10 Breakthrough Technologies 2017

These technologies all have staying power. They will affect the economy and our politics, improve medicine, or influence our culture. Some are unfolding now; others will take a decade or more to develop. But you should know about all of them right now.

1. Reversing Paralysis 
   Scientists are making remarkable progress at using brain implants to restore the freedom of movement that spinal cord injuries take away.
   
   
2. Self-Driving Trucks Tractor-trailers without a human at the wheel will soon barrel onto highways near you. What will this mean for the nation’s 1.7 million truck drivers?
   
   
3. Paying with Your Face
   Face-detecting systems in China now authorize payments, provide access to facilities, and track down criminals. Will other countries follow?
   
   
4. Practical Quantum Computing
   Advances at Google, Intel, and several research groups indicate that computers with previously unimaginable power are finally within reach.
   
    
5. The 360-Degree Selfie
   Inexpensive cameras that make spherical images are opening a new era in photography and changing the way people share stories.
   
    
6. Hot Solar Cells
   By converting heat to focused beams of light, a new solar device could create cheap and continuous power.
   
    
7. Gene Therapy 2.0
   Scientists have solved fundamental problems that were holding back cures for rare hereditary disorders. Next we’ll see if the same approach can take on cancer, heart disease, and other common illnesses.
   
   
8. The Cell Atlas
   Biology’s next mega-project will find out what we’re really made of.
   
   
9. Botnets of Things
   The relentless push to add connectivity to home gadgets is creating dangerous side effects that figure to get even worse.
   
   
10. Reinforcement Learning
    By experimenting, computers are figuring out how to do things that no programmer could teach them.
    
    

ORIGINAL: Technology Review

It’s No Myth: Robots and Artificial Intelligence Will Erase Jobs in Nearly Every Industry

It’s No Myth: Robots and Artificial Intelligence Will Erase Jobs in Nearly Every Industry AI, Fulfillment, Human, Jobs, Robotics, Self Driving Cars, Google, Telecommuting,

Image Credit: Tupungato/Shutterstock.com

With the unemployment rate falling to 5.3 percent, the lowest in seven years, policy makers are heaving a sigh of relief. Indeed, with the technology boom in progress, there is a lot to be optimistic about.

• Manufacturing will be returning to U.S. shores with robots doing the job of Chinese workers; 
• American carmakers will be mass-producing self-driving electric vehicles; 
• technology companies will develop medical devices that greatly improve health and longevity; 
• we will have unlimited clean energy and 3D print our daily needs. 

The cost of all of these things will plummet and make it possible to provide for the basic needs of every human being.

I am talking about technology advances that are happening now, which will bear fruit in the 2020s.

But policy makers will have a big new problem to deal with: the disappearance of human jobs. Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers. Almost every industry and profession will be impacted and this will create a new set of social problems — because most people can’t adapt to such dramatic change.

If we can develop the economic structures necessary to distribute the prosperity we are creating, most people will no longer have to work to sustain themselves. They will be free to pursue other creative endeavors. The problem, however, is that without jobs, they will not have the dignity, social engagement, and sense of fulfillment that comes from work. The life, liberty and pursuit of happiness that the constitution entitles us to won’t be through labor, it will have to be through other means.

It is imperative that we understand the changes that are happening and find ways to cushion the impacts.

The technology elite who are leading this revolution will reassure you that there is nothing to worry about because we will create new jobs just as we did in previous centuries when the economy transitioned from agrarian to industrial to knowledge-based. Tech mogul Marc Andreessen has called the notion of a jobless future a “Luddite fallacy,” referring to past fears that machines would take human jobs away. Those fears turned out to be unfounded because we created newer and better jobs and were much better off.

True, we are living better lives. But what is missing from these arguments is the timeframe over which the transitions occurred. The industrial revolution unfolded over centuries. Today’s technology revolutions are happening within years. We will surely create a few intellectually-challenging jobs, but we won’t be able to retrain the workers who lose today’s jobs. They will experience the same unemployment and despair that their forefathers did. It is they who we need to worry about.

The first large wave of unemployment will be caused by self-driving cars. These will provide tremendous benefit by eliminating traffic accidents and congestion, making commuting time more productive, and reducing energy usage. But they will eliminate the jobs of millions of taxi and truck drivers and delivery people. Fully-automated robotic cars are no longer in the realm of science fiction; you can see Google’s cars on the streets of Mountain View, Calif. There are also self-driving trucks on our highways and self-driving tractors on farms. Uber just hired away dozens of engineers from Carnegie Mellon University to build its own robotic cars. It will surely start replacing its human drivers as soon as its technology is ready — later in this decade. As Uber CEO Travis Kalanick reportedly said in an interview, “The reason Uber could be expensive is you’re paying for the other dude in the car. When there is no other dude in the car, the cost of taking an Uber anywhere is cheaper. Even on a road trip.”

The dude in the driver’s seat will go away.

