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Artificial Intelligence, Employment, and Income | AI Magazine
|
Artificial Intelligence, Employment, and Income
|
https://ojs.aaai.org
|
[
"Nils J. Nilsson"
] |
In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of ...
|
Authors Nils J. Nilsson
Abstract Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil. We also note that some people fear the automation of work by machines and the resulting of unemployment. Yet, since the majority of us probably would rather use our time for activities other than our present jobs, we ought thus to greet the work-eliminating consequences of AI enthusiastically. The paper discusses two reasons, one economic and one psychological, for this paradoxical apprehension. We conclude with discussion of problems of moving toward the kind of economy that will be enabled by developments in AI.
| 1984-06-15T00:00:00 |
https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/433
|
[
{
"date": "1984/06/15",
"position": 44,
"query": "artificial intelligence employment"
},
{
"date": "1984/06/15",
"position": 45,
"query": "artificial intelligence employment"
}
] |
|
Views from Those Who Expect AI and Robotics to Displace More ...
|
Views from Those Who Expect AI and Robotics to Displace More Jobs than They Create by 2025
|
https://www.pewresearch.org
|
[
"Aaron Smith",
"Janna Anderson",
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"Font-Family Var --Wp--Preset--Font-Family--Sans-Serif",
"Font-Size Font-Weight Gap Important Line-Height",
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] |
People will be displaced, businesses will slowly transition their older workforce to different jobs and not hire younger people or veterans.
|
[will]
Advances in technology will absolutely reduce human jobs—this process is already underway, and the logic of our economy and technological advancement make it a sure thing to continue
Many of the experts in our survey who expect technology to be a net job destroyer also looked to the history of technology and employment in making their case. But in contrast to the group discussed above, these respondents see a much different story—one in which advances in automation have been taking jobs and putting downward pressure on wage growth for years.
Alex Halavais, associate professor of social and behavioral sciences at Arizona State University, predicted, “They have probably already replaced more jobs than they have created. The slow recovery in the U.S. is closely tied to our worker productivity, which is in turn related to our use of technology. We are only at the cusp of this, and I suspect it will be far more obvious and pronounced by 2025. I suspect that ATMs and self-checkout are just the starting points. The biggest shift will be fairly invisible in the next 10 years, because they will be in manufacturing, particularly at small scale. Tesla’s factory is new standard for relatively small-scale production. As some kinds of standardized service jobs become more easily addressed by scalable technology, they will go the way of phone operators and bank tellers. That is, they will not disappear entirely, but they will be radically reduced. Right now, things like food service, travel, and hospitality are being kept human for cultural rather than purely economic reasons, I suspect.”
Larry Gell, founder and director of the International Agency for Economic Development (IAED), responded, “After 50+ years working for the heads of the world’s biggest corporations all over the globe—watching them cut costs every place starting with the biggest cost: PEOPLE; moving labor to cheapest markets, then replacing them as fast as possible with robots and automation—why would it stop? It will accelerate. Anything and everything that can be automated to replace humans will be done. You can bet on it!”
Mary Joyce, an Internet researcher and digital activism consultant, replied, “To the extent that human workers can be replaced by robots and algorithms, they will be. There’s no reason to believe that firms would behave in any other ways. And social forces, like unions, that would limit these actions, don’t have the strength to prevent these changes.”
Karl Fogel, a partner with Open Tech Strategies and president of QuestionCopyright.org, wrote, “The reason people are investing in machine agents is precisely that they will replace more (lower-paid) humans than the number of (more highly-paid) humans needed to build and maintain the machines. But this is not a new phenomenon—it’s been going on for more than a century. We’re going to have to come to grips with a long-term employment crisis and the fact that—strictly from an economic point of view, not a moral point of view—there are more and more ‘surplus humans.’”
John Wilbanks, chief commons officer for Sage Bionetworks, wrote, “There remain enormous market gaps where digital tools can replace people, from parking lot attendants to call centers to checkout lanes in retail. Those jobs will go and won’t come back.”
A distinguished engineer working in networking for Dell wrote, “It’s a given that computers will get more powerful and be able to perform more and more intelligent tasks. This is going to create more unemployment and I’m not sure how all this gets resolved.”
Dean Thrasher, founder of Infovark, Inc., wrote, “More and more fields seem ripe for automation, but it’s hard to think of areas of our economy that are suffering from lack of staff—possibly teaching or healthcare? Yet we are applying more robotics and AI in these fields as well. I think technology’s negative impact on employment is likely to grow worse in the near future, rather than better. It’s easier to think of the few areas that will be resistant to robotics: sports leagues, symphony orchestras, craft brewing, ballet, and fine art. If the human touch is not essential to the task, it’s fair to assume that it could be automated away.”
Bernard Glassman wrote, “I’m honestly trying to think about the last time I heard anyone of any importance argue with a straight face that we should adopt a new technology because it will create jobs. At best, new robotics technologies move people into the service sector, at least until the service itself can be automated. Take 3-D printing—can we honestly believe that it will generate more high-level jobs than it kills?”
Lyman Chapin, co-founder and principal of Interisle Consulting Group, wrote, “Anything that can be automated will be, and to a greater or lesser extent depending on circumstances businesses will be reluctant to hire people to perform tasks that can be performed by robots, digital agents, or AI applications.”
Dave Kissoondoyal, CEO for KMP Global Ltd. and Internet consultant active in Internet governance activities, wrote, “We have already witnessed the effects of mechanization and automation on the labor force. Similarly—networked, automated, artificial intelligence (AI) applications and robotic devices will have displaced more jobs than they have created by 2025. The effects this time will be for both white and blue-collar jobs.”
Mark Johnson, CTO and vice president for architecture at MCNC wrote, “The trend towards automation of every job seems inexorable. This probably has a disproportionate effect on older workers of all kinds who are less agile in their ability to move about in the economy than younger workers.”
Serge Marelli, a past member of IEEE and ACM, wrote, “Automated cars can cheaply replace public transportation drivers, and automated cleaners and caregivers might very well replace human-help and caretakers for the sick and elderly. While this may seem in the short term more economic, it will be a fatal blow to the last local jobs for those with less skills (formal education).”
These advances are different from what has come before them—the changes are more rapid, and are going to impact people and professions that have thus far been insulated from automation
Many respondents worry that the current wave of technological change is going to impact previously insulated professions, and will happen so quickly as to prevent people from adjusting to new career paths.
Jeremy Epstein, a senior computer scientist at SRI International, responded, “The net number of jobs displaced will be fairly small, but they will be disproportionately blue-collar and pink-collar jobs going away and new white-collar jobs created. Just as travel agents (a pink collar job) have been largely replaced by Kayak and the like, many other service jobs like taxi drivers will largely disappear. There are no elevator operators left in the Western world (I’ve seen them still in India, though), why would anyone need a human to pilot a car to a location? Having a human driver may be seen as a status symbol for the wealthy, but even they will see the value in not having to worry about their driver’s sobriety or willingness to share overheard secrets. Blue-collar jobs like construction will still exist, because the costs of automation are too high. However, even they will be reduced as there’s more factory-built housing, which allows for cost effective use of robots in the construction process. It’s hard for me to guess how things like garbage collecting will be affected—use of equipment has reduced the number of people involved, and self-driving vehicles could reduce it further, but given the low wages it might not be worth eliminating people altogether.”
Joel Halpern, a distinguished engineer at Ericsson, wrote, “While the advent of automated assistive technology will enable many new jobs, it will likely render irrelevant many current jobs. I expect that in the same time frame other technologies will likely create many opportunities, but in terms of the direct job destruction, creation, and disruption from automated operational technologies such as implied by the question will likely be negative in terms of numbers of jobs. While the effect will be felt more on the ‘blue-collar’ level, it will likely also occur at the ‘white-collar’ level as well.”
An attorney at a major law firm responded, “The field within which I work currently employs many thousands to review documents. They are already being replaced by predictive coding algorithms. By 2025, those jobs will not exist for any but the most opaque documents and thus there will be many thousands of lawyers out of work. I find it difficult to imagine any industry which is more knowledge and thought intensive than law and we are already being replaced by machines. I suspect this will disrupt most industries.”
David Allen, an academic and advocate engaged with the development of global Internet governance, replied, “The underlying, fundamental determinant is rate of change, between invention and the workforce. The last century plus has seen the most phenomenal acceleration in the rate of change for innovation. The rate seems likely to continue high. On the other hand, people change and adapt to these changes in the real world only with difficulty. If this is correct, then the rate of change in invention will continue to overwhelm the ability of people—in this case the workforce—to adjust to that change.”
The CEO of a company that makes intelligent machines to make you smarter about your money wrote, “Most information work isn’t all that complicated. Rarely, in fact, does it require the kind of creative manipulation of symbols that usually counts for human intelligence. Where such tasks can be automated, they will be with an appropriate reduction in the human effort required. We’re just seeing the first fruits of this automation today, in fields like banking, where traditional retail banking services have been reduced to a couple of clicks in a mobile application—who needs a branch teller when you can have that teller in your pocket? This goes double for truly mundane tasks like securities trading, where algorithms running in server farms located in the same co-lo as the exchanges execute 50% of a day’s trades on many markets. Where the money goes, so goes the society. I expect the service industries will survive for another 50 years or so past 2025, but then they will be ripe for automation as well, once we can build computers that can process natural human language more accurately, and robots that can simulate human behavior more closely.”
A university professor from the United States wrote, “The impact of AIs and robotics is often, I think, overstated, but automation of vehicles and improvements in robotics in warehousing operations should lead to a steady loss of employment in all areas of logistics, with the impact felt initially in warehouse operations and then moving into delivery of goods/materials. If Amazon is already seriously contemplating delivery-by-drone, I cannot believe they are not also planning on automating warehouse operations to a greater extent than they already have.”
Mike Osswald, vice president for experience innovation at Hanson Inc., wrote, “Many jobs—truck drivers, customer support, light assembly, bank tellers and store checkout staff—will be diminished for businesses who can afford the upfront implementation costs. People will be displaced, businesses will slowly transition their older workforce to different jobs and not hire younger people or veterans. Businesses who let go of many people when adding robots will face backlash from citizens, but only for a time.”
Tom Folkes, an Internet professional, replied, “We will shortly be able to replace low level information workers—these being teachers, lawyers and librarians. In the not distant future, taxi, bus, and truck drivers. Delivery and food workers will be replaced by 3D printing. The number of people required to develop these systems will be relatively small.”
As the split between highly skilled workers and others continues to grow, current problems with inequality are going to get even worse
A number of these experts offered thoughts on how advances in AI and robotics may lead to increased income inequality and contribute to the ongoing hollowing-out of the middle class.
Bob Briscoe, chief researcher in networking and infrastructure for British Telecom, replied, “Robotics is more likely to have displaced blue-collar jobs, deepening the divide between the haves and the have-nots, and protecting the ‘haves’ from withdrawal of labor and similar industrial action. Rather than increasing leisure time, the ‘haves’ will use the freed-up time to achieve more, because maintaining the previous level of achievement would be rewarded less (relative to a living wage). The greater intensity of economic activity will maintain employment for blue-collar workers, but with similar levels of unemployment as today.”
Robert Cannon, Internet law and policy expert, wrote, “During the Industrial Revolution, although Adam Smith will disagree, our economy has been based primarily on labor. The Industrial Revolution displaced labor from agriculture to the city—but the labor existed. Where there was work to be done, humans were the best “machines” to do the labor. The humans would be paid for their labor; the humans would then pay for goods produced by other people’s labor. As production became more efficient, labor continued but moved into non-essential vocations (where essential is food and shelter). In the future, that foundation of our economy—labor—will be gone. Humans will not be the best “machines” to get work done. What will be left? Capital (ownership) and creativity (human contribution), and perhaps competition (sports, other competitions of humans as we are keen on the realization of the best among us). This will be a massive displacement of the middle class. There will be an ownership class and there will be a poor class that works at a rate below what would economically justify bringing in automation.”
S. Craig Watkins, a professor and author based at the University of Texas-Austin, replied, “This is already happening and while the rise of intelligent machines will contribute to the loss of jobs it will also create new jobs—managing, designing, building, and managing the new systems that will emerge. The challenge is will those new jobs require high skills that only a select portion of the population will be able to acquire? In general, the jobs loss will not likely be matched by the jobs created, thus creating a net loss of jobs overall.”
Henning Schulzrinne, an Internet Hall of Famer and technology developer and professor at Columbia University observed, “Many routine information aggregation and information routing jobs (e.g., in sales, customer support, health care and legal support) will be endangered, as well as some janitorial tasks. I don’t see self-driving cars displacing livery or truck drivers, as they are more likely to be used for parts of driving (e.g., on interstates) or to support drivers. You still need to unload delivery trucks, for example. However, in some cases, jobs won’t be replaced, but rather be down-skilled or bifurcated into a small number of high-skill, high-pay and a much larger number of low-skill, low-pay positions.”
John Anderson, director of broadcast journalism at Brooklyn College, wrote, “It’s the same pattern we saw in manufacturing: the de-skilling of some forms of work due to improvements in technology. The social consequences are also the same: displacement, increased insecurity, growing inequality.”
A professor of communication at the University of Southern California and well-known researcher of Internet uses and users replied, “I worry that these technological developments will further erode opportunities for working class labor in the United States and around the world, further destabilizing the employment situation for many people and further exaggerating the divide between have and have not. I don’t think smashing the machines has ever worked as a response to such developments, but this points all the more urgently to the needs of governments and citizens to more directly address inequalities in economic opportunity.”
A private law firm partner specializing in telecommunications and Internet regulatory issues wrote, “The ability of robots and AI to take on many basic tasks and jobs will relentlessly increase. That means that our total output/production may well increase even as the number of people required to generate that production goes down. That will create vexing problems of distribution of wealth/income, as the folks who own the robots etc. will claim entitlement to all or nearly all the production—yet the ability of people to buy that production will be in the aggregate declining. Over time (again, decades, not 11 years) I suspect that there will be a move towards, and an increase in the value of, unique personal-service type jobs. But that will simply highlight the conflict between different groups.”
A college professor wrote, “This has already begun happening. If we’re lucky, we’ll all be put on middle-class welfare to keep people from becoming destitute and desperate. We are not creative enough to make meaningful jobs out of nothing—and that’s what we’ll be left with when we give all the skilled labor and unskilled labor to the machines.”
An Internet engineer and machine intelligence researcher responded, “With the erosion of manufacturing and manual labor jobs, the underpinning economies of the lower and middle classes have been and will continue to be undermined. Wealth will continue to migrate towards the select few who have control over information resources. The control of information will be markedly enhanced by advances in machine intelligence.”
Mikey O’Connor, one of two elected representatives to ICANN’s GNSO Council, representing the ISP and connectivity provider constituency, wrote, “There will always be a LOT of jobs that are more cheaply performed by extremely low-wage humans than technology. Life at the grinding bottom of the income ladder will be largely unchanged, with any hope of improvement coming from other sectors and technologies. Life in the middle will be changed dramatically. A decreasing few will graduate into wealth and comfort, while most will slip towards the bottom. The middle will continue to become a smaller proportion of the population. Robotic and AI technology, once hoped to mitigate this trend, again disappoints. Professionals are coming under increasing pressure and have joined the middle class on the knife-edge between jumping up or sliding down. Their lives will become ever more stressful as they fight to maintain their position. Life at the top will not change much, although it will be more luxurious (if that’s possible to imagine).”
