebook Messenger instead.) You might think of WebMD, but Gutman calls that a “dinosaur.” Using a library of 4.2 billion vetted answers, HealthTap can serve you up a free, instant, automated response, or you can ask to speak to a live doctor (it has 100,000 on the platform; they are contractors a la Uber) and it will connect you with one within minutes.

HealthTap is just one example of a company that jumped on the new Messenger service. David Marcus, in his presentation, mentioned Spring, a shopping app, as well as Poncho, a weather app that delivers the weather report to you from a character named Poncho the Weather Cat. (Seriously.) All of these are eager corporate friends of Facebook, and it’s easy to see why more brands will be happy to hop on board and create bots for the Messenger platform.

Last year, Facebook Messenger was the fastest-growing app in the U.S. Second fastest? Facebook. Between Messenger and WhatsApp, people are sending 60 billion messages a day, Zuckerberg said. But it’s not enough: 900 million people use Facebook Messenger. The company wants that number to be higher, and it hopes instant connection with services can be the way to do it.

For a change, this chat-bot rollout is a utilitarian bet—on the usefulness and functionality of getting things done—more than a social bet. With 1.5 billion monthly active global users on the social network, it’s a bet that will likely pay off.

MIT TR

An Impressive Walking Google Robot Tries to Vacuum the Stairs

An odd-looking bipedal bot, created by a Japanese subsidiary of Alphabet, can climb stairs and carry heavy objects around a home.

by Will Knight April 11, 2016

These strange-looking, two-legged robots might be the predecessor of a machine that someday helps with chores around the home.

The bipedal bot, which has yet to be named, was developed by Schaft, a Japanese robotics company that is part of X, the research lab owned by Alphabet (previously Google). It was revealed at an event in Japan hosted by Andy Rubin, who started Google’s robotics project before leaving the company at the end of 2014 to create his own hardware incubator.

A video shot by someone at the event shows the robot carrying a heavy-looking gym weight, slipping on a tube without falling over, and cleaning a set of stairs with a vacuum cleaner brush attachment on its feet. It can also be seen walking through a forest and along a rocky beach.

It looks like the robot’s low center of gravity might help with its dynamic balancing. Usually such robots are very power-hungry, so it would be interesting to know how much that helps with power consumption.

The demo is especially interesting in light of Alphabet’s decision to sell off another robotics company, Boston Dynamics, that's working on walking robots. Perhaps the fact that Schaft’s robot is seen doing housework is a sign that Alphabet thinks it can commercialize the company’s technology sooner.

Schaft was spun out of the JSK Robotics Laboratory at the University of Tokyo, and one of the company’s robots took part in the first stage of DARPA’s Robotic Challenge. In fact, Schaft’s robot dominated the competition, demonstrating remarkable control, dynamic balance, and power-efficiency. But it was withdrawn by Google from the second stage of the contest for undisclosed reasons.

VIDEO @

https://www.youtube.com/watch?v=iyZE0psQsX0

NYT

Technology

Arms Control Groups Urge Human Control of Robot WeaponryBy JOHN MARKOFFAPRIL 11, 2016

Photo

A long-range anti-ship missile prototype being launched from a B-1 Bomber. Credit Defense Advanced Research Projects Agency

Two international arms control groups on Monday issued a report that called for maintaining human control over a new generation of weapons that are increasingly capable of targeting and attacking without the involvement of people.

The report, which came from Human Rights Watch and the Harvard Law School International Human Rights Clinic at the opening of a weeklong United Nations meeting on autonomous weapons in Geneva, potentially challenges an emerging United States military strategy that will count on technology advantages and increasingly depend on weapons systems that blend humans and machines.

That strategy has been described as the Third Offset strategy and it seeks to exploit technologies to maintain American military superiority. Pentagon officials have recently stated that the new technologies — and particularly artificial intelligence software — will help, rather than replace, human soldiers who must make killing decisions.

“Machines have long served as instruments of war, but historically humans have always dictated how they are used,” the report, titled “Killer Robots and the Concept of Meaningful Human Control,” said.

While some have argued that in the future, autonomous weapons might be able to better adhere to the laws of war than humans, an international debate is now emerging over whether it is possible to limit the evolution of weapons that make killing decisions without human involvement.

Current United States military guidelines, published in 2012, call for commanders and weapons operators to exercise “appropriate levels of human judgment” over the use of force. The guidelines do not completely prohibit autonomous weapons, but require that high-level Pentagon officials authorize them. They draw a line between semiautonomous weapons, whose targets are chosen by a person, and fully autonomous weapons that can hunt and engage targets without intervention.

New weapons that will enter the United States arsenal as early as 2018 may make the distinction a vital one. One example is a missile, known as the Long Range Anti-Ship Missile, or L.R.A.S.M., which was initially designed by the Defense Advanced Research Projects Agency and will be manufactured by Lockheed Martin. This year, the Pentagon asked Congress to authorize $927 million over the next five years for the system.

The missile is being developed in large part because of concerns that American carriers will be required to operate farther from China because of its growing military power.

Yet the missile has raised concerns among critics because it is designed to be launched by a human operator and then fly to a targeted ship out of human contact and make final targeting decisions autonomously.

“I would argue that L.R.A.S.M. is intended primarily to threaten China and Russia and is only likely to be used in the opening shots of a nuclear war that would quite likely destroy our civilization and kill a large fraction, or most, or nearly all human beings,” said Mark A. Gubrud, a physicist and member of the International Committee for Robot Arms Control, a group working for the prohibition of autonomous weapons.

The ability to recall a weapon may be a crucial point in any ban on autonomous weapons, said Bonnie Docherty, the author of the report and a lecturer on law and senior clinical instructor at the International Human Rights Clinic at Harvard Law School.

Weapons specialists said the exact capabilities of systems like L.R.A.S.M. are often protected as classified information.

“We urge states to provide more information on specific technology so the international community can better judge what type and level of control should be required,” Ms. Docherty said.

The United States is not the only nation pursuing automated weapons. Britain, Israel and Norway have deployed missiles and drones that carry out attacks against enemy radar, or tanks without direct human control.

The most recent United States military budget for the 2017 fiscal year calls for spending $3 billion on what it describes as “human machine combat teaming.” As machines become more capable and the pace of warfare quickens because of automation, many weapons specialists think that it will be challenging to keep humans in control.

Some nations are now calling for some kind of international agreement that limits the weapons.

“There seems to be a broad consensus that, at some level, humans should be involved in lethal force,” said Paul Scharre, a senior fellow at the Center for New American Security in Washington.

Medium

Scott SantensMar 1614 min read

18-time world champion Lee Se-dol learning something new from AlphaGo - defeat

Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines

(An alternate version of this article was originally published in the Boston Globe)

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.

The language is a new class of machine learning known as deep learning, and the “whispered word” was a computer’s use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat. Many who read this news, considered that as impressive, but in no way comparable to a match against Lee Se-dol instead, who many consider to be one of the world’s best living Go players, if not the best. Imagining such a grand duel of man versus machine, China’s top Go player predicted that Lee would not lose a single game, and Lee himself confidently expected to possibly lose one at the most.

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 machine, 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:

Source: St. Louis Fed

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.

Big Data

Big data isn’t just some buzzword. It’s information, and when it comes to information, we’re creating more and more of it every day. In fact we’re creating so much that a 2013 report by SINTEF estimated that 90% of all information in the world had been created in the prior two years. This incredible rate of data creation is even doubling every 1.5 years thanks to the Internet, where in 2015 every minute we were liking 4.2 million things on Facebook, uploading 300 hours of video to YouTube, and sending 350,000 tweets. Everything we do is generating data like never before, and lots of data is exactly what machines need in order to learn to learn. Why?

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 .

This combination of deep learning and big data has resulted in astounding accomplishments just in the past year. Aside from the incredible accomplishment of AlphaGo, Google’s DeepMind AI learned how to read and comprehend what it read through hundreds of thousands of annotated news articles. DeepMind also taught itself to play dozens of Atari 2600 video games better than humans , just by looking at the screen and its score, and playing games repeatedly. An AI named Giraffe taught itself how to play chess in a similar manner using a dataset of 175 million chess positions, attaining International Master level status in just 72 hours by repeatedly playing itself. In 2015, an AI even passed a visual Turing test by learning to learn in a way that enabled it to be shown an unknown character in a fictional alphabet, then instantly reproduce that letter in a way that was entirely indistinguishable from a human given the same task. These are all major milestones in AI.

However, despite all these milestones, when asked to estimate when a computer would defeat a prominent Go player, the answer even just months prior to the announcement by Google of AlphaGo’s victory, was by experts essentially, “Maybe in another ten years.” A decade was considered a fair guess because Go is a game so complex I’ll just let Ken Jennings of Jeopardy fame, another former champion human defeated by AI, describe it:

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 10¹ board ⁷⁰positions in Go, and only 10 atoms in the universe. That means that if there were ⁸⁰as many parallel universes as there are atoms in our universe (!), then the total number 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.

A world with Amelia and Viv — and the countless other AI counterparts coming online soon — in combination with robots like Boston Dynamics’ next generation Atlas portends, is a world where machines can do all four types of jobs and that means serious societal reconsiderations. If a machine can do a job instead of a human, should any human be forced at the threat of destitution to perform that job? Should income itself remain coupled to employment, such that having a job is the only way to obtain income, when jobs for many are entirely unobtainable? If machines are performing an increasing percentage of our jobs for us, and not getting paid to do them, where does that money go instead? And what does it no longer buy? Is it even possible that many of the jobs we’re creating don’t need to exist at all, and only do because of the incomes they provide? These are questions we need to start asking, and fast.

Decoupling Income From Work

Fortunately, people are beginning to ask these questions, 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.

The future is a place of accelerating changes. It seems unwise to continue looking at the future as if it were the past, where just because new jobs have historically appeared, they always will. The WEF started 2016 off by estimating the creation by 2020 of 2 million new jobs alongside the elimination of 7 million. That’s a net loss, not a net gain of 5 million jobs. In a frequently cited paper, an Oxford study estimated the automation of about half of all existing jobs by 2033. Meanwhile self-driving vehicles, again thanks to machine learning, have the capability of drastically impacting all economies — especially the US economy as I wrote last year about automating truck driving — by eliminating millions of jobs within a short span of time.

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.

IT News

MIT uses 4D maps to help robot teams navigate moving obstacles

Audi drives repairs with telepresence robots' help

A new, decentralized planning algorithm for teams of robots factors in moving obstacles.

Credit: Christine Daniloff/MIT

A new algorithm incorporates time as a fourth mapping dimension

By Katherine Noyes

IDG News Service | Apr 21, 2016 4:35 PM PT

It's one thing to keep robots from crashing into fixed obstacles like walls or furniture, but preventing collisions with other moving things is a much tougher challenge. Targeting teams of robots working together, MIT on Thursday announced a new algorithm that helps robots avoid moving objects.

Planning algorithms for robot teams can be centralized, in which a single computer makes decisions for the whole team, or decentralized, in which each robot makes its own decisions. The latter approach is much better in terms of incorporating local observations, but it's also much trickier, since each robot must essentially guess what the others are going to do.

MIT's new algorithm takes a decentralized approach and factors in not just stationary obstacles but also moving ones. Each robot uses its own observations to map out an obstacle-free region in its immediate environment. It then passes that map to its nearest neighbors. When a robot receives a map from a neighbor, it calculates the intersection of that map with its own and passes that on to other neighbors.

Because each robot communicates only with its close neighbors, the bandwidth required for communications is greatly reduced, particularly when there are a lot of robots. And each robot ends up with a map that reflects all of the obstacles detected by the whole team.

The algorithm accounts for obstacles in motion by including time as a fourth mapping dimension. With that dimension included, it describes how a three-dimensional map would have to change to accommodate the obstacle’s change of location over a span of a few seconds.

In simulations involving squadrons of mini helicopters, the algorithm came up with the same flight plans that a centralized version did but allowed for small variations as conditions required.

“It’s a really exciting result, because it combines so many challenging goals,” said Daniela Rus, a professor in MIT’s Department of Electrical Engineering and Computer Science and director of the Computer Science and Artificial Intelligence Laboratory.

“Your group of robots has a local goal, which is to stay in formation, and a global goal, which is where they want to go or the trajectory along which you want them to move," Rus explained. "You allow them to operate in a world with static obstacles but also unexpected dynamic obstacles, and you have a guarantee that they are going to retain their local and global objectives.”

Each robot updates its map several times per second, calculating the trajectory that will maximize both local and global objectives.

To simulate environments in which humans and robots work together, the researchers are also testing a version of their algorithm on wheeled robots whose goal is to collectively carry an object across a room where humans are also moving.

They'll present their algorithm next month at the International Conference on Robotics and Automation.

The Economist

Robotic surgery

Who wields the knife?

A machine carries out an operation almost unaided May 7th 2016 | From the print edition

THEY don’t drink, they don’t get tired and they don’t go on strike. To hospital managers, the idea of robots operating on patients without human intervention is an attractive one. To patients, though, the crucial question is, “are they better than human surgeons?” Surgery is messy and complicated. A routine operation can become life-threatening in minutes.

Such considerations have meant that the role of robots in operating theatres has been limited until now to being little more than motorised, precision tools for surgeons to deploy—a far cry from the smart surgical pods and “med-bays” of science fiction. But a paper published this week in Science Translational Medicine, by Peter Kim of the Children’s National Health System in Washington, DC, and his colleagues, brings the idea of real robot surgeons, operating under only the lightest of human supervision, a step closer. Though not yet let loose on people, it has successfully stitched up the intestines of piglets.

To build their robodoc, dubbed the Smart Tissue Autonomous Robot (STAR), Dr Kim and his team fitted a robotic arm with an articulated suturing tool and a force sensor to detect the tension in the surgical thread during the operation. They equipped the arm with cameras that could create a three-dimensional image, to guide it as it deployed the tool, and also a thermal-imaging device to help distinguish between similar-looking tissues. A computer program written by the team controlled the arm. This had a repertoire of stitches, knots and manoeuvres that permitted it to plan and carry out a procedure, known as anastomosis, which involves sewing together two parts of a bodily tube.

No pig in a poke

Before each of the trial operations, the team anaesthetised a piglet and opened its abdomen to expose part of its small intestine. They then severed this and highlighted pertinent areas with fluorescent dye, to help

guide the arm. Under a surgeon’s supervision, STAR sewed the piglet’s gut together again. In the four operations reported in the paper it carried out about 60% of the procedure without human intervention, and the rest with only minor adjustments to its stitches. Since the team submitted their results for publication, however, they say STAR has successfully completed the entire process unaided.

Comparing STAR’s work with that of experienced surgeons operating both with and without the assistance of existing robotic tools, Dr Kim and his colleagues reckoned STAR’s stitches were more evenly spaced and the sutured gut less leaky. None of the pigs suffered complications.

STAR did, it is true, take much longer than a human surgeon would to create the suture. It averaged 50 minutes for the operation, whereas a person would take about eight. But that will surely get faster. And even if STAR never quite matches a human being at work for speed, the better final product it seems to deliver would, if translated into regular clinical practice, reduce readmission rates.