Manufacturing will be the next industry to be transformed. Robots have, for many years, been able to perform surgery, milk cows, do military reconnaissance and combat, and assemble goods. But they weren’t dexterous enough to do the type of work that humans do in installing circuit boards. The latest generation of industrial robots by ABB of Switzerland and Rethink Robotics of Boston can do this however. ABB’s robot, Yumi, can even thread a needle. It costs only $40,000.

China, fearing the demise of its industry, is setting up fully-automated robotic factories in the hope that by becoming more price-competitive, it can continue to be the manufacturing capital of the world. But its advantage only holds up as long as the supply chains are in China and shipping raw materials and finished goods over the oceans remains cost-effective. Don’t forget that our robots are as productive as theirs are; they too don’t join labor unions (yet) and will work around the clock without complaining. Supply chains will surely shift and the trickle of returning manufacturing will become a flood.

But there will be few jobs for humans once the new, local factories are built.

With advances in artificial intelligence, any job that requires the analysis of information can be done better by computers. This includes the jobs of physicians, lawyers, accountants, and stock brokers. We will still need some humans to interact with the ones who prefer human contact, but the grunt work will disappear. The machines will need very few humans to help them.

This jobless future will surely create social problems — but it may be an opportunity for humanity to uplift itself. Why do we need to work 40, 50, or 60 hours a week, after all? Just as we were better off leaving the long and hard agrarian and factory jobs behind, we may be better off without the mindless work at the office. What if we could be working 10 or 15 hours per week from anywhere we want and have the remaining time for leisure, social work, or attainment of knowledge?

Yes, there will be a booming tourism and recreation industry and new jobs will be created in these — for some people.

There are as many things to be excited about as to fear. If we are smart enough to develop technologies that solve the problems of disease, hunger, energy, and education, we can — and surely will — develop solutions to our social problems. But we need to start by understanding where we are headed and prepare for the changes. We need to get beyond the claims of a Luddite fallacy — to a discussion about the new future.

ORIGINAL: Singularity Hub

BY VIVEK WADHWA

ON JUL 07, 2015

Vivek Wadhwa is a fellow at Rock Center for Corporate Governance at Stanford University, director of research at Center for Entrepreneurship and Research Commercialization at Duke, and distinguished fellow at Singularity University.

His past appointments include Harvard Law School, University of California Berkeley, and Emory University. Follow him on Twitter @wadhwa.

An executive’s guide to machine learning

An executive’s guide to machine learning

It’s no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix.

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.

Stanford's Fei-Fei Li

In 2007 Fei-Fei Li, the head of Stanford’s Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 Last November, Li’s team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy. IBM’s Watson machine relied on a similar self-generated scoring system among hundreds of potential answers to crush the world’s best Jeopardy! players in 2011.

Dazzling as such feats are, machine learning is nothing like learning in the human sense (yet). But what it already does extraordinarily well—and will get better at—is relentlessly chewing through any amount of data and every combination of variables. Because machine learning’s emergence as a mainstream management tool is relatively recent, it often raises questions. In this article, we’ve posed some that we often hear and answered them in a way we hope will be useful for any executive. Now is the time to grapple with these issues, because the competitive significance of business models turbocharged by machine learning is poised to surge. Indeed, management author Ram Charan suggests that “any organization that is not a math house now or is unable to become one soon is already a legacy company.2

1. How are traditional industries using machine learning to gather fresh business insights?

Well, let’s start with sports. This past spring, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum, a California machine-learning start-up. By digitizing the past few seasons’ games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, “a bad shooter who takes good shots and a good shooter who takes bad shots”—and to adjust his decisions accordingly.

You can’t get more venerable or traditional than General Electric, the only member of the original Dow Jones Industrial Average still around after 119 years. GE already makes hundreds of millions of dollars by crunching the data it collects from deep-sea oil wells or jet engines to optimize performance, anticipate breakdowns, and streamline maintenance. But Colin Parris, who joined GE Software from IBM late last year as vice president of software research, believes that continued advances in data-processing power, sensors, and predictive algorithms will soon give his company the same sharpness of insight into the individual vagaries of a jet engine that Google has into the online behavior of a 24-year-old netizen from West Hollywood.

2. What about outside North America?

In Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10 percent increases in sales of new products, 20 percent savings in capital expenditures, 20 percent increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built microtargeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene.

Closer to home, as a recent article in McKinsey Quarterly notes,3 our colleagues have been applying hard analytics to the soft stuff of talent management. Last fall, they tested the ability of three algorithms developed by external vendors and one built internally to forecast, solely by examining scanned résumés, which of more than 10,000 potential recruits the firm would have accepted. The predictions strongly correlated with the real-world results. Interestingly, the machines accepted a slightly higher percentage of female candidates, which holds promise for using analytics to unlock a more diverse range of profiles and counter hidden human bias.