Oscar Gandy, an emeritus professor at the Annenberg School, University of Pennsylvania, wrote, “If ‘displaced’ means or includes ‘replaced with lower paying jobs’, there is no question in my mind about that: this is a process already clearly visible. While not the only determinant, the hollowing out of the middle class that we are seeing is due in no small part to the replacement of mental/creative/analytical workers with software/systems. This can only increase.”
A retired software engineer and IETF participant responded, “To the extent that our culture focuses on monetary value, and to the extent that labor cost has become the primary dimension in which Western corporations are able to optimize, the only way that automation will be permitted to create more jobs than it destroys will be if those new jobs are at substantially lower wages than the existing ones.”
Stuart Umpleby, a systems theory expert and professor at George Washington University, sees these advances leading to a new type of digital divide: “It is very easy to make a digital device that will make a routine decision. This frees up time to do other things. However, it also makes life more complicated, because one then needs to monitor and control one’s digital agents. It also requires a different type of thinking. For example, instead of going to the store to buy food, one needs to learn how to sign on to a website, order food, monitor delivery and payment. One lives increasingly in an informational environment rather than a physical environment. A virtual environment is more easily monitored by businesses and simulated by scam artists. People must learn how to identify scams, which most likely will become more sophisticated. The gap between those who live primarily in a virtual world and those who live primarily in a physical world will grow.”
We run the risk of creating a “permanent underclass”
A notable number of respondents expressed concern that we will see the emergence of a large class of people who have lost their jobs to automation, and who have little hope of gaining the skills needed to obtain meaningful employment in the future.
Bill Woodcock, executive director for the Packet Clearing House, responded, “We’re seeing AI and expert systems beginning to replace or augment customer-service jobs now, and that trend will continue. I believe that’s a good thing, as they’re replacing jobs starting with the most tedious, leaving the ones that require the most critical thinking and ingenuity for humans. As always, people will find ways to occupy themselves, and I believe AI are not a problem here. Far more troublesome is the trend toward greater social divide, that leaves a larger portion of the world’s population in poverty and unable to garner any advantage from self-driving cars or robot vacuum cleaners, because they simply can’t afford cars or vacuum cleaners of any sort, nor services that come with customer service, whether AI or human. Implicit in this question is an assumption about a middle class that still makes up the bulk of the population of Western nations, and to which many developing countries aspire, but which is, in reality, facing a decline if current trends continue.”
A technology writer observed, “Look at yard maintenance, which employs hundreds of thousands. As soon as there’s a safe, cost-effective, lawn-mowing robot, that robot will take over all the lawn mowing jobs there are. Artificial intelligence that will be able to answer questions over the telephone will displace the average call center employee for most calls. Those with only minimal education will be forced even more to the margins of society. Likely there will have to be a new social safety net for those that are simply unable to earn more than a poverty wage.”
Mark Johns, a professor of media studies at a liberal arts college in the U.S., said, “Many manufacturing and service jobs will be eliminated by intelligent agents in the next decade. Social problems associated with a growing “underclass” will increase…The middle class will continue to shrink, and there will be a greater gap between the educated and tech-savvy ‘haves’ and the uneducated ‘have-nots’.”
The research director at a technology trade association responded, “More people will be forced out of growing sectors of the workforce, with downward mobility, unemployment and underemployment resulting. Growing alienation and fear of the future will mark the lives of some members of the baby boomer population. Traditional jobs across the board, from entry-level service jobs through higher-skilled production and intellectually-challenging jobs, will be reduced in number.”
Jamais Cascio, a writer and futurist specializing in possible futures scenario outcomes, sees this new underclass having a gender component when he writes, “Unlike the numerically-controlled factory robots of the 1970s, today’s general purpose machines are designed to be easily-adapted to new job requirements. It won’t just be dropping a robot into the human’s seat…The self-checkout system at many grocery stores is a perfect example of what I mean: we didn’t just build a robot checker, we made machines that split the checkout task with the customer. Digital travel websites replacing travel agents is another example. We’re already seeing some grey-collar and specialized white-collar jobs being absorbed by machines, from legal assistants to surgeons. I expect that to continue, even accelerate. The biggest exception will be jobs that depend upon empathy as a core capacity—schoolteacher, personal service worker, nurse. These jobs are often those traditionally performed by women. One of the bigger social questions of the mid-late 2020s will be the role of men in this world.”
Dan Coates of YPulse responded, “A great thinker in this space is Tyler Cowen who in his book Average is Over outlines a dual track economic reality wherein those who leverage automation enjoy an escalating standard of living, while those displaced by automation descend into a dramatically reduced standard of living.”
A doctoral student in information science at the Universidade Estadual Paulista, in Sao Paolo, Brazil, wrote, “Robots and automatization will only release qualified personnel from heavy duties. But big masses of unqualified people will still be available, but now, competing with machines. Wages will reduce as well as labor protection in advanced countries. In undeveloped countries the situation will remain the similar as today. Big masses of poor people will suffer starvation and pandemias in developing countries.”
Frank Pasquale, a law professor at a state university, wrote, “The key here is not that there is some predetermined path tech will take. Rather, current levels of inequality will be reinforced by robotization as more of these computers are used both to a) do present human-performed jobs better and b) suppress dissent or political action designed to better distribute the gains from technological advance. Think about Occupy Wall Street being dispersed on its first day by land-based robotic policemen and aerial LRADs. They will make the lives of the top 5% or so a virtual paradise, and will surveill and discipline the bottom 95% to keep them in line.”
A principal engineer for Cisco wrote, “Robotics will add a new twist to the global redistribution of manufacturing; if a robot can operate as cheaply in Detroit as in Shenzhen, why pay to ship materials and finished goods around the world? The social consequences will be driven by chronic underemployment and how we choose to manage it economically. Traditional unemployment schemes will not suffice. Some kind of negative income tax based system may be needed to ensure that everyone has enough to live on. Nevertheless a huge social and economic gulf will open up between those who work (even occasionally), and those who never work, and this will have dramatic political consequences.”
If we aren’t careful, increased income inequality and mass unemployment may lead to social unrest
Taken to their logical extreme, these trends—increased unemployment, widespread inequality, the emergence of a permanent poverty class—caused a number of experts to predict riots and other types of social instability in the relatively near future.
The director of innovation a multi-country company aiming to tap into the gigabit Internet wrote, “I am participating in several international projects to develop agents and to bring about factories of the future (including hybrid factories with a mix of robots and blue-collar workers). I’m also participating in two international projects with major cities as partners, looking at ways of introducing enabling technologies such as the Internet of things. And I live in a city that has been chosen as a test site for replacement of some bus routes by AI-based vehicles. All of this leads me to set the job-displacement date earlier, 2020. And between 2020 and 2025 I expect a lot of social unrest, because insufficient attention is being paid to the needs of people displaced by technology. This will lead to a Winner Take All society, in which such workers can earn 10 or 20 times their current salary. Many of those citizens currently pay for part-time or full-time cleaners, gardeners, handy-people. Most of those local jobs will go (to judge from the people I know who already have robot cleaners and robot mowers). Very, very sad for the people affected.”
A technology risk and cybersecurity expert for a U.S.-based financial services association responded, “We have already observed how automation reduces employment, creates gaps in skills needed to be valued workers in multiple industries including the automotive industry. While it may be more efficient, leads to global trade, and moves complex supply chains, it also creates new challenges and problems for individuals and society. One of these challenges/problems is the gap in the skills and training that is necessary for workers to be valued. Another is increasing income inequality between those that have the valued skills and employed and those who do not and are unemployed or underemployed. Unless industry and government steps in to provide the necessary training, this could lead to greater political unrest.”
A browser engineer at Mozilla wrote, “Current trends indicate that the economy in its current form is ill-suited to support large numbers of low- and un-skilled workers. As more jobs become replaceable, I predict large societal upheavals as the gap between highly skilled (and highly paid) workers and a high proportion of partially, or totally, unemployed people continues to widen.”
| 2014-08-06T00:00:00 |
2014/08/06
|
https://www.pewresearch.org/internet/2014/08/06/views-from-those-who-expect-ai-and-robotics-to-displace-more-jobs-than-they-create-by-2025/
|
[
{
"date": "2014/08/06",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 36,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 35,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 90,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 36,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 35,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 32,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 74,
"query": "robotics job displacement"
}
] |
AI, Robotics, and the Future of Jobs - Pew Research Center
|
AI, Robotics, and the Future of Jobs
|
https://www.pewresearch.org
|
[
"Aaron Smith",
"Janna Anderson",
".Wp-Block-Prc-Block-Bylines-Display Background Inherit Box-Sizing Inherit Color Inherit Color Var --Wp--Preset--Color--Text-Color",
"Font-Family Var --Wp--Preset--Font-Family--Sans-Serif",
"Font-Size Font-Weight Gap Important Line-Height",
"Margin-Bottom Text-Transform Uppercase .Wp-Block-Prc-Block-Bylines-Display A Text-Decoration None Important .Wp-Block-Prc-Block-Bylines-Display A Hover Text-Decoration Underline Important .Wp-Block-Prc-Block-Bylines-Display .Prc-Platform-Staff-Bylines__Separator Margin-Left"
] |
The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To ...
|
Key Findings
The vast majority of respondents to the 2014 Future of the Internet canvassing anticipate that robotics and artificial intelligence will permeate wide segments of daily life by 2025, with huge implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. But even as they are largely consistent in their predictions for the evolution of technology itself, they are deeply divided on how advances in AI and robotics will impact the economic and employment picture over the next decade.
We call this a canvassing because it is not a representative, randomized survey. Its findings emerge from an “opt in” invitation to experts who have been identified by researching those who are widely quoted as technology builders and analysts and those who have made insightful predictions to our previous queries about the future of the Internet. (For more details, please see the section “About this Report and Survey.”)
Key themes: reasons to be hopeful Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. Ultimately, we as a society control our own destiny through the choices we make. Key themes: reasons to be concerned Impacts from automation have thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well. Certain highly-skilled workers will succeed wildly in this new environment—but far more may be displaced into lower paying service industry jobs at best, or permanent unemployment at worst. Our educational system is not adequately preparing us for work of the future, and our political and economic institutions are poorly equipped to handle these hard choices.
Some 1,896 experts responded to the following question:
The economic impact of robotic advances and AI—Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?
Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.
The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution.
These two groups also share certain hopes and concerns about the impact of technology on employment. For instance, many are concerned that our existing social structures—and especially our educational institutions—are not adequately preparing people for the skills that will be needed in the job market of the future. Conversely, others have hope that the coming changes will be an opportunity to reassess our society’s relationship to employment itself—by returning to a focus on small-scale or artisanal modes of production, or by giving people more time to spend on leisure, self-improvement, or time with loved ones.
A number of themes ran through the responses to this question: those that are unique to either group, and those that were mentioned by members of both groups.
The view from those who expect AI and robotics to have a positive or neutral impact on jobs by 2025
JP Rangaswami, chief scientist for Salesforce.com, offered a number of reasons for his belief that automation will not be a net displacer of jobs in the next decade: “The effects will be different in different economies (which themselves may look different from today’s political boundaries). Driven by revolutions in education and in technology, the very nature of work will have changed radically—but only in economies that have chosen to invest in education, technology, and related infrastructure. Some classes of jobs will be handed over to the ‘immigrants’ of AI and Robotics, but more will have been generated in creative and curating activities as demand for their services grows exponentially while barriers to entry continue to fall. For many classes of jobs, robots will continue to be poor labor substitutes.”
Rangaswami’s prediction incorporates a number of arguments made by those in this canvassing who took his side of this question.
Argument #1: Throughout history, technology has been a job creator—not a job destroyer
Jonathan Grudin, principal researcher for Microsoft, concurred: “Technology will continue to disrupt jobs, but more jobs seem likely to be created. When the world population was a few hundred million people there were hundreds of millions of jobs. Although there have always been unemployed people, when we reached a few billion people there were billions of jobs. There is no shortage of things that need to be done and that will not change.”
Argument #2: Advances in technology create new jobs and industries even as they displace some of the older ones
Amy Webb, CEO of strategy firm Webbmedia Group, wrote, “There is a general concern that the robots are taking over. I disagree that our emerging technologies will permanently displace most of the workforce, though I’d argue that jobs will shift into other sectors. Now more than ever, an army of talented coders is needed to help our technology advance. But we will still need folks to do packaging, assembly, sales, and outreach. The collar of the future is a hoodie.”
Argument #3: There are certain jobs that only humans have the capacity to do
A number of respondents argued that many jobs require uniquely human characteristics such as empathy, creativity, judgment, or critical thinking—and that jobs of this nature will never succumb to widespread automation.
Michael Glassman, associate professor at the Ohio State University, wrote, “I think AI will do a few more things, but people are going to be surprised how limited it is. There will be greater differentiation between what AI does and what humans do, but also much more realization that AI will not be able to engage the critical tasks that humans do.”
Argument #4: The technology will not advance enough in the next decade to substantially impact the job market
Another group of experts feels that the impact on employment is likely to be minimal for the simple reason that 10 years is too short a timeframe for automation to move substantially beyond the factory floor. David Clark, a senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, noted, “The larger trend to consider is the penetration of automation into service jobs. This trend will require new skills for the service industry, which may challenge some of the lower-tier workers, but in 12 years I do not think autonomous devices will be truly autonomous. I think they will allow us to deliver a higher level of service with the same level of human involvement.”
Christopher Wilkinson, a retired European Union official, board member for EURid.eu, and Internet Society leader said, “The vast majority of the population will be untouched by these technologies for the foreseeable future. AI and robotics will be a niche, with a few leading applications such as banking, retailing, and transport. The risks of error and the imputation of liability remain major constraints to the application of these technologies to the ordinary landscape.”
Argument #5: Our social, legal, and regulatory structures will minimize the impact on employment
A final group suspects that economic, political, and social concerns will prevent the widespread displacement of jobs. Glenn Edens, a director of research in networking, security, and distributed systems within the Computer Science Laboratory at PARC, a Xerox Company, wrote, “There are significant technical and policy issues yet to resolve, however there is a relentless march on the part of commercial interests (businesses) to increase productivity so if the technical advances are reliable and have a positive ROI then there is a risk that workers will be displaced. Ultimately we need a broad and large base of employed population, otherwise there will be no one to pay for all of this new world.”
Andrew Rens, chief council at the Shuttleworth Foundation, wrote, “A fundamental insight of economics is that an entrepreneur will only supply goods or services if there is a demand, and those who demand the good can pay. Therefore any country that wants a competitive economy will ensure that most of its citizens are employed so that in turn they can pay for goods and services. If a country doesn’t ensure employment driven demand it will become increasingly less competitive.”
The view from those who expect AI and robotics to displace more jobs than they create by 2025
An equally large group of experts takes a diametrically opposed view of technology’s impact on employment. In their reading of history, job displacement as a result of technological advancement is clearly in evidence today, and can only be expected to get worse as automation comes to the white-collar world.
Argument #1: Displacement of workers from automation is already happening—and about to get much worse
[also]
Argument #2: The consequences for income inequality will be profound
For those who expect AI and robotics to significantly displace human employment, these displacements seem certain to lead to an increase in income inequality, a continued hollowing out of the middle class, and even riots, social unrest, and/or the creation of a permanent, unemployable “underclass”.