For now, STAR remains a tool rather than a truly autonomous agent. But such autonomy is probably not far away. Dr Kim hopes, for example, that a souped-up version will soon be able to remove an appendix without any assistance from doctors.

STAR’s existence does, though, highlight two questions being raised more and more in what is an increasingly robotised society. These are: “will people trust robots with their lives?” and, “who is liable if something goes wrong?”

The answer to the first will probably depend on the level of supervision the machines are subject to. It would not take much, for example, to turn airliners into drones, but passengers are reassured by the presence of a flight crew, so this is unlikely to happen soon. The same will probably be true of surgical robots, however good they become. In answer to the second, the lawyers are already circling. Intuitive Surgical, a maker of surgical robots based in Sunnyvale, California, has been on the receiving end of lawsuits alleging (which the firm denies) that surgeons were inadequately trained to use its machines or that the robots were defective. Machines may get the better of humans in the operating theatre, but the courtroom will also determine how fast they spread.

MIT Technology Review

Robots Learn How to Make Friends and Influence People

If robots can learn to respect human social norms, they will become much better at navigating busy spaces like airports, malls, or city sidewalks.

by Will Knight May 17, 2016

If robots are going to take over the world, they could at least have the courtesy not to bump into us while they’re at it. That’s not as easy as it sounds, though, especially when a robot is trying to make its way through a bustling space like a mall, hospital, or crowded city street.

Thankfully, researchers have developed an algorithm that could give robots the ability to deftly maneuver through spaces packed with unpredictable humans.

Robots are gradually leaving controlled spaces like labs and factories and edging into more settings in which they will inevitably encounter human beings (see “Are You Ready for a Robot Colleague?”). We navigate hectic spaces by reading other people’s movements and planning our paths accordingly. Robots tend to just barrel ahead, and then stop suddenly when someone gets in the way.

Stanford's JackRabbot robot will explore busy spaces while trying to respect people's boundaries.

“The challenge is how to program these devices to respect human social conventions,” says Silvio Savarese at Stanford University.

Savarese and colleagues developed a computer-vision algorithm that predicts the movement of people in a busy space. They trained a deep-learning neural network using several publicly available data sets containing video of people moving around crowded areas. And they found their software to be better at predicting peoples’ movements than existing approaches for several of those data sets.

Savarese's team is testing its algorithm on a mobile robot called JackRabbot developed at Stanford. The two-wheeled robot, which is equipped with cameras, range sensors, and GPS, will explore busy indoor and outdoor spaces to test the approach in real situations.

At the moment, the most notable example of robots interacting directly with members of the public is Google’s self-driving vehicles. The company has acknowledged that its cars, while predominantly safe, have indirectly contributed to accidents due to a failure to understand the social norms of the road (“Google’s Self-Driving Car Chief Defends Safety Record”). As robots begin to proliferate into settings like shops and offices, awkward run-ins could become more common.

“The first problem is to understand the mostly unstated rules that people follow,” Savarese says. “How do people behave in crowds? How do they share resources, like sidewalks, parking spots? When should a person (or a robot) take its turn?”

A startup called Starship Technologies, which makes robots that deliver packages, is also working on this problem. The company has been testing its robots at several locations in the U.S. and the U.K., and besides dealing with uneven sidewalks and navigating around random obstacles, encounters with pedestrians pose the biggest challenge.

“Our robots have now come into contact with over 230,000 people around the world,” says Henry Harris-Burland, a spokesperson for Starship. Engineers at the company monitor the robots remotely as they go about mock deliveries. “Social acceptance is a core focus at the moment,” he says.

Jodi Forlizzi, at Carnegie Mellon University’s Human Computer Interaction Institute, says the Stanford algorithm adds to other research aimed at making robot behavior more humanlike. “Much research in human-robot interaction has looked at whether we can replicate the norms of human social interaction,” she says.

That goes way beyond just predicting a person’s movement. Forlizzi’s own research has involved trying to get robots to move around spaces in such a way that they form natural-seeming clusters with people. She says there is a definite need to teach robots how to blend in.

“There’s a whole class of robots that will be working with people and close to people also, so we need to understand how they should behave,” Forlizzi says.

SA

When Will Computers Have Common Sense? Ask FacebookThe social network is ramping up artificial intelligence to teach machines to figure out what users want—without human help

By Larry Greenemeier on June 20, 2016

Credit: Courtesy of Getty Images/iStockphoto Thinkstock Images \ VLADGRIN

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users’ individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network’s AI researchers are trying to take this technology to the next level—from pure data-crunching logic to a nuanced form of “common sense” rivaling that of humans.

AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning. For machines to truly have common sense—to be able to figure out how the world works and make reasonable decisions based on that knowledge—they must be able to teach themselves without human supervision. Though this will not happen on a significant scale anytime soon, researchers are taking steps in that direction. In a blog posted Monday, for example, Facebook director of AI Yann LeCun and research engineer Soumith Chintala describe efforts at unsupervised machine learning through a technique called adversarial training.

This approach consists of two artificial neural networks, so called because they use algorithms designed to help them function a little like a human brain. A “generator” network creates images based on random data that it is fed. Researchers train the second “discriminator” network through machine learning to be able to tell the difference between a real image and a data file containing nonsensical patterns of shapes and colors. The discriminator then analyzes a series of files, some from a database of real images and others created by the generator network. Initially, the generator is not very good at creating realistic images and the discriminator easily flags them as fakes. Eventually, however, the generator is supposed to learn from the discriminator’s responses and begin to produce increasingly more realistic images. In this way the generator and discriminator are adversaries, with the former trying to fool the latter and the latter trying to avoid being fooled, according to Chintala.

Adversaries

Adversarial network generators tested up to now in AI labs—at Facebook and elsewhere—have typically failed to show significant improvement even after interacting with discriminators. In an attempt to remedy this, Facebook researchers created generators that have specially crafted structures of interconnected layers, an arrangement known in the AI community as a “deep convolutional generative adversarial network,” or DCGAN. Each of these layers consists of a particular algorithm applied to the input that the network gets. The generator’s first layer runs an algorithm that extracts raw pixels and simple motifs from a dataset representing an image. The next layer combines these motifs into slightly more complex arrangements. The next layers detect parts of objects, assemble them into objects and create scenes, respectively, until the

entire image is created. “There’s a hierarchy of layers, which is where the word ‘deep’ comes from,” LeCun says.

The researchers found that their DCGANs could, among other things, learn to draw specific objects as the training progressed. They also got a better understanding of what happens as data move from layer to layer within the neural network. In addition, LeCun, Chintala and their colleagues tested their generator’s predictive capabilities by having it use raw data to produce video frames. In one experiment they fed the generator four frames of video and had it produce the next two frames based on those data. The resulting AI-generated frames looked like a realistic continuation of the action, whether it was a person walking or simply making head movements.

More intelligent assistants

LeCun thinks such predictive abilities could enhance Facebook’s ability to engage users, using the common sense the site has developed to essentially make educated guesses about them. “If we know how to build dialogue systems that have an idea of what the person dialoguing wants or thinks, that means we can have chatbots that are actually useful and interact with you in a natural way,” LeCun says. Improved predictive capabilities could likewise help improve the Facebook M virtual assistant, which faces growing competition from Apple’s Siri, Google’s upcoming Google Assistant, Amazon's Alexa and Microsoft's Cortana.

“There is still a long way—very long way—to go before [machines have common sense], but I share with [LeCun and his colleagues] the belief that exploring better unsupervised learning algorithms is a crucial key towards human-level AI,” says Yoshua Bengio, a University of Montreal computer science professor and a co-author of the 2014 study that introduced much of the AI world to generative adversarial networks. Bengio, who was not involved in the Facebook AI research, addressed deep learning’s progress in the June 2016 Scientific American article titled “Machines Who Learn.”

Facebook’s interest in unsupervised machine learning is part of a larger trend that has some of the largest Internet companies—including Amazon, Apple, Google, Microsoft and Twitter—buying AI startup companies and investing in their own studies. Earlier this month Microsoft Research announced its effort to develop a system that could tell a story based on a series of related images. Google’s AlphaGo program made headlines in March when it convincingly beat one of the world’s best Go players at his own game. AI likewise plays a crucial role in efforts by Alphabet, Inc.—Google’s parent company—to develop a driverless car. Apple executives in the past week talked at the company’s Worldwide Developer Conference about wrestling with efforts to advance AI in its products without hurting widely publicized efforts to ensure customer privacy. “It's obvious [that AI] is likely to completely change their business as well as the whole world's economy in a major way,” Bengio says.

LeCun agrees that AI is clearly a very strategic technology for any company that operates on the Web or has any kind of digital presence. “Not just for user interfaces or content filtering but in general,” he says. “People will interact with machines in a very natural way, and we need to get machines to understand people.”

Yahoo Finance

How robots paved the way for Donald Trump

July 14, 2016

See any humans? A Ford assembly plant in Missouri.

Think we don’t make anything in America any more? Think again.

US manufacturing output is close to a record high, even when adjusted for inflation. The reason that sounds surprising is manufacturing jobs have been disappearing since the late 1980s, and now that number is just 12.3 million. Since 1989, manufacturing output has surged 69% while employment has fallen by 32%.

Manufacturers are doing more with less because of technology: computerized machines, streamlined processes, and on just about any factory floor that’s been built or revamped during the last 20 years, robots. “Automation is eating jobs from the inside out,” says Moshe Vardi, a professor of computational engineering at Rice University in Houston. “It’s the major cause of job losses in manufacturing.”

There’s a different storyline in the presidential campaign, with Donald Trump blaming bad trade deals and unfair labor practices in China and Mexico for the loss of decent-paying blue-collar jobs in the United States. “They’re eating our lunch,” Trump often says of trading partners that pay their workers below US standards and sell billions of cheap imports to Americans.

It’s easier to blame other countries for the loss of American jobs than it is to blame technology entrepreneurs, many of them American, who have revolutionized manufacturing and will continue to do so. But the numbers do suggest that technology has made many manufacturers far more productive and cut the need for human workers. These two charts show manufacturing output and employment during the last 30 years – and they’re clearly going in opposite directions.

Screen Shot 2016-07-13 at 2.04.46 PM

Screen Shot 2016-07-13 at 2.05.02 PM

The loss of manufacturing jobs is probably causing more damage to the middle-class than anything else, as families try to cope with stagnant or declining pay, shrinking opportunity and the alarming prospect that today’s digital economy is simply leaving them behind. Trump, more than any political figure of modern times, has tapped into that anxiety with his call to “make America great again” and remake trade deals he blames for the plight of the formerly middle class.

But Trump, the likely Republican nominee for president, may be targeting the wrong problem by focusing on trade rather than technology. If elected, it’s possible he could rework trade deals only to find that US manufacturers still aren’t hiring and the trend toward replacing workers with machines only intensifies.

Economist Larry Summers has argued that a large-scale substitution of technology for workers might be underway, and only just getting started. In restaurants, kiosks and tablets are replacing humans who used to take your order. Robots process packages in distribution warehouses. Some white-collar work, such as legal research, can be computerized. Researchers at Oxford University and consulting firm McKinsey estimate that nearly half of all jobs are susceptible to automation. The next sector likely to be robotified is transportation, as self-driving trucks, ships and construction machinery begin to come online.

In theory, people whose jobs are displaced by new technology are supposed to get retrained, move if necessary, and find new work in a growing field. But instead of “upscaling,” most displaced workers end up “downscaling” – taking lower-paying jobs and in many cases falling out of the middle class. “The economy for working-class people has been miserable,” says Vardi. “They are not sharing in economic growth from the Internet.”

While workers connected to the global, digital economy — the top 20% of earners, more or less – are generally doing fine, others are reeling from the twin crush of globalization and digital technology. The percentage of adult men who have a job or are looking for one is a scant 69.2%, nearly the lowest level on record. In the years following World War II, more than 85% of men had a job or wanted one. The decline has been most pronounced since 2008, and that growing segment of economic dropouts tend to be older white men without a college degree – Trump’s strongest supporters.

Trump wants importers such as China and Mexico to pay larger tariffs on goods they ship to the United States, to make home-grown goods more competitive and boost hiring at home. But he hasn’t said a word about disarming the robots. The march of technology may be the tougher challenge.

NYT

A Robot May Be Training to Do Your Job. Don’t Panic.Preoccupations

By ALEXANDRA LEVIT SEPT. 10, 2016

In my speaking engagements, when I mention the terms “the future of work” and “automation” in the same sentence, I often see the audience squirm. People’s worst fear is that their job will soon be taken over by the equivalent of Rosie the Robot from “The Jetsons.” But even though we’re only in the beginning stages of work force automation, I’m optimistic about the effect it will have on human workers.

Over the last two decades, machines have indeed usurped many human jobs in industries like manufacturing, hospitality, transportation and customer service. But here’s what I find interesting: We hang our hats on the idea that there are certain professions, such as teaching and caregiving, in which humans could never be replaced by robots because of the level of personal interaction required.

But according to Richard Yonck, executive director and analyst for Intelligent Future Consulting and author of the forthcoming book “Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence,” we should never say never.

“Starting in the mid-’00s, due to better computer hardware and algorithms, we made some major leaps forward in deep learning,” he said in an interview. “As a result, we’re now developing emotional computing and software programs that are aware of our moods and intentions and are able to respond accordingly.”

For example, I might be fuming at my desk, punching out an irate email to an inconsiderate client. Through text analysis or facial expression recognition, a program on my laptop could identify the high level of emotion, understand what’s about to happen, and warn me to take a breather before I send something I’ll regret.

Emotion recognition software is making waves in education, too. Researchers from North Carolina State University showed that software that tracks facial expressions can accurately assess the emotions of students engaged in interactive online learning, then predict the effectiveness of online tutoring sessions. The researchers’ program, JavaTutor, responds not only to what a student knows, but to each student’s feelings of frustration or engagement, just as a human teacher would.

Affective computing and emotional awareness in software are likely to come into common use sooner than in robotics. So can we stop worrying that humanoid robots will take our jobs as, let’s say, health workers?

Not necessarily. In Japan, the rapidly aging population and shrinking work force have led to significant advances in social robotics. Riken and Sumitomo Riko Company have released Robear, a nursing robot that looks like a tall, white bear and can lift patients out of bed and help them move. Strong, gentle and nonthreatening, Robear can converse and interact with patients on a rudimentary level.

Then, there’s Jibo, which, at 11 inches tall, is used mostly in the home — for now. Jibo, designed at M.I.T., uses speech and facial recognition, and natural language processing, to learn from its interactions with people. This little guy is on my wish list. I can’t wait for it to suggest what I should have for dinner and take video of my child’s birthday party without being prompted. I’m sure I’ll get mad at it sometimes, but we’ll make up as soon as I see its movements mimicking human sadness. Maybe I’ll be the first person to hire Jibo as a garden-variety junior staff member.

Realistically, these technologies have far to go. For an idea of how long it might be before social robots can do your job, look at Microsoft Windows’ personal assistant Clippy. It took 25 years for that irritating paper clip to evolve to the current Cortana, a more intelligent personal assistant that helps you find things on your machine, manages your schedule and tells jokes that it knows you’ll find funny.

