Rise of the machines – The future of robotics and automation

So many of the tasks that we now take for granted once had to be done manually. Washing a load of laundry no longer takes all day; our phone calls are directed to the correct departments by automated recordings; and many of our online orders are now selected and packed by robots.

Developments in this area are accelerating at an incredible rate. But as exciting as these new discoveries may be, they raise question after question around whether the research needed to deliver such innovations is viable, both from an economical and an ethical point of view.

As expert manufacturers of engineering parts that help to keep hundreds of different automated processes up and running, electronic repair specialists Neutronic Technologies are understandably very interested in where the future is going to take us. Is it going to take hundreds, if not thousands, of years for us to reach the kinds of automation that are lodged in the imaginations of sci-fi enthusiasts? Or are we a great deal closer to a machine takeover than we think?

According to the International Federation of Robotics, there are five countries in the developed world that manufacture at least 70 per cent of our entire robotics supply: Germany, the United States, South Korea, China and Japan.

By 2018, the Federation of Robotics predicts that there will be approximately 1.3 million industrial robots working in factories around the world. That’s less than two years away.

The development of automation has received a great deal more attention over the past few years. And undoubtedly what has brought it to people’s attention is the popularisation of the subject following the explosion of science fiction books and movies such as Isaac Asimov’s ‘i, Robot’ and ‘The Bicentennial Man’. And this has continued to emerge throughout the decades and has likely only heightened our curiosity about the world of robots.

Why are we even exploring robotics?

Developing robotics is the next stage in our search for automation. We already have automation integrated into so many aspects of our daily lives, from doors that open due to motion sensors to assembly lines and automobile production, robotics is simply the next step along that path.

I predict that the biggest developments in the automation world will come from the automobile industry – so the likes of self-driving cars that are already being tested – and the internet.

Another area of development within automation is likely to come from the growth of the internet. The concept of the ‘Internet of Things’ has been gaining momentum for some years now, even decades amongst technology companies, but the idea has only recently started to break into a mainstream conversation.

We have already seen glimpses of the future starting to creep into reality, most notably with the introduction of Amazon Dash. Linked to the person’s account and programmed to a certain item, all you have to do is press the button and an order is placed and delivered. Of course, this process is currently only half automated; a button still has to be manually pressed and Amazon shippers still post and deliver the item, but it certainly shows the direction in which we are headed.

But ultimately the Internet of Things can go even further than creating smart homes. The term ‘smart cities’ has been coined that could theoretically include connected traffic lights to control vehicle flow, smart bins that inform the right people when they need to be emptied, to even the monitoring of crops growing in fields.

How do we reach these automation goals?

Ultimately, the end goal of any research into robotics or automation is to emulate the actions of humans. People across the world engage in heated debates about whether machines will ever have the ability to think like people – a subject known as A.I. or Artificial Intelligence which is worthy of its own exploration. Whether that will become a reality in the future we cannot currently tell for sure, but researchers are hard at work across the world trying to inch our way closer.

There are, of course, issues that arise when we try to develop machines to take over certain tasks from humans, most notably to do with quality control and the increased margin for error. Some question whether a machine, that doesn’t necessarily have the capacity to consider extenuating circumstances or raise certain questions or react in a way, would be able to perform these tasks.

Let’s look at self-driving cars for example. So much of driving depends on the person behind the wheel being able to react in seconds to any changes around them. It is, therefore, essential that machines are able to “think” as close to humans as possible. If artificial intelligence and technology alone cannot achieve this, it would be very difficult for such vehicles to become road legal. However, experts in the industry have suggested a very clever solution.

Are there any disadvantages to the research?

As with any major development, there are always going to be people who oppose it, or at the very least point out reasons why we should proceed with caution – and with good reason.

One of the biggest, and indeed most realistic, fears that many people express, is all to do with economics and jobs. It’s no secret that the UK’s economy, and indeed the world’s economy, has been somewhat shaky over the past few years. This has led to many people showing concern that the development of automated processes, which are able to perform certain tasks with precision and accuracy that surpasses humans and at a much faster speed, will mean that many people’s jobs will become redundant.

