AI News, 9 Ideas That Summarise The Future of Work (And How You Can ... artificial intelligence

If robots are the future of work, where do humans fit in?

In his new book, The Age of Em, the economist explains: you take the best and brightest 200 human beings on the planet, you scan their brains and you get robots that to all intents and purposes are indivisible from the humans on which they are based, except a thousand times faster and better.

Harari predicts the rise of the useless class: humans who don’t know what to study because they have no idea what skills will be needed by the time they finish, who can’t work because there’s always a cheaper and better robot, and spend their time taking drugs and staring at screens.

It is a question articulated precisely by Stephen Hawking last year, when he noted: “Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up miserably poor if the machine owners successfully lobby against wealth redistribution.” Like so many things, from debt cancellation to climate change, the reality of the situation is easily understood by scientists, academics, philosophers from the left and right, activists from within and without the establishment;

The question of how to distribute wealth in the future curves back round to meet a conundrum raised by the past: how do we remake the social safety net so that it embodies solidarity, generosity and trust, rather than the welfare state of the present, rickety with the woodworm of mutual suspicion.

The idea of a universal basic income is generally framed as a way to “shift from the Beveridge principle of national insurance based on contributions and the sharing of risk, to a system of income as of right” (as described in a Compass paper by Howard Reed and Stewart Lansley).

There is work to be done on the numbers – whether this income needs to be supplemented for housing, in what form it has its most progressive effect, whether and how it is taxed back in the higher deciles, how it can be affordable at the same time as genuinely livable.

There is also work to be done on the surrounding incentives, whether a basic income would capsize the work ethic and leave the world understaffed while we await the robot takeover (a pilot scheme in Canada concluded the only groups who worked less with an income were mothers of young babies and teenagers still in education;

The real risks of artificial intelligence

As smart systems become involved in ever more decisions in arenas ranging from healthcare to finance to criminal justice, there is a danger that important parts of our lives are being made without sufficient scrutiny.

Very simply, it’s machines doing things that are considered to require intelligence when humans do them: understanding natural language, recognising faces in photos, driving a car, or guessing what other books we might like based on what we have previously enjoyed reading.

It’s the difference between a mechanical arm on a factory production line programmed to repeat the same basic task over and over again, and an arm that learns through trial and error how to handle different tasks by itself.

The leading approach to AI right now is machine learning, in which programs are trained to pick out and respond to patterns in large amounts of data, such as identifying a face in an image or choosing a winning move in the board game Go.

Google’s artificial intelligence company DeepMind are collaborating with the UK’s National Health Service in a handful of projects, including ones in which their software is being taught to diagnose cancer and eye disease from patient scans.

For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone.

Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port.

AIs are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control.

This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying.

Navigating the future of work: Can we point business, workers, and social institutions in the same direction? Deloitte Review, issue 21

As science-fiction novelist William Gibson reminded us, “The future is already here—it’s just not evenly distributed.” The biggest challenge in understanding the future of work comes in surfacing the implications for three broad constituencies—the individual, businesses and other employers, and social and governmental institutions—and getting all three pointed in the same direction.

Technological advances—for example, in the areas of robotics, artificial intelligence (AI), sensors, and data—have created entirely new ways of getting work done that are, in some cases, upending the way we use and think about our tools and how people and machines can complement and substitute for one another.

Where once most workers were full-time, on-balance-sheet employees with benefits and defined salaries, employers of the future will also execute a significant proportion of their activities through individuals engaged in alternative work arrangements, from freelancing to crowdsourcing to contract-based work.

As just one example, by helping us “see” much more richly the evolving world around us, applications based on augmented reality (AR) can help us focus our curiosity, imagination, and creativity on early signals of the potential changes ahead that really matter.2 Already, AR technology is helping workers out in the field, far from their desktop computers, to assess unexpected developments and focus their effort on the actions that could have the greatest impact.3 And it’s hardly just cognitive technologies such as AR: In the robotics space, prosthetics and other augmentation devices are helping technicians and others to perform operations unimaginable a decade ago.