[said that he]
Alex Howard, a writer and editor based in Washington, D.C., said, “I expect that automation and AI will have had a substantial impact on white-collar jobs, particularly back-office functions in clinics, in law firms, like medical secretaries, transcriptionists, or paralegals. Governments will have to collaborate effectively with technology companies and academic institutions to provide massive retraining efforts over the next decade to prevent massive social disruption from these changes.”
Point of agreement: The educational system is doing a poor job of preparing the next generation of workers
A consistent theme among both groups is that our existing social institutions—especially the educational system—are not up to the challenge of preparing workers for the technology- and robotics-centric nature of employment in the future.
Point of agreement: The concept of “work” may change significantly in the coming decade
On a more hopeful note, a number of experts expressed a belief that the coming changes will allow us to renegotiate the existing social compact around work and employment.
Possibility #1: We will experience less drudgery and more leisure time
Francois-Dominique Armingaud, retired computer software engineer from IBM and now giving security courses to major engineering schools, responded, “The main purpose of progress now is to allow people to spend more life with their loved ones instead of spoiling it with overtime while others are struggling in order to access work.”
Possibility #2: It will free us from the industrial age notion of what a “job” is
A notable number of experts take it for granted that many of tomorrow’s jobs will be held by robots or digital agents—and express hope that this will inspire us as a society to completely redefine our notions of work and employment.
Peter and Trudy Johnson-Lenz, founders of the online community Awakening Technology, based in Portland, Oregon, wrote, “Many things need to be done to care for, teach, feed, and heal others that are difficult to monetize. If technologies replace people in some jobs and roles, what kinds of social support or safety nets will make it possible for them to contribute to the common good through other means? Think outside the job.”
Bob Frankston, an Internet pioneer and technology innovator whose work helped allow people to have control of the networking (internet) within their homes, wrote, “We’ll need to evolve the concept of a job as a means of wealth distribution as we did in response to the invention of the sewing machine displacing seamstressing as welfare.”
Jim Hendler, an architect of the evolution of the World Wide Web and professor of computer science at Rensselaer Polytechnic Institute, wrote, “The notion of work as a necessity for life cannot be sustained if the great bulk of manufacturing and such moves to machines—but humans will adapt by finding new models of payment as they did in the industrial revolution (after much upheaval).”
Tim Bray, an active participant in the IETF and technology industry veteran, wrote, “It seems inevitable to me that the proportion of the population that needs to engage in traditional full-time employment, in order to keep us fed, supplied, healthy, and safe, will decrease. I hope this leads to a humane restructuring of the general social contract around employment.”
Possibility #3: We will see a return to uniquely “human” forms of production
Another group of experts anticipates that pushback against expanding automation will lead to a revolution in small-scale, artisanal, and handmade modes of production.
Kevin Carson, a senior fellow at the Center for a Stateless Society and contributor to the P2P Foundation blog, wrote, “I believe the concept of ‘jobs’ and ‘employment’ will be far less meaningful, because the main direction of technological advance is toward cheap production tools (e.g., desktop information processing tools or open-source CNC garage machine tools) that undermine the material basis of the wage system. The real change will not be the stereotypical model of ‘technological unemployment,’ with robots displacing workers in the factories, but increased employment in small shops, increased project-based work on the construction industry model, and increased provisioning in the informal and household economies and production for gift, sharing, and barter.”
[efforts]
A network scientist for BBN Technologies wrote, “To some degree, this is already happening. In terms of the large-scale, mass-produced economy, the utility of low-skill human workers is rapidly diminishing, as many blue-collar jobs (e.g., in manufacturing) and white-collar jobs (e.g., processing insurance paperwork) can be handled much more cheaply by automated systems. And we can already see some hints of reaction to this trend in the current economy: entrepreneurially-minded unemployed and underemployed people are taking advantages of sites like Etsy and TaskRabbit to market quintessentially human skills. And in response, there is increasing demand for ‘artisanal’ or ‘hand-crafted’ products that were made by a human. In the long run this trend will actually push toward the re-localization and re-humanization of the economy, with the 19th- and 20th-century economies of scale exploited where they make sense (cheap, identical, disposable goods), and human-oriented techniques (both older and newer) increasingly accounting for goods and services that are valuable, customized, or long-lasting.”
Point of agreement: Technology is not destiny … we control the future we will inhabit
In the end, a number of these experts took pains to note that none of these potential outcomes—from the most utopian to most dystopian—are etched in stone. Although technological advancement often seems to take on a mind of its own, humans are in control of the political, social, and economic systems that will ultimately determine whether the coming wave of technological change has a positive or negative impact on jobs and employment.
Seth Finkelstein, a programmer, consultant and EFF Pioneer of the Electronic Frontier Award winner, responded, “The technodeterminist-negative view, that automation means jobs loss, end of story, versus the technodeterminist-positive view, that more and better jobs will result, both seem to me to make the error of confusing potential outcomes with inevitability. Thus, a technological advance by itself can either be positive or negative for jobs, depending on the social structure as a whole….this is not a technological consequence; rather it’s a political choice.”
Jason Pontin, editor in chief and publisher of the MIT Technology Review, responded, “There’s no economic law that says the jobs eliminated by new technologies will inevitably be replaced by new jobs in new markets… All of this is manageable by states and economies: but it will require wrestling with ideologically fraught solutions, such as a guaranteed minimum income, and a broadening of our social sense of what is valuable work.”
| 2014-08-06T00:00:00 |
2014/08/06
|
https://www.pewresearch.org/internet/2014/08/06/future-of-jobs/
|
[
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"date": "2014/08/06",
"position": 48,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "AI unemployment rate"
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"date": "2014/08/06",
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{
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},
{
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{
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{
"date": "2014/08/06",
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"query": "AI unemployment rate"
},
{
"date": "2014/08/06",
"position": 74,
"query": "AI unemployment rate"
},
{
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{
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},
{
"date": "2014/08/06",
"position": 59,
"query": "AI impact jobs"
},
{
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{
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},
{
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] |
Report Finds Rise Of Artificial Intelligence Could Spark ... - IFLScience
|
Report Finds Rise Of Artificial Intelligence Could Spark Mass Unemployment And Inequality
|
https://www.iflscience.com
|
[
"Benjamin Taub",
"Freelance Writer"
] |
Report Finds Rise Of Artificial Intelligence Could Spark Mass Unemployment And Inequality. ... among business owners. This could generate ...
|
Fears about human workers losing their jobs to machines have been fueled by a 72 percent increase in the number of industrial robots in the U.S. over the past decade, although with investment in artificial intelligence (AI) soaring, things could be about to get a lot worse. This is according to a new 300-page report by Bank of America Merrill Lynch (BAML), which details the potential impact of the impending robot revolution on the job market.
The report claims that advances in robotics and AI are leading to a phenomenon known as “creative disruption,” whereby benefits in the shape of increased productivity and reduced costs are offset against disruptions to labor markets, with huge numbers of workers set to lose out. For instance, a San Francisco-based start-up has created a fully-automated burger-flipping machine, which is being tipped to replace workers in fast food restaurants. Elsewhere, plans have been announced to introduce “fully intelligent robot” police officers in the United Arab Emirates before the end of the decade, with the intention of providing “better services without hiring more people.”
While this may sound more like science fiction than reality, the report insists that such innovations are not beyond the realms of possibility, largely thanks to a predicted three-fold increase in the size of the robotics market over the next five years. As a result, it is claimed that “the combination of AI, machine learning, deep learning, and natural user interfaces (such as voice recognition) are making it possible to automate many knowledge worker tasks that were long regarded as impossible or impractical for machines to perform.”
According to Ray Kurzweil, director of engineering at Google, this could soon lead to what he calls the “Singularity,” whereby sentient devices overtake humans as the most intelligent beings on the planet.
On a slightly less apocalyptic but equally alarming note, the BAML report suggests that the rise of automated workforces could result in social and economic inequality, as wealth becomes concentrated among business owners. This could generate “winner-takes-all and monopolistic outcomes,” with the proprietors of technological patents accumulating huge amounts of wealth while unskilled workers struggle.
However, BAML also recognizes that similar fears have been raised several times in the past, with the catastrophic consequences of increased mechanization ultimately failing to materialize. For instance, while the introduction of technology to agricultural processes may have replaced man hours on farms, it also led to the creation of entirely new job markets and has not therefore led to mass unemployment.
Many will be hoping for a similar effect as new robotic technologies continue to revolutionize the global workspace, leading to what BAML is calling a “paradigm shift which will change the way we live and work.”
| 2015-11-09T00:00:00 |
2015/11/09
|
https://www.iflscience.com/artificial-intelligence-could-cause-mass-unemployment-and-inequality-31868
|
[
{
"date": "2015/11/09",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
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},
{
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},
{
"date": "2015/11/09",
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},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
"position": 51,
"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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},
{
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{
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{
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] |
The Robots Are Coming … to Take Your Job - Knowledge at Wharton
|
The Robots Are Coming … to Take Your Job
|
https://knowledge.wharton.upenn.edu
|
[] |
Today, robots are increasingly handling many jobs in manufacturing that were done by human hands not more than 20 years ago.
|
Today, robots are increasingly handling many jobs in manufacturing that were done by human hands not more than 20 years ago. This sea change has affected a variety of industries, and it’s one reason why the jobs recovery of the past few years hasn’t included as many manufacturing jobs. Those jobs weren’t just destroyed — they were lost to smart machines.
But while we’re living in a time when computer programs already dominate Wall Street, and when driverless cars and delivery drones are moving from science fiction to mundane fact, those developments may be just the tip of the iceberg. Martin Ford, author of Rise of the Robots: Technology and the Threat of a Jobless Future, recently appeared on the Knowledge at Wharton show on Wharton Business Radio on SiriusXM channel 111 to talk about how the robot revolution has affected businesses in a host of industries, what it means for jobs in the years ahead, and what other surprises might be on the horizon.
An edited transcript of the conversation appears below.
Knowledge at Wharton: We’ve seen these changes going on. Where does your concern lie for the future?
Martin Ford: It’s really across the board. Traditionally, robots have been in factories, but I think that over the next 10 to 20 years, automation in the form of robots, smart software and machine learning is really going to invade pretty much across the board. It’s going to start impacting jobs at all skill levels. It’s not just going to be about low-wage people who don’t have lots of education. It’s really starting to impact also professional jobs.
Knowledge at Wharton: For years, when you did have robots involved, they were viewed as a supplement to the workers, but that clearly isn’t the case now in some areas. From what you’re saying, we’re going to see this even more going forward.
Ford: That’s right. I really think we’re in the midst of a transition where in the past, machines have always been tools that have been used by people and made those people more productive, but increasingly, the technology is really becoming a replacement or a substitute for more and more workers. That’s going to be a huge issue over the coming decade.
Knowledge at Wharton: Based on the level of adoption by businesses so far, what has been the effect on the economy? It’s probably somewhat marginal at this point, but it’s certainly going to be growing.
Ford: That’s right. Clearly, we have not seen actual massive unemployment as a result of this. That’s obvious. But what we have seen is, for decades now, wages have been stagnant, and even now, as the economy has been recovering and we’ve seen the unemployment rate falling, we haven’t seen anything in terms of wage increases. I do think that technology is probably one of the main forces that’s driving that stagnation of wages. It’s important to note the way that stagnation is happening — even as, over the long run, productivity has continued to increase. We see this decoupling of productivity and wages that really points to this transition that’s unfolding.
Knowledge at Wharton: You work in software development, so this is an area that you have focused on professionally for quite some time. How quickly are we going to see this continue to grow? I was watching a segment on a TV show earlier today where they were showing off a driverless pick-up truck. That technology is getting closer and closer to being a part of our everyday life.
Ford: That’s right. All of this is subject to a continuing acceleration, and for that reason, it’s going to unfold at a rate that may surprise us. To take the example of driverless cars: It’s just a few years ago — really, back in 2009 — that I wrote my first book on this topic, and I never imagined at that time that driverless cars would be feasible any time soon. It seemed like an almost impossible task, even to me. Yet now, virtually every auto manufacturer, as well as a whole bunch of companies that haven’t traditionally been in the car industry, are working on this, and it’s looking like it’s going to be feasible within 10, 15 years, at least. So it’s pretty amazing how fast things are moving.
Knowledge at Wharton: You expect that in a lot of the normal, white-collar jobs, we’re going to see more and more robot adoption and automation come into play. We certainly see it in some respects already.
“It’s not about the skill level or how much education you have. Really, the primary question is, is the job on some level routine, repetitive and predictable?”
Ford: On Wall Street, most trading is now done by algorithms. There have been lots and lots of jobs that have disappeared already, and again, the important thing is that in many cases, these are skilled jobs. It’s not about the skill level or how much education you have. The primary question is, is the job on some level routine, repetitive and predictable? In other words, can the actions that a worker undertakes in that field be predicted based on what they’ve done in the past?
If the answer to that is yes, then it’s going to be susceptible to machine learning, which is really the central technology that’s driving all of this. It’s a huge range of jobs, and it includes a lot of jobs that are good jobs that people need to go to school for. So that really kind of throws a wrench into our conventional thinking about how all of this has worked in the past.
Knowledge at Wharton: What are some of the areas that appear to be on the cusp of seeing this great change that you’re talking about and will see greater adoption of robots?
Ford: We already see systems that are beginning to impact journalism that can crank out news stories based on data streams. We see the field of law being impacted, with algorithms that do document review taking over a lot of the more routine work that used to be done by lawyers and paralegals. A lot of that is driven by machine learning, and is going to scale across a whole bunch of the knowledge economy. I can imagine that over the next couple of decades anyone who has a job sitting in front of a computer doing something that is some on level routine and predictable — cranking out the same analysis or the same report every month — that type of thing is going to be susceptible to this. That’s an enormous number of white-collar jobs out there. At the same time, there’s going to be a huge impact on many more routine, lower-skill jobs as well — areas like fast food, driving vehicles. So it’s really very, very broad-based.
Knowledge at Wharton: It is interesting you mention even fast food. We’ve already started to see places like McDonald’s in some locations put in automation where you don’t have the connection with a person up at the counter to put in your order. You’re just going to put your order in at a menu board, and eventually that food’s going to be prepared by somebody, but it’s going to come out to you without you talking to anybody.
Ford: That’s right. There are companies also working on the preparation, what’s happening in the back. There’s a company in San Francisco that’s working on a hamburger robot that can crank out about 400 hamburgers an hour. So you’re eventually going to see automation both in the front, at the counter in terms of the ordering, and also in the back in terms of the food preparation. That again scales to any kind of fast food or beverage — Starbucks, everything. So it’s inevitable that there will be an impact there, and those are jobs that a lot of people rely on. In many cases, people take jobs in fast food because they don’t have better opportunities. Those are a last resort, almost a safety net for workers who don’t have opportunities.
Knowledge at Wharton: You mentioned education a little bit ago, obviously, from the aspect of how it plays in to the whole process because of algorithms being such an important piece. But what about the educational system as a business entity? How will it be changed going forward?
“There’s going to be a huge impact on many more routine, lower-skill jobs as well — areas like fast food, driving vehicles. So it’s really very, very broad-based.”