Where are we headed?

It is unlikely that we are going to see any robot uprisings anytime soon. But the potential threats that an increase in automation brings to our society should not be underestimated. With the economic state of the world already so fragile, any attempts to research areas that could result in unemployment should be very carefully considered before implementation.

That being said, we are living in exciting times where we are able to witness such developments taking place. So much has already occurred over the past few years that many people may not be aware of. We may not have reached the exciting level of developments as seen in the movies – not yet anyway – but with the amount of ideas and research taking place in the world, the sky really is the limit.

Source: itproportal.com – Rise of the machines – The future of robotics and automation

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4 Unique Challenges Of Industrial Artificial Intelligence

Robots are probably the first thing you think of when asked to imagine AI applied to industrials and manufacturing. Indeed, many innovative companies like Rodney Brooks’ Rethink Robotics have developed friendly-looking robot factory workers who hustle alongside their human colleagues. Industrial robots have historically been designed to perform specific niche tasks, but modern-day robots can be taught new tasks and make real-time decisions.

As sexy and shiny as robots are, the bulk of the value of AI in industrials lies in transforming data from sensors and routine hardware into intelligent predictions for better and faster decision-making. 15 billion machines are currently connected to the Internet. By 2020, Cisco predicts the number will surpass 50 billion. Connecting machines together into intelligent automated systems in the cloud is the next major step in the evolution of manufacturing and industry.

In 2015, General Electric launched GE Digital to drive software innovation and cloud connectivity across all departments. Harel Kodesh, CTO of GE Digital, shares the unique challenges of applying AI to industrials that differ from consumer applications.

1. Industrial Data Is Often Inaccurate

“For machine learning to work properly, you need lots of data. Consumer data is harder to misunderstand, for example when you buy a pizza or click on an ad,” says Kodesh. “When looking at the industrial internet, however, 40% of the data coming in is spurious and isn’t useful”.

Let’s say you need to calculate how far a combine needs to drill and you stick a moisture sensor into the ground to take important measurements. The readings can be skewed by extreme temperatures, accidental man-handling, hardware malfunctions, or even a worm that’s been accidentally skewered by the device. “We are not generating data from the comfort and safety of a computer in your den,” Kodesh emphasizes.

2. AI Runs On The Edge, Not On The Cloud

Consumer data is processed in the cloud on computing clusters with seemingly infinite capacity. Amazon can luxuriously take their time to crunch your browsing and purchase history and show you new recommendations. “In consumer predictions, there’s low value to false negatives and to false positives. You’ll forget that Amazon recommended you a crappy book,” Kodesh points out.

On a deep sea oil rig, a riser is a conduit which transports oil from subsea wells to a surface facility. If a problem arises, several clamps must respond immediately to shut the valve. The sophisticated software that manages the actuators on those clamps tracks minute details in temperature and pressure. Any mistake could mean disaster.

The stakes and responsiveness are much higher for industrial applications where millions of dollars and human lives can be on the line. In these cases, industrial features cannot be trusted to run on the cloud and must be implemented on location, also known as “the edge.”

Industrial AI is built as an end-to-end system, described by Kodesh as a “round-trip ticket”, where data is generated by sensors on the edge, served to algorithms, modeled on the cloud, and then moved back to the edge for implementation. Between the edge and the cloud are supervisor gateways and multiple nodes of computer storage since the entire system must be able to run the the right load at the right places.

Source: forbes.com-4 Unique Challenges Of Industrial Artificial Intelligence

The Countries Most (and Least) Likely to be Affected by Automation

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology. In all, 1.2 billion full time equivalents and $14.6 trillion in wages are associated with activities that are technically automatable with current technology. This automation potential differs among countries, with the range spanning from 40% to 55%. Four economies—China, India, Japan, and the United States—dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting currently demonstrated technologies.