More broadly, an expanding array of technologies, ranging from 3D printing to biosynthesis, are making productive tools accessible to smaller and smaller businesses, thereby eroding some of large companies’ traditional advantages in developing and producing new products and services.

More and more knowledge is being created—with other knowledge becoming obsolete—at an accelerating rate, making it necessary to update our skills and job descriptions ever more rapidly to keep up.4 The supply of workers is rapidly evolving globally as a result of shifting demographics, enhanced longevity, and increased focus on the inclusion of marginalized segments of the population.5 The workforce in many economies—especially the developed economies and China—is rapidly aging, as figure 2 illustrates.

For a variety of reasons, ranging from financial need to a desire to continue to make a difference, many older workers are extending their careers well beyond traditional retirement age.6 The prospect of older generations working for longer periods as their physical capability to remain employed improves could affect the pace at which younger talent and ideas renew organizations—and potentially intensify the intergenerational competition for jobs.

This dynamic is playing out in digital product markets such as music, video, and software, but it has the potential to rapidly extend into physical products and services, as the technology trends outlined above make it far more feasible for niche vendors to access the means of production.

The result is likely to be a growing fragmentation of product and service businesses, with small companies employing more of the overall labor force.8 On the supply side, labor markets are evolving in ways that enhance organizations’ ability to access and work with talent when and where needed.

The “power of pull” forces described above can spur growing demand for more creative work as customers shift away from mass-market products and services, as workers in smaller businesses gain greater access to the means of production, and as platforms help to connect niche product and service providers with smaller segments of customers globally.

Routine tasks will be increasingly automated, while technology-aided creative work expands and evolves in response to a growing array of unmet needs.9 The industrial era defined work largely in the form of highly specialized and standardized tasks that became increasingly tightly integrated.

The greater opportunity to enhance productivity may lie in reinventing and reimagining work around solving business problems, providing new services, and achieving new levels of productivity and worker satisfaction and passion.10 The growing availability of cognitive technologies and data also presents an opportunity to radically reengineer business processes leveraging the breadth and unique capabilities of people, machines, and data to achieve desired outcomes.

Research suggests that more than 30 percent of high-paying new jobs will be social and “essentially human” in nature.11 Increasing diversity in the workforce will likely enhance the shift from routine tasks to more creative work, and we will see the emergence of hybrid jobs that increasingly integrate technical, design, and project management skills.

Recent MIT research, for instance, explores industrial robots’ negative impact on employment and wages.12 For instance, a Mercedes-Benz production facility in Germany recently announced plans to reduce the number of robots on its production line and replace them with human labor—with increasing demand for customized auto options, reprogramming and switching out robots was more costly than shifting the line using human workers.13 Technology is transforming more than the way individual jobs are done—it’s changing the way companies source labor.

Deloitte’s Center for Health Solutions and Center for Financial Services collaborated with insurance company specialists, for example, on an online platform provided by Wikistrat, in four days generating 44 use cases regarding the potential for using blockchain technology in insurance.14 Online platforms are playing a key role in accelerating the growth of this kind of crowdsourcing.

(In a 2013 study, 87 percent of UK students with first- or second-class degrees said freelancing is a “highly attractive and lucrative career option.”)15 And a third factor driving the growth of the gig economy is the desire of workers who are marginalized or underemployed—younger workers in developing economies, older workers in developed economies, and unskilled workers around the world—to find some productive work, even if it may not be full-time employment.

These more creative gigs—if they still qualify as gigs—will likely be increasingly done by small teams or workgroups that will collaborate on different projects over extended periods of time.18 In the new landscape of work, personal success will largely depend on accelerating learning throughout one’s lifetime.

As rapid technological and marketplace change shrinks the useful lifespan of any given skill set, workers will need to shift from acquiring specific skills and credentials to pursuing enduring and essential skills for lifelong learning.

And as work evolves, individuals should cultivate a “surfing” mind-set, always alert to emerging, high-value skills and catching the wave at an early stage to capture the most value from these skills.19 To avoid getting stretched too thin and stay motivated, they must filter a growing array of skill opportunities through their personal passions.