The widespread adoption of social robotics in the workplace faces a host of potential problems, including a lack of infrastructure and power requirements, deficient awareness of surroundings, and public resistance. Eventually, though, the moment will come when machines possess empathy, the ability to innovate and other traits we perceive as uniquely human. What then? How will we sustain our own career relevance?

I think the only way forward is to look at artificial intelligence developments as an opportunity rather than a threat. We need the mind-set that success is no longer about our level of knowledge but about our level of creative intelligence. If we accept the process of lifelong learning, in which we adapt to new ways of working as technology improves, we’ll always find roles that take advantage of our best qualities.

Maybe I’m overly optimistic, but I also believe that behind every highly intelligent machine will be humans who help build it, train it, distribute it, advise it and repair it when things go wrong. And until (unless?) machines acquire consciousness, they’ll have trouble mastering the most complex aspects of human behavior — many of which we still don’t understand. I can’t, for example, imagine a machine that, without human guidance or input, knows the perfect way to motivate a team of disparate human personalities that has just received bad news.

Mr. Yonck agrees. “Social robots will interact with people, not just replace them,” he said. “Human and machine will partner to provide products and services in ways we haven’t before — each providing its own strengths.”

Personally, I look forward to the day when my work-from-home job isn’t quite so lonely because Jibo is keeping me company.

ALEXANDRA LEVIT is a workplace consultant and the author of “Blind Spots: The 10 Business Myths You Can’t Afford to Believe on Your New Path to Success.”

Computerworld

News

A.I. and robotics could replace 6% of U.S. jobs by 2021More like this

Scientists look at how A.I. will change our lives by 2030

Lowe's will introduce the LoweBot, an autonomous robot by Fellow Robots, in 11 Lowe's stores throughout the San Francisco Bay area.

Credit: Lowe's

Those same systems also could create new jobs

By Sharon Gaudin

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Computerworld | Sep 14, 2016 1:04 PM PT

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In just five years, intelligent systems and robots may have taken up to 6% of U.S. jobs, according to Forrester Research in a report released this week.

As artificial intelligence (A.I.) advances to better understand human behavior and make decisions on its own in complicated situations, it will enable smart software and robots to take on increasingly challenging jobs.

That means robotics should be able to take over some jobs traditionally held by humans by 2021.

For instance, Forrester predicts that smart systems like autonomous robots, digital assistants, A.I. software and chatbots will take over customer service rep jobs and eventually even serve as truck and taxi drivers.

"Intelligent agents have emerged, but wide adoption is not yet mainstream," Forrester analysts wrote in their report. "As cognitive elements are added, capabilities will expand and target more use cases."

Forrester also noted that by 2021, A.I. is expected to evolve significantly beyond today's relatively simple machine learning and natural language processing capabilities. Advanced applications will focus more on self-learning and more complex scenarios.

This isn't a new scenario for the American workforce -- and it's also not as bad as it sounds.

In January, the Geneva-based World Economic Forum reported that technologies like A.I. and machine learning could mean the loss of more than 7 million jobs over the next several years.

However, the Forum also reported that these same technologies could lead to the gain of 2 million jobs in fields related to computer science, engineering and mathematics.

Tom Davenport, co-author of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, echoed that idea According to Davenport, artificial intelligent systems and robotics will become our assistants and co-workers, helping many people do their jobs better.

"We have a new generation of technologies and we need to work with them if we're going to be productive and effective," Davenport told Computerworld in April. "I think that in many cases, we'll be working with these machines as colleagues.... I think the people who prosper will be the ones who like working with machines."

Patrick Moorhead, an analyst with Moor Insights & Strategy, said Forrester's estimate seems a bit high. He expects the number to be closer to 3% or 4%.

"I don't necessarily buy into customer service jobs being replaced very quickly," he added. "Most of the jobs impacted would be in transportation, like cabbies, limo drivers, large highway truck and small truck city drivers [whose jobs are] hit by autonomous vehicles. And jobs where people are checking on things, like oil pipeline inspectors, will be impacted."

Moorhead noted that if many of these smart systems and devices are made here in the United States, there may not be much of a net job loss.

WP

novationsopinion

Robots could eventually replace soldiers in warfare. Is that a good thing?By Vivek Wadhwa and Aaron Johnson October 5 at 7:00 AM

The United States has on its Aegis-class cruisers a defense system that can track and destroy anti-ship missiles and aircraft. Israel has developed a drone, the Harpy, that can detect and automatically destroy radar emitters. South Korea has security-guard robots on its border with North Korea that can kill humans.

All of these can function autonomously — without any human intention.

Indeed, the early versions of the Terminator are already here. And there are no global conventions limiting their use. They deploy artificial intelligence to identify targets and make split-second decisions on whether to attack.

The technology is still imperfect, but it is becoming increasingly accurate — and lethal. Deep learning has revolutionized image classification and recognition and will soon allow these systems to exceed the capabilities of an average human soldier.

But are we ready for this? Do we want Robocops policing our cities? The consequences, after all, could be very much like we’ve seen in dystopian science fiction. The answer surely is no.

For now, the U.S. military says that it wants to keep a human in the loop on all life-or-death decisions. All of the drones currently deployed overseas fall into this category: They are remotely piloted by a human (or usually multiple humans). But what happens when China, Russia and rogue nations develop their autonomous robots and acquire with them an advantage over our troops? There will surely be a strong incentive for the military to adopt autonomous killing technologies.

The rationale then will be that if we can send a robot instead of a human into war, we are morally obliged to do so, because it will save lives — at least, our soldiers’ lives, and in the short term. And it is likely that robots will be better at applying the most straightforward laws of war than humans have proven to be. You wouldn’t have the My Lai massacre of the Vietnam War if robots could enforce basic rules, such as “don’t shoot women and children.”

And then there will be questions of chain of command. Who is accountable in the event that something goes wrong? If a weapons system has a design or manufacturing issue, the manufacturer can be held accountable. If a system was deployed when it should not have been deployed, all commanders going up the chain are responsible. Ascribing responsibility will still be a challenging task, as it is with conventional weapons, but the more important question is: Should the decision to take a human life be made by a machine?

Lethal autonomous weapons systems would violate human dignity. The decision to take a human life is a moral one, and a machine can only mimic moral decisions, not actually consider the implications of its actions. We can program it, or show it examples, to derive a formula to approximate these decisions, but that is different from making them for itself. This decision goes beyond enforcing the written laws of war, but even that requires using judgment and considering innumerable subtleties.

And the steady seepage of military technologies into civilian life will see these military systems being deployed in our cities.

Artificial systems have the benefit of not experiencing destructive emotions, such as rage. But they also lack critical positive emotions, such as sympathy and compassion. As Maj. Daniel Davis of the U.S. Army points out: “In virtually every war involving the U.S. … the enemy discovered that although GIs could be as ruthless and vicious as any opponent, the same soldier could extend mercy when appropriate.” The point of war is to attain peace on our terms; the human connection is an important part of facilitating it.

The only way to avoid untenable situations is to create and enforce an international ban on lethal autonomous weapons systems. Unilateral disarmament is not viable. As soon as an enemy demonstrates this technology, we will quickly work to catch up: a robotic cold war.

The precedent for this sort of ban is well established. Barbed spears, chemical weapons and blinding lasers are all weapons that society has agreed should never be used. (Unfortunately, nuclear weapons are not

specifically banned, though their use may violate other international laws limiting civilian casualties and long-lasting effects; the main factor curtailing their use is the fear of massive retaliation.)

There is hope for such a ban. Efforts are underway by the U.N. Convention on Certain Conventional Weapons (CCW), leading scientists and the Campaign to Stop Killer Robots to have the world’s governments consider a multilateral treaty that would remove the temptation to build a bigger, better swarm of autonomous killer robots and deploy them sooner than the next potential enemy can. But we are collectively responsible for considering these moral questions and deciding whether we want this technology to be used in war.

Robotics and artificial intelligence both offer great potential for helping society — from searching collapsed buildings for survivors, to sifting massive data for new treatments for cancer. It is up to us whether we harness their potential to build peace and enrich our lives or to ensure endless war and cheapen human life.

Vivek Wadhwa is Distinguished Fellow and professor at Carnegie Mellon University Engineering at Silicon Valley and a director of research at Center for Entrepreneurship and Research Commercialization at Duke. His past appointments include Stanford Law School, the University of California, Berkeley, Harvard Law School, and Emory University.

Article McKinsey Quarterly July 2016

Where machines could replace humans—and where they can’t (yet)By Michael Chui, James Manyika, and Mehdi Miremadi Article Actions

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The technical potential for automation differs dramatically across sectors and activities.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won’t be replaced by machines?

In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail. Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

Video From science fiction to business fact McKinsey’s Michael Chui explains how automation is transforming work.

These conclusions rest on our detailed analysis of 2,000-plus work activities for more than 800 occupations. Using data from the US Bureau of Labor Statistics and O*Net, we’ve quantified both the amount of time spent on these activities across the economy of the United States and the technical feasibility of automating each of them. The full results, forthcoming in early 2017, will include several other countries,1 but we released some initial findings late last year and are following up now with additional interim results.

Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today. In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation. Within each category, we discuss the sectors and occupations where robots and other machines are most—and least—likely to serve as substitutes in activities humans currently perform. Toward the end

of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.

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Understanding automation potential

In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2 Each whole occupation is made up of multiple types of activities, each with varying degrees of technical feasibility. Exhibit 1 lists seven top-level groupings of activities we have identified.

Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment). Since all of these constituent activities have a different automation potential, we arrive at an overall estimate for the sector by examining the time workers spend on each of them during the workweek.

Exhibit 1

Technical feasibility is a necessary precondition for automation, but not a complete predictor that an activity will be automated. A second factor to consider is the cost of developing and deploying both the hardware and the software for automation. The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it. A fourth factor to consider is the benefits beyond labor substitution, including higher levels of output, better quality, and fewer errors. These are often larger than those of reducing labor costs. Regulatory and social-acceptance issues, such as the degree to which machines are acceptable in any particular setting, must also be weighed. A robot may, in theory, be able to replace some of the functions of a nurse, for example. But for now, the prospect that this might actually happen in a highly visible way could prove unpalatable for many patients, who expect human contact. The potential for automation to take hold in a sector or occupation reflects a subtle interplay between these factors and the trade-offs among them.

Even when machines do take over some human activities in an occupation, this does not necessarily spell the end of the jobs in that line of work. On the contrary, their number at times increases in occupations that have been partly automated, because overall demand for their remaining activities has continued to grow. For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3 It also enabled a number of innovations, including increased promotions. But cashiers were still needed; in fact, their employment grew at an average rate of more than 2 percent between 1980 and 2013.

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The most automatable activities

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate. Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2). Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.

Exhibit 2

In manufacturing, for example, performing physical activities or operating machinery in a predictable environment represents one-third of the workers’ overall time. The activities range from packaging products to loading materials on production equipment to welding to maintaining equipment. Because of the prevalence of such predictable physical work, some 59 percent of all manufacturing activities could be

automated, given technical considerations. The overall technical feasibility, however, masks considerable variance. Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent. The potential varies among companies as well. Our work with manufacturers reveals a wide range of adoption levels—from companies with inconsistent or little use of automation all the way to quite sophisticated users.

Manufacturing, for all its technical potential, is only the second most readily automatable sector in the US economy. A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food; cleaning food-preparation areas; preparing hot and cold beverages; and collecting dirty dishes. According to our analysis, 73 percent of the activities workers perform in food service and accommodations have the potential for automation, based on technical considerations.

Some of this potential is familiar. Automats, or automated cafeterias, for example, have long been in use. Now restaurants are testing new, more sophisticated concepts, like self-service ordering or even robotic servers. Solutions such as Momentum Machines’ hamburger-cooking robot, which can reportedly assemble and cook 360 burgers an hour, could automate a number of cooking and food-preparation activities. But while the technical potential for automating them might be high, the business case must take into account both the benefits and the costs of automation, as well as the labor-supply dynamics discussed earlier. For some of these activities, current wage rates are among the lowest in the United States, reflecting both the skills required and the size of the available labor supply. Since restaurant employees who cook earn an average of about $10 an hour, a business case based solely on reducing labor costs may be unconvincing.

Retailing is another sector with a high technical potential for automation. We estimate that 53 percent of its activities are automatable, though, as in manufacturing, much depends on the specific occupation within the sector. Retailers can take advantage of efficient, technology-driven stock management and logistics, for example. Packaging objects for shipping and stocking merchandise are among the most frequent physical activities in retailing, and they have a high technical potential for automation. So do maintaining records of sales, gathering customer or product information, and other data-collection activities. But retailing also requires cognitive and social skills. Advising customers which cuts of meat or what color shoes to buy requires judgment and emotional intelligence. We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.

As we noted above, however, just because an activity can be automated doesn’t mean that it will be—broader economic factors are at play. The jobs of bookkeepers, accountants, and auditing clerks, for example, require skills and training, so they are scarcer than basic cooks. But the activities they perform cost less to automate, requiring mostly software and a basic computer.

Considerations such as these have led to an observed tendency for higher rates of automation for activities common in some middle-skill jobs—for example, in data collection and data processing. As automation advances in capability, jobs involving higher skills will probably be automated at increasingly high rates.

The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4

Exhibit 3

Activities and sectors in the middle range for automation

Across all occupations in the US economy, one-third of the time spent in the workplace involves collecting and processing data. Both activities have a technical potential for automation exceeding 60 percent. Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials. But as technology progresses, computers are helping to increase the scale and quality of these activities. For example, a number of companies now offer solutions that automate entering paper and PDF invoices into computer systems or even processing loan applications. And it’s not just entry-level workers or low-wage clerks who collect and process data; people whose annual incomes exceed $200,000 spend some 31 percent of their time doing those things, as well.

Financial services and insurance provide one example of this phenomenon. The world of finance relies on professional expertise: stock traders and investment bankers live off their wits. Yet about 50 percent of the overall time of the workforce in finance and insurance is devoted to collecting and processing data, where the technical potential for automation is high. Insurance sales agents gather customer or product information and underwriters verify the accuracy of records. Securities and financial sales agents prepare sales or other contracts. Bank tellers verify the accuracy of financial data.

As a result, the financial sector has the technical potential to automate activities taking up 43 percent of its workers’ time. Once again, the potential is far higher for some occupations than for others. For example, we estimate that mortgage brokers spend as much as 90 percent of their time processing applications. Putting in place more sophisticated verification processes for documents and credit applications could reduce that proportion to just more than 60 percent. This would free up mortgage advisers to focus more of their time on advising clients rather than routine processing. Both the customer and the mortgage institution get greater value.

Other activities in the middle range of the technical potential for automation involve large amounts of physical activity or the operation of machinery in unpredictable environments. These types of activities make up a high proportion of the work in sectors such as farming, forestry, and construction and can be found in many other sectors as well.

Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms. The latter two activities are unpredictable largely because the environment keeps changing. Schoolchildren leave bags, books, and coats in a seemingly random manner. Likewise, in a hotel room, different guests throw pillows in different places, may or may not leave clothing on their beds, and clutter up the floor space in different ways.