As ever more of the analog world gets digitized, our ability to learn from data by developing and testing algorithms will only become more important for what are now seen as traditional businesses. Google chief economist Hal Varian calls this “computer kaizen.” For “just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he says, “so continuous [and often automatic] experimentation will improve the way we optimize business processes in our organizations.”4

3. What were the early foundations of machine learning?

Machine learning is based on a number of earlier building blocks, starting with classical statistics. Statistical inference does form an important foundation for the current implementations of artificial intelligence. But it’s important to recognize that classical statistical techniques were developed between the 18th and early 20th centuries for much smaller data sets than the ones we now have at our disposal. Machine learning is unconstrained by the preset assumptions of statistics. As a result, it can yield insights that human analysts do not see on their own and make predictions with ever-higher degrees of accuracy.

More recently, in the 1930s and 1940s, the pioneers of computing (such as Alan Turing, who had a deep and abiding interest in artificial intelligence) began formulating and tinkering with the basic techniques such as neural networks that make today’s machine learning possible. But those techniques stayed in the laboratory longer than many technologies did and, for the most part, had to await the development and infrastructure of powerful computers, in the late 1970s and early 1980s. That’s probably the starting point for the machine-learning adoption curve. New technologies introduced into modern economies—the steam engine, electricity, the electric motor, and computers, for example—seem to take about 80 years to transition from the laboratory to what you might call cultural invisibility. The computer hasn’t faded from sight just yet, but it’s likely to by 2040. And it probably won’t take much longer for machine learning to recede into the background.

4. What does it take to get started?

C-level executives will best exploit machine learning if they see it as a tool to craft and implement a strategic vision. But that means putting strategy first. Without strategy as a starting point, machine learning risks becoming a tool buried inside a company’s routine operations: it will provide a useful service, but its long-term value will probably be limited to an endless repetition of “cookie cutter” applications such as models for acquiring, stimulating, and retaining customers.

We find the parallels with M&A instructive. That, after all, is a means to a well-defined end. No sensible business rushes into a flurry of acquisitions or mergers and then just sits back to see what happens. Companies embarking on machine learning should make the same three commitments companies make before embracing M&A. Those commitments are,

• first, to investigate all feasible alternatives; 
• second, to pursue the strategy wholeheartedly at the C-suite level; and, 
• third, to use (or if necessary acquire) existing expertise and knowledge in the C-suite to guide the application of that strategy.

The people charged with creating the strategic vision may well be (or have been) data scientists. But as they define the problem and the desired outcome of the strategy, they will need guidance from C-level colleagues overseeing other crucial strategic initiatives. More broadly, companies must have two types of people to unleash the potential of machine learning.

• “Quants” are schooled in its language and methods. 
• “Translators” can bridge the disciplines of data, machine learning, and decision making by reframing the quants’ complex results as actionable insights that generalist managers can execute.

Access to troves of useful and reliable data is required for effective machine learning, such as Watson’s ability, in tests, to predict oncological outcomes better than physicians or Facebook’s recent success teaching computers to identify specific human faces nearly as accurately as humans do. A true data strategy starts with identifying gaps in the data, determining the time and money required to fill those gaps, and breaking down silos. Too often, departments hoard information and politicize access to it—one reason some companies have created the new role of chief data officer to pull together what’s required. Other elements include putting responsibility for generating data in the hands of frontline managers.

Start small—look for low-hanging fruit and trumpet any early success. This will help recruit grassroots support and reinforce the changes in individual behavior and the employee buy-in that ultimately determine whether an organization can apply machine learning effectively. Finally, evaluate the results in the light of clearly identified criteria for success.

5. What’s the role of top management?

Behavioral change will be critical, and one of top management’s key roles will be to influence and encourage it. Traditional managers, for example, will have to get comfortable with their own variations on A/B testing, the technique digital companies use to see what will and will not appeal to online consumers. Frontline managers, armed with insights from increasingly powerful computers, must learn to make more decisions on their own, with top management setting the overall direction and zeroing in only when exceptions surface. Democratizing the use of analytics—providing the front line with the necessary skills and setting appropriate incentives to encourage data sharing—will require time.

C-level officers should think about applied machine learning in three stages: machine learning 1.0, 2.0, and 3.0—or, as we prefer to say,

1. description, 
2. prediction, and 
3. prescription. 

They probably don’t need to worry much about the description stage, which most companies have already been through. That was all about collecting data in databases (which had to be invented for the purpose), a development that gave managers new insights into the past. OLAP—online analytical processing—is now pretty routine and well established in most large organizations.

There’s a much more urgent need to embrace the prediction stage, which is happening right now. Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future—for example, by helping credit-risk officers at banks to assess which customers are most likely to default or by enabling telcos to anticipate which customers are especially prone to “churn” in the near term (exhibit).

Exhibit

A frequent concern for the C-suite when it embarks on the prediction stage is the quality of the data. That concern often paralyzes executives. In our experience, though, the last decade’s IT investments have equipped most companies with sufficient information to obtain new insights even from incomplete, messy data sets, provided of course that those companies choose the right algorithm. Adding exotic new data sources may be of only marginal benefit compared with what can be mined from existing data warehouses. Confronting that challenge is the task of the “chief data scientist.”