Ford: We see already the beginnings of that. There’s a lot of focus right now on so-called MOOCs or massive open online courses. There are essentially robotic teaching systems that are becoming more and more powerful, where, for example, a student can use the system online, and there will be a tutor who will monitor their progress and help assign them tasks and adjust the level of difficulty and so forth based on their capabilities. So you’re seeing some pretty important advances there. Right now, education is one of the sectors that has been kind of lagging. If you look at manufacturing vs. education — there have been just tremendous increases in manufacturing. In education, we haven’t seen that, but there are reasons to believe that we may be on the brink of a big disruption. But it’s important to note that if that happens, it’s going to be kind of a double-edged sword. It will make education a lot more accessible, but at the same time, it could impact a lot of jobs in the education sector.
Knowledge at Wharton: What about the area of artificial intelligence?
Ford: Well, that’s a pretty broad area. What we’re seeing right now are tremendous advances in specialized areas of artificial intelligence: machine learning and a particular area called deep learning, which is based on neural networks. That’s really generating some amazing progress in areas like pattern recognition — systems that can recognize images better than people. Microsoft demonstrated a system that could translate spoken Mandarin into English in real time. Not perfectly, obviously, but the fact that it could do it all was incredible. So we’re really just seeing some amazing advances in specialized areas of artificial intelligence.
Knowledge at Wharton: You also bring up the concept of “the Singularity,” and I wanted you to go into that a little bit. It’s something that I think people that are listening to the show would find very interesting.
Ford: Right. The Singularity is a future time when, essentially, everything is accelerating so rapidly that it becomes almost incomprehensible. The general thinking is that the thing that’s going to bring that about will be true artificial intelligence. It will be when we build a machine that can think and have cognitive ability at the same level as a human being. Eventually, that machine will turn its efforts to building better versions of itself, and those will become a super-intelligence. Then we’ll have this entirely new phenomenon in the universe — something that’s more intelligent than human beings. That’s what’s going to drive this incredible acceleration.
Among the big proponents of the idea of the Singularity are people like Ray Kurzweil who is fairly famous in Silicon Valley. It’s a fascinating concept. There have been some kind of outlandish ideas that, I think, have become attached to it. Many people who believe in the promise of the Singularity also believe that they’re going to live forever because these technologies are going to result in immortality for humans, and that’s fairly controversial.
It’s an interesting concept, and there are some elements of it that are useful, but I also think that there’s a lot of kind of crazy baggage that’s attached to it.
Knowledge at Wharton: And it’s still a fairly long way off.
Ford: Yeah, the most optimistic predictions have it maybe 20 years out, and I would personally guess that if anything like that is going to happen, it’s even further out than that.
“Some of the safest jobs are going to be areas like being an electrician or a plumber or maybe a car mechanic because it’s really hard to build a robot that can do all of those things.”
Knowledge at Wharton: Al is [calling from] Toronto, Canada. Al, welcome to the show.
Al: Thank you.… My question is how do you expect automation to affect trade labor and essentially the most common denominator of construction, which is manual labor?
Ford: Right. First of all, it’s true that many trade jobs are for the moment relatively safe, because a lot of trade jobs require a combination of visual perception, dexterity, mobility as well as problem solving. Some of the safest jobs are going to be areas like being an electrician or a plumber or maybe a car mechanic because it’s really hard to build a robot that can do all of those things. But over the longer term, that would not necessarily be true, and there are also other technologies that could disrupt those jobs in ways that you might not expect. Speaking specifically of construction, building a robot that can do what a construction worker does is really tough — although people are working on it. But one of the things that could really have a big impact is construction-scale 3D printing, where you build these massive 3D printers that can actually lay down a house or a building. There is some experimentation with that already. So automation can take a completely different form than having a robot do what a person now does. And that may actually be the bigger impact over the longer term.
Knowledge at Wharton: There are robots used in the auto industry right now, and obviously, they are not used to the level of being a mechanic. How far of a reach is it to take a robot within the auto industry working on an assembly line, putting the car together, to one that’s able to replace parts in a shop?
“There’s a company in San Francisco that’s working on a hamburger robot that can crank out about 400 hamburgers an hour.”
Ford: That’s going to become increasingly feasible, but it will happen in part because the design of cars will change. In other words, we’ll start to design cars specifically so that they’re very modular and can be perhaps repaired by robots. But if you take your old car from the 1970s, for example, that would be obviously an almost impossible challenge for a robot to repair.
Knowledge at Wharton: How much do you see the health care industry advancing in this area in the next 20, 30 years?
Ford: Health care across the board is one of the areas where we most want a disruption. The cost of health care, especially as our population gets older, is really becoming just a staggering burden — and that’s especially true in the United States. We still have really a problematic health care system relative to other countries in terms of the cost.
I think that it is starting to happen. One of the things you certainly will see is more artificial intelligence leveraged in areas like diagnosis and medicine in general. Some areas of nursing and elder care are beginning to be impacted by robots. That’s being driven, in particular, by Japan, which is doing a lot of research into robotics to help care for the elderly, because they have such a rapidly aging population. So all of that is going to begin to have an impact.
Having said that, some areas of health care, like nursing — really going around and interacting with patients and taking care — is really just tremendously hard to automate. I mean, that requires at this point what we would think of as a science-fiction robot. But we are beginning to see progress.
Knowledge at Wharton: Given that there are natural areas where it’s very tough to replicate the experience of a human being, we can expect these are areas that are going to continue to be strong employment areas going forward. Are we going to have another shift in terms of what jobs people are going to be really pushed toward over the next couple of decades?
Ford: In general, if a college student says to me, “What should I study so that it can be safe?” I think health care is a good bet, if you are doing something that is interacting directly with patients and requires lots of mobility and dexterity … as opposed to, for example, being a radiologist where you’re looking at images and interpreting. That kind of thing can be automated quite easily. But yes, it does foreshadow a shift in terms of where the jobs are going to be. I don’t tend to believe that there will be enough jobs in health care to make up for all of the jobs that are going to be impacted in other areas of the economy. But certainly health care as a particular segment is going to be relatively safe.
| 2016-03-02T00:00:00 |
https://knowledge.wharton.upenn.edu/article/the-robots-are-coming-to-take-your-job/
|
[
{
"date": "2016/03/02",
"position": 80,
"query": "robotics job displacement"
}
] |
|
Robohub roundtable: Job loss through automation, Foxconn ...
|
Robohub roundtable: Job loss through automation, Foxconn controversy
|
https://robohub.org
|
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Automation cutting jobs. Robots stealing jobs. The loss of human workers. How many times have different iterations appeared in the media ...
|
Robohub roundtable: Job loss through automation, Foxconn controversy
Every few weeks, Robohub will post a roundtable chat and discuss an engaging topic relating to robotics. In this edition, we looked at the controversial job loss of 60K jobs by Foxconn. Is this substantial job loss a preview to come with automation, or largely overblown hype? We strongly encourage our Robohub readers to chime in and be part of the conversation!
This chat features Sabine Hauert, Andra Keay, Kassie Perlongo, Yannis Erripis, John Payne, providing a range of perspectives from research, business, and the general public.
Automation cutting jobs. Robots stealing jobs. The loss of human workers. How many times have different iterations appeared in the media with these all too similar headlines? When news broke about Foxconn cutting 60,000 jobs from their factory, mass speculation spread like wildfire. Chinese companies say they are looking to combine people with robotics, and that it will improve product quality whilst also ensure labour supply is not a factor to production. So, is China looking at a long-term gain, with a short-term loss, so they can continue to make their own products on their mainland?
Once the offshore labour capital of the world, China has rising costs and a higher standard of living, making it difficult to find workers. President Xi Jinping called for an “industrial robot revolution” in 2014, looking to place automation’s role in raising productivity. Due to this, Chinese companies are turning to robots, announcing measures — such as subsidies and tax incentives — to encourage ‘more industrial automation and development of home-grown robotics.’ 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.
But back to Foxconn. After the news broke, they released a statement: “We are applying robotics engineering and other innovative manufacturing technologies to replace repetitive tasks previously done by employees, and through training, also enabling our employees to focus on higher value-added elements in the manufacturing process, such as research and development, process control and quality control. We will continue to harness automation and manpower in our manufacturing operations, and we expect to maintain our significant workforce in China.”
The company still employs more than 1.2 million people. If businesses grow more efficient and competitive due to robotics, are robots in some ways helping to prevent businesses from shutting down, with potentially more people losing jobs? Some tasks currently done by humans can be automated at low-cost while others, such as fine manipulation or visual inspection, will require sophisticated hardware and software to mechanise.
The roundtable began by discussing how robotics alleviates risk with people working in dirty, dangerous conditions. And also how this plays into the government’s plan to infuse capital into the mainland robotics industry over the next few years.
“The increase in automation is partly due to the increase in labour costs, but it is also in response to things that happened, like the factory explosion due to unsafe working conditions, killing people. The government pledged 2 billion yuan in subsidies for companies to install industrial robotics and production lines,” said Andra.
“I remember reading exposés about terrible working conditions. It’s not necessarily a bad thing to be introducing robots,” said Andra. “It may be ‘viewed’ as threatening, but it can be beneficial. So instead of people being pushed into horrible working conditions, robots will take care of it,” said Andra.
((As an aside, Foxconn echoes similar concerns, with Day Chia-Peng, general manager of Foxconn’s automation technology development committee, saying that the company was motivated to focus on this area due to safety concerns and manpower shortages in recent years.))
“There are always going to be people working in some of the most dangerous jobs in society, so there’s a question of whether robotics can improve the quality of some of the worst jobs for these people,” Andra continued.
“People often forget how difficult it is to make a robot. The goal is not to make robots that replace humans, but robots that work alongside humans, performing specific tasks. There is a large push to develop collaborative robots (cobots) that are easy to program and deploy by workers. This will allow the workers to focus on more fulfilling high-level tasks that require human-level cognition, and the robots, to focus on specific repetitive tasks,” said Sabine.
The roundtable also discussed China’s strategy for investing and utilising industrial robotics. As noted by the Financial Times, their working age population is expected to fall from one billion people last year to 960 million in 2030, and 800 million by 2050. Automation can be a way to fill the labour gap.
“China has increased their robotics to 1 million robots in 2 years,” said Andra. “The number of robots of China is increasing. They have a middle class that is ageing, and they are expecting a higher standard of living and a higher quality of job. The cost of labour in China has increased, and manufacturing has shifted to other parts of the world.”
“What we’re seeing today, is that robots introduced in factories have contributed overall to an increase in productivity. Higher production leads to more jobs down the road in other sectors such as sales, customer support, and for the overall deployment, installation, maintenance, and management of high-tech factories. While it’s true that the nature of work might change, the net number of jobs may not necessary decrease. The question is how to retrain workers,” said Sabine.
The roundtable mentions that China has relied on a seemingly endless supply of cheap labour for decades to power its economic expansion. “Fast forward to today and the production of robots and China is now the largest consumer of industrial robots and is approaching becoming one of the largest producers,” said Andra.
“Is there also a problem that it’s all happening too quickly and not a gradual pace for people to find new jobs?” asked Kassie.
“I think the pace the automation is increasing, but we’re still talking about a fairly small density,” said Andra.
“One hope is that the jobs of tomorrow are more fulfilling. The nature of work has always evolved, driven in part by the introduction of technology. We don’t manually wash our clothes anymore, and I don’t see why we should be performing the same repetitive task on an assembly line either,” said Sabine.
“Job displacement doesn’t mean everyone needs to become an engineer, although we are very much in need for more people, especially women, in the STEM fields. There is also room for jobs that profoundly build on human interactions and creativity. In fact, I hope all professions will become more human,” said Sabine.
“Augmented reality will make training easier and human workers more effective,” said John. “By replacing shortages of health care professionals (not doctors) robots can help here — replacing tasks, not replacing jobs. I don’t think we have to leave people out of the meaningful economy. Eyes, brains, and hands are wonderful things. Add augmented reality and you have a powerful combination.”
Final thoughts…
In 2013, the most widely noted report on the subject came from Oxford University’s Carl Benedikt Frey and Michael Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” However, a new report has also been making waves. The report from Organisation for Economic Co-operation and Development (OECD), “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis,” by Melanie Arntz, Terry Gregory, and Ulrich Zierahn, finds that there actually will be jobs for people, but still foresees difficulties, particularly for low-skilled workers. It’s also important to note that these reports do not specifically discuss robotics, but also software automation.
Also, just because something can be automated doesn’t mean it necessarily will be. Although we have current technology to purchase coffee by machine, there are still people lining up at coffee houses, like Starbucks, to order their favourite brew. The human aspect is more important.
Finally, the robotic component of this overhaul in China will be about more than just installing more robots in manufacturing plants; and perhaps, robots shouldn’t be seen as a cure-all solution for the nation’s labour shortages and economic slowdown.
Read some of our previous Robohub roundtables below:
See all the latest robotics news on Robohub, or sign up for our weekly newsletter.
Robohub Editors
tags: c-Industrial-Automation
| 2016-06-21T00:00:00 |
https://robohub.org/robohub-roundtable-job-loss-through-automation-foxconn-controversy/
|
[
{
"date": "2016/06/21",
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"query": "robotics job displacement"
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"query": "robotics job displacement"
},
{
"date": "2016/06/21",
"position": 72,
"query": "robotics job displacement"
}
] |
|
A Good Disruption - SYSTEMIQ
|
A Good Disruption
|
https://www.systemiq.earth
|
[] |
Artificial Intelligence (AI) can play a powerful role in supporting climate action while boosting sustainable and inclusive economic growth.
|
Disruptive technology is one of the defining economic trends of our age with companies such as Airbnb, Uber and Apple dramatically transforming their industries. But what is the true impact of such disruption on the world’s economies, and does it have the potential to solve global problems such as low growth, inequality and environmental degradation? Why not seize this opportunity and make it a good disruption?
A Good Disruption highlights some of the huge costs that are at stake and argues that managing such disruption will be the defining business challenge of the next decade. In order for us to meet that challenge, the book sets out a bold, timely and inspirational vision for a more robust and sustainable economic model, demonstrating how we can use technology to have a prospering economy while nature thrives.
Marrying the latest thinking from Silicon Valley, the Paris climate meetings and traditional economics, A Good Disruption is rich in relevant case studies, incorporates industry examples from around the world and features a number of interviews with leading business influencers including Nobel laureate Mohamed Yunus and Lord Nicholas Stern.
from left to right: Per-Anders Enkvist, Dr. Klaus Zumwinkel, Dr. Martin Stuchtey
| 2016-10-20T00:00:00 |
https://www.systemiq.earth/resource-category/a-good-disruption/
|
[
{
"date": "2016/10/20",
"position": 95,
"query": "AI economic disruption"
},
{
"date": "2016/10/20",
"position": 94,
"query": "AI economic disruption"
}
] |
|
Blame Displacement of Jobs on Automation, Not Offshoring and ...
|
Blame Displacement of Jobs on Automation, Not Offshoring and Immigration
|
https://futurism.com
|
[] |
High-profile personalities such as Stephen Hawking, as well as economists, have begun to shine the spotlight on this issue of technological ...
|
A New Wave of Automation
As the world continues to achieve unprecedented levels of advancement in AI and robotics, we must, at the same time, come to terms with the fact that our fundamental understanding of technology is also being challenged. Technology was once viewed as a tool that drove human progress forward. Today, technology is threatening the employment and job security of millions.
High-profile personalities such as Stephen Hawking, as well as economists, have begun to shine the spotlight on this issue of technological unemployment—the displacement of human jobs by increasingly sophisticated means of automation.