These activities, requiring greater flexibility than those in a predictable environment, are for now more difficult to automate with currently demonstrated technologies: their automation potential is 25 percent. Should technology advance to handle unpredictable environments with the same ease as predictable ones, the potential for automation would jump to 67 percent. Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.

Activities with low technical potential for automation

The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent). These activities, often characterized as knowledge work, can be as varied as coding software, creating menus, or writing promotional materials. For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to

determine the proper goals, interpret results, or provide commonsense checks for solutions. The importance of human interaction is evident in two sectors that, so far, have a relatively low technical potential for automation: healthcare and education.

Four fundamentals of workplace automation Read the article

Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients. For example, we estimate that less than 30 percent of a registered nurse’s activities could be automated, based on technical considerations alone. For dental hygienists, that proportion drops to 13 percent.

Nonetheless, some healthcare activities, including preparing food in hospitals and administering non-intravenous medications, could be automated if currently demonstrated technologies were adapted. Data collection, which also accounts for a significant amount of working time in the sector, could become more automated as well. Nursing assistants, for example, spend about two-thirds of their time collecting health information. Even some of the more complex activities that doctors perform, such as administering anesthesia during simple procedures or reading radiological scans, have the technical potential for automation.

Of all the sectors we have examined, the technical feasibility of automation is lowest in education, at least for now. To be sure, digital technology is transforming the field, as can be seen from the myriad classes and learning vehicles available online. Yet the essence of teaching is deep expertise and complex interactions with other people. Together, those two categories—the least automatable of the seven identified in the first exhibit—account for about one-half of the activities in the education sector.

Even so, 27 percent of the activities in education—primarily those that happen outside the classroom or on the sidelines—have the potential to be automated with demonstrated technologies. Janitors and cleaners, for example, clean and monitor building premises. Cooks prepare and serve school food. Administrative assistants maintain inventory records and personnel information. The automation of these data-collection and processing activities may help to reduce the growth of the administrative expenses of education and to lower its cost without affecting its quality.

Looking ahead

As technology develops, robotics and machine learning will make greater inroads into activities that today have only a low technical potential for automation. New techniques, for example, are enabling safer and more enhanced physical collaboration between robots and humans in what are now considered unpredictable environments. These developments could enable the automation of more activities in sectors such as construction. Artificial intelligence can be used to design components in engineer-heavy sectors.

One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people. In retailing, such natural-language advances would increase the technical potential for automation from 53 percent of all labor time to 60 percent. In finance and insurance, the leap would be even greater, to 66 percent, from 43 percent. In healthcare, too, while we don’t believe currently demonstrated technologies could accomplish all of the activities needed to diagnose and treat patients, technology will become more capable over time. Robots may not be cleaning your teeth or teaching your children quite yet, but that doesn’t mean they won’t in the future.

As stated at the outset, though, simply considering the technical potential for automation is not enough to assess how much of it will occur in particular activities. The actual level will reflect the interplay of the

technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.

Leading more automated enterprises

Automation could transform the workplace for everyone, including senior management. The rapid evolution of technology can make harnessing its potential and avoiding its pitfalls especially complex. In some industries, such as retailing, automation is already changing the nature of competition. E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy). In mining, autonomous haulage systems that transport ore inside mines more safely and efficiently than human operators do could also deliver a step change in productivity.

Top executives will first and foremost need to identify where automation could transform their own organizations and then put a plan in place to migrate to new business processes enabled by automation. A heat map of potential automation activities within companies can help to guide, identify, and prioritize the potential processes and activities that could be transformed. As we have noted, the key question will be where and how to unlock value, given the cost of replacing human labor with machines. The majority of the benefits may come not from reducing labor costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.

It is never too early to prepare for the future. To get ready for automation’s advances tomorrow, executives must challenge themselves to understand the data and automation technologies on the horizon today. But more than data and technological savvy are required to capture value from automation. The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever. Senior leaders, for their part, will need to “let go” in ways that run counter to a century of organizational development.5

Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6 It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.

Could a machine do your job? Find out on Tableau Public, where we analyzed more than 800 occupations to assess the extent to which they could be automated using existing technology.

About the author(s)

Michael Chui is a partner in McKinsey’s San Francisco office, where James Manyika is a senior partner; Mehdi Miremadi is a partner in the Chicago office.

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Robot Exoskeletons March in to Link Mind and BodyOriginally designed to give soldiers superhuman strength, exoskeletons are enabling heroic efforts to help patients re-learn to walk

By Larry Greenemeier on October 13, 2016

The Ekso GT is a combination of metal struts, sensors, straps and software that provides assistance based on a patient’s physical capabilities. Credit: EKSO BIONICS

The prosthetic exoskeleton sits bolt upright in a chair, looking as if a robot has stood up, walked away and left part of itself behind. Roughly three minutes later Kevin Oldt is strapped into the metal frame and ready to stand. He closes his eyes and takes a deep breath, stretching his arms away from his body like a high diver about to take a plunge. Except Oldt holds a crutch in each hand, and when it’s go time he pushes upward with his powerful arms. The exoskeleton’s four electric motors kick in with a low whir, straightening Oldt’s lower body as he steadies himself with the crutches.

Once Oldt is standing, a physical therapist checks the exoskeleton’s settings on a digital screen connected to its back support, and gives him the okay. Oldt takes a few steps, looks up and says, “I’m learning how to walk all over again.”

When a 49-year-old man says that, one assumes he has been through something terrible. For Oldt that was a snowmobile accident more than 14 years ago that injured his spine and left him in a wheelchair. After more than a decade of physical therapy and hard work he is back on his feet several times a week, with the help of a robotic medical exoskeleton.

More science than fiction

The device offers Oldt the support his legs no longer provide. “It almost feels like I am walking, with a little bit of help from the motors,” he says as he strides across the room, the exoskeleton clicking and

purring. His steps are surprisingly fluid, given that they are a combination of the machine’s programming and the remaining strength in his legs. The exoskeleton’s software calibrates how much assistance Oldt needs by sensing how much force he generates as he lifts his foot off the ground and pushes forward. “I’m always trying to use my mind to initiate my leg to go forward,” he says, his forehead moist from the effort it takes to work with the device. “I look down because I can’t feel my feet, but I can at least see where they’re going.” That, he says, helps reconstruct the missing connection between his mind and his body.

Mechanical exoskeletons have been in development for decades, but for most of that time the focus was on creating hydraulic-powered suits that soldiers could wear to carry heavy loads. These exist mostly in the form of prototypes as the U.S. government’s Defense Advanced Research Projects Agency (DARPA) and contractors try to figure out how to make them practical for military operations, in terms of cost and logistics.

But Oldt’s exoskeleton—the Ekso GT, made by Ekso Bionics—and a variety of similar products from other companies have had much more impact in recent years on medical rehabilitation for spinal cord injury patients. In April the GT became the first exoskeleton approved by the U.S. Food and Drug Administration for use with stroke patients as well as patients suffering injuries as far up the spine as the cervical region (just below the neck), thanks to the device’s tall back plate. In March the FDA granted Parker Hannifin Corp. approval to sell its Indego robotic exoskeletons both to hospitals and directly to patients. Argo Medical Technologies—makers of the ReWalk exoskeleton—is reportedly the only other company that can sell directly to patients. In December 2015 the U.S. Department of Veterans Affairs began covering the cost of the ReWalk exoskeleton for eligible paralyzed veterans.

Standard care is very hands-on—two or three physical therapists often support and guide each step a patient takes. Often one of those therapists must manipulate the patient’s legs if the patient does not have the strength to move. That technique can be effective over time in helping patients regain some strength and mobility in their lower bodies, but measuring a patient’s progress is difficult and the work is very strenuous for therapists and patients alike. Another recent option has been assistive standing devices such as Hocoma’s Lokomat, which places a patient in an exoskeleton suspended by cables over a treadmill. Patients walk on the treadmill with the Lokomat exoskeleton’s help but do not have the untethered freedom that freestanding exoskeletons provide.

Robotic rehab

Exoskeletons such as the one Oldt uses require a single therapist. They can be tuned to provide varying levels of support to meet different patients’ needs, and they measure a patient’s progress more precisely. This leads to more effective therapy sessions, says Tom Looby, Ekso Bionics’ president and CEO. During a patient’s first rehabilitation session a team of physical therapists working without an exoskeleton can normally get that person to take eight “quality” steps, meaning the patient is not trying to contort his or her body to compensate for a lack of strength or balance, Looby says. He claims the same patient in an Ekso GT can take 400 quality steps during that first session.

Robotic “gait trainer” exoskeletons like these have become increasingly popular as a rehabilitation option, says Liza McHugh, a physical therapist at Kennedy Krieger Institute in Baltimore. The GT enables individuals with paralysis to have long therapy sessions during which they are able step in a way that “we believe is good for restoring the nervous system after spinal cord injury,” McHugh says, adding that Kennedy Krieger has treated dozens of patients with the device since it arrived in August 2015.

The latest exoskeletons allow therapists to measure and document statistics including the length and number of steps, how much power the suit’s motors are using to assist a patient, and how the patients shift their weight as they step. This allows therapists to measure progress more precisely than in the past, McHugh says, explaining that physical therapists have traditionally judged progress based largely on subjective observations.

Working out the kinks

But exoskeletons still have several drawbacks. In addition to being expensive—one can cost $70,000 to $150,000—and requiring a lot of special training for therapists, they are designed to be used only on surfaces that are solid, dry and level, McHugh says. A patient’s ability to walk in the clinic might not translate to wet, sandy or uneven terrain. The Ekso GT is adjustable for heights only between 1.5 and 1.8 meters, so “we are unable to use it with our pediatric population,” she adds.

There is also a lack of concrete evidence that exoskeletons are more successful than conventional physical therapy at rehabilitating patients. Ekso is sponsoring a study that compares the progress of 160 spinal cord injury patients undergoing rehabilitation with the GT, with hands-on therapy and with no therapy, over 12 weeks. In August the company enrolled its first patient in its WISE, or “walking improvement for spinal cord injury with exoskeletons,” clinical trial.

“Generally, all things being equal, those using exoskeletons are able to get more therapy—do more work—in a shorter amount of time,” says Dylan Edwards, WISE’s lead investigator and director of the Burke Medical Research Institute’s Laboratory for Non-Invasive Brain Stimulation and Human Motor Control in White Plains, N.Y. Edwards and his colleagues plan to evaluate whether robotic gait training can improve a patient’s walking speed—progress that would indicate that the brain, spinal cord, peripheral nerves and muscles are beginning to work together more effectively. The researchers will also evaluate patient pain and muscle spasticity, as well as economic factors such as number of physical therapists and staff required during training. “I’m trying to build an argument that we should embrace this technology in the physical therapy profession,” Edwards says.

Iron Man

Kevin Oldt is perhaps the best endorsement for exoskeletons thus far. He has been helping promote its technology for the past several years through demonstrations for the press and public. He says he relishes the relative freedom that wearing the Ekso GT gives him, even for a brief amount of time. And he claims that consistent use of the exoskeleton three days per week for the past few years has helped him regain some strength and motor control in his legs.

Oldt acknowledges that the technology needed to help him walk again with minimal or no exoskeleton help is years away. Still, his experience has given him hope. “Sixteen years ago there was nothing,” he says. “This was just a comic book design—reading about Iron Man. Now it’s reality.”

Hydraulic-powered, mind-controlled support suits aren’t just for superheroes. Soon you might have to wear one to work. Larry Greenemeier reports

https://www.scientificamerican.com/podcast/episode/robotic-exoskeletons-giving-and-gaining-support/

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Exoskeleton defines a new class of warrior [Video]

STAFFBy Larry Greenemeier on September 27, 2010

Technology has always defined how wars are fought, from swords to bows and arrows through the invention of gunpowder and the dawn of the aircraft and, now, to the presence of laser-guided unmanned aerial drones and bomb-diffusing robots. The U.S. military is now hoping the next decade will see a new class of warrior—a faster, stronger and more durable exoskeleton-empowered

infantryman.

Such an "iron man" was unveiled Monday at a demonstration of Raytheon Company's new Exoskeleton (XOS 2) at the company's research facility in Salt Lake City, Utah. XOS 2 was designed to be stronger and allow soldier wearing the exoskeleton to execute movements more fluidly than its XOS 1 predecessor, first unveiled in May 2008 (riding the publicity at the time that led up to the release of the Error! Hyperlink reference not valid.).

The 95-kilogram XOS 2 is about 40 percent stronger than its 88-kilogram predecessor—the XOS-1 could lift about 16 kilograms with each arm, XOS-2 can lift about 23 kilograms.

Whereas XOS 2 was designed to use half the amount of power as its predecessor, Raytheon is hoping to ultimately develop a version that uses 20 percent of the power as the XOS 1 to perform the same tasks.

Reduced power consumption is key to making the exoskeleton practical to the military. The system is powered by an internal combustion engine, and its electrical systems are run by a wire that tethers the XOS 2 to a power source. Raytheon decided not to use batteries because the company's engineers didn't trust the safety of Lithium-ion batteries in close proximity to the person wearing the exoskeleton.

The engine, tether and even a battery all pose potential limitations to the exoskeleton's range. (Marvel Comic's Iron Man also had issues with range when the character first appeared in 1963.) In order to increase the amount of time the XOS 2 can remain out in the field, Raytheon's engineers are examining both the exoskeleton's internal combustion engine and the impact of the device's high-pressure hydraulics on power consumption. Raytheon doesn't plan to take on the task of developing a better internal combustion engine because there are already many efforts underway to do this, according to the company. However, the company did develop its own hydraulic components and control strategies for the exoskeleton's movement and will continue to look for ways to optimize the efficient use of high-pressure hydraulic fluid.

When Raytheon's exoskeletons first become available to the military (planned for 2015), they will also likely be tethered by power cables, followed three to five years later by untethered versions. The exoskeletons are expected to be used initially to help soldiers to carry heavier loads farther, whether they are performing combat or logistical operations.

The exoskeleton has been under

development since 2000 by a team led by Stephen Jacobsen at Raytheon Sarcos. Raytheon released a video [see below] Monday in which XOS 2 test engineer Rex Jameson breaks wood boards, lifts weights and does push ups without much exertion.

XOS 2 is just the latest in a long history of efforts to develop exoskeletons for military, industrial and medical uses. In 2008, Japan's CYBERDYNE, Inc. introduced a sleek, white exoskeleton called the Hybrid Assistive Limb (HAL) under development to augment the body's own strength or do the work of ailing (or missing) limbs. The company (whose name is taken from the fictional company in the Terminator movies that created the deadly Skynet) claims to have signed an agreement with Denmark's Rehabilitation Center in Odense University Hospital to introduce HAL for clinical trials.

Honda Motor Company and the Massachusetts Institute of Technology's (M.I.T.) Media Lab's Biomechatronics Group are likewise developing exoskeleton technology.

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Robots won’t kill the workforce. They’ll save the global economy.Across the world, the labor pool isn't growing fast enough to support our needs.