 

Around the world, automation is transforming work, business, and the economy. China is already the largest market for robots in the world, based on volume. All economies, from Brazil and Germany to India and Saudi Arabia, stand to gain from the hefty productivity boosts that robotics and artificial intelligence will bring. The pace and extent of adoption will vary from country to country, depending on factors including wage levels. But no geography and no sector will remain untouched.

In our research we took a detailed look at 46 countries, representing about 80% of the global workforce. We examined their automation potential today — what’s possible by adapting demonstrated technologies — as well as the potential similarities and differences in how automation could take hold in the future.

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting demonstrated technology. As we’ve described previously, our focus is on individual work activities, which we believe to be a more useful way to examine automation potential than looking at entire jobs, since most occupations consist of a number of activities with differing potential to be automated.

In all, 1.2 billion full-time equivalents and $14.6 trillion in wages are associated with activities that are automatable with current technology. This automation potential differs among countries, ranging from 40% to 55%.

The differences reflect variations in sector mix and, within sectors, the mix of jobs with larger or smaller automation potential. Sector differences among economies sometimes lead to striking variations, as is the case with Japan and the United States, two advanced economies. Japan has an overall automation potential of 55% of hours worked, compared with 46% in the United States. Much of the difference is due to Japan’s manufacturing sector, which has a particularly high automation potential, at 71% (versus 60% in the United States). Japanese manufacturing has a slightly larger concentration of work hours in production jobs (54% of hours versus the U.S.’s 50%) and office and administrative support jobs (16% versus 9%). Both of these job titles comprise activities with a relatively high automation potential. By comparison, the United States has a higher proportion of work hours in management, architecture, and engineering jobs, which have a lower automation potential since they require application of specific expertise such as high-value engineering, which computers and robots currently are not able to do.

On a global level, four economies — China, India, Japan, and the United States — dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting demonstrated technologies. Together, China and India may account for the largest potential employment impact — more than 700 million workers between them — because of the relative size of their labor forces. Technical automation potential is also large in Europe: According to our analysis, more than 60 million full-time employee equivalents and more than $1.9 trillion in wages are associated with automatable activities in the five largest economies (France, Germany, Italy, Spain, and the United Kingdom).

We also expect to see large differences among countries in the pace and extent of automation adoption. Numerous factors will determine automation adoption, of which technical feasibility is only one. Many of the other factors are economic and social, and include the cost of hardware or software solutions needed to integrate technologies into the workplace, labor supply and demand dynamics, and regulatory and social acceptance. Some hardware solutions require significant capital expenditures and could be adopted faster in advanced economies than in emerging ones with lower wage levels, where it will be harder to make a business case for adoption because of low wages. But software solutions could be adopted rapidly around the world, particularly those deployed through the cloud, reducing the lag in adoption time. The pace of adoption will also depend on the benefits that countries expect automation to bring for things other than labor substitution, such as the potential to enhance productivity, raise throughput, and improve accuracy and regulatory and social acceptance.

Regardless of the timing, automation could be the shot in the arm that the global economy sorely needs in the decades ahead. Declining birthrates and the trend toward aging in countries from China to Germany mean that peak employment will occur in most countries within 50 years. The expected decline in the share of the working-age population will open an economic growth gap that automation could potentially fill. We estimate that automation could increase global GDP growth by 0.8% to 1.4% annually, assuming that people replaced by automation rejoin the workforce and remain as productive as they were in 2014. Considering the labor substitution effect alone, we calculate that, by 2065, the productivity growth that automation could add to the largest economies in the world (G19 plus Nigeria) is the equivalent of an additional 1.1 billion to 2.2 billion full-time workers.

The productivity growth enabled by automation can ensure continued prosperity in aging nations and could provide an additional boost to fast-growing ones. However, automation on its own will not be sufficient to achieve long-term economic growth aspirations across the world. For that, additional productivity-boosting measures will be needed, including reworking business processes or developing new products, services, and business models.