Eatsa, an automated restaurant chain where customers have zero interaction with a human staff. Credit: Jason Henry for The New York Times.
In a column published in The Guardian, Hawking points out that, “[...]the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.”
Economists are not discounting the fact that globalization is at least partially to blame for unemployment. They cite trade relations with China during the 2000s as an example, which according to researchers from MIT, led to the loss of over two millions jobs. Still, the impact of automation will have a greater, more disruptive effect on the labor force.
The New Economy
Some argue that the situation isn’t nearly as dire as some imagine it to be.
Elon Musk, who believes that rising automation will lead to the implementation of universal basic income, sees it as an opportunity. "People will have time to do other things, more complex things, more interesting things," says Musk. "Certainly more leisure time."
It’s also entirely possible that as industries begin to assimilate technology into their business models, that it will create new jobs.
“It’s literally the story of the economic development of the world over the last 200 years...just as most of us today have jobs that weren’t even invented 100 years ago, the same will be true 100 years from now,” argued Marc Andreesen, a venture capitalist who was also responsible for creating Mosaic, the first widely used web browser.
Automation can also serve to complement human skills. As Stefan Hajkowicz illustrated in his article in The Conversation: “Spreadsheets didn't kill off accounting jobs. On the contrary, smart accountants learned how to use spreadsheets to become more productive and more employable.”
True. But experts think this industrial revolution is different.
Machines right now might be only capable of doing repetitive, formulaic jobs, but even so, it was already enough to displace thousands of human workers. What happens when prototypes of robots that were taught to mimic the human mind become available? It’s not hard to imagine that knowledge-based, creative, and service-oriented jobs will eventually be overtaken as well.
Our society is evolving—this is the inescapable reality, and change is the watchword of our age. Uncertainty and fear are the inevitable corollaries of the enormous changes stealing upon us; we feel as the cotton picker must have felt at the arrival of the cotton gin, or the coachman beholding the first horseless carriage. Some speak of a melding of our biological minds with the mechanical AI we create, and new phases of human evolution; but these are remote fantasies, of small comfort to the man or woman whose livelihood is rendered obsolete by the march of progress.
But our species' most remarkable trait is its adaptability—with any luck, we'll weather this storm as we've weathered so many before, and doubtless the people of 2117 will marvel at and even long for our quaint, unsophisticated age and our uncomplicated lives.
| 2017-01-21T00:00:00 |
https://futurism.com/2-evergreen-blame-displacement-of-jobs-on-automation-not-offshoring-and-immigration
|
[
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 97,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2017/01/21",
"position": 94,
"query": "robotics job displacement"
}
] |
|
Robot taxes and universal basic income: How do we manage our ...
|
Robot taxes and universal basic income: How do we manage our automated future?
|
https://newatlas.com
|
[
"Stefan Bohrer",
"Rich Has Written For A Number Of Online",
"Print Publications Over The Last Decade While Also Acting As Film Critic For Several Radio Broadcasters",
"Podcasts. His Interests Focus On Psychedelic Science",
"New Media",
"Science Oddities. Rich Completed A Masters Degree In The Arts Back In Before Joining New Atlas In",
"Bob Stuart"
] |
Elon Musk maintains that the idea of a universal basic income is the best solution, while Bill Gates advocates for a robot tax. It's undeniable, ...
|
As more and more jobs are becoming automated, the world faces a dramatic shift in the underlying structures of its labor economies over the next 20 to 50 years. The conversation is slowly becoming more prominent in the mainstream with several major figures highlighting the problem and proposing different solutions. Elon Musk maintains that the idea of a universal basic income is the best solution, while Bill Gates advocates for a robot tax.
It's undeniable, we are entering a revolution in our labor economy. Numerous recent reports have reached some confronting conclusions as to the effects of automation and artificial intelligence on our current work force. A striking report from Oxford University in 2013 estimated that about 47 percent of the total current US work force is at risk of becoming redundant due to automation or artificial intelligence. Another study in 2015 found that 45 percent of jobs in the US right now could be replaced by currently demonstrated technologies.
Late in 2016, Obama's White House released a report warning that measures needed to be taken to manage the millions of jobs that could be lost in the coming years due to technological advances. Despite societies having faced similar labor transitions in the past due to technological advancements displacing workers, we seem to be speeding through a transitional phase at a pace that may exceed our ability to naturally adapt. In an interview with Wired in 2016, President Obama expressed his concern saying, "I do think that we may be in a slightly different period now, simply because of the pervasive applicability of AI and other technologies."
The two big solutions to our looming unemployment crisis currently hitting the mainstream conversation are the institution of a tax on robots and a universal basic income.
How do we tax robots?
Billionaire philanthropist Bill Gates came out in favor of a robot tax in a recent interview with Quartz. Gates explained that in our current climate you have workers who earn income that is taxed, but when their job is replaced by a robot you are losing that income tax revenue. The clearest solution he sees is to tax the robots at a similar level to the human worker it has displaced.
Bill Gates thinks we should tax the robot that takes your job
The revenue garnered from these robot taxes could then be used to support and retrain those unemployed workers, ultimately moving them into new forms of employment. It's an enticingly simple solution to a complex global problem. It's also becoming popular in certain political arenas, with prominent advocacy from surprise socialist front-runner in the upcoming French election, Benoit Hamon.
So what's wrong with a robot tax?
Well, apart from a confusing burden of implementation (for example, how much automation in a job would equal a taxable rate?), this really amounts to a tax on businesses that would inevitably result in a trickle down to the consumer. If a business was to make the decision to automate a percentage of its workforce, the technology itself would be a significant up-front cost before even adding an ongoing robot tax. The tax would ultimately either slow the rate of automated adoption and robotic development, or result in a sharp inflation of costs to the general public.
This is, of course, a simplistic interpretation of events, but not an unreasonable one. In fact, just recently the European Parliament debated this very issue, and while approving a raft of robot law proposals to regulate and manage the growing industry, they roundly rejected the idea of a robot tax.
The big alternative being touted around the world by many is the idea of a universal basic income.
Elon Musk is an outspoken advocate of universal basic income Steve Jurvetson (CC BY 2.0)
Money for nothing
Elon Musk has been a vocal proponent of a universal basic income (UBI) for several years now. Most recently in February 2017 he reiterated his support of the idea at a summit in Dubai saying, "I don't think we're going to have a choice, I think it's going to be necessary. There will be fewer and fewer jobs that a robot cannot do better."
UBI is an even more simplistic solution to our oncoming problems than the robot tax. The idea proposes that all citizens of a country receive a regular unconditional sum of money, either monthly or annually, that is calculated to cover basic living expenses. The UBI is touted as being an efficient way to replace the unwieldy bureaucratic mechanisms of many governmental social welfare systems.
Economists are still debating the cost-effectiveness of UBI, but there are many who believe that the total cost of current large and inefficient welfare systems are higher than the potential cost of UBI. Charles Murray, a prominent advocate of UBI has prolifically written of a proposal to eliminate all current welfare systems in the United States and replace them with a universal US$10,000 guaranteed income for every citizen. He estimated that this would be cheaper than the combined costs of current systems in place, but that has been debated with others claiming his numbers are fundamentally wrong.
When his proposal was put to a panel of expert economists, 58 percent either disagreed or strongly disagreed that it was a better policy than the status quo. It is worth noting many economists surveyed in that particular poll explained that they were responding more to the generalized extremity and lack of nuance in Murray's specific system.
Pushing aside the economic factor for a moment, the most heated debates surrounding an implementation of UBI tend to be concern over the social consequences. After all, if people didn't need to work then why would they? Would we basically be funding a future of lazy, unmotivated human beings?
A group of activists in Switzerland have started up the collective 'Robots For Basic Income' Gen. Grundeinkommen/CC 2.0
But what would we all do?
This is a common concern railed against UBI, but it may be more of a philosophical concern than a pragmatic one. Several pilot UBI projects have shown that households receiving cash handouts have actually increased their labor and production outputs. In India several NGOs piloted UBI and found that those who received the grants doubled their production work when compared to similar households not receiving the grants.
An extensive trial in a small Canadian town in the 1970s produced similar results, showing the guaranteed income system resulted in a larger volume of high school students reaching the 12th grade, as well as an 8.5 percent drop in hospital visits and a reduction in domestic violence cases.
"It's surprising to find that it actually works, that people don't quit their jobs," remarked Evelyn Forget, a sociology professor who recently reevaluated the records from that Canadian pilot study in the 1970s, "There's this fear that if we have too much freedom, we might misuse it."
In 2013 Swiss activists gathered 125,000 signatures forcing a referendum on the issue of UBI. In 2016 an overwhelming 76 percent of the population voted against implementing the system Stefan Bohrer / WikiCommons
At its core we return to the philosophical questions regarding the general effects of UBI on society. For generations our occupations have guided and structured our lives and our identities. It is undeniable that UBI would alter this, but it could easily be argued that this ideological shift is already happening anyway.
The new generation of millennials are currently redefining older perceptions of work and career. They are known as the job-hopping generation and have be shown to care more about personal fulfilment in their job over money.
These attitudes seem to signal that for the younger generations, UBI would offer a safety net allowing personal creative explorations, enhanced education, and even the ability to take the time to cultivate new income streams from different endeavors. As we look to almost half of our current workforce becoming redundant through automation over the coming years, we will certainly need to be working as a society to develop new industries, occupations and income streams. Is UBI the most straight-forward way to achieve this outcome?
Whatever you believe is the best way forward, be it robot taxes, UBI or its more pragmatic variant, the negative income tax, everyone can agree that our labor economies are facing some dramatic looming changes. Something will certainly need to be done and the discussion needs to be had, so let's have it.
We'd love to hear your thoughts on the topic. Sound off in the comments below.
| 2017-02-21T00:00:00 |
2017/02/21
|
https://newatlas.com/robot-tax-universal-basic-income-future-work/48014/
|
[
{
"date": "2017/02/20",
"position": 94,
"query": "universal basic income AI"
}
] |
Work and social policy in the age of artificial intelligence | Brookings
|
Work and social policy in the age of artificial intelligence
|
https://www.brookings.edu
|
[
"Darrell M. West",
"Mark Maccarthy",
"Eduardo Levy Yeyati"
] |
The White House report, entitled “Artificial Intelligence, Automation and the Economy”, concluded that AI-driven automation suggests the need ...
|
Since its inception some sixty years ago, artificial intelligence (AI) has evolved from an arcane academic field into a powerful driver of social and economic change. AI is now the basis for a wide range of mainstream technologies including web search, medical diagnosis, smart phone applications, and most recently, autonomous vehicles. Deep learning —a form of machine learning based on layered representations or neural networks—has dramatically improved pattern recognition, speech recognition, and natural language processing.
In 2013, the Oxford Martin School published a report predicting that 47 percent of jobs in the United States could be under threat of automation within two decades due to advances in AI technologies. Last year, the Obama administration raised similar concerns in a presidential report on AI. The White House report, entitled “Artificial Intelligence, Automation and the Economy”, concluded that AI-driven automation suggests the need for aggressive public policies and a more robust safety net in order to combat labor disruption. Of course, predictions on total number of job losses is fundamentally uncertain, but according to a recent study by McKinsey, technology is expected to automate portions of jobs over the next decade.
Given the prospects of an economic future in which large swaths of the working population are at risk of losing their jobs or seeing them diminish in quality, how might government mitigate the impact of AI? A recent report from the University of Toronto’s Mowat Centre has explored this conundrum in some detail, outlining potential options for policymakers to consider. In the near-term, and with relatively little difficulty, governments can look to their own mandates and to partnerships with the private sector and organized labor to consider the following social policies:
| 2017-02-28T00:00:00 |
https://www.brookings.edu/articles/work-and-social-policy-in-the-age-of-artificial-intelligence/
|
[
{
"date": "2017/02/28",
"position": 54,
"query": "government AI workforce policy"
}
] |
|
The Jobs That Artificial Intelligence Will Create
|
The Jobs That Artificial Intelligence Will Create
|
https://sloanreview.mit.edu
|
[
"H. James Wilson",
"Paul R. Daugherty",
"Nicola Morini-Bianzino",
"Massachusetts Institute Of Technology",
"About The Authors"
] |
Our research reveals three new categories of AI-driven business and technology jobs. We label them trainers, explainers, and sustainers.
|
The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur.
It can be a distressing picture.
But here’s what we’ve been overlooking: Many new jobs will also be created — jobs that look nothing like those that exist today.
In Accenture PLC’s global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs. These roles are not replacing old ones. They are novel, requiring skills and training that have no precedents. (Accenture’s study, “How Companies Are Reimagining Business Processes With IT,” will be published this summer.)
More specifically, our research reveals three new categories of AI-driven business and technology jobs. We label them trainers, explainers, and sustainers. Humans in these roles will complement the tasks performed by cognitive technology, ensuring that the work of machines is both effective and responsible — that it is fair, transparent, and auditable.
Trainers
This first category of new jobs will need human workers to teach AI systems how they should perform — and it is emerging rapidly. At one end of the spectrum, trainers help natural-language processors and language translators make fewer errors. At the other end, they teach AI algorithms how to mimic human behaviors.
Customer service chatbots, for example, need to be trained to detect the complexities and subtleties of human communication. Yahoo Inc. is trying to teach its language processing system that people do not always literally mean what they say. Thus far, Yahoo engineers have developed an algorithm that can detect sarcasm on social media and websites with an accuracy of at least 80%.
Consider, then, the job of “empathy trainer” — individuals who will teach AI systems to show compassion. The New York-based startup Kemoko Inc., d/b/a Koko, which sprung from the MIT Media Lab, has developed a machine-learning system that can help digital assistants such as Apple’s Siri and Amazon’s Alexa address people’s questions with sympathy and depth.
About the Authors H. James Wilson is managing director of IT and business research at Accenture Research. Paul R. Daugherty is Accenture’s chief technology and innovation officer. Nicola Morini-Bianzino is global lead of artificial intelligence at Accenture.
| 2017-03-23T00:00:00 |
2017/03/23
|
https://sloanreview.mit.edu/article/will-ai-create-as-many-jobs-as-it-eliminates/
|
[
{
"date": "2017/03/23",
"position": 58,
"query": "artificial intelligence employment"
},
{
"date": "2017/03/23",
"position": 50,
"query": "AI job creation vs elimination"
},
{
"date": "2017/03/23",
"position": 60,
"query": "artificial intelligence employment"
},
{
"date": "2017/03/23",
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"query": "artificial intelligence employment"
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"date": "2017/03/23",
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"query": "AI job creation vs elimination"
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"date": "2017/03/23",
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"query": "AI job creation vs elimination"
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"date": "2017/03/23",
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"query": "artificial intelligence employment"
},
{
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"query": "artificial intelligence employment"
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},
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{
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"query": "artificial intelligence employment"
},
{
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"query": "AI job creation vs elimination"
},
{
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},
{
"date": "2017/03/23",
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"query": "AI job creation vs elimination"
},
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"query": "AI job creation vs elimination"
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{
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"query": "artificial intelligence employment"
}
] |
Robots, Automation, and Jobs: A Primer for Policymakers | ITIF
|
Robots, Automation, and Jobs: A Primer for Policymakers
|
https://itif.org
|
[
"Robert D. Atkinson"
] |
... robotics and artificial intelligence on employment. This ... Automation does not lead to net job loss, either. Even if automation ...
|
Read foreign language translations: Chinese, French, German, Spanish.