By Ruchir Sharma December 2 2016Ruchir Sharma, chief global strategist at Morgan Stanley Investment Management, is the author of “The Rise and Fall of Nations: Forces of Change in the The Post-Crisis World,” from which this essay is adapted.

A robot collects dishes to be cleaned at Chilli Padi Nonya Cafe in Singapore. (REUTERS/Edgar Su)

The United Nations forecasts that the global population will rise from 7.3 billion to nearly 10 billion by 2050, a big number that often prompts warnings about overpopulation. Some have come from neo-Malthusians, who fear that population growth will outstrip the food supply, leaving a hungry planet. Others appear in the tirades of anti-immigrant populists, invoking the specter of a rising tide of humanity as cause to slam borders shut. Still others inspire a chorus of neo-Luddites, who fear that the “rise of the robots” is rapidly making human workers obsolete, a threat all the more alarming if the human population is exploding.

Before long, though, we’re more likely to treasure robots than to revile them. They may be the one thing that can protect the global economy from the dangers that lie ahead.

An increase of 2.5 billion people may sound catastrophic. But what matters for economic growth is not the number of people but the rate of population growth. Since its peak in the 1960s, that rate has slumped by almost half to just 1 percent, and the U.N. forecast assumes that this slowdown will continue. Women are having fewer children, so fewer people are entering the working ages between 15 and 64, and labor-force growth is poised to decline from Chile to China. At the same time, owing to rapid advances in health care and medicine, people are living longer , and most of the coming global population increase will be among the retirement crowd. These trends are toxic for economic growth, and boosting the number of robots may be the easiest answer for many countries.

One simple way to estimate how fast an economy can grow is by adding working-age population growth and productivity growth: If the number of workers and output per worker are both increasing by 1 percent a year, then economic output should rise by roughly 2 percent. Over the past decade, both sides of that equation have declined dramatically across the world. In the United States, productivity growth has fallen by almost half from its postwar average, but growth in the labor force has slid even faster, dropping by two-thirds to an average pace of 0.5 percent, according to calculations performed for my book. Though many explanations have been offered for the slow recovery from the global financial crisis of 2008, the clearest answer may be aging populations. Something will have to fill the void left by, say, retiring farmers, and particularly at a time of rising hostility to immigrants, it is likely to be farmbots.

It may not be long before economists are worrying about a global shortage of robots. In many industrial countries, from Germany to Japan to South Korea, growth in the working-age population has already peaked, acting as a drag on the economy. Widely overlooked, however, is the fact that the population-growth slowdown is unfolding even faster in the emerging world, according to my research.

Consider the turning point that China hit last year. For the first time since records began in the 1950s, its working-age population growth was negative. As a result, China’s labor force is expected to lose 1 million workers each year for the foreseeable future, and it is also aging rapidly. Studies by Evercore ISI, a research firm, show that the elderly share of the population is rising more than twice as fast as it did in the United States and more than four times faster than in France at similar stages of development. Asked by an alarmed dinner companion about the threat robots posed to jobs in China, Nobel economist Daniel Kahneman responded: “You just don’t get it. In China, the robots are going to come just in time.” No wonder Beijing now offers heavy subsidies to companies involved in industrial automation.

And timing is critical. Those who fear the job-destroying impact of machines say this generation of technology is different because it is coming so fast. If older generations created tools for use by humans, such as sewing machines, the new forms of automation are imbued with artificial intelligence, capable of “machine learning” and of rapidly replacing humans in a broad swath of jobs, from manufacturing to services — even jobs that involve writing about robots. Concern about this disruptive advance has been stirred up by authorities such as Oxford University researchers Carl Benedikt Frey and Michael Osborne, who predicted in 2013 that nearly half of U.S. jobs would be at risk from automation in the next decade or two.

These alarms have sounded before, however. The Machine Intelligence Research Institute at the University of California at Berkeley has found that today, the average forecast for when artificial intelligence will arrive is about 20 years. But that was also the standard prediction in 1955. And often, humans find a way of working with their automated creations. After the introduction of supermarket scanners, the number of cashiers grew. Though legal-discovery software appeared to threaten the jobs of paralegals, their ranks increased, too. Now, many fear that self-driving trucks will displace millions of American truckers, but they may create more and better jobs for those who service those increasingly complex vehicles.

If automation was displacing human workers as fast as implied in recent books like Martin Ford’s “The Rise of the Robots,” then we should be seeing a negative impact on jobs already. We’re not. Since 2008, economic growth has been weak compared with that in other post-crisis recoveries, but job growth in the major industrial countries has been relatively strong. In the Group of Seven, the world’s top industrial countries, unemployment has fallen faster than expected in the face of weak economic growth, and faster than in any comparable period since at least the 1970s. The Japanese economy is growing at 0.8 percent, yet it is at full employment. According to my research, the job picture has been particularly strong in Germany, Japan and South Korea — the industrial countries that employ the most robots .

True, robots do represent a new obstacle for some poorer nations, namely those few that do not suffer from population decline. In the postwar era, countries like China escaped poverty by moving a rising young population off the farm and into more productive jobs in factories. Indeed, it was unusual for any country to sustain rapid growth unless the working-age population was increasing faster than 2 percent a year. My analysis shows that, in the 1980s, 17 of the 20 largest emerging economies had a working-age population expanding that fast, according to my research, but now there are only two: Nigeria and Saudi Arabia. And they will have a hard time moving a large segment of their young populations into industrial jobs, given that they now have to compete with robotic manufacturing elsewhere.

Yet for the rising number of countries facing population decline, the effort to lift the labor force has begun. Starting in the 1980s, led by Singapore, nations from Chile to Australia have offered baby bonuses for women to have more children, but many have found that these bonuses are ineffective in the face of stronger cultural forces, including the desire of many women to pursue a career before having children. Others have tried with some success to boost the workforce directly by raising the retirement age, offering

women incentives to join or return to the labor force after having kids, and opening doors to immigrant workers.

The simple math, however, shows that particularly in rapidly aging, conservative societies such as Japan and Germany, none of these groups has the potential to make up for coming declines in the working-age population. Germany decided to admit roughly 1 million refugees in 2015, in part for economic reasons, but the resulting controversy has reduced the flow. Germany would have to admit 1.5 million each year through 2030 to fully offset the economic impact of its aging population. Japan, which on average admits fewer than 70,000 immigrants per year, would have to admit 1 million annually. Given the widespread political backlash against immigration, increases this large are unlikely.

So far, robots are drawing comparatively little populist fire, perhaps in part because their numbers are still quite low. Worldwide, the industrial labor force includes about 320 million humans, compared with just 1.6 million robots. That’s a huge gap, even counting the superior strength and speed of the robots. And most of them fall in the category of unintelligent machines, committed to a single task such as turning a bolt or painting a car door. Nearly half of them work in the auto industry, which is still the largest employer (of humans) in the United States.

In the future, economists may start counting robots the way they now count gains in the working-age population, as a driver of growth. For much of the world, robots will stand alongside immigrants, women and the elderly as a fourth pool of labor.

Whether by design or accident, many of the countries with the most rapidly aging populations already have the most robots. According to the International Federation of Robotics, the nations with the highest density of industrial robots include South Korea, with 531 per 10,000 employees, Japan with 305 and Germany with 301. The United States ranks eighth with 176. China is well behind with only 49, but on the bright side — arguably — it had the world’s fastest-growing robot population.

Today, population trends are the most powerful force shaping the rise and fall of nations, the starting point of any discussion about an economy’s prospects. Most of the world is graying fast, and the economic answer to aging will be all hands on deck, no matter what they’re made of.

NYT

The Opinion Pages | Turning Points

The Robot Revolution Will Be the Quietest OneBy LIU CIXINDEC. 7, 2016

Credit Renaud Vigourt via The New York Times

This is an article from Turning Points, a magazine that explores what critical moments from this year might mean for the year ahead.

Turning Point: Though the first fatal crash involving an autonomous car took place in July 2016, self-driving vehicles have been adopted around the world.

In 2016, self-driving cars made inroads in several countries, many of which rewrote their laws to accommodate the new technology. As a science-fiction writer, it’s my duty to warn the human race that the robot revolution has begun — even if no one has noticed yet.

When a few autonomous test cars appeared on the roads over the last few years, we didn’t think of them as robots because they didn’t have the humanoid shape that science-fiction movies taught us to expect. In 2016, they were adopted widely: as buses in the United Arab Emirates and the Netherlands, taxis in Singapore and private cars in the United States and China. There was a fatal accident in Florida involving an autonomous car, which caused some concerns, but this did not significantly affect our embrace of this technology.

Instead of arming ourselves against this alien presence, as some of my fellow science-fiction writers have fearfully suggested, we gawked as the vehicles pulled up to the curb. The driverless vehicles, some of which had no steering wheels or gas pedals, merged into traffic and stopped at stop signs, smoothly taking us to our destinations. We lounged in comfort, occasionally taking selfies.

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Uber’s driverless system navigating the streets of Pittsburgh. Self-driving taxis, buses and cars operated in several countries in 2016. Credit Jeff Swensen for The New York Times

Machine learning has been an important tool for autonomous car companies as they develop the systems that pilot their vehicles. Instead of rigidly following programming as an app on your phone does, an A.I. system can try to learn to do a task itself, using techniques borrowed from human learning, like pattern recognition and trial and error, and may use hardware modeled on the architecture of a human brain. Currently, the responsibilities of artificial intelligence are mostly limited to tasks like translating texts, helping with medical diagnoses and writing simple articles for media companies. But we can expect to see unimaginable progress in this field in future — and the widespread use of the autonomous car is going to accelerate that process as automobile and technology companies invest ever more resources in its development.

Let’s try to envision that future. As during every other technological revolution, the robots will first transform our economy. People who drive for a living will lose their jobs — around 3 million in the United States alone. E-commerce may experience further booms because of automation, and car ownership is likely to become nearly obsolete as more targeted car sharing and public transportation systems are developed. Eventually, the robot cars could be integrated with other transportation systems. Say that you live in New York City and want to go to China’s Henan Province: You will enter the address into an app, a car will take you to your plane at the airport, and after you land, another will take you directly to your destination.

Robots will begin to creep into other areas of our lives — serving as busboys or waiters, for example — as our investments in robotic transport improve their prowess in areas such as environmental detection and modeling, hyper-complex problem solving and fuzzy-logic applications. With every advance, the use of A.I.-powered robots will expand into other fields: health care, policing, national defense and education.

There will be scandals when things go wrong and backlash movements from the new Luddites. But I don’t think we’ll protest very much. The A.I. systems that drive our cars will teach us to trust machine intelligence over the human variety — car accidents will become very rare, for example — and when given an opportunity to delegate a job to a robot, we will placidly do so without giving it much thought.

In all previous technological revolutions, people who lost their jobs mostly moved to new ones, but that will be less likely when the robots take over. A.I. that can learn from experience will replace many accountants, lawyers, bankers, insurance adjusters, doctors, scientific researchers and some creative professionals. Intelligence and advanced training will no longer mean job stability.

Gradually the A.I. era will transform the essence of human culture. When we’re no longer more intelligent than our machines, when they can easily outthink and outperform us, making the sort of intuitive leaps in research and other areas that we currently associate with genius, a sort of learned helplessness is likely to set in for us, and the idea of work itself may cease to hold meaning.

Photo

Liu Cixin. Credit The New York Times

As A.I. takes over, the remaining jobs may dwindle to a fraction of what they were, employing perhaps 10 percent or even less of the total population. These may be highly creative or complex jobs that robots can’t do, such as senior management, directing scientific research or nursing and child care.

In the dystopian scenario, as jobless numbers rise across the globe, our societies sink into prolonged turmoil. The world could be engulfed by endless conflicts between those who control the A.I. and the rest of us. The technocratic 10 percent could end up living in a gated community with armed robot guards.

There is a second, utopian scenario, where we’ve anticipated these changes and come up with solutions beforehand. Those in political power have planned a smoother, gentler transition, perhaps using A.I. to help them anticipate and modulate the strife. At the end of it, almost all of us live on social welfare.

How we will spend our time is hard to predict. “He who does not work, neither shall he eat” has been the cornerstone of civilizations through the ages, but that will have vanished. History shows that those who haven’t had to work — aristocrats, say — have often spent their time entertaining and developing their artistic and sporting talents while scrupulously observing elaborate rituals of dress and manners.

In this future, creativity is highly valued. We sport ever more fantastic makeup, hairstyles and clothing. The labor of past ages seems barbaric.

But the aristocrats ruled nations; in the A.I. era, machines are doing all the thinking. Because, over the decades, we’ve gradually given up our autonomy, step by step, allowing ourselves to be transformed into A.I.’s docile, fabulously pampered pets. As A.I. whisks us from place to place — visits to family members, art galleries and musical events — we will look out the windows, as unaware of its plans for us as a poodle on its way to the groomer’s.

The science-fiction writer Liu Cixin is a nine-time winner of the Galaxy Award, China’s highest honor for science-fiction writing. He is the first Chinese writer to receive the Hugo Award for Best Novel, which he received for his international best seller “The Three-Body Problem.” A 3-D film adaptation will be released in 2017.

NYT

Robot Revolution

The Long-Term Jobs Killer Is Not China. It’s Automation.

Claire Cain Miller @clairecm DEC. 21, 2016

A worker at a steel minimill in California. Minimill technology has enabled steel plants to cut 75 percent of employees over five decades, while keeping production the same. Credit David McNew/Getty Images

The first job that Sherry Johnson, 56, lost to automation was at the local newspaper in Marietta, Ga., where she fed paper into the printing machines and laid out pages. Later, she watched machines learn to do her jobs on a factory floor making breathing machines, and in inventory and filing.

“It actually kind of ticked me off because it’s like, How are we supposed to make a living?” she said. She took a computer class at Goodwill, but it was too little too late. “The 20- and 30-year-olds are more up to date on that stuff than we are because we didn’t have that when we were growing up,” said Ms. Johnson, who is now on disability and lives in a housing project in Jefferson City, Tenn.

Donald J. Trump told workers like Ms. Johnson that he would bring back their jobs by clamping down on trade, offshoring and immigration. But economists say the bigger threat to their jobs has been something else: automation.

“Over the long haul, clearly automation’s been much more important — it’s not even close,” said Lawrence Katz, an economics professor at Harvard who studies labor and technological change.

No candidate talked much about automation on the campaign trail. Technology is not as convenient a villain as China or Mexico, there is no clear way to stop it, and many of the technology companies are in the United States and benefit the country in many ways.

Mr. Trump told a group of tech company leaders last Wednesday: “We want you to keep going with the incredible innovation. Anything we can do to help this go along, we’re going to be there for you.”

Andrew F. Puzder, Mr. Trump’s pick for labor secretary and chief executive of CKE Restaurants, extolled the virtues of robot employees over the human kind in an interview with Business Insider in March. “They’re always polite, they always upsell, they never take a vacation, they never show up late, there’s never a slip-and-fall, or an age, sex or race discrimination case,” he said.

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At Eatsa, an automated restaurant chain, customers never interact with a human. Credit Jason Henry for The New York Times

Globalization is clearly responsible for some of the job losses, particularly trade with China during the 2000s, which led to the rapid loss of 2 million to 2.4 million net jobs, according to research by economists including Daron Acemoglu and David Autor of M.I.T.