Prescription—the third and most advanced stage of machine learning—is the opportunity of the future and must therefore command strong C-suite attention. It is, after all, not enough just to predict what customers are going to do; only by understanding why they are going to do it can companies encourage or deter that behavior in the future. Technically, today’s machine-learning algorithms, aided by human translators, can already do this. For example, an international bank concerned about the scale of defaults in its retail business recently identified a group of customers who had suddenly switched from using credit cards during the day to using them in the middle of the night. That pattern was accompanied by a steep decrease in their savings rate. After consulting branch managers, the bank further discovered that the people behaving in this way were also coping with some recent stressful event. As a result, all customers tagged by the algorithm as members of that microsegment were automatically given a new limit on their credit cards and offered financial advice.

The prescription stage of machine learning, ushering in a new era of man–machine collaboration, will require the biggest change in the way we work. While the machine identifies patterns, the human translator’s responsibility will be to interpret them for different microsegments and to recommend a course of action. Here the C-suite must be directly involved in the crafting and formulation of the objectives that such algorithms attempt to optimize.

6. This sounds awfully like automation replacing humans in the long run. Are we any nearer to knowing whether machines will replace managers?

It’s true that change is coming (and data are generated) so quickly that human-in-the-loop involvement in all decision making is rapidly becoming impractical. Looking three to five years out, we expect to see far higher levels of artificial intelligence, as well as the development of distributed autonomous corporations. These self-motivating, self-contained agents, formed as corporations, will be able to carry out set objectives autonomously, without any direct human supervision. Some DACs will certainly become self-programming.

One current of opinion sees distributed autonomous corporations as threatening and inimical to our culture. But by the time they fully evolve, machine learning will have become culturally invisible in the same way technological inventions of the 20th century disappeared into the background. The role of humans will be to direct and guide the algorithms as they attempt to achieve the objectives that they are given. That is one lesson of the automatic-trading algorithms which wreaked such damage during the financial crisis of 2008.

No matter what fresh insights computers unearth, only human managers can decide the essential questions, such as which critical business problems a company is really trying to solve. Just as human colleagues need regular reviews and assessments, so these “brilliant machines” and their works will also need to be regularly evaluated, refined—and, who knows, perhaps even fired or told to pursue entirely different paths—by executives with experience, judgment, and domain expertise.

The winners will be neither machines alone, nor humans alone, but the two working together effectively.

7. So in the long term there’s no need to worry?

It’s hard to be sure, but distributed autonomous corporations and machine learning should be high on the C-suite agenda. We anticipate a time when the philosophical discussion of what intelligence, artificial or otherwise, might be will end because there will be no such thing as intelligence—just processes. If distributed autonomous corporations act intelligently, perform intelligently, and respond intelligently, we will cease to debate whether high-level intelligence other than the human variety exists. In the meantime, we must all think about what we want these entities to do, the way we want them to behave, and how we are going to work with them.

About the authors

Dorian Pyle is a data expert in McKinsey’s Miami office, and Cristina San Jose is a principal in the Madrid office.

ORIGINAL: McKinsey

by Dorian Pyle and Cristina San Jose

June 2015

Meet Amelia, the AI Platform That Could Change the Future of IT

Chetah Dube. Image credit: Photography by Jesse Dittmar

Her name is Amelia, and she is the complete package: smart, sophisticated, industrious and loyal. No wonder her boss, Chetan Dube, can’t get her out of his head.

“My wife is convinced I’m having an affair with Amelia,” Dube says, leaning forward conspiratorially. “I have a great deal of passion and infatuation with her.”

He’s not alone. Amelia beguiles everyone she meets, and those in the know can’t stop buzzing about her. The blue-eyed blonde’s star is rising so fast that if she were a Hollywood ingénue or fashion model, the tabloids would proclaim her an “It” girl, but the tag doesn’t really apply. Amelia is more of an IT girl, you see. In fact, she’s all IT.

Amelia is an artificial intelligence platform created by Dube’s managed IT services firm IPsoft, a virtual agent avatar poised to redefine how enterprises operate by automating and enhancing a wide range of business processes. The product of an obsessive and still-ongoing 16-year developmental cycle, she—yes, everyone at IPsoft speaks about Amelia using feminine pronouns—
leverages cognitive technologies to interface with consumers and colleagues in astoundingly human terms,

• parsing questions, 
• analyzing intent and 
• even sensing emotions to resolve issues more efficiently and effectively than flesh-and-blood customer service representatives.

Install Amelia in a call center, for example, and her patent-pending intelligence algorithms absorb in a matter of seconds the same instruction manuals and guidelines that human staffers spend weeks or even months memorizing. Instead of simply recognizing individual words, Amelia grasps the deeper implications of what she reads, applying logic and making connections between concepts. She relies on that baseline information to reply to customer email and answer phone calls; if she understands the query, she executes the steps necessary to resolve the issue, and if she doesn’t know the answer, she scans the web or the corporate intranet for clues. Only when Amelia cannot locate the relevant information does she escalate the case to a human expert, observing the response and filing it away for the next time the same scenario unfolds.

Robert Reich: The Nightmarish Future for American Jobs and Incomes Is Here

Even knowledge-based jobs will disappear as wealth gets more concentrated at the top in the next 10 years.