There is considerable interest, if not consternation, about the potential effects of emerging technologies such as robotics and artificial intelligence on employment. There is also considerable confusion about the interaction between automation, technology, and jobs. Here are 13 key points that are important for policymakers to understand about that interaction:
| 2017-05-08T00:00:00 |
2017/05/08
|
https://itif.org/publications/2017/05/08/robots-automation-and-jobs-primer-policymakers/
|
[
{
"date": "2017/05/08",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/05/08",
"position": 98,
"query": "robotics job displacement"
},
{
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"query": "robotics job displacement"
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"query": "robotics job displacement"
},
{
"date": "2017/05/08",
"position": 95,
"query": "robotics job displacement"
}
] |
Is Your Job Safe From the Rise of the Robots? | The Motley Fool
|
Is Your Job Safe From the Rise of the Robots?
|
https://www.fool.com
|
[
"Keith Speights"
] |
The World Economic Forum (WEF) projects a loss of 7.1 million jobs to robots in the world's 15 leading countries, including the US, by 2020.
|
The robots aren't just coming. They're already here. And your job just might be at risk.
Serious threat or overblown hype?
That's the question many have about recent reports that robots could replace millions of jobs currently held by human workers. On one hand, there's the research by global accounting and consulting company PricewaterhouseCoopers (PwC) that projects 38% of American jobs could be at risk of replacement by automation within the next 15 or so years. On the other hand, U.S. Treasury Secretary Steven Mnuchin thinks that we're so far away from seeing artificial intelligence take American jobs that "it's not even on [his] radar screen."
Is your job safe from the rise of the robots? The best answer right now: It depends.
How things stand today
First, when people talk about the rise of the robots, they're not necessarily referring to smart machines that resemble humans. Instead, the general use of the word "robots" includes all technology that can handle tasks that humans have performed in the past. The reality is that robots are already an important part of the U.S. economy.
The top 14 industrial robot manufacturers have an estimated 1.6 million robots in use at manufacturing, distribution, and other sites across the world. The automobile industry uses robots extensively. But while robots have replaced some jobs, U.S. automakers have actually been adding jobs overall during the past several years.
Many robots haven't replaced jobs but have instead augmented them. Intuitive Surgical's (ISRG 0.85%) da Vinci robotic surgical system is a great example.
Surgeons and other medical professionals are still just as involved with procedures as they are without using the robotic system. However, instead of having the surgeon stand over the patient, he or she is several feet away at a console. Da Vinci allows the surgeon to see a crisp, high-definition 3-D image of the patient's anatomy. It translates the surgeon's hand movements on the system's controls into precise micro-movements that enable less-invasive surgery.
Da Vinci was used in over 560,000 surgeries in the U.S. last year. How many jobs were displaced? Zero. The technology actually resulted in a net gain of jobs, since Intuitive Surgical employees nearly 3,800 people.
The near future
The World Economic Forum (WEF) projects a loss of 7.1 million jobs to robots in the world's 15 leading countries, including the U.S., by 2020. Which jobs are most likely to be lost? The WEF paints a dire picture for office and administrative workers, estimating that these occupations will make up roughly two-thirds of the total job losses.
Research firm Forrester (FORR 0.41%) has a similar outlook. The company estimates that cognitive technologies, including robots, artificial intelligence (AI), machine learning, and automation, will replace 16% of U.S. jobs by 2025. Like the WEF, Forrester thinks that office and administrative support positions will be most affected.
It's not all bad news, though. The WEF estimates that 2 million new jobs will be created by 2020, especially in certain skilled positions such as data analysts and specialist sales representatives. Forrester projects that around 8.9 million new jobs will be created in the U.S. by 2025, particularly for robot monitoring professionals, data scientists, automation specialists, and content curators. As a result of this growth, Forrester thinks the overall impact of technology will result in a net loss of 7% of American jobs by 2025.
By the early 2030s
Workers in the U.S. and across the world could face the most significant threat in the years after 2025. And it won't be just office support staff.
PwC thinks job losses in the wholesale, retail, and manufacturing industries could be even worse than in the administrative and support services area. In the U.S., financial and insurance sector jobs could be hit hard.
Adoption of driverless cars could wreak havoc in the transportation industry. Taxi and Uber drivers could be forced to find work elsewhere as AI replaces them. The same could happen with many of the estimated 3.5 million truck drivers in the United States.
In general, workers who spend a significant amount of time performing manual or repetitive tasks will be at most risk of losing their jobs to robots over the next 15 years. PwC projects that the greatest impact will be on employees in jobs that require lower levels of education.
What to do
Some think that teachers, registered nurses, and dental hygienists should be in low danger for a while. Over the longer run, however, there are probably few jobs that can be considered completely safe. The potential for AI is so tremendous that it could be just a matter of time before robots can perform nearly every job that humans have.
What can you do if you have years to go before retirement? Getting more education could help. Perhaps you might want to take a look at some of the new jobs that could be created, such as data scientist.
I like the idea of investing in companies that should benefit from the rise of the robots. Intuitive Surgical would be a good one. So would Google's parent, Alphabet (GOOG 0.85%) (GOOGL 0.79%), which is a pioneer in driverless-car technology and in artificial intelligence, in general. My colleagues at The Motley Fool have identified Alphabet as a top driverless-car stock, as well as a top AI stock to buy. I totally agree with their assessments.
If you can't beat them, join them. Investing now seems like a smart way to join the robots before they take millions of jobs in the future.
| 2017-06-04T00:00:00 |
2017/06/04
|
https://www.fool.com/careers/2017/06/04/is-your-job-safe-from-the-rise-of-the-robots.aspx
|
[
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] |
Artificial intelligence in healthcare: past, present and future
|
Artificial intelligence in healthcare: past, present and future
|
https://svn.bmj.com
|
[
"Author Affiliations"
] |
We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and ...
|
The AI devices: ML and NLP
In this section, we review the AI devices (or techniques) that have been found useful in the medial applications. We categorise them into three groups: the classical machine learning techniques,26 the more recent deep learning techniques27 and the NLP methods.28
Classical ML ML constructs data analytical algorithms to extract features from data. Inputs to ML algorithms include patient ‘traits’ and sometimes medical outcomes of interest. A patient’s traits commonly include baseline data, such as age, gender, disease history and so on, and disease-specific data, such as diagnostic imaging, gene expressions, EP test, physical examination results, clinical symptoms, medication and so on. Besides the traits, patients’ medical outcomes are often collected in clinical research. These include disease indicators, patient’s survival times and quantitative disease levels, for example, tumour sizes. To fix ideas, we denote the jth trait of the ith patient by X ij , and the outcome of interest by Y i . Depending on whether to incorporate the outcomes, ML algorithms can be divided into two major categories: unsupervised learning and supervised learning. Unsupervised learning is well known for feature extraction, while supervised learning is suitable for predictive modelling via building some relationships between the patient traits (as input) and the outcome of interest (as output). More recently, semisupervised learning has been proposed as a hybrid between unsupervised learning and supervised learning, which is suitable for scenarios where the outcome is missing for certain subjects. These three types of learning are illustrated in figure 4. Figure 4 Request permissions Graphical illustration of unsupervised learning, supervised learning and semisupervised learning. Clustering and principal component analysis (PCA) are two major unsupervised learning methods. Clustering groups subjects with similar traits together into clusters, without using the outcome information. Clustering algorithms output the cluster labels for the patients through maximising and minimising the similarity of the patients within and between the clusters. Popular clustering algorithms include k-means clustering, hierarchical clustering and Gaussian mixture clustering. PCA is mainly for dimension reduction, especially when the trait is recorded in a large number of dimensions, such as the number of genes in a genome-wide association study. PCA projects the data onto a few principal component (PC) directions, without losing too much information about the subjects. Sometimes, one can first use PCA to reduce the dimension of the data, and then use clustering to group the subjects. On the other hand, supervised learning considers the subjects’ outcomes together with their traits, and goes through a certain training process to determine the best outputs associated with the inputs that are closest to the outcomes on average. Usually, the output formulations vary with the outcomes of interest. For example, the outcome can be the probability of getting a particular clinical event, the expected value of a disease level or the expected survival time. Clearly, compared with unsupervised learning, supervised learning provides more clinically relevant results; hence AI applications in healthcare most often use supervised learning. (Note that unsupervised learning can be used as part of the preprocessing step to reduce dimensionality or identify subgroups, which in turn makes the follow-up supervised learning step more efficient.) Relevant techniques include linear regression, logistic regression, naïve Bayes, decision tree, nearest neighbour, random forest, discriminant analysis, support vector machine (SVM) and neural network.27 Figure 5 displays the popularity of the various supervised learning techniques in medical applications, which clearly shows that SVM and neural network are the most popular ones. This remains the case when restricting to the three major data types (image, genetic and EP), as shown in figure 6. Figure 5 Request permissions The machine learning algorithms used in the medical literature. The data are generated through searching the machine learning algorithms within healthcare on PubMed. Figure 6 Request permissions The machine learning algorithms used for imaging (upper), genetic (middle) and electrophysiological (bottom) data. The data are generated through searching the machine learning algorithms for each data type on PubMed. Below we will provide more details about the mechanisms of SVM and neural networks, along with application examples in the cancer, neurological and cardiovascular disease areas.
Support vector machine SVM is mainly used for classifying the subjects into two groups, where the outcome Y i is a classifier: Y i = −1 or 1 represents whether the ith patient is in group 1 or 2, respectively. (The method can be extended for scenarios with more than two groups.) The basic assumption is that the subjects can be separated into two groups through a decision boundary defined on the traits X ij , which can be written as: where w j is the weight putting on the jth trait to manifest its relative importance on affecting the outcome among the others. The decision rule then follows that if a i >0, the ith patient is classified to group 1, that is, labelling Y i = −1; if a i <0, the patient is classified to group 2, that is, labelling Y i =1. The class memberships are indeterminate for the points with a i =0. See figure 7 for an illustration with , , a 1 =1, and a 2 =−1. Figure 7 Request permissions An illustration of the support vector machine. The training goal is to find the optimal w j s so that the resulting classifications agree with the outcomes as much as possible, that is, with the smallest misclassification error, the error of classifying a patient into the wrong group. Intuitively, the best weights must allow (1) the sign of a i to be the same as Y i so the classification is correct; and (2) |a i | to be far away from 0 so the ambiguity of the classification is minimised. These can be achieved by selecting w j s that minimise a quadratic loss function.29 Furthermore, assuming that the new patients come from the same population, the resulting w j s can be applied to classify these new patients based on their traits. An important property of SVM is that the determination of the model parameters is a convex optimisation problem so the solution is always global optimum. Furthermore, many existing convex optimisation tools are readily applicable for the SVM implementation. As such, SVM has been extensively used in medical research. For instance, Orrù et al applied SVM to identify imaging biomarkers of neurological and psychiatric disease.30 Sweilam et al reviewed the use of SVM in the diagnosis of cancer.31 Khedher et al used the combination of SVM and other statistical tools to achieve early detection of Alzheimer’s disease.32 Farina et al used SVM to test the power of an offline man/machine interface that controls upper-limb prostheses.22
Neural network One can think about neural network as an extension of linear regression to capture complex non-linear relationships between input variables and an outcome. In neural network, the associations between the outcome and the input variables are depicted through multiple hidden layer combinations of prespecified functionals. The goal is to estimate the weights through input and outcome data so that the average error between the outcome and their predictions is minimised. We describe the method in the following example. Mirtskhulava et al used neural network in stroke diagnosis.33 In their analysis, the input variables X i 1 , . . . , X ip are p=16 stroke-related symptoms, including paraesthesia of the arm or leg, acute confusion, vision, problems with mobility and so on. The outcome Y i is binary: Y i =1/0 indicates the ith patient has/does not have stroke. The output parameter of interest is the probability of stroke, a i , which carries the form of In the above equation, the w 10 and w 20 ≠0 guarantee the above form to be valid even when all X ij , f k are 0; the w 1l and 2l s are the weights to characterise the relative importance of the corresponding multiplicands on affecting the outcome; the f k s and are prespecified functionals to manifest how the weighted combinations influence the disease risk as a whole. A stylised illustration is provided in figure 8. Figure 8 Request permissions An illustration of neural network. The training goal is to find the weights w ij , which minimise the prediction error 2. The minimisation can be performed through standard optimisation algorithms, such as local quadratic approximation or gradient descent optimisation, that are included in both MATLAB and R. If the new data come from the same population, the resulting w ij can be used to predict the outcomes based on their specific traits.29 Similar techniques have been used to diagnose cancer by Khan et al, where the inputs are the PCs estimated from 6567 genes and the outcomes are the tumour categories.34 Dheeba et al used neural network to predict breast cancer, with the inputs being the texture information from mammographic images and the outcomes being tumour indicators.35 Hirschauer et al used a more sophisticated neural network model to diagnose Parkinson’s disease based on the inputs of motor, non-motor symptoms and neuroimages.36
Deep learning: a new era of ML Deep learning is a modern extension of the classical neural network technique. One can view deep learning as a neural network with many layers (as in figure 9). Rapid development of modern computing enables deep learning to build up neural networks with a large number of layers, which is infeasible for classical neural networks. As such, deep learning can explore more complex non-linear patterns in the data. Another reason for the recent popularity of deep learning is due to the increase of the volume and complexity of data.37 Figure 10 shows that the application of deep learning in the field of medical research nearly doubled in 2016. In addition, figure 11 shows that a clear majority of deep learning is used in imaging analysis, which makes sense given that images are naturally complex and high volume. Figure 9 Request permissions An illustration of deep learning with two hidden layers. Figure 10 Request permissions Current trend for deep learning. The data are generated through searching the deep learning in healthcare and disease category on PubMed. Figure 11 Request permissions The data sources for deep learning. The data are generated through searching deep learning in combination with the diagnosis techniques on PubMed. Different from the classical neural network, deep learning uses more hidden layers so that the algorithms can handle complex data with various structures.27 In the medical applications, the commonly used deep learning algorithms include convolution neural network (CNN), recurrent neural network, deep belief network and deep neural network. Figure 12 depicts their trends and relative popularities from 2013 to 2016. One can see that the CNN is the most popular one in 2016. Figure 12 Request permissions The four main deep learning algorithm and their popularities. The data are generated through searching algorithm names in healthcare and disease category on PubMed. The CNN is developed in viewing of the incompetence of the classical ML algorithms when handling high dimensional data, that is, data with a large number of traits. Traditionally, the ML algorithms are designed to analyse data when the number of traits is small. However, the image data are naturally high-dimensional because each image normally contains thousands of pixels as traits. One solution is to perform dimension reduction: first preselect a subset of pixels as features, and then perform the ML algorithms on the resulting lower dimensional features. However, heuristic feature selection procedures may lose information in the images. Unsupervised learning techniques such as PCA or clustering can be used for data-driven dimension reduction. The CNN was first proposed and advocated for the high-dimensional image analysis by Lecun et al.38 The inputs for CNN are the properly normalised pixel values on the images. The CNN then transfers the pixel values in the image through weighting in the convolution layers and sampling in the subsampling layers alternatively. The final output is a recursive function of the weighted input values. The weights are trained to minimise the average error between the outcomes and the predictions. The implementation of CNN has been included in popular software packages such as Caffe from Berkeley AI Research,39 CNTK from Microsoft40 and TensorFlow from Google.41 Recently, the CNN has been successfully implemented in the medical area to assist disease diagnosis. Long et al used it to diagnose congenital cataract disease through learning the ocular images.24 The CNN yields over 90% accuracy on diagnosis and treatment suggestion. Esteva et al performed the CNN to identify skin cancer from clinical images.20 The proportions of correctly predicted malignant lesions (ie, sensitivity) and benign lesions (ie, specificity) are both over 90%, which indicates the superior performance of the CNN. Gulshan et al applied the CNN to detect referable diabetic retinopathy through the retinal fundus photographs.25 The sensitivity and specificity of the algorithm are both over 90%, which demonstrates the effectiveness of using the technique on the diagnosis of diabetes. It is worth mentioning that in all these applications, the performance of the CNN is competitive against experienced physicians in the accuracy for classifying both normal and disease cases.