How could automation play out among countries? We have divided our 46 focus nations into three groups, each of which could use automation to further national economic growth objectives, depending on its demographic trends and growth aspirations. The three groups are:

  • Advanced economies. These include Australia, Canada, France, Germany, Italy, Japan, South Korea, the United Kingdom, and the United States. They typically face an aging workforce, though the decline in working-age population growth is more immediate in some (Germany, Italy, and Japan) than in others. Automation can provide the productivity boost required to meet economic growth projections that they otherwise would struggle to attain. These economies thus have a major interest in pursuing rapid automation development and adoption.
  • Emerging economies with aging populations. This category includes Argentina, Brazil, China, and Russia, which face economic growth gaps as a result of projected declines in the growth of their working population. For these economies, automation can provide the productivity injection needed to maintain current GDP per capita. To achieve a faster growth trajectory that is more commensurate with their developmental aspirations, these countries would need to supplement automation with additional sources of productivity, such as process transformations, and would benefit from rapid adoption of automation.
  • Emerging economies with younger populations. These include India, Indonesia, Mexico, Nigeria, Saudi Arabia, South Africa, and Turkey. The continued growth of the working-age population in these countries could support maintaining current GDP per capita. However, given their high growth aspirations, and in order to remain competitive globally, automation plus additional productivity-raising measures will be necessary to sustain their economic development.

For all the differences between countries, many of automation’s challenges are universal. For business, the performance benefits are relatively clear, but the issues are more complicated for policy makers. They will need to find ways to embrace the opportunity for their economies to benefit from the productivity growth potential that automation offers, putting in place policies to encourage investment and market incentives to encourage innovation. At the same time, all countries will need to evolve and create policies that help workers and institutions adapt to the impact on employment.

 

Source: Harvard Business Review-The Countries Most (and Least) Likely to be Affected by Automation

Bill Gates Is Wrong: The Solution to AI Taking Jobs Is Training, Not Taxes

Let’s take a breath: Robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven’t happened.

AI has massive potential. Taxing this promising field of innovation is not only reactionary and antithetical to progress, it would discourage the development of technologies and systems that can improve everyday life.

Imagine where we would be today if policy makers, fearing the unknown, had feverishly taxed personal computer software to protect the typewriter industry, or slapped imposts on digital cameras to preserve jobs for darkroom technicians. Taxes to insulate telephone switchboard operators from the march of progress could have trapped our ever-present mobile devices on a piece of paper in an inventor’s filing cabinet.

There simply is no proof that levying taxes on technology protects workers. In fact, as former US treasury secretary Lawrence Summers recently wrote, “Taxes on technology are more likely to drive production offshore than create jobs at home.”

Calls to tax AI are even more stunning because they represent a fundamental abandonment of any responsibility to prepare employees to work with AI systems. Those of us fortunate enough to influence policy in this space should demonstrate real faith in the ability of people to embrace and prepare for change. The right approach is to focus on training workers in the right skills, not taxing robots.

There are more than half a million open technology jobs in the United States, according to the Department of Labor, but our schools and universities are not producing enough graduates with the right skills to fill them. In many cases, these are “new collar jobs” that, rather than calling for a four-year college degree, require sought-after skills that can be learned through 21st century vocational training, innovative public education models like P-TECH (which IBM pioneered), coding camps, professional certification programs and more. These programs can prepare both students and mid-career professionals for new collar roles ranging from cybersecurity analyst to cloud infrastructure engineer.

At IBM, we have seen countless stories of motivated new collar professionals who have learned the skills to thrive in the digital economy. They are former teachers, fast food workers, and rappers who now fight cyber threats, operate cloud platforms and design digital experiences for mobile applications. WIRED has even reported how, with access to the right training, former coal miners have transitioned into new collar coding careers.