Photo Credit: via YouTube

What will happen to American jobs, incomes, and wealth a decade from now?

Predictions are hazardous but survivable. In 1991, in my book The Work of Nations, I separated almost all work into three categories, and then predicted what would happen to each of them.

The first category I called "routine production services," which entailed the kind of repetitive tasks performed by the old foot soldiers of American capitalism through most of the twentieth century -- done over and over, on an assembly line or in an office.

I estimated that such work then constituted about one-quarter of all jobs in the United States, but would decline steadily as such jobs were replaced by 

• new labor-saving technologies and 
• by workers in developing nations eager to do them for far lower wages. 

I also assumed the pay of remaining routine production workers in America would drop, for similar reasons.

I was not far wrong.

The second category I called "in-person services." This work had to be provided personally because the "human touch" was essential to it. It included retail sales workers, hotel and restaurant workers, nursing-home aides, realtors, childcare workers, home health-care aides, flight attendants, physical therapists, and security guards, among many others.

In 1990, by my estimate, such workers accounted for about 30 percent of all jobs in America, and I predicted their numbers would grow because -- given that their services were delivered in person -- neither advancing technologies nor foreign-based workers would be able to replace them.

I also predicted their pay would drop. They would be competing with 

• a large number of former routine production workers, who could only find jobs in the "in-person" sector. 
• They would also be competing with labor-saving machinery such as automated tellers, computerized cashiers, automatic car washes, robotized vending machines, and self-service gas pumps -- 
• as well as "personal computers linked to television screens" through which "tomorrow's consumers will be able to buy furniture, appliances, and all sorts of electronic toys from their living rooms -- examining the merchandise from all angles, selecting whatever color, size, special features, and price seem most appealing, and then transmitting the order instantly to warehouses from which the selections will be shipped directly to their homes. 
• So, too, with financial transactions, airline and hotel reservations, rental car agreements, and similar contracts, which will be executed between consumers in their homes and computer banks somewhere else on the globe."

Here again, my predictions were not far off. But I didn't foresee how quickly advanced technologies would begin to make inroads even on in-person services. Ten years from now I expect Amazon will have wiped out many of today's retail jobs, and Google's self-driving car will eliminate many bus drivers, truck drivers, sanitation workers, and even Uber drivers.

The third job category I named "symbolic-analytic services." Here I included all the problem-solving, problem-identifying, and strategic thinking that go into the manipulation of symbols—data, words, oral and visual representations.

I estimated in 1990 that symbolic analysts accounted for 20 percent of all American jobs, and expected their share to continue to grow, as would their incomes, because the demand for people to do these jobs would continue to outrun the supply of people capable of doing them. This widening disconnect between symbolic-analytic jobs and the other two major categories of work would, I predicted, be the major force driving widening inequality.

Again, I wasn't far off. But I didn't anticipate how quickly or how wide the divide would become, or how great a toll inequality and economic insecurity would take. I would never have expected, for example, that the life expectancy of an American white woman without a high school degree would decrease by five years between 1990 and 2008.

We are now faced not just with labor-replacing technologies but with knowledge-replacing technologies. The combination of 

• advanced sensors, 
• voice recognition, 
• artificial intelligence, 
• big data, 
• text-mining, and 
• pattern-recognition algorithms, 

is generating smart robots capable of quickly learning human actions, and even learning from one another. A revolution in life sciences is also underway, allowing drugs to be tailored to a patient's particular condition and genome.

If the current trend continues, many more symbolic analysts will be replaced in coming years. The two largest professionally intensive sectors of the United States -- health care and education -- will be particularly affected because of increasing pressures to hold down costs and, at the same time, the increasing accessibility of expert machines.

We are on the verge of a wave of mobile health applications, for example, measuring everything from calories to blood pressure, along with software programs capable of performing the same functions as costly medical devices and diagnostic software that can tell you what it all means and what to do about it.

Schools and universities will likewise be reorganized around smart machines (although faculties will scream all the way). Many teachers and university professors are already on the way to being replaced by software -- so-called "MOOCs" (Massive Open Online Courses) and interactive online textbooks -- along with adjuncts that guide student learning.

As a result, income and wealth will become even more concentrated than they are today. Those who create or invest in blockbuster ideas will earn unprecedented sums and returns. The corollary is they will have enormous political power. But most people will not share in the monetary gains, and their political power will disappear. The middle class's share of the total economic pie will continue to shrink, while the share going to the very top will continue to grow.

But the current trend is not preordained to last, and only the most rigid technological determinist would assume this to be our inevitable fate. We can -- indeed, I believe we must -- ignite a political movement to reorganize the economy for the benefit of the many, rather than for the lavish lifestyles of a precious few and their heirs. (I have more to say on this in my upcoming book, Saving Capitalism: For the Many, Not the Few, out at the end of September.) 

Robert B. Reich has served in three national administrations, most recently as secretary of labor under President Bill Clinton. He also served on President Obama's transition advisory board. His latest book is "Aftershock: The Next Economy and America's Future." His homepage is www.robertreich.org.