Natural language processing The image, EP and genetic data are machine-understandable so that the ML algorithms can be directly performed after proper preprocessing or quality control processes. However, large proportions of clinical information are in the form of narrative text, such as physical examination, clinical laboratory reports, operative notes and discharge summaries, which are unstructured and incomprehensible for the computer program. Under this context, NLP targets at extracting useful information from the narrative text to assist clinical decision making.28 An NLP pipeline comprises two main components: (1) text processing and (2) classification. Through text processing, the NLP identifies a series of disease-relevant keywords in the clinical notes based on the historical databases.42 Then a subset of the keywords are selected through examining their effects on the classification of the normal and abnormal cases. The validated keywords then enter and enrich the structured data to support clinical decision making. The NLP pipelines have been developed to assist clinical decision making on alerting treatment arrangements, monitoring adverse effects and so on. For example, Fiszman et al showed that introducing NLP for reading the chest X-ray reports would assist the antibiotic assistant system to alert physicians for the possible need for anti-infective therapy.43 Miller et al used NLP to automatically monitor the laboratory-based adverse effects.44 Furthermore, the NLP pipelines can help with disease diagnosis. For instance, Castro et al identified 14 cerebral aneurysms disease-associated variables through implementing NLP on the clinical notes.45 The resulting variables are successfully used for classifying the normal patients and the patients with cerebral, with 95% and 86% accuracy rates on the training and validation samples, respectively. Afzal et al implemented the NLP to extract the peripheral arterial disease-related keywords from narrative clinical notes. The keywords are then used to classify the normal and the patients with peripheral arterial disease, which achieves over 90% accuracy.42
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| 2017-12-20T00:00:00 |
2017/12/20
|
https://svn.bmj.com/content/2/4/230
|
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Artificial Intelligence Salary | Free-Work
|
Artificial Intelligence Salary
|
https://www.free-work.com
|
[] |
Artificial Intelligence Salary · Research Scientist · *A percentile is a measure used in statistics indicating the value below which a given ...
|
Research Scientist
Job Description --> Research scientists are responsible for designing, undertaking and analysing information from controlled laboratory-based investigations, experiments and trials.
*A percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. For example, the 20th percentile is the value below which 20% of the observations may be found
Software Engineer
Job Description --> Software engineers tend to specialize in a few areas of development, such as networks, operating systems, databases or applications, and each area requires fluency in its own set of computer languages and development environments.
C# Developer
Job Description --> C# is a modern, general purpose, object-oriented programming language designed around the Common Language Infrastructure. A great C# developer is capable of handling many aspects of developing an application, including but not limited to performance, scalability, security, testing, and more. C# developers can develop modern applications that run on desktop computers, or even sophisticated back-end processes powering modern web applications.
Information Security Engineer
Job Description --> Information Security Engineers, also called Information Security Analysts, help to safeguard organization's computer networks and systems. They plan and carry out security measures to monitor and protect sensitive data and systems from infiltration and cyber-attacks.
Software Development Manager
Job Description --> As a software developer you'll be playing a key role in the design, installation, testing and maintenance of software systems.
Java Developer
Job Description --> A
| 2017-07-18T00:00:00 |
https://www.free-work.com/en-gb/tech-it/blog/it-skills/artificial-intelligence-salary
|
[
{
"date": "2017/07/18",
"position": 84,
"query": "artificial intelligence wages"
}
] |
|
Robots won't steal our jobs if we put workers at center of AI revolution
|
Robots won’t steal our jobs if we put workers at center of AI revolution
|
https://theconversation.com
|
[
"Lee Dyer",
"Thomas Kochan"
] |
One of the McKinsey study's key findings was that about a third of the tasks performed in 60 percent of today's jobs are likely to be eliminated ...
|
The technologies driving artificial intelligence are expanding exponentially, leading many technology experts and futurists to predict machines will soon be doing many of the jobs that humans do today. Some even predict humans could lose control over their future.
While we agree about the seismic changes afoot, we don’t believe this is the right way to think about it. Approaching the challenge this way assumes society has to be passive about how tomorrow’s technologies are designed and implemented. The truth is there is no absolute law that determines the shape and consequences of innovation. We can all influence where it takes us.
Thus, the question society should be asking is: “How can we direct the development of future technologies so that robots complement rather than replace us?”
The Japanese have an apt phrase for this: “giving wisdom to the machines.” And the wisdom comes from workers and an integrated approach to technology design, as our research shows.
Lessons from history
There is no question coming technologies like AI will eliminate some jobs, as did those of the past.
More than half of the American workforce was involved in farming in the 1890s, back when it was a physically demanding, labor-intensive industry. Today, thanks to mechanization and the use of sophisticated data analytics to handle the operation of crops and cattle, fewer than 2 percent are in agriculture, yet their output is significantly higher.
But new technologies will also create new jobs. After steam engines replaced water wheels as the source of power in manufacturing in the 1800s, the sector expanded sevenfold, from 1.2 million jobs in 1830 to 8.3 million by 1910. Similarly, many feared that the ATM’s emergence in the early 1970s would replace bank tellers. Yet even though the machines are now ubiquitous, there are actually more tellers today doing a wider variety of customer service tasks.
So trying to predict whether a new wave of technologies will create more jobs than it will destroy is not worth the effort, and even the experts are split 50-50.
It’s particularly pointless given that perhaps fewer than 5 percent of current occupations are likely to disappear entirely in the next decade, according to a detailed study by McKinsey.
Instead, let’s focus on the changes they’ll make to how people work.
Reuters/John Gress
It’s about tasks, not jobs
To understand why, it’s helpful to think of a job as made up of a collection of tasks that can be carried out in different ways when supported by new technologies.
And in turn, the tasks performed by different workers – colleagues, managers and many others – can also be rearranged in ways that make the best use of technologies to get the work accomplished. Job design specialists call these “work systems.”
One of the McKinsey study’s key findings was that about a third of the tasks performed in 60 percent of today’s jobs are likely to be eliminated or altered significantly by coming technologies. In other words, the vast majority of our jobs will still be there, but what we do on a daily basis will change drastically.
To date, robotics and other digital technologies have had their biggest effects on mostly routine tasks like spell-checking and those that are dangerous, dirty or hard, such as lifting heavy tires onto a wheel on an assembly line. Advances in AI and machine learning will significantly expand the array of tasks and occupations affected.
Reuters/Vasily Fedosenko
Creating an integrated strategy
We have been exploring these issues for years as part of our ongoing discussions on how to remake labor for the 21st century. In our recently published book, “Shaping the Future of Work: A Handbook for Change and a New Social Contract,” we describe why society needs an integrated strategy to gain control over how future technologies will affect work.
And that strategy starts with helping define the problems humans want new technologies to solve. We shouldn’t be leaving this solely to their inventors.
Fortunately, some engineers and AI experts are recognizing that the end users of a new technology must have a central role in guiding its design to specify which problems they’re trying to solve.
The second step is ensuring that these technologies are designed alongside the work systems with which they will be paired. A so-called simultaneous design process produces better results for both the companies and their workers compared with a sequential strategy – typical today – which involves designing a technology and only later considering the impact on a workforce.
An excellent illustration of simultaneous design is how Toyota handled the introduction of robotics onto its assembly lines in the 1980s. Unlike rivals such as General Motors that followed a sequential strategy, the Japanese automaker redesigned its work systems at the same time, which allowed it to get the most out of the new technologies and its employees. Importantly, Toyota solicited ideas for improving operations directly from workers.
In doing so, Toyota achieved higher productivity and quality in its plants than competitors like GM that invested heavily in stand-alone automation before they began to alter work systems.
Similarly, businesses that tweaked their work systems in concert with investing in IT in the 1990s outperformed those that didn’t. And health care companies like Kaiser Permanente and others learned the same lesson as they introduced electronic medical records over the past decade.
Each example demonstrates that the introduction of a new technology does more than just eliminate jobs. If managed well, it can change how work is done in ways that can both increase productivity and the level of service by augmenting the tasks humans do.
Reuters/Yuriko Nakao
Worker wisdom
But the process doesn’t end there. Companies need to invest in continuous training so their workers are ready to help influence, use and adapt to technological changes. That’s the third step in getting the most out of new technologies.
And it needs to begin before they are introduced. The important part of this is that workers need to learn what some are calling “hybrid” skills: a combination of technical knowledge of the new technology with aptitudes for communications and problem-solving.
Companies whose workers have these skills will have the best chance of getting the biggest return on their technology investments. It is not surprising that these hybrid skills are now in high and growing demand and command good salaries.
None of this is to deny that some jobs will be eliminated and some workers will be displaced. So the final element of an integrated strategy must be to help those displaced find new jobs and compensate those unable to do so for the losses endured. Ford and the United Auto Workers, for example, offered generous early retirement benefits and cash severance payments in addition to retraining assistance when the company downsized from 2007 to 2010.
Examples like this will need to become the norm in the years ahead. Failure to treat displaced workers equitably will only widen the gaps between winners and losers in the future economy that are now already all too apparent.
In sum, companies that engage their workforce when they design and implement new technologies will be best-positioned to manage the coming AI revolution. By respecting the fact that today’s workers, like those before them, understand their jobs better than anyone and the many tasks they entail, they will be better able to “give wisdom to the machines.”
| 2017-08-30T00:00:00 |
2017/08/30
|
https://theconversation.com/robots-wont-steal-our-jobs-if-we-put-workers-at-center-of-ai-revolution-82474
|
[
{
"date": "2017/08/30",
"position": 64,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 61,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 62,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 67,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 63,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 64,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 69,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 67,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 68,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 66,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 75,
"query": "robotics job displacement"
}
] |
The Impact of Automation on Employment - Part I
|
The Impact of Automation on Employment
|
https://www.ncci.com
|
[] |
Over the same period, US manufacturing labor productivity grew 140.1% III. Automation in manufacturing has decreased production costs, making US ...
|
Page Content
Automation has the potential to transform future jobs and the structure of the labor force. As we discussed in the March edition of the QEB, automation in manufacturing has steadily decreased costs for decades, making US manufactures more competitive while also reducing the amount of labor required to produce them. Looking forward, technical advances in computing power, artificial intelligence, and robotics have created the potential for automation to penetrate deeply into occupations beyond manufacturing. The prospect that future automation might transform jobs and the labor force on a systemic scale raises some important questions for workers compensation:
What jobs are more susceptible to automation and what jobs are less susceptible?
How is the composition of employment across economic sectors likely to change?
What happens to workers displaced from occupations impacted by automation?
Because of the breadth of this topic, we will present our analysis in two parts. This edition of the QEB provides historical context for automation and explains why automation in the future has the potential to change labor markets more dramatically than in the past. We review several recent studies that quantify the potential for automation expansion across occupations and economic sectors, and address the question of which jobs are most susceptible to automation. In a follow-up piece to be published later, we will present scenarios for automation penetration across different economic sectors comprising the US economy, from which we will address the questions above concerning changes in the composition of the labor force and what could happen to workers displaced by automation.
AUTOMATION HISTORICALLY
Automation is not a new occurrence, and has contributed to significant shifts in employment throughout the 20th century. With the widespread adoption of the gas-powered tractor in the early 20th century, farmers experienced many benefits including the more efficient use of labor and increased productionI. This contributed to a decline in agricultural employment from close to 40% of total employment in 1900 to less than 2% since 2000, as shown in Figure 1.
Likewise, manufacturing’s share of employment has also declined from a peak of over 25% in the 1950s to less than 10% currently. Much of this decline is due to automation. A Ball State University study found that 87% of the job losses in manufacturing from 2000 to 2010 were due to automation, while 13% were due to globalization and tradeII.
Automation has also contributed to an increase in output, as seen in Figure 2. Since 1990, manufacturing output grew 71.8% while manufacturing employment fell 30.7%. This means that in 2016 the United States produced almost 72% more goods than in 1990, but with only about 70% of the workers. Over the same period, US manufacturing labor productivity grew 140.1%III. Automation in manufacturing has decreased production costs, making US manufactures less expensive and more competitive by reducing the amount of labor required to produce them.
Notwithstanding these dramatic impacts, only a small portion of manufacturing tasks are currently automated. By one estimate, only about 10% of manufacturing tasks globally were performed by robots in 2015. But that percentage is expected to rise to 25% by 2025 as robots become less expensive and easier to program, making them more accessible, particularly to small factoriesIV.
AUTOMATION PENETRATION ACROSS SECTORS
Automation is starting to take hold in other sectors beyond manufacturing. Some well-known examples include kiosks and tablets to place orders and pay in restaurants, robots to process packages in warehouses, and self-driving trucks in transportationV.
Until recently, automation displaced routine tasks that were predictable and could be easily programmed. This included assembly line robots in factories and computers to replace certain occupations such as switchboard operators. Today, advances in artificial intelligence and machine learning enable software to detect patterns in data, allowing some nonroutine tasks and judgmental decisions to be automated. Combined with advances in mobile robotics, machine learning can even permit nonroutine manual tasks to be automated—tasks that could only be performed by humans in the past.
Two recent major studies have attempted to identify which tasks are most susceptible to automation with current technology: "A Future That Works: Automation, Employment and Productivity," by the McKinsey Global InstituteVI; and "The Future of Employment: How Susceptible Are Jobs to Computerisation?" by Carl Frey and Michael Osborne at the University of OxfordVII. We discuss the findings of these studies in the following two sections. Subsequent sections contain a brief survey of related studies by other authors, and a discussion of how the labor market impacts envisioned by recent research on automation differs from more conventional labor market forecasts, such as those published by the US Bureau of Labor Statistics.
MCKINSEY STUDY
Using data from the US Department of Labor for 800 occupations, the McKinsey study identified 2,000 distinct work activities. Each work activity requires some combination of 18 performance capabilities, which come from five groups: sensory perception, cognitive capabilities, natural language processing, social and emotional capabilities, and physical capabilities. For each occupation and activity, McKinsey determined which performance capabilities are demanded, and whether the required level of performance is below, at, or above a median level of human performance. McKinsey then rated the potential for existing technology to substitute for humans in each performance capability in each occupation and activity.
Finally, McKinsey aggregated its 2,000 work activities into seven broad categories. Table 1 shows the average percentage of time spent on each activity category across all occupations, as well as its potential for automation. For example, a 69% automation potential for processing data means that over two-thirds of the time currently spent on this activity by human workers across all occupations might be saved by automation with existing technology.