People who work in parts of the country most affected by imports generally have greater unemployment and reduced income for the rest of their lives, Mr. Autor found in a paper published in January. Still, over time, automation has had a far bigger effect than globalization, and would have eventually eliminated those jobs anyway, he said in an interview. “Some of it is globalization, but a lot of it is we require many fewer workers to do the same amount of work,” he said. “Workers are basically supervisors of machines.”

When Greg Hayes, the chief executive of United Technologies, agreed to invest $16 million in one of its Carrier factories as part of a Trump deal to keep some jobs in Indiana instead of moving them to Mexico, he said the money would go toward automation.

“What that ultimately means is there will be fewer jobs,” he said on CNBC.

Take the steel industry. It lost 400,000 people, 75 percent of its work force, between 1962 and 2005. But its shipments did not decline, according to a study published in the American Economic Review last year. The reason was a new technology called the minimill. Its effect remained strong even after controlling for management practices; job losses in the Midwest; international trade; and unionization rates, found the authors of the study, Allan Collard-Wexler of Duke and Jan De Loecker of Princeton.

Another analysis, from Ball State University, attributed roughly 13 percent of manufacturing job losses to trade and the rest to enhanced productivity because of automation. Apparel making was hit hardest by trade, it said, and computer and electronics manufacturing was hit hardest by technological advances.

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A vacant factory parking lot in Luzerne County, Pa., which flipped from blue to red. Donald J. Trump railed against trade and offshoring, but automation has killed more jobs. Credit Mark Makela for The New York Times

Over time, automation has generally had a happy ending: As it has displaced jobs, it has created new ones. But some experts are beginning to worry that this time could be different. Even as the economy has improved, jobs and wages for a large segment of workers — particularly men without college degrees doing manual labor — have not recovered.

Even in the best case, automation leaves the first generation of workers it displaces in a lurch because they usually don’t have the skills to do new and more complex tasks, Mr. Acemoglu found in a paper published in May.

Robert Stilwell, 35, of Evansville, Ind., is one of them. He did not graduate from high school and worked in factories building parts for tools and cars, wrapping them up and loading them onto trucks. After he was laid off, he got a job as a convenience store cashier, which pays a lot less.

“I used to have a really good job, and I liked the people I worked with — until it got overtaken by a machine, and then I was let go,” he said.

Dennis Kriebel’s last job was as a supervisor at an aluminum extrusion factory, where he had spent a decade punching out parts for cars and tractors. Then, about five years ago, he lost it to a robot.

“Everything we did, you could program a robot to do it,” said Mr. Kriebel, who is 55 and lives in Youngstown, Ohio, the town about which Bruce Springsteen sang, “Seven hundred tons of metal a day/Now sir you tell me the world’s changed.”

Since then, Mr. Kriebel has barely been scraping by doing odd jobs. Many of the new jobs at factories require technical skills, but he doesn’t own a computer and doesn’t want to.

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The Skechers distribution center in Moreno Valley, Calif., is fully automated. Credit Monica Almeida/The New York Times

Labor economists say there are ways to ease the transition for workers whose jobs have been displaced by robots. They include retraining programs, stronger unions, more public-sector jobs, a higher minimum wage, a bigger earned-income tax credit and, for the next generation of workers, more college degrees. Few are policies that Mr. Trump has said he will pursue.

“Just allowing the private market to automate without any support is a recipe for blaming immigrants and trade and other things, even when it’s the long impact of technology,” said Mr. Katz, who was the Labor Department’s chief economist under President Clinton.

The changes are not just affecting manual labor: Computers are rapidly learning to do some white-collar and service-sector work, too. Existing technology could automate 45 percent of activities people are paid to do, according to a July report by McKinsey. Work that requires creativity, management of people or caregiving is least at risk.

Ms. Johnson in Tennessee said both her favorite and highest-paying job, at $8.65 an hour, was at an animal shelter, caring for puppies.

It was also the least likely to be done by a machine, she said: “I would hope a computer couldn’t do that, unless they like changing dirty papers and giving them love and attention.

Technology Review

Robotics China Is Building a Robot Army of Model Workers

Can China reboot its manufacturing industry—and the global economy—by replacing millions of workers with machines?

by Will Knight April 26, 2016

Above: A robot arm moves circuit boards around for testing inside CIG’s factory in Shanghai. Previously the work was done by hand.

Inside a large, windowless room in an electronics factory in south Shanghai, about 15 workers are eyeing a small robot arm with frustration. Near the end of the production line where optical networking equipment is being packed into boxes for shipping, the robot sits motionless.

“The system is down,” explains Nie Juan, a woman in her early 20s who is responsible for quality control. Her team has been testing the robot for the past week. The machine is meant to place stickers on the boxes containing new routers, and it seemed to have mastered the task quite nicely. But then it suddenly stopped working. “The robot does save labor,” Nie tells me, her brow furrowed, “but it is difficult to maintain.”

The hitch reflects a much bigger technological challenge facing China’s manufacturers today. Wages in Shanghai have more than doubled in the past seven years, and the company that owns the factory, Cambridge Industries Group, faces fierce competition from increasingly high-tech operations in Germany, Japan, and the United States. To address both of these problems, CIG wants to replace two-thirds of its 3,000 workers with machines this year. Within a few more years, it wants the operation to be almost

entirely automated, creating a so-called “dark factory.” The idea is that with so few people around, you could switch the lights off and leave the place to the machines.

But as the idle robot arm on CIG’s packaging line suggests, replacing humans with machines is not an easy task. Most industrial robots have to be extensively programmed, and they will perform a job properly only if everything is positioned just so. Much of the production work done in Chinese factories requires dexterity, flexibility, and common sense. If a box comes down the line at an odd angle, for instance, a worker has to adjust his or her hand before affixing the label. A few hours later, the same worker might be tasked with affixing a new label to a different kind of box. And the following day he or she might be moved to another part of the line entirely.

Despite the huge challenges, countless manufacturers in China are planning to transform their production processes using robotics and automation at an unprecedented scale. In some ways, they don’t really have a choice. Human labor in China is no longer as cheap as it once was, especially compared with labor in rival manufacturing hubs growing quickly in Asia. In Vietnam, Thailand, and Indonesia, factory wages can be less than a third of what they are in the urban centers of China. One solution, many manufacturers—and government officials—believe, is to replace human workers with machines.

The results of this effort will be felt globally. Almost a quarter of the world’s products are made in China today. If China can use robots and other advanced technologies to retool types of production never before automated, that might turn the country, now the world’s sweatshop, into a hub of high-tech innovation. Less clear, however, is how that would affect the millions of workers recruited to China’s booming factories.

There are still plenty of workers around now as I tour CIG’s factory with the company’s CEO, Gerald Wong, a compact man who earned degrees from MIT in the 1980s. We watch a team of people performing delicate soldering on circuit boards, and another group clicking circuit boards into plastic casings. Wong stops to demonstrate a task that is proving especially hard to automate: attaching a flexible wire to a circuit board. “It’s always curled differently,” he says with annoyance.

Gerald Wong, CEO of CIG, is developing an automated electronics factory.

But there are some impressive examples of automation creeping through Wong’s factory, too. As we walk by a row of machines that stamp chips into circuit boards, a wheeled robot roughly the size of a mini-fridge rolls by ferrying components in the other direction. Wong steps in front of the machine to show me how it

will detect him and stop. In another part of the factory, we watch a robot arm grab finished circuit boards from a conveyor belt and place them into a machine that automatically checks their software. Wong explains that his company is testing a robot that does the soldering work we saw earlier more quickly and reliably than a person.

After we finish the tour, he says, “It is very clear in China: people will either go into automation or they will go out of the manufacturing business.”

Automate or bust

China’s economic miracle is directly attributable to its manufacturing industry. Approximately 100 million people are employed in manufacturing in China (in the U.S., the number is around 12 million), and the sector accounts for almost 36 percent of China’s gross domestic product. During the last few decades, manufacturing empires were forged around the Yangtze River Delta, Bohai Bay outside Beijing, and the Pearl River Delta in the south. Millions of low-skilled migrant workers found employment in gigantic factories, producing an unimaginable range of products, from socks to servers. China accounted for just 3 percent of global manufacturing output in 1990. Today it produces almost a quarter, including 80 percent of all air conditioners, 71 percent of all mobile phones, and 63 percent of the world’s shoes. For consumers around the world, this manufacturing boom has meant many low-cost products, from affordable iPhones to flat-screen televisions.

In recent years, though, China’s manufacturing engine has started to stall. Wages have increased at a crippling 12 percent per year on average since 2001. Chinese exports fell last year for the first time since the financial crisis of 2009. And toward the end of 2015 the Caixin Purchasing Managers’ Index, a widely used indicator of manufacturing activity, showed that the sector had contracted for the 10th month in a row. Just as China’s manufacturing boom fed the global economy, the prospect of its decline has already started to spook the world’s financial markets.

Within a few years, CIG plans to have a largely automated operation—what’s sometimes called a “dark factory.”

Automation appears to offer an enticing technological solution. China already imports a huge number of industrial robots, but the country lags far behind competitors in the ratio of robots to workers. In South Korea, for instance, there are 478 robots per 10,000 workers; in Japan the figure is 315; in Germany, 292; in the United States it is 164. In China that number is only 36.

The Chinese government is keen to change this. On March 16, officials approved the latest Five Year Plan for China’s economy, which is reported to include an initiative that will make billions of yuan available for manufacturers to upgrade to technologies including advanced machinery and robots. The government also plans to create dozens of innovation centers across the country to showcase advanced manufacturing technologies. Some regional authorities in China have been especially bold in their own efforts. Last year the government of Guangdong, a province that contains many large manufacturing operations, promised to spend $150 billion equipping factories with industrial robots and creating two new centers dedicated to advanced automation.

The goal is to overtake Germany, Japan, and the United States in terms of manufacturing sophistication by 2049, the 100th anniversary of the founding of the People’s Republic of China. To make that happen, the government needs Chinese manufacturers to adopt robots by the millions. It also wants Chinese companies to start producing more of these robots.

Workers at CIG retrieve items from one of several mobile robots that ferry materials around the facility.

The hope is that this will create a virtuous cycle, helping to birth a new high-tech industry and inspiring innovations that could spill over from manufacturing into other sectors and products.

Introducing hordes of robot workers is hardly something that can be done overnight, however. That much is clear from the struggles faced by Foxconn, a $130 billion Taiwanese manufacturer famous for employing hundreds of thousands of workers in city-size factories—and for making, among other products, Apple’s iPhones. In 2011, Foxconn’s founder and CEO, Terry Gou, said he expected to have a million robots in his company’s plants by 2014. Three years later, the effort had proved more challenging than expected, and just a few tens of thousands of robots had been deployed.

The transition to robot workers may upend Chinese society, since so many people work in manufacturing.

Despite the challenges, Day Chia-peng, general manager of Foxconn’s automation technology development committee, says the company is automating a growing number of tasks on its lines. These include the manufacture of displays and printed circuit boards, although processes that involve bending or snapping components into place still pose challenges. The company is even exploring ways that products themselves can be redesigned to make automated manufacturing easier. And it recently said it will sell some of the robots it has developed in-house to other manufacturers.

The transition from human to robot workers may upend Chinese society. Some displaced factory workers could find employment in the service sector, but not all of the 100 million now employed in factories will find such jobs a good match. So a sudden shift toward robots and automation could cause economic hardship and social unrest. “You can make the argument that robotic technology is the way to save manufacturing in China,” says Yasheng Huang, a professor at MIT’s Sloan School of Management. “But China also has a huge labor force. What are you going to do with them?”

Dancing bots

A few days before visiting CIG, I went to China’s first major robotics event, the World Robot Conference, held inside a vast exhibition hall located within Beijing’s Olympic Park. The city was in the grip of an

unusually cold spell, and producing the electricity to meet its heating needs had resulted in lung-searing air pollution from nearby coal power plants. But the snow and smog had done nothing to deter hundreds of researchers and companies, and thousands of attendees, from coming to the event.

A CIG worker inspects a custom-made machine for building circuit boards.

First came a theatrical opening ceremony, during which a huge video wall showed innovations from China’s ancient history spliced, somewhat oddly, with clips of robots from science fiction movies. The guest list included several high-ranking Chinese politicians. Li Yuanchao, China’s vice president, read messages of congratulations from President Xi Jinping and Premier Li Keqiang. The vice president said that investing in robotics research would not only feed the country’s manufacturing industry but encourage greater domestic innovation.

After watching several talks, I wandered past endless demos set up by robot companies and research institutes. I watched as an enormous industrial robot fitted with a fork-like appendage went through some sort of routine factory work at terrifying speed. Other demos were more whimsical, like a small industrial machine performing a mesmerizing rendition of a traditional Chinese dragon dance (in full costume), and a

mobile robot equipped with two racquets playing badminton with excited attendees. A humanoid robot with flashing eyes was carrying a small automated vacuum cleaner around on a tray.

It was also possible to grasp just how ambitious China will be in trying to replace human workers in its factories. HIT Robot Group, a company affiliated with one of the country’s foremost technical universities, Harbin Institute of Technology, had mocked up a battery production line that itself seemed like one giant robot. Robotic vehicles ferried components between various manufacturing machines. The only spots for humans were inside a control room in the center and on a line where especially fiddly manual work needed to be done. I later learned that HIT estimates the new factory could reduce human labor by as much as 85 percent.

But it was also evident that as a country with a history of seemingly endless cheap labor, China had to date been outpaced in the robot revolution. Rethink Robotics, a Boston-based company, was showing off a pair of flexible and intelligent industrial machines. Unlike conventional industrial robots, these products, called Baxter and Sawyer, require very little programming, and they are equipped with sensors that allow them to recognize objects and avoid hitting people. They also cost between $20,000 and $30,000 instead of the hundreds of thousands typical of an industrial robot. Speaking to me after the event, Rethink founder and robotics pioneer Rodney Brooks said that China represents a huge potential market for his company, which recently opened offices in Shanghai. Chinese robot makers are likely to start making more flexible and intelligent robots, too. But for now their products lag behind those of Western manufacturers.

“A game we often play when we go to a trade show in the Far East is we go and see the industrial robots from little companies and say, ‘Oh, that’s a copy of that, and that’s a copy of that,’” Brooks said. It will, he suggested, take time for China’s robotics companies to catch up.

Reinvented in China

To see for myself how far China’s researchers have to go, I visited Shanghai Jiao Tong University, one of the country’s most prestigious institutions and home to China’s oldest academic robotics lab, founded in 1979. I found myself on a lush and sprawling campus in a quiet suburb in south Shanghai, surrounded by students cycling around on squeaky bicycles. There, I found a modern-looking building that housed the robotics lab.

Researchers at Shanghai Jiao Tong University are developing humanoid and walking robots.