In our research into diverse work environments where there is sustained extreme performance improvement—everything from extreme sports to online war games—we identified the one common element as participants having a very specific form of passion—something that we call the “passion of the explorer.” This form of passion has three components: a long-term commitment to making an increasing impact in a domain, a questing disposition that actively seeks out new challenges, and a connecting disposition that seeks to find others who can help them get to a better answer faster.20 Tapping into this kind of passion can shift people from the fear of change to excitement about the opportunity to learn something new and to have a greater impact.

To take effective advantage of technology, organizations will likely need to redesign work itself, moving beyond process optimization to find ways to enhance machine-human collaboration, drawing out the best of both and expanding access to distributed talent.

How can companies use robotics to provide workers with access to environments that would be far too dangerous for humans?21 What are some ways in which AI-based technology can complement human judgment and contextual knowledge to achieve better outcomes than either human or machine alone?22 This is perhaps the greatest challenge for businesses in the next decade: how to plan for the redesign and reinvention of work to combine the capabilities of machines and people, create meaningful jobs and careers, and help employees with the learning and support to navigate these rapidly evolving circumstances.

As the continuum of talent resources expands and becomes more diversified, organizations will need to develop richer relationships in larger business ecosystems and find ways to participate more effectively on scalable platforms to access expertise and enhance the ability to work together to accelerate performance improvement.24 Organizations will need to cultivate new leadership and management approaches that can help build powerful learning cultures and motivate workers to go beyond their comfort zone.

Governments around the world are considering and revisiting basic income guarantees in various forms, and some recent proposals have surfaced to tax robots as a way to provide funding for transition support programs.25 Reassess legal and regulatory policies.

What role can all dimensions of public policy play in accelerating broader inclusion in the workforce, talent development, and innovation capability?26 Governments should consider updating the definitions of employment to account for freelance and gig economy work and the provision and access to government health, pension, and other social benefits through micro-payment programs.

The goal of this framework is to inform and motivate individuals, various forms of organizations, and public policy makers to proactively navigate the future of work and to come together and act now to make the transition as positive, productive, and smooth as possible.

The optimist’s guide to the robot apocalypse

Nearly 500 years ago, Queen Elizabeth I cited the same fear when she denied an English inventor named William Lee a patent for an automated knitting contraption. “I have too much regard for the poor women and unprotected young maidens who obtain their daily bread by knitting to forward an invention which, by depriving them of employment, would reduce them to starvation,” she told Lee, according to one account of the incident. The lack of patent didn’t ultimately stop factories from adopting the machine.

Johnson’s labor secretary had recently commented that new machines had ”skills equivalent to a high school diploma” (though then, and now, machines have trouble doing simple things like recognizing objects in photos or packing a box), and the economists were worried that machines would soon take over service industry jobs.

The optimist’s take on this trend is that robots help Amazon keep prices low, which means people buy more stuff, which means the company needs more people to man its warehouses even though it needs fewer human hours of labor per package. Bruce Welty, the founder of a fulfillment company that ships more than $1 billion of ecommerce orders each year and another company called Locus Robotics that sells warehouse robots, says he thinks the threat to jobs from the latter is overblown—especially as the rise of ecommerce creates more demand for warehouse workers.

About a third of new jobs created in the United States over the past 25 years didn’t exist (or just barely existed) at the beginning of that period, and predicting what jobs might be created in the next 25 years is just guessing.

But, the report’s authors note, “Predicting future job growth is extremely difficult, as it depends on technologies that do not exist today.” In 2013, researchers at Oxford sparked fear of the robot revolution when they estimated that almost half of US occupations were likely to be automated. But three years later, McKinsey arrived at a very different number.

McKinsey’s conclusion was not that machines will take all of these jobs, but rather, “more occupations will change than will be automated away.” Our CEO, for example, won’t spend time analyzing reports if artificial intelligence can draw conclusions more efficiently, so he can spend more time coaching his team.