McKinsey estimates that only 7% of time is spent managing and developing people, and that this is the work activity with the lowest automation potential of 9%. The next three work activities—applying expertise, interfacing, and performing unpredictable physical activities—make up 42% of time, with automation potentials ranging from 18% to 26%. The three remaining categories—collecting data, processing data, and performing predictable physical activities—comprise 51% of all work activities and have high automation potentials ranging from 64% to 81%.
Figure 3 presents McKinsey’s estimates of automation potential by economic sector. Automation potential in an economic sector depends on the shares of time spent on the different work activity categories from Table 1. Circle sizes indicate the amounts of time spent on each work activity, while their colors indicate the automation potential for that activity in the indicated sector. The bar at the right indicates the overall automation potential for the associated sector.
With automation potential of 73%, accommodation and food services is the sector most susceptible to automation with existing technology. A large percentage of work time in this sector is spent on predictable physical activities, the activity category with highest automation potential. Manufacturing and agriculture rank next highest, suggesting that historical automation trends in these sectors are likely to continue. Least susceptible to automation are jobs relating to healthcare, information, professional and management services, and education. These jobs demand higher shares of time spent on managing and developing people and applying expertise to decision making, the two activity categories with the lowest automation potential.
McKinsey’s estimates indicate that the potential for workplace automation depends on the type of occupation. While fewer than 5% of all occupations might be fully automated, about 60% of occupations could have at least 30% of their activities automated. In McKinsey’s baseline scenario, 49% of all of today’s work activities may be automated using currently available technology by 2055. However, McKinsey throws a wide confidence band around this timeline by suggesting that the same degree of automation penetration might occur 20 years earlier in 2035, or 20 years later in 2075. Clearly, estimating the technical potential for automation is one thing, but estimating the speed with which automation occurs is another. McKinsey notes that numerous factors are likely to impact the timing and extent of automation penetration in different occupations, including technical complications in specific job applications, capital investment necessary to develop and deploy solutions, labor costs including benefits and liabilities, and regulatory and social acceptance.
OXFORD STUDY
The Oxford study is based on descriptions of specific tasks for 702 different occupations from the US Department of Labor. Using these descriptions and working with machine learning experts, authors Frey and Osborne identified 70 occupations that they consider to be either entirely automatable or entirely nonautomatable. In doing so, they considered the degree to which each occupation requires any of three bottlenecks to automation: perception and manipulation, creative intelligence, and social intelligence. Perception and manipulation includes finger dexterity, manual dexterity, and awkward positions; creative intelligence includes originality and fine arts; and social intelligence includes social perceptiveness, negotiation, persuasion, and assisting and caring for others. Finally, using these 70 occupations as a basis, Frey and Osborne applied a classification algorithm to assign a degree of automation potential between zero and one to each of the remaining 632 occupations.
Their results suggest that 47% of total US employment is in occupations at high risk for automation (probabilities greater than 70%), while 19% of employment is in occupations at medium risk (probabilities between 30% and 70%), and 33% of employment is in occupations at low risk for automation (probabilities less than 30%). Categories of occupations comprising the highest shares of employment in high- and low-risk automation groups are listed in Table 2.
Although they use different approaches, the Oxford and McKinsey studies reach similar conclusions. Occupations with higher (or lower) automation risk in the Oxford study are exactly those that are typical in the economic sectors identified by the McKinsey study as having similar degrees of automation risk. Economy-wide estimates of automation potential are also similar for both studies: the McKinsey study estimates that 49% of today’s work activities could be automated with currently available technology; the Oxford study concludes that 47% of US employment is in occupations at high risk for automation.
Like the McKinsey study, the Oxford study makes no definite predictions about a timeline for automation, but emphasizes that the speed of automation in various occupations will depend on a variety of factors. Among these, the Oxford study points to the difficulty of overcoming automation bottlenecks like perception and social intelligence, resistance from stakeholders, and the level of wages relative to the cost of automation. Both studies conclude that industries or occupations with a higher potential for automation are likely to be impacted sooner than those with lower potential.
ADDITIONAL STUDIES
This section contains a survey of additional research on automation, some of which extends results from the McKinsey and Oxford studies.
Citigroup . VIII The Citigroup study extends the Oxford estimates both globally and regionally. Using World Bank data for over 50 countries, Citigroup found that the average share of employment at high risk for automation across Organization for Economic Cooperation and Development (OECD) countries is 57%, with shares as high as 69% in India and 77% in China. Citigroup also estimates the share of employment at high risk of automation for US cities: Fresno, CA and Las Vegas, NV had the highest shares of employment at risk of automation, 53.8% and 49.1%, respectively. Cities at high automation risk tend to specialize in industries such as services, sales, office support, and manufacturing. These also tend to be cities with lower wages. Boston, MA and Washington, DC had the lowest employment shares at risk of automation, 38.4% for both. Cities at lower risk generally have more diversified labor forces and more skilled workers in technological industries.
. The Citigroup study extends the Oxford estimates both globally and regionally. University of Redlands . IX The University of Redlands study combines the Oxford study’s estimates with employment data to identify US metropolitan areas most susceptible to automation. Most US metropolitan areas are at risk of losing over 55% of their current jobs due to automation. Metropolitan areas Las Vegas, NV; El Paso TX; and Riverside-San Bernardino, CA have more than 63% of their jobs at risk of automation. These areas have concentrations of lower wage jobs relating to office support, food preparation and serving, sales, and transportation and material moving. High-tech metropolitan areas such as Boston and Silicon Valley have fewer jobs at risk.
. The University of Redlands study combines the Oxford study’s estimates with employment data to identify US metropolitan areas most susceptible to automation. OECD . X This study evaluates the potential for automation for 21 OECD countries. It argues that the McKinsey and Oxford studies may overstate the number of workers at risk from automation for several reasons: First, the OECD stresses the importance of economic, legal, or societal factors that may inhibit the adoption of new technologies. Where the cost of the new technology exceeds the cost of labor, adoption will be slower. Legal questions may slow technology adoption: for example, assigning liability in accidents with a driverless car. People may prefer that certain tasks be performed by humans instead of machines, like nursing. Second, the OECD considers what might happen to human workers in a more automated workplace. As new technology is adopted, workers may switch within their workplaces to more complex tasks that cannot be automated, such as programming and monitoring. New technology related to automation may create jobs or raise wages. Job creation related to production and service of automation tools and technologies. By reducing production costs, automation will lead to higher output in affected industries, ameliorating the displacement effect on workers. Automation which increases worker productivity may also increase wages.
. This study evaluates the potential for automation for 21 OECD countries. It argues that the McKinsey and Oxford studies may overstate the number of workers at risk from automation for several reasons:
DEPARTURE FROM CONVENTIONAL LABOR FORCE FORECASTS
While subject to a lot of conjecture and uncertainty, the McKinsey and Oxford studies point to magnitudes of feasible job automation that go far beyond conventional forecasts for future output and employment in the US economy. Conventional forecasts, like those from the US Bureau of Labor Statistics (BLS) or Moody’s Analytics, are typically based on econometric models that extrapolate historical labor productivity trends into the future. In contrast, the McKinsey and Oxford studies suggest that automation penetration could accelerate dramatically in many economic sectors, with the consequence that labor productivity in those sectors would also jump relative to historical trends.
For example, the most recent output and employment forecast from the BLS projects a cumulative labor productivity gain from 2017 to 2024 of 12% for the US economy excluding government. Over the same period, if automation were to substitute for BLS’s estimate of new jobs at McKinsey’s 49% estimate, then US labor productivity would instead increase 14%.
Similarly, the BLS’s output and employment forecast indicates cumulative productivity gains over the same period of 7% in healthcare and social assistance, and 11% in construction—two sectors with high rates of projected employment growth. If we apply McKinsey’s estimates for technically feasible automation in these sectors to substitute for labor in the BLS’s estimates of new job creation, then the labor productivity gain in healthcare and social assistance would increase to 12%, and in construction to 16%. More automation means that workers in these sectors could become substantially more productive, but also that fewer workers would then be needed to produce a given amount of output or services.
WHAT MIGHT AUTOMATION MEAN FOR THE FUTURE LABOR MARKET?
In a follow-up piece to this QEB, we will present scenarios for automation penetration across economic sectors of interest to workers compensation, which collectively comprise most covered employment in the US. Using McKinsey’s estimates for potential automation penetration in different economic sectors, we will address the following questions at the national level:
What happens to workers in economic sectors impacted by automation? In an industry or sector, automation increases worker productivity, which means that the same amount of output could be produced with fewer workers. However, more productive workers also earn higher wages, and lower production costs mean lower prices and more output demanded, which tends to raise employment. Total employment in a sector will depend on the interplay of these effects.
How might the distribution of employment across economic sectors change as the result of automation? In the national economy, employment shares can be expected to shift from sectors more impacted by automation to other sectors that are less impacted. How might re-equilibration in the labor market affect the overall size of the labor force and the employment shares of different industries and occupations?
FORTHCOMING RESEARCH: SCENARIOS FOR AUTOMATION AND THE FUTURE LABOR MARKET
A follow-up piece will present scenarios for the potential impact of automation on the US labor market, addressing what might happen to workers in economic sectors impacted by automation, and how the distribution of employment across economic sectors may change as a result of automation.
© Copyright 2017 National Council on Compensation Insurance, Inc. All Rights Reserved.
THE RESEARCH ARTICLES AND CONTENT DISTRIBUTED BY NCCI ARE PROVIDED FOR GENERAL INFORMATIONAL PURPOSES ONLY AND ARE PROVIDED “AS IS.” NCCI DOES NOT GUARANTEE THEIR ACCURACY OR COMPLETENESS NOR DOES NCCI ASSUME ANY LIABILITY THAT MAY RESULT IN YOUR RELIANCE UPON SUCH INFORMATION. NCCI EXPRESSLY DISCLAIMS ANY AND ALL WARRANTIES OF ANY KIND INCLUDING ALL EXPRESS, STATUTORY AND IMPLIED WARRANTIES INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
Note: Analysis and charts prepared in May and June 2017.
| 2017-10-10T00:00:00 |
https://www.ncci.com/Articles/Pages/II_Insights_QEB_Impact-Automation-Employment-Q2-2017-Part1.aspx
|
[
{
"date": "2017/10/10",
"position": 57,
"query": "job automation statistics"
},
{
"date": "2017/10/10",
"position": 57,
"query": "job automation statistics"
},
{
"date": "2017/10/10",
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},
{
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{
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},
{
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{
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{
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{
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{
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] |
|
Most US workers want to see more AI and robots in the office | ZDNET
|
Most US workers want to see more AI and robots in the office
|
https://www.zdnet.com
|
[
"Eileen Brown",
"Oct.",
"At A.M. Pt"
] |
Although workers want robots in the workplace to give them more time to do their primary job duties, almost all still want the human touch ...
|
Video: No, robots won't take your job -- just part of it
Currently, workers spend only 44 percent of their time on their primary duties, mainly because email, meetings, and non-essential tasks take up the bulk of their working week.
Cloud-based enterprise work management solutions provider Workfront released a report showing that US workers are largely optimistic about the impact automation will have in the workplace.
Its annual State of Enterprise Work report aims to capture how work is currently being done and what challenges office workers see in the present.
Workfront surveyed 2001 US residents who worked for companies with over 500 employees. These employees worked on computers and collaborated on projects. It wanted to record how workers see current workplace trends playing out in the near future.
The report highlighted three major themes:
Wasteful practices and tools, namely email and meetings, continue to thwart worker productivity. Poorly used meetings and email topped the list of things that prevent knowledge workers from getting work done. US workers have an average of 199 unopened emails in their inboxes at any given time. This indicates that email has reached the limits of its effectiveness as a work management tool.
Flexibility is on the rise. The report shows that 79 percent of knowledge workers now have the ability to use flexible working. Companies are seeing the benefits of allowing their team members to work outside the office and outside standard business hours. The average knowledge worker now works from home for eight hours every week.
Automation is the future. Four out of five knowledge workers see automation as a chance to rethink work in new and exciting ways. Sixty-nine percent believe work automation will give them back time to perform their primary job duties better. The only uncertainty is in how much of work will ultimately be done by machines and how much will still require the human touch
So, while the overwhelming view on automation was positive, one in three (34 percent) feared that "men and women in my line of work will be competing with robots, machines, and/or artificial intelligence." Eighty-six percent of respondents agreed with the sentiment that "the use of automation in the workplace will let us think of work in new and innovative ways," whilst 82 percent expressed excitement at the chance "to learn new things as the workforce moves toward more automation."
Read also: Chuwi SurBook hands-on: A high-performing Surface clone | Mgcool Explorer 1S hands-on: A low-cost action camera with some great features | Hands on with the Destek V4 VR Headset: Great quality for an affordable price
Alex Shootman, president and CEO of Workfront, said: "Popular culture may depict automation in dystopian terms, but the reality is that the majority of workers are optimistic about automation because they understand how it helps them focus on high-value tasks at work.:
Although automation seems like it is poised to take over our lives at home and work, there is hope. More than half or respondents (52 percent) agreed that "no matter how sophisticated artificial intelligence becomes, there will always be the need for the human touch in the workplace."
Even though humans might be just fixing the machines in the fully automated office.
Previous and related coverage
As AI floods the market, which chatbots deliver the best ROI for enterprises?
A recent report shows that AI and chatbots can bring a huge ROI (return on investment) for the enterprise. But which solution should you choose?
Although chatbot use rises, we still prefer talking to humans
Research has forecast that bot interactions in the banking sector, completed without human assistance, will move from 12 percent to over 90 percent in 2022. Will this mean the end of the contact centre agent as bots take over?
Will robots ever really become part of our daily lives?
Tesla CEO Elon Musk sees artificial intelligence as the future. Professor Stephen Hawking thinks robots will one day destroy all human life. Who do you side with on the AI debate?
| 2017-10-24T00:00:00 |
https://www.zdnet.com/article/four-out-of-five-workers-want-to-see-more-ai-and-robots-in-the-office/
|
[
{
"date": "2017/10/24",
"position": 92,
"query": "AI workers"
}
] |
|
How Many Jobs can be Automated? - Medium
|
How Many Jobs can be Automated?
|
https://medium.com
|
[
"Joe Psotka"
] |
Automation of labor is an increasingly important topic to researchers, businesses, and unions. There are so many opinions about how many ...
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Member-only story How Many Jobs can be Automated? Joe Psotka 6 min read · Oct 27, 2017 -- Share
Automation of labor is an increasingly important topic to researchers, businesses, and unions. There are so many opinions about how many jobs can be automated that range from 10% to 75% so that it is very hard to make any sense of the data. Existing estimates are wide ranging. One of the best studies, Frey and Osborne estimate that 47% of U.S. employment is at high risk of computerization in the foreseeable future, while an alternative OECD study concludes a more modest 9% of employment is at risk. How many of these estimators have any real idea about the capabilities of AI and robots?Very few can have, since the whole field is in rapid transition.
An article by Robert D. Atkinson and John Wu in May of this year pooh - poohs the whole idea of dramatic exponential change in employment by showing that job churn has actually decreased since the last century when agricultural jobs were dramatically disappearing. They argue that this dynamic will NOT change in the future for the simple reason that consumer wants are far from satisfied. This seems a potentially troubling argument, since job loss implies a loss of consumer demand. A future dystopia would leave most consumer desires unsatisfied.
| 2017-10-31T00:00:00 |
2017/10/31
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https://medium.com/@joepsotka/how-many-jobs-can-be-automated-4505220c16e7
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