Zhu Xiangyang, a professor in his late 40s with thin glasses and a fleece sweater-vest, welcomed me to his office with tea and an irrepressible smile. The lab has a few dozen professors and research scientists and more than 100 doctoral and master’s students, and Zhu is justifiably proud of its research. In one room was a brain-controlled robotic wheelchair, operated by means of an electroencephalogram cap worn by a graduate student. A video showed a cyborg cockroach fitted with a wireless implant that connected to its peripheral nervous system and made it possible to control the creature’s movements from a computer. In another room, a researcher demonstrated snakelike and soft-bodied robots capable of reaching or crawling through narrow spaces. Inside a garage, a prototype self-driving car, not unlike one of Google’s, is being developed in collaboration with a Chinese carmaker called Chery.

“More and more, we need to get into more advanced robots. That can help make a dark factory.”

Despite the impressive research projects at places like Jiao Tong, I kept wondering just how China will fulfill its manufacturing ambitions. Kai Yu is the founder of a startup called Horizon Robotics and was previously the head of an AI-focused research lab set up by Baidu, China’s dominant Internet company. Within the Baidu lab, Yu and colleagues were focused on a field of AI called deep learning, which involves training large simulated neural networks to recognize patterns in data. Researchers are now starting to explore how machine learning might make the next generation of industrial robots even smarter and more flexible. “In the future, what I see is China being more creative [in robotics],” Yu told me. “Original design, original ideas, but also some of the fundamental technologies, like deep learning, neural networks, artificial intelligence.”

Yu believes that the AI techniques developed by China’s big Internet companies for search, e-commerce, and other purposes could be applied to robots. “China has a very good opportunity to catch up,” he said. “The skills they have learned in the last five years can be transferred to making intelligent machines.”

When I later toured CIG’s factory, it wasn’t too hard to imagine how such advances could start feeding into Wong’s efforts to automate his operation. For one thing, a robot capable of learning and adapting presumably wouldn’t be baffled by a misaligned box that needs labeling.

After the tour, Wong took me through a PowerPoint presentation that laid out the company’s plan for the next few years, and then the conversation turned to intelligent robotics. “We’re going to use standard robots at first,” Wong said. “But then we’re going to use more advanced ones. More and more, we need to get into more advanced robotics. That can help make a dark factory.”

Given the economic imperative, the government’s determination, and the country’s growing technological sophistication, it seems very likely that manufacturing companies across China will automate successfully and that the country will become a leader in the technologies of advanced automation.

And yet it’s strange to think about the changes in store for Chinese manufacturing workers. At one point during our tour we had passed a group of about 20 people taking an afternoon break. Everyone was apparently snoozing, heads rested on arms folded in front of them. That’s hardly something a robot needs to do. But I couldn’t help wondering what will happen to these workers once robots have taken their jobs. Wong says they will most likely return to their hometowns and find employment there, on a farm or perhaps in a shop or restaurant. That may be so, but for some it won’t be so simple.

A week after leaving China, I received an e-mail from Wong with some more information about his plans, along with a characteristically bold promise. “Stay in touch,” he wrote. “We will make the dark factory happen.”

FT

6/6/16

China’s robot revolution Factories in China are replacing humans with robots in a new automation-driven industrial revolution. How will this effect be felt around the globe?

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Artificial Intelligence and Robotics China’s robot revolution Factories in China are replacing humans with robots in a new automation-driven industrial revolution. How will this effect be felt around the globe? Read next Banks’ AI plans threaten thousands of jobs © Zeng Han June 6, 2016 by: Ben Bland

The Ying Ao sink foundry in southern China’s Guangdong province does not look like a factory of the future. The sign over the entrance is faded; inside, the floor is greasy with patches of mud, and a thick metal dust — the by-product of the stainless-steel polishing process — clogs the air. As workers haul trolleys across the factory floor, the cavernous, shed-like building reverberates with a loud clanging.

Guangdong is the growth engine of China’s manufacturing industry, generating $615bn in exports last year — more than a quarter of the country’s total. In this part of the province, the standard wage for workers is about Rmb4,000 ($600) per month. Ying Ao, which manufactures sinks destined for the kitchens of Europe and the US, has to pay double that, according to deputy manager Chen Conghan, because conditions in the factory are so unpleasant. So, four years ago, the company started buying machines to replace the ever more costly humans. Nine robots now do the job of 140 full-time workers. Robotic arms pick up sinks from a pile, buff them until they gleam and then deposit them on a self-driving trolley that takes them to a computer-linked camera for a final quality check. The company, which exports 1,500 sinks a day, spent more than $3m on the robots. “These machines are cheaper, more precise and more reliable than people,” says Chen. “I’ve never had a whole batch ruined by robots. I look forward to replacing more humans in future,” he adds, with a wry smile. Across the manufacturing belt that hugs China’s southern coastline, thousands of factories like Chen’s are turning to automation in a government-backed, robot-driven industrial revolution the likes of which the world has never seen. Since 2013, China has bought more industrial robots each year than any other country, including high-tech manufacturing giants such as Germany, Japan and South Korea. By the end of this year, China will overtake Japan to be the world’s biggest operator of industrial robots, according to the International Federation of Robotics (IFR), an industry lobby group. The pace of disruption in China is “unique in the history of robots,” says Gudrun Litzenberger, general secretary of the IFR, which is based in Germany, home to some of the world’s leading industrial-robot makers. China’s technological transformation still has far to go — the country has just 36 robots per 10,000 manufacturing workers, compared with 292 in Germany, 314 in Japan and 478 in South Korea. But it is already changing the face of the global manufacturing industry. In the process, it is raising broader questions: can emerging economies still hope to follow the traditional route to prosperity that the developed world has relied upon since Britain’s industrial revolution in the 18th century? Or will robots assume many of the jobs that once pulled hundreds of millions out of poverty? Chen Conghan, deputy manager at Ying Ao: ‘These machines are cheaper, more precise and more reliable than people’ © Zeng Han China’s spending splurge on industrial robots has its roots in a pressing economic problem. From the 1980s onwards, as Beijing’s Communist rulers opened up to global trade, the country’s huge, cheap workforce helped make it the world’s biggest exporter of manufactured goods. Breakneck economic

growth lifted hundreds of millions of Chinese out of poverty and transformed swaths of the country, as workers migrated from the countryside to the city. But a growing middle class and an ageing population have led to rising wages, eroding China’s competitive advantage. Partly because of the one-child policy, formally phased out in 2015, China’s working-age population is expected to fall from one billion people last year to 960 million in 2030, and 800 million by 2050. In recent years, China’s central planners have been promoting automation as a way to fill the labour gap. They have promised generous subsidies — to be doled out by local governments — to smooth the way for Chinese companies both to use and build robots. In 2014, President Xi Jinping called for a “robot revolution” that would transform first China, and then the world. “Our country will be the biggest market for robots,” he said in a speech to the Chinese Academy of Sciences, “but can our technology and manufacturing capacity cope with the competition? Not only do we need to upgrade our robots, we also need to capture markets in many places.” The march of the machines, not just in China but around the world, has been accelerated by sharp falls in the price of industrial robots and a steady increase in their capabilities. Boston Consulting Group, a management consultancy, predicts that the price of industrial robots and their enabling software will drop by 20 per cent over the next decade, while their performance will improve by 5 per cent each year. connected business Man or machine? Building robots like us Liu Hui, an entrepreneur in his forties, is making the most of China’s robot boom. In 2001, when he opened his first factory in Foshan, an industrial city of seven million people in Guangdong, he started out making knock-offs of electric fans. As his business grew, he moved to bona fide manufacturing, producing components for Chinese home appliance brands. Then, in 2012, spotting an opportunity in a growing market, he jumped into the emerging world of robotics. Liu now imports robotic arms from suppliers such as the Swedish-Swiss conglomerate ABB, and sells them on to Chinese manufacturers, helping them integrate the machines into their production lines. It is a highly specialised business. Most of his customers are component-makers who supply motors and other parts to large Chinese home-appliance brands such as Midea and Galanz, which produce air conditioners, refrigerators and more. E-Deodar: Robot arms are programmed to complete repetitive tasks © Zeng Han Business has expanded so quickly in the past year that Liu does not have enough space in his factory for all the machinery he is assembling. He has to store parts designed to support a $23,000 ABB robot under a makeshift lean-to outside. “Things are changing rapidly,” he says. “The cost of labour is rising every year, and young people don’t want to work on the production line like their parents did, so we need machines to replace them.” The stereotypical image of China’s factories can still be found in many places: tens of thousands of people in long lines hunched over sewing machines or slotting components into a printed circuit board. But that mode of manufacturing is starting to be replaced by a more mixed picture: partially automated production lines, with human workers interspersed at a few key points. © Zeng Han Meanwhile, China is developing its own robot makers. In September last year, Ningbo Techmation, a Shanghai-listed producer of machinery for the plastics industry, launched a subsidiary, E-Deodar, making robots that are 20-30 per cent cheaper than those produced by international companies such as ABB, Germany’s Kuka or Japan’s Kawasaki. The E-Deodar factory in Foshan, with its café, chill-out zone and open-plan production line, looks more like the offices of a Silicon Valley tech start-up than a Chinese industrial workhorse. “Our global rivals are very good at making robots but their costs are higher and they are not so good at understanding the needs of local customers,” says Zhang Honglei, the company’s 35-year-old, spiky-haired technical director. The cost of labour is rising and young people don’t want to work on the production line like their parents did Liu Hui, Chinese entrepreneur This year, Zhang plans to produce 350 distinctively green-coloured robots, which are designed for use in plastic factories and sell for between $14,000 and $18,000 each; in three years’ time he hopes to produce 3,000 a year. “We have to move fast because automation is a scale business,” he says. “The bigger the better.” Chinese manufacturers, which bought 66,000 of the 240,000 industrial robots sold globally last year, still largely prefer to buy international brands, according to Litzenberger of the IFR. But she expects that to change, particularly in the wake of the Beijing government throwing its full support behind the domestic robot industry in recent years. “They are developing very fast,” she says. Zhang Peng, vice-director of the economy and technology bureau, Shunde, Foshan © Zeng Han At an imposing, colonnade-fronted government building — known locally as the “White House” — in the Shunde district of Foshan, officials are trying to put President Xi’s call for a robot revolution into practice. The province of Guangdong has vowed to invest $8bn between 2015 and 2017 on automation. Zhang Peng, vice-director of Shunde’s economy and technology bureau, recently had his office in the building reduced in size, in line with the Communist party’s call for bureaucratic austerity. But the budget for industrial automation was unaffected. Zhang says robots are vital to overcome labour shortages and help Chinese companies make better quality, more competitive products. Unusually straight-talking for a

Chinese official, he warns: “If manufacturing companies don’t improve, they won’t be able to survive.” How will robots really change our world? In this series, the FT meets the robots and talks to people already living and working with them to find out if they will be good or bad for humanity Government support for the integration of ever cheaper and more efficient industrial robots is good news for factory owners in China, who are facing a weak global economy and a slowdown in domestic demand. But the benefits of the robot revolution will not be shared equally across the world. Developing countries from India to Indonesia and Egypt to Ethiopia have long hoped to follow the example of China, as well as Japan, South Korea and Taiwan before them: stimulating job creation and economic growth by moving agricultural workers into low-cost factories to make goods for export. Yet the rise of automation means that industrialisation is likely to generate significantly fewer jobs for the next generation of emerging economies. “Today’s low-income countries will not have the same possibility of achieving rapid growth by shifting workers from farms to higher-paying factory jobs,” researchers from the US investment bank Citi and the University of Oxford concluded in a recent report, The Future Is Not What It Used to Be, on the impact of technological change. They argue that China’s rising labour costs are a “silver lining” for the country because they are driving technological advancement, in much the same way that an increase in wages in 18th-century Britain provided impetus to the world’s first industrial revolution. At the same time, according to Johanna Chua, an economist at Citi in Hong Kong, industrial laggards in parts of Asia and Africa face a “race against the machines” as they struggle to create sufficient manufacturing jobs before they are wiped out by the gathering robot army in China and beyond. Tom Lembong, Indonesia’s 45-year-old trade minister, and a leading voice for liberalisation and reform within the government of Southeast Asia’s biggest economy, is aware of the risks. “Many people don’t realise we’re seeing a quantum leap in robotics,” he says. “It’s a huge concern and we need to acknowledge the looming threat of this new industrial revolution. But as a political and business elite, we’re still stuck on debates about industrialisation that were settled in the 20th and even 19th centuries.” Countries such as Indonesia are already suffering from something that the Harvard economist Dani Rodrik has dubbed “premature de-industrialisation”. This describes a trend where emerging economies see their manufacturing sector begin to shrink long before the countries have reached income levels comparable to the developed world. Despite rapid economic growth over the past 15 years, Indonesia saw its manufacturing industry’s share of the economy peak in 2002. Analysts believe this is partly because of a failure to invest in infrastructure, and the country’s uncompetitive trade and investment policy, and partly due to globalisation. Rodrik believes the country will never be able to grow at the kind of rapid rate experienced by China or South Korea. “Traditionally, manufacturing required very few skills and employed a lot of people,” he says. “Because of automation, the skills required have increased significantly and many fewer people are employed to run factories. What do you do with these extra workers? They won’t turn into IT entrepreneurs or entertainers; and, if they become restaurant workers, they will be paid much less than in a factory.” Five factories a year have left the industrial park on the Indonesian island of Batam © Muhammad Fadli The spread of robots makes it much harder for developing countries to get on the “escalator” of economic growth, he argues. That is bad news for the estimated two million young people who enter the workforce every year in Indonesia, a nation of 255 million, where 40 per cent live on $3 a day or less. Mahami Jaya Lumbanraja, a 22-year-old job-seeker on the Indonesian industrial island of Batam, is feeling the effects of the premature de-industrialisation phenomenon. For seven months he has been looking for a factory job in Batam, which sits just 20 miles from prosperous Singapore, but he has had no luck. Wearing faded jeans, a grey hoodie and an endearing smile, Lumbanraja says that although he has one year of experience working for Shimano, the Japanese manufacturer of bicycle gears and fishing tackle, he is not experienced enough to secure anything more than an entry-level position, and that there are many more job hunters than openings. “I can survive on the little money I get from busking and helping friends with construction work but I must get a proper factory job to save enough money so I can set up my own small shop later,” he says. Wages in Batam — around $230 per month — are double what Lumbanraja could earn in his home city of Medan, on the island of Sumatra. So he feels he must stay until he finds work. Lumbanraja is one of about 700 Indonesians in their late teens and early twenties who visit the community centre at the Batamindo industrial park every day looking for work. In February, 3,000 people applied in person for just 80 positions at a Japanese-owned wiring factory there, a gathering so large that executives initially feared it was a labour protest. Mahami Jaya Lumbanraja is one of 700 Indonesians who visit Batamindo every day looking for work © Muhammad Fadli Batamindo is a joint venture between Singaporean and Indonesian investors that was backed by Presidents Lee Kuan Yew and Suharto — the two nations’ respective rulers — when it opened in 1990. Intended as the showpiece of Indonesia’s industrialisation strategy, it has become a symbol of everything that is wrong with it. In recent times, an