The nation needs a massive expansion of the number and reach of programs students and workers can access to build new skills. Closing the skills gap could fill an estimated 1 million US jobs by 2020, but only if large-scale public private partnerships can better connect many more workers to the training they need. This must be a national priority.

First, Congress should update and expand career-focused education to help more people, especially women and underrepresented minorities, learn in-demand skills at every stage. This should include programs to promote STEM careers among elementary students, which increase interest and enrollment in skills-focused courses later in their educational careers. Next, high-school vocational training programs should be reoriented around the skills needed in the labor market. And updating the Federal Work-Study program, something long overdue, would give college students meaningful, career-focused internships at companies rather than jobs in the school cafeteria or library. Together, high-school career training programs and college work study receive just over $2 billion in federal funding. At around 3 percent of total federal education spending, that’s a pittance. We can and must do more.

Second, Congress should create and fund a 21st century apprenticeship program to recruit and train or retrain workers to fill critical skills gaps in federal agencies and the private sector. Allowing block grants to fund these programs at the state level would boost their effectiveness and impact.

Third, Congress should support standards and certifications for new collar skills, just as it has done for other technical skills, from automotive technicians to welders. Formalizing these national credentials and accreditation programs will help employers recognize that candidates are sufficiently qualified, benefiting workers and employers alike.

Taking these steps now will establish a robust skills-training infrastructure that can address America’s immediate shortage of high-tech talent. Once this foundation is in place, it can evolve to focus on new categories of skills that will grow in priority as the deployment of AI moves forward.

AI should stand for augmented—not artificial—intelligence. It will help us make digital networks more secure, allow people to lead healthier lives, better protect our environment, and more. Like steam power, electricity, computers, and the internet before it, AI will create more jobs than it displaces. What workers really need in the era of AI are the skills to compete and win. Providing the architecture for 21st century skills training requires public policies based on confidence, not taxes based on fear.

Source: Wired-Bill Gates Is Wrong: The Solution to AI Taking Jobs Is Training, Not Taxes

Don’t Confuse Uncertainty with Risk

We are living in a digital era increasingly dominated by uncertainty, driven in part by the rise of exponential change. The problem is, we are generally clogging up the gears of progress and growth in our companies by treating that uncertainty as risk and by trying to address it with traditional mitigation strategies. The economist Frank Knight first popularized the differentiation between risk and uncertainty almost a century ago. Though it is a dramatic oversimplification, one critical difference is that risk is – by definition – measurable while uncertainty is not. [1]

 

Proof and Confidence. One way to parse uncertainty from risk, and in turn to assess differing levels of risk, is to consider what it should take for your organization to make a certain strategic move. One dimension of this is the “level of evidence required” in order to make the move. In other words, what amount of data and supporting information is necessary to understand the contours of the unknown and to shift from inaction to action? A

The first dimension is the “level of evidence required” in order to make the move. The second dimension is the “level of confidence” that we have in making the move in the first place.

Risk in the Known or Knowable. Since anything that can be called risky is measurable (e.g. via scenario modeling, financial forecasting, sensitivity analysis, etc.), it is by definition close enough to the standard and “knowable” business of today. Uncertainty is the realm outside of that: it’s “unknowable” and not measurable.

In risky areas, the level of analysis we do – and how much time we take to try to understand the risk and make a decision – should vary. The graphic above frames the three levels of risk described in more detail below, along with examples from some of our clients of the types of projects that we see falling into these categories:

 

1. Risky, Without Precursor – These are moves for which there is no “precursor” or analog that we have seen from elsewhere. We really want to do our homework when opportunities fall here, as exposure (e.g. financial or reputational) is high, and we have very little experience with the move and/or supporting data in the form of other’s success stories, analogs, etc.