ORIGINAL: AlterNet

By Robert Reich / RobertReich.org

May 7, 2015

ROBERT B. REICH, Chancellor’s Professor of Public Policy at the University of California at Berkeley and Senior Fellow at the Blum Center for Developing Economies, was Secretary of Labor in the Clinton administration. Time Magazine named him one of the ten most effective cabinet secretaries of the twentieth century. He has written thirteen books, including the best sellers “Aftershock" and “The Work of Nations." His latest, "Beyond Outrage," is now out in paperback. He is also a founding editor of the American Prospect magazine and chairman of Common Cause. His new film, "Inequality for All," is now available on Netflix, iTunes, DVD, and On Demand.

Rise of the Machines: The Future has Lots of Robots, Few Jobs for Humans

Martin Ford

The robots haven’t just landed in the workplace—they’re expanding skills, moving up the corporate ladder, showing awesome productivity and retention rates, and increasingly shoving aside their human counterparts. One multi-tasker bot, from Momentum Machines, can make (and flip) a gourmet hamburger in 10 seconds and could soon replace an entire McDonalds crew. A manufacturing device from Universal Robots doesn’t just solder, paint, screw, glue, and grasp—it builds new parts for itself on the fly when they wear out or bust. And just this week, Google won a patent to start building worker robots with personalities.  

Fast Food Company Develops Robots

Universal Robots: UR3: The world’s most flexible, light-weight table-top robot to work alongside humans

As intelligent machines begin their march on labor and become more sophisticated and specialized than first-generation cousins like Roomba or Siri, they have an outspoken champion in their corner: author and entrepreneur Martin Ford. In his new book, Rise of the Robots, he argues that AI and robotics will soon overhaul our economy. 

There’s some logic to the thesis, of course, and other economists such as Andrew (The Second Machine Age) McAfee have sided generally with Ford’s outlook. Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. And if even half that number is closer to the mark, workers are in for a rude awakening. 

In Ford’s vision, a full-on worker revolt is on the horizon, followed by a radically new economic state whereby humans will live more productive and entrepreneurial lives, subsisting on guaranteed incomes generated by our amazing machines. (Don’t laugh — even some conservative influencers believe this may be the ultimate means of solving the wealth-inequality dilemma.) 

Sound a little nuts? We thought so—we’re human, after all—so we invited Ford to defend his turf. 

Rise of the Robots

Critics say your vision of a jobless future isn’t founded in good research or logic. What makes you so convinced this phenomenon is real? 

I see the advances happening in technology and it’s becoming evident that computers, machines, robots, and algorithms are going to be able to do most of the routine, repetitive types of jobs. That’s the essence of what machine learning is all about. What types of jobs are on some level fundamentally predictable? A lot of different skill levels fall into that category. It’s not just about lower-skilled jobs either. People with college degrees, even professional degrees, people like lawyers are doing things that ultimately are predictable. A lot of those jobs are going to be susceptible over time. 

Right now there’s still a lot of debate over it. There are economists who think it’s totally wrong, that problems really stem from things like globalization or the fact that we’ve wiped out unions or haven’t raised the minimum wage. Those are all important, but I tend to believe that technology is a bigger issue, especially as we look to the future. 

Eventually I think we’ll get to the point where there’s less debate about whether this is really happening or not. There will be more widespread agreement that it really is a problem and at that point we’ll have to figure out what to do about it. 

Aren’t you relying on some pretty radical and unlikely assumptions? 

People who are very skeptical tend to look at the historical record. It’s true that the economy has always adapted over time. It has created new kinds of jobs. The classic example of that is agriculture. In the 1800s, 80 percent of the U.S. labor force worked on farms. Today it’s 2 percent. Obviously mechanization didn’t destroy the economy; it made it better off. Food is now really cheap compared to what it was relative to income, and as a result people have money to spend on other things and they’ve transitioned to jobs in other areas. Skeptics say that will happen again. 

The agricultural revolution was about specialized technology that couldn’t be implemented in other industries. You couldn’t take the farm machinery and have it go flip hamburgers. Information technology is totally different. It’s a broad-based general purpose technology. There isn’t a new place for all these workers to move. 

You can imagine lots of new industries—nanotechnology and synthetic biology—but they won’t employ many people. They’ll use lots of technology, rely on big computing centers, and be heavily automated. 

So in the all-automated economy, what will ambitious 20-somethings choose to do with their lives and careers? 

My proposed solution is to have some kind of a guaranteed income that incentivizes education. We don’t want people to get halfway through high school and say, ‘Well if I drop out I’m still going to get the same income as everyone else.’ 

Then I believe that a guaranteed income would actually result in more entrepreneurship. A lot of people would start businesses just as they do today. The problem with these types of businesses you can start online today is it’s hard to put enough together to generate a middle-class income. 

If people had an income floor, and if the incentives were such that on top of that they could do other things and still keep that extra money, without having it all taxed away, then I think a lot of people would pursue those opportunities. 