average of five factories a year have left the industrial park for other countries and the number of people employed there has dropped to just 46,000, from a peak of 80,000 in 2000. That is despite the fact that wages today are between one-third and one-half of the level paid in China’s Guangdong province. Lembong, a Harvard graduate who ran his own Singapore-based private equity firm before he was appointed trade minister in August, says the government is determined to tackle the twin problems at the heart of Indonesia’s economic malaise: weak infrastructure and over-regulation. But some argue that reform will come too late. During its period of rapid industrialisation, China invested in the modern highways, railways and ports necessary to support its manufacturing sector. In contrast, the physical infrastructure in Batam and much of Indonesia has “not changed much since the 1970s,” says Mook Sooi Wah, general manager of Batamindo. © Zeng Han Indonesia actually had a slightly higher “robot density” than China when the latest figures were collated by the International Federation of Robotics in 2014, although the situation is likely to have changed dramatically since then given the pace of Beijing’s automation push. This anomaly was largely the result of China’s manufacturing workforce being so much bigger than that of Indonesia, which still has no government plan or backing for industrial automation. Many people don’t realise we’re seeing a quantum leap in robotics Tom Lembong, Indonesia’s trade minister Indonesia’s regulatory process is as fusty as its infrastructure. Recently, legitimate shipments from a paper factory were held by customs at the Batam port because of a rule meant to stop the export of illegally sourced wood. These problems leave even Batam’s boosters exasperated. Stefan Roll, a German manufacturing veteran who worked in China during its industrial take-off in the 1990s, enjoys living and working in Indonesia. But he worries that the country is missing its “golden opportunity” to become efficient enough to compete on a global scale. “When you are dealing with multinationals, time is money,” Roll says as he shows off his new factory in Batam, which assembles coffee machines for Nestlé. “But you can only do just-in-time manufacturing if you have good roads and infrastructure.” While few doubt the depth of the challenges facing developing countries, not everyone sees the dilemma in such bleak terms. With wages in countries such as Indonesia and India much lower than in China and their populations still relatively young, some analysts believe they can attract more labour-intensive industries, such as garment-making, where widespread automation is not yet suitable. © Zeng Han “As China moves up the industrial chain, it’s actually freeing up a lot of opportunities for Southeast Asia and India,” says Anderson Chow, a robotics industry analyst at the investment bank HSBC, in Hong Kong. Hal Sirkin, an expert on manufacturing at Boston Consulting Group, says that from the perspective of an economy such as India’s, it does not make sense to automate now because it would drive up the price of goods — “when they have one billion people who can make things cheaply”. He is among the tech optimists who believe that, in the medium term, automation will also create new business niches for emerging economies, mitigating the damage from the jobs that will be eradicated. “We think you’re going to see more localisation rather than more scale,” says Sirkin. “I can put up a plant, change the software and manufacture all sorts of things, not in the hundreds of millions but runs of five million or 10 million units.” But Carl Frey, an expert on employment and technology at the University of Oxford, warns that without better education and more skills, developing countries will struggle to take advantage of advancements in manufacturing. “Technology is becoming increasingly skill-based,” he says. “Many of these countries don’t have a skilled workforce so they’re not very good at adopting these technologies.” The Shangpin Home Collection factory where the use of robots to cut and drill wooden planks improved productivity by 40 per cent © Zeng Han China itself is not immune from the negative consequences of automation. More than 40 per cent of its 1.4 billion population still live in the countryside, many in poverty, having benefited only marginally from the urban economic miracle. But the government is betting that the benefits of promoting cutting-edge manufacturing will outweigh the damage from the potential jobs lost. The industrial strategy announced by Beijing last year — known as Made in China 2025 — is designed not only to improve the technological capability of its factories but also to support the development of Chinese brands internationally. Chow, the HSBC analyst, says that as Chinese companies try to increase their exports to alleviate the impact of the domestic slowdown, they are likely to focus more on the quality of their products: “Quite often part of that development is a better production process, involving robotics.” Every year, the amount of time it takes for a company’s investment in a robot to pay off — known as the “payback period” — is narrowing sharply, making it more attractive for small Chinese companies and workshops to invest in automation. The payback period for a welding robot in the Chinese automotive industry, for instance, dropped from 5.3 years to 1.7 years between 2010 and 2015, according to calculations by analysts at Citi. By 2017, the payback period is forecast to shrink to just 1.3 years. Li Gan, general manager of Shangpin Home Collection, Foshan © Zeng Han Automation is not just about putting cheaper and more efficient robot arms

on the production line. Li Gan, the general manager of Shangpin Home Collection, which makes and sells customised home furniture, says the greater opportunity is to integrate robots on the factory floor with real-time data from customers and automated logistics systems. Thanks to the use of robots, the factory that Shangpin opened in Foshan in 2014 was 40 per cent more productive than its previous plant, even though it employs 20 per cent fewer people. Later this year, it will start up the machines at its newest and biggest production base, where it hopes to improve productivity fourfold with just double the number of staff, by using more robots to move supplies around the factory floor and help pack outbound shipping containers. Drilling wooden slats for the company’s diverse range of beds, wardrobes and other bespoke furniture used to be a painstaking and sometimes dangerous process. Now a worker simply picks up each piece of wood, scans a barcode and puts the wood on a conveyor belt that takes it to the robot arm. The finished product returns on another belt. The process in between is strikingly complicated: Shangpin had to design a device to make sure that each slat would be aligned in the right way for it to be grasped by the robot arm, and the drilling specifications for the slats have to be pre-programmed and recorded in a barcode, because the robots do not yet have any artificial intelligence capability. Li Gan points out that human oversight and decision-making is still crucial. “Automation is just a technical process but what is more important is our thinking about how best to do this,” he says. “Every time we change something, we ask: is it more effective to do this using humans or robots?” Every time we change something, we ask: is it more effective to do this using humans or robots? Li Gan, general manager of Shangpin Home Collection, Foshan Boston Consulting Group forecasts that the percentage of tasks handled by advanced robots will rise from 8 per cent today to 26 per cent by the end of the decade, driven by China, Germany, Japan, South Korea and the US, which together will account for 80 per cent of robot purchases. Sirkin at BCG says that the rapid expansion of automation could be compared to the difference between the “human learning curve” and Moore’s Law, which posited that computing power could double every 18 months to two years. “Even if you’re very good, humans can only double their productivity at best every 10 years,” he says. In contrast, researchers can push robots to double their productivity every four years, he estimates. “Compounded over time, that makes a big difference.” As China and other industrial leaders build more and better robots, the tasks they can take on will expand. Butchery, for example, was long considered the sort of skill that machines would struggle to develop, because of the need for careful hand-eye co-ordination and the manipulation of non-uniform slabs of meat. But Sirkin has watched robots cut the fat off meat much more efficiently than humans, thanks to the use of cheaper and more responsive sensors. “It’s becoming economically feasible to use machines to do this because you save another 3 or 4 per cent of the meat — and that’s worth a lot on a production line, where you can move quickly. “There are things that humans can do better than robots,” he adds. “But they are getting less and less.” Ben Bland is the FT’s South China correspondent and a former Indonesia correspondent Photographs by Zeng Han and Muhammad Fadli A brief guide to industrial robots What is an industrial robot? Many automated tasks in factories are performed by robotic arms programmed to complete repetitive and sometimes dangerous tasks. A growing number of factories are also buying self-driving freight carriers and autonomous forklift trucks to reduce manpower. Most industrial robots operate at a distance from their co-workers, sometimes in cages for safety reasons, but a new generation of humanoid robots is being developed to work alongside humans. The $25,000 Baxter robot, for instance, which is designed to perform simple tasks such as packing and loading, has a screen with an animated face that frowns when there is a problem. What can they do well? Three-quarters of all industrial robots operate in four sectors: computers and electronic goods; home appliances and components; transportation equipment; and machinery. That’s partly because automation makes financial sense in these industries and partly because of the limited nature of the existing technology. Industrial robots are usually fixed in a single location and are adept at performing uniform tasks with objects that are stationary or that move at a predictable speed. Robots can work more quickly than humans, with a high degree of consistency and precision, and do not need breaks to sleep or eat, of course. What can’t they do well? Other than a few cutting-edge “intelligent” robots, most of these machines have to be pre-programmed for each specific task on the production line. Precision can be a challenge too. Robots have no problem placing electronic components on to a flat circuit board, for instance, but struggle to assemble a car battery, which has many small parts that must be installed at odd angles. Very labour-intensive tasks such as sewing garments and shoemaking have seen minimal automation so far. But that is starting to change, with Adidas opening a robot-dominated shoe factory in Germany this year.

WP

Why China won’t own next-generation manufacturingBy Vivek Wadhwa August 26, 2016

Chinese workers check carbon fiber silk thread at a carbon fiber factory in Lianyungang, China, on Aug. 1. (AFP/Getty Images)

After three decades of dramatic growth, China’s manufacturing engine has largely stalled. With rising salaries, labor unrest, environmental devastation and intellectual property theft, China is no longer an attractive place for Western companies to move their manufacturing. Technology has also eliminated the labor cost advantage, so companies are looking for ways to bring their high-value manufacturing back to the United States and Europe.

China is well aware that it has lost its advantage, and its leaders want to use the same technologies that have leveled the playing field to give the country a new strategic edge. In May 2015, China launched a 10-year plan, called Made in China 2025, to modernize its factories with advanced manufacturing technologies, such as robotics, 3-D printing and the Industrial Internet. And then, in July 2015, it launched another national plan, called Internet Plus, “to integrate mobile Internet, cloud computing, big data and the Internet of Things with modern manufacturing.”

China has made this a national priority and is making massive investments. Just one province, Guangdong, committed to spending $150 billion to equip its factories with industrial robots and create two centers dedicated to advanced automation. But no matter how much money it spends, China simply can’t win with next-generation manufacturing. It built its dominance in manufacturing by offering massive subsidies, cheap labor and lax regulations. With technologies such as robotics and 3-D printing, it has no edge.

After all, American robots work as hard as Chinese robots. And they also don’t complain or join labor unions. They all consume the same electricity and do exactly what they are told. It doesn’t make economic sense for American industry to ship raw materials and electronics components across the globe to have Chinese robots assemble them into finished goods that are then shipped back. That manufacturing could be done locally for almost the same cost. And with shipping eliminated, what once took weeks could be done in days and we could reduce pollution at the same time.

Most Chinese robots are also not made in China. An analysis by Dieter Ernst of the East-West Center showed that 75 percent of all robots used in China are purchased from foreign firms (some with assembly lines in China), and China remains heavily dependent on the import of core components from Japan. By Ernst’s count, there are 107 Chinese companies producing robots but many have low quality and safety and design standards. He anticipates that fewer than half of them will survive.

The bigger problem for China is its workforce. Even though China is graduating far more than 1 million engineers every year, the quality of their education is so poor that they are not employable in technical

professions. This was documented by my research teams at Duke and Harvard. Western companies already have great difficulty in recruiting technical talent in China. This will get worse because advanced manufacturing requires management and communication skills and the ability to operate complex information-based factories. Ernst predicts that the increasing scarcity of specialized skills may be the Achilles’ heel of China’s push into advanced manufacturing and services.

Even if China solves its skills problem, builds its own high-quality industrial robots, and develops innovative industrial processes, it won’t be able to maintain its advantage for long. We could simply import the Chinese robots and copy its industrial innovations. I doubt that even Donald Trump’s immigration walls would keep the foreign robots out.

There is little doubt in my mind that over the next five to 10 years, manufacturing will return, en masse, to the United States. It will once again become a local industry. Yes, it won’t employ the numbers of workers that old-line manufacturing did, but advanced manufacturing will create hundreds of thousands of high-skilled, high-paying jobs. With its massive investments, China is only accelerating the demise of its export-oriented manufacturing industry.

Science

Technology continues to pervade our lives.

Michel Royon/Wikimedia Commons

When will I have my sidekick robot?By Lindzi WesselFeb. 20, 2017 , 4:00 PMBOSTON—From Netflix recommendations to credit card fraud detection, artificial intelligence (AI) is already part of our daily lives. But as AI expands, where do we draw the line on how intimate we become with this new technology?  Swarm engineer Sabine Hauert of the University of Bristol in the United Kingdom is part of a Royal Society working group asking just that. Hauert, a swarm engineer who works with nanoparticles, has spent time speaking with members of the public about fears and hopes for advancing artificial intelligence. Here on Saturday at the

annual meeting of AAAS, which publishes Science, she gave a talk titled, "AI and Policy Engagement: Understanding the Public's Views of Social Risk". Hauert sat down with Science to discuss the issue. This interview has been edited for clarity and length.  Q: You’ve found that only 9% of people in the United Kingdom have heard of machine learning. But everyone has heard of AI. How do they relate? A: AI is an abstract concept—different people have different definitions. Very often what people think when they say artificial intelligence is humanlike intelligence. Machine learning is a concrete process that is really the science of computers learning from data. We might be looking at one specific task with one specific set of data and be able to come up with a prediction or a solution based on that. And were using that as a starting point so that we don't get lost in all the discussions about what is AI and what does this technology do.  Q: Where do we see machine learning in our lives? A: There are examples of machine learning all around us. We see it in our spam filters, recommendations online—whether it's movies or with things that we'd like to purchase—and we see it in credit card fraud detection. And there are a number of areas where we are going to see more machine learning in the future.

Swarm engineer Sabine Hauert 

Sabine Hauert

Q: What are the goals of your Royal Society working group?

A: They’re creating a report looking at the potential for machine learning in the next 5 to 10 years, and also the barriers to achieving that potential. They're engaging with a number of stakeholders across the U.K. who'd be interested in this technology whether its industry, policymakers, academia, or the public. And they're trying to look at it from a number of perspectives: ethical, legal, scientific, and societal. 

What I love about the project is that actually a big chunk of this working group’s role is to engage with the public. We surveyed people across the U.K. and asked them what they think of machine learning. We've also had focus groups where we spend more time with small groups of people to dig in and understand what they want from this technology. 

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Q: And what are the responses from members of the public you’ve worked with?

A: It's very much context dependent. People won't feel the same way if you're talking about autonomous cars versus something that can help doctors do better diagnostics. When they do see areas that benefit them, there's genuine excitement about the technology. People are worried about making sure algorithms can work with humans. They want to make sure the algorithms are safe and trustworthy. And there is the discussion about robots replacing human jobs.

Q: And how do we move forward in this field without replacing humans?

A: Well it’s about tasks, not jobs, in terms of the way that we're building the future. We now have algorithms that can detect markers of cancer in images. But the goal is to create tools for the doctors rather than replacing them. 

Q: What's your favorite example of AI in science fiction?

A: The movie Robot and Frank. It's the story of an elderly person who gets a caregiving robot for the home. He convinces the robot that to be happy he needs the robot as a sidekick to become a robber. It’s just a really nice story of the limitations of the technology, in that the person quickly understands how he can manipulate it, but also of a partnership. And even though the motivation is dubious, in the end these two end up as a genuine team.

Q: So when will I have my sidekick robot?

A: I think you'll have different technologies for different tasks, just like you have lots of apps on your phone. I'm guessing in the future we're going to get more and more of these helpers that are really focusing on a specific area. Having a fully functional system that can do everything is just so far away.