“Any time in history we’ve seen automation occur, people don’t all of the sudden stop being creative and wanting to do interesting new things,” says Aaron Levie, the CEO of enterprise software company Box and an automation optimist.

“From what we’ve actually seen on the ground, in real business operations, we’ve seen almost zero job loss,” says Alastair Bathgate, CEO of Blue Prism, a software company that helps automate tasks within customer service, accounting, and other jobs.

This was a process that had never been done by humans, because it would be too tedious and expensive. Another bank used the software to allow customer service representatives to direct customers who had a credit card stolen to an automated system that would input their information and close the account. What do they do now?

But there’s a lot of stuff going on outside of technological developments, argue the automation optimists, like the decline of unions, weakening of labor laws, tax laws that benefit rich people, and education policies that haven’t adapted to a changing world—these are policy problems, and we should fix them rather than blaming technology.

“What happens if [in the near future], each period of disruption comes so quickly, that it never recovers?” “There will be fewer and fewer jobs that a robot cannot do better,” Tesla and SpaceX CEO Elon Musk recently mused at the World Government Summit in Dubai, before suggesting that a universal basic income would be necessary.

“AI can seem dystopian,” tweeted Box CEO Levie, “because it’s easier to describe existing jobs disappearing than to imagine industries that never existed appearing.” He doesn’t deny that automated technology will make some labor obsolete—he just focuses on the long-term, big-picture opportunity for potential benefits.

His case wasn’t that impending technology doomed society to prolonged massive unemployment, but rather that a reaction to new technology should neither assume the end of the world or refuse to recognize that world had changed. From his essay, Economic Possibilities For Our Grandchildren: The prevailing world depression, the enormous anomaly of unemployment in a world full of wants, the disastrous mistakes we have made, blind us to what is going on under the surface to the true interpretation, of the trend of things.

For I predict that both of the two opposed errors of pessimism which now make so much noise in the world will be proved wrong in our own time-the pessimism of the revolutionaries who think that things are so bad that nothing can save us but violent change, and the pessimism of the reactionaries who consider the balance of our economic and social life so precarious that we must risk no experiments.

These proposed solutions are not so dissimilar to those provided to President Johnson in 1964, which included ”a massive program to build up our educational system” and “a major revision of our tax structure.” Even so, little progress has been made since then in making the US more resilient to job displacement caused by automation.

Artificial Intelligence

Intelligent machines are no longer science fiction and experts seem divided as to whether artificial intelligence should be feared or welcomed. In this video I ...

What is Artificial Intelligence (or Machine Learning)?

What is AI? What is machine learning and how does it work? You've probably heard the buzz. The age of artificial intelligence has arrived. But that doesn't mean ...

Joe Rogan - Elon Musk on Artificial Intelligence

Taken from Joe Rogan Experience #1169:

How smart is today's artificial intelligence?

Current AI is impressive, but it's not intelligent. Subscribe to our channel! Sources: ..

What is the impact of technology on the future of work and what adjustments must we make?

Financial Times Special Projects Editor Robin Kwong, FT Employment Correspondent Sarah O'Connor, author Margaret Heffernan, CIPD Chief Executive Peter ...

The Future Is Now With Artificial Intelligence For Marketing

[30:37] For some time now Bernie has been talking about the growing importance of learning how to use Artificial Intelligence for marketing. While we still have a ...

The future of language jobs in the age of innovation, artificial intelligence n entrepreneurship

This is the summary of 10 years of my research in the field of Language Entrepreneurship. This deals with the role of language translation service in the age of ...

The Singularity, Skynet, and the Future of Computing: Crash Course Computer Science #40

In our SERIES FINALE of Crash Course Computer Science we take a look towards the future! In the past 70 years electronic computing has fundamentally ...

The future of artificial intelligence and self-driving cars

Stanford professors discuss their innovative research and the new technologies that will transform lives in the 21st century. At a live taping of The Future of ...

How A.I. Works? Machine Learning Basics Explained! Simple Visual Example!

Artificial Intelligence and Machine Learning are such a buzzwords, but what is the difference between them? How Artificial Intelligence works? What is Machine ...