There’s a phenomenon called the Peltzman Effect, based on research from an economist at the University of Chicago who studied auto accidents. He found that when you introduce more safety features like seat belts into cars, the number of fatalities and injuries doesn’t drop. The reason is that people compensate for it. When you have a safety net in place, people will take more risks. That probably is true of the economic arena as well. 

People say that having a guaranteed income will turn everyone into a slacker and destroy the economy. I think the opposite might be true, that it might push us toward more entrepreneurship and more risk-taking. 

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ORIGINAL: Wired

'Highly creative' professionals won't lose their jobs to robots, study finds

A University of Oxford study finds that there are some things that a robot won’t be able to do. Unfortunately, these gigs don’t pay all that well.

Many people are in “robot overlord denial,” according to a recent online poll run by jobs board Monster.com. They think computers could not replace them at work. Sadly, most are probably wrong.

University of Oxford researchers Carl Benedikt Frey and Michael Osborne estimated in 2013 that 47% of total U.S. jobs could be automated by 2033. The combination of robotics, automation, artificial intelligence, and machine learning is so powerful that some white collar workers are already being replaced — and we’re talking journalists, lawyers, doctors, and financial analysts, not the person who used to file all the incoming faxes.

But there’s hope, at least for some. According to an advanced copy of a new report that U.K. non-profit Nesta sent to Fortune, 21% of US employment requires people to be “highly creative.” Of them, 86% (18% of the total workforce) are at low or no risk from automation. In the U.K., 87% of those in creative fields

“Artists, musicians, computer programmers, architects, advertising specialists … there’s a very wide range of creative occupations,” said report co-author Hasan Bakhshi, director of creative economy at Nesta, to Fortune. Some other types would be financial managers, judges, management consultants, and IT managers. “Those jobs have a very high degree of resistance to automation.”

The study is based on the work of Frey and Osborne, who are also co-authors of this new report. The three researchers fed 120 job descriptions from the US Department of Labor into a computer and analyzed them to see which were most likely to require extensive creativity, or the use of imagination or ideas to make something new.

Creativity is one of the three classic bottlenecks to automating work, according to Bakhshi. “Tasks which involve a high degree of human manipulation and human perception — subtle tasks — other things being equal will be more difficult to automate,” he said. For instance, although goods can be manufactured in a robotic factory, real craft work still “requires the human touch.”

So will jobs that need social intelligence, such as your therapist or life insurance agent.

Of course, the degree of creativity matters. Financial journalists who rewrite financial statements are already beginning to be supplanted by software. The more repetitive and dependent on data the work is, the more easily a human can be pushed aside.

In addition, just because certain types of creative occupations can’t easily be replaced doesn’t mean that their industries won’t see disruption. Packing and shipping crafts can be automated, as can could some aspects of the film industry that aren’t such things as directing, acting, and design. “These industries are going to be disrupted and are vulnerable,” Bakhshi said.

Also, not all these will necessarily provide a financial windfall. The study found an “inverse U-shape” relationship between the probability of an occupation being highly creative and the average income it might deliver. Musicians, actors, dancers, and artists might make relatively little, while people in technical, financial, and legal creative occupations can do quite well. So keeping that creative job may not seem much of a financial blessing in many cases.

Are you in a “creative” role that will be safe from automation? You can find out what these Oxford researchers think by taking their online quiz.

ORIGINAL Fortune

by Erik Sherman 

APRIL 22, 2015

Apple co-founder on artificial intelligence: ‘The future is scary and very bad for people’

Steve Wozniak speaks at the Worldwebforum in Zurich on March 10. (Steffen Schmidt/European Pressphoto Agency)

The Super Rich Technologists Making Dire Predictions About Artificial Intelligence club gained another fear-mongering member this week: Apple co-founder Steve Wozniak.

In an interview with the Australian Financial Review, Wozniak joined original club members Bill Gates, Stephen Hawking and Elon Musk by making his own casually apocalyptic warning about machines superseding the human race.

"Like people including Stephen Hawking and Elon Musk have predicted, I agree that the future is scary and very bad for people," Wozniak said. "If we build these devices to take care of everything for us, eventually they'll think faster than us and they'll get rid of the slow humans to run companies more efficiently."

[Bill Gates on dangers of artificial intelligence: ‘I don’t understand why some people are not concerned’]

Doling out paralyzing chunks of fear like gumdrops to sweet-toothed children on Halloween, Woz continued: "Will we be the gods? Will we be the family pets? Or will we be ants that get stepped on? I don't know about that … But when I got that thinking in my head about if I'm going to be treated in the future as a pet to these smart machines … well I'm going to treat my own pet dog really nice."

Seriously? Should we even get up tomorrow morning, or just order pizza, log onto Netflix and wait until we find ourselves looking through the bars of a dog crate? Help me out here, man!

Wozniak's warning seemed to follow the exact same story arc as Season 1 Episode 2 of Adult Swim's "Rick and Morty Show." Not accusing him of apocalyptic plagiarism or anything; just noting.

For what it's worth, Wozniak did outline a scenario by which super-machines will be stopped in their human-enslaving tracks. Citing Moore's Law -- "the pattern whereby computer processing speeds double every two years" -- Wozniak pointed out that at some point, the size of silicon transistors, which allow processing speeds to increase as they reduce size, will eventually reach the size of an atom, according to the Financial Review.

"Any smaller than that, and scientists will need to figure out how to manipulate subatomic particles — a field commonly referred to as quantum computing — which has not yet been cracked," Quartz notes.

Wozniak's predictions represent a bit of a turnaround, the Financial Review pointed out. While he previously rejected the predictions of futurists such as the pill-popping Ray Kurzweil, who argued that super machines will outpace human intelligence within several decades, Wozniak told the Financial Review that he came around after he realized the prognostication was coming true.

"Computers are going to take over from humans, no question," Wozniak said, nearly prompting me to tender my resignation and start watching this cute puppies compilation video until forever.

"I hope it does come, and we should pursue it because it is about scientific exploring," he added. "But in the end we just may have created the species that is above us."

In January, during a Reddit AMA, Gates wrote: "I am in the camp that is concerned about super intelligence." His comment came a month after Hawking said artificial intelligence "could spell the end of the human race."

British inventor Clive Sinclair has also said he thinks artificial intelligence will doom humankind. "Once you start to make machines that are rivaling and surpassing humans with intelligence, it's going to be very difficult for us to survive," he told the BBC. "It's just an inevitability."

Musk was among the earliest members of this club. Speaking at the MIT aeronautics and astronautics department’s Centennial Symposium in October, the Tesla founder said: "With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, it’s like, yeah, he’s sure he can control the demon. Didn't work out."

MORE READING:

• Elon Musk, Neil deGrasse Tyson laugh about artificial intelligence turning the human race into its pet lab
• Elon Musk: Human-driven cars may be outlawed because they’re ‘too dangerous’
• Stephen Hawking just got an artificial intelligence upgrade, but still thinks AI could bring an end to humankind

ORIGINAL: Washington Post

By Peter Holley  

March 24, 2015

AI Won’t End the World, But It Might Take Your Job

AI Won’t End the World, But It Might Take Your Job AI, Ethics, Jobs, Andrew NG, People2Watch, Baidu, Economy, Education,

Andrew Ng. Ariel Zambelich/WIRED

There’s been a lot of fear about the future of artificial intelligence.

Stephen Hawking and Elon Musk worry that AI-powered computers might one day become uncontrollable super-intelligent demons. So does Bill Gates.

But Baidu chief scientist Andrew Ng—one of the world’s best-known AI researchers and a guy who’s building out what is likely one of the world’s largest applied AI projects—says we really ought to worry more about robot truck drivers than the Terminator.

In fact, he’s irritated by the discussion about scientists somehow building an apocalyptic super-intelligence. “I think it’s a distraction from the conversation about…serious issues,” Ng said at an AI conference in San Francisco last week.

Ng isn’t alone in thinking this way. A select group of AI luminaries met recently at a closed door retreat in Puerto Rico to discuss ethics and AI. WIRED interviewed some of them, and the consensus was that there are short-term and long-term AI issues to worry about. But it’s the long-term questions getting all the press.
Artificial intelligence is likely to start having an important effect on society over the next five to 10 years, according to Murray Shanahan, a professor of cognitive robotics with Imperial College, Professor of Cognitive Robotics. “It’s hard to predict exactly what’s going on,” he told WIRED a few weeks ago, “but we can be pretty sure that these technologies are going to impact and society quite a bit. ”

The way Ng sees it, it took the US about 200 years to switch from an agricultural economy where 90 percent of the country worked on farms, to our current economy, where the number is closer to 2 percent. The AI switchover promises to come must faster, and that could make it a bigger problem.

That’s an idea echoed in two MIT academics, Erik Brynjolfsson and Andrew McAfee, who argue that we’re entering a “second machine age,” where the accelerating rate of change brought on by digital technologies could leave millions of medium-and-low skilled workers behind.

Some AI technologies, such as the self-driving car, could be extremely disruptive, but over a much shorter period of time than the industrial revolution. There are three million truck drivers in the US, according to the American Trucking Association. What happens if self-driving vehicles put them all out of a job in a matter of years?

With recent advances in perception, the range of things that machines can do is getting a boost. Computers are better at understanding what we say and analyzing data in a way that used to be the exclusive domain of humans.

Last month, Audi’s self-driving car took WIRED’s Alex Davies for a 500 mile ride. In Cupertino, California’s Aloft Hotel a robot butler can deliver you a toothbrush. Paralegals are now finding their work performed by data-sifting computers. And just last year, Google told us about a group of workers who were doing mundane image recognition work for the search giant—jobs like figuring out the difference between telephone numbers and street addresses on building walls. Google figured out how to do this by machine, and so they’ve now moved onto other things.

Ng, who also co-founded the online learning company Coursera, says that if AI really starts taking jobs, retraining all of those workers could present a major challenge. When it comes to retraining workers, he said, “our education system has historically found it very difficult.”

ORIGINAL: Wired

By Robert McMillan
02.02.15