Typical initiative: Collaborative and/or ecosystem-driven solution development – The City of Columbus was awarded the U.S. Department of Transportation’s $40MM Smart City Challenge in June of last year. The competition involved submissions from 78 cities “to develop ideas for an integrated, first-of-its-kind smart transportation system that would use data, applications, and technology to help people and goods move more quickly, cheaply, and efficiently.”[2] The solutions that were envisioned as part of the challenge were generally known (or at least had an identifiable development path) but required a complex ecosystem to deliver them. Columbus was awarded the prize because they created a compelling vision and because they were able to bring the right “burden of proof” to the USDOT that they would be able to pull it off – i.e. that they had ways to manage down the execution risk.

 

2. Risky, With Precursor – Exposure may be high, but we are highly confident about making the move. The argument for why the move makes sense should be reasonably straightforward.

Typical initiativeSensor-based business models and data monetization – A major aerospace sub-system provider had long been an industry leader in developing high-tech industrial parts and products. In recent years, new competitors had been coming online, and the company knew they needed to innovate to stay ahead of the game. In one initiative, they began adding sensors to their aircraft and aerospace products, initially for predictive maintenance needs. As they began rolling this out, they realized the data could be valuable in many other ways and actually create a whole new source of revenue from a whole new customer: pilots. Using this data, they decided to build a mobile platform that would allow pilots to view operating information from the parts and understand better ways to fly from point A to point B. The level of evidence they had was high – it was clear from many other industries that data could be used in this way to produce business value, but the confidence that it was the right decision for the brand was low at the beginning. They had to test it to find out. In this case, it was enormously successful, opening up a new business model and customer set that the company had never served before.

 

3. Low Risk, No Brainer – This is the domain of “just go do it,” perhaps because lots of solutions exist already and the opportunity for immediate economic value is high. There isn’t much reason to go study this to death.

Typical initiative: Robotic Process Automation (RPA) – RPA technology is essentially a software robot that has been coded to be able to do repetitive, highly logical and structured tasks. It has been around for a while, and there are extensive examples and case studies across industries, especially in banking. So, when JP Morgan decided to look into using softbots to automate higher order processes with investment banking, the right decision seemed obvious.[3] With growing pressure on margins, and with the success within the industry in automating structured tasks, raising demands on automation technology seemed like a logical next step. It was clear this was where the industry is going, and it was just a matter of time before all competitors would be doing it. Choosing not to innovate seemed like the bigger risk in this situation.

 

Dealing with the Uncertainty Quadrant. This is the domain of the “unknowable.” Operating in this space, many companies spend lots of time running around collecting data to reduce risk, in the attempt to make it more knowable. But if the action is truly uncertain, extensive research to lower your risk is just a waste of time.

The only way to consider a highly uncertain action is to “just go do it” – usually through prototyping and market testing – but in a way that minimizes financial or reputational exposure. Consider an old story about Palm Computing, a favorite of my friend Larry Keeley’s. As I have heard Larry tell it, the genesis story of Palm is rooted in a condition we are all too familiar with today: a low-level hypothesis that digital would matter when it came to being organized and connected, but with a high degree of uncertainty about how that would (and should) play out. This was a time of “spontaneous simultaneity” as various players worked with designs and technological solutions. The one who got it right (for a time) was the one who just did it.

Jeff Hawkins, one of the founders of Palm, epitomized the activity of prototyping. The (perhaps apocryphal) story is that in the very early days, Jeff would work in his garage to cut multiple pieces of balsa wood into organizer-shaped rectangles. He would load a bunch of those into his shirt pocket and carry them to meetings, sketching on each one in the moment something that occurred to him as being particularly helpful at the time. Contact entry, instant contact sharing, notes, calendar access, etc.: all started to appear on pieces of wood and craft an overall vision for the most important functionality to be built into the Palm. And unlike computers of the era, he discovered the criticality of instant-on functionality. To steal a phrase from the design world, the device ended up being “well-behaved” from the beginning because it was founded upon how people actually interacted. The rise and fall of Palm is a much longer story. But in the early days, Hawkins demonstrated the handling of uncertainty while minimizing exposure exquisitely.

As we carry these principles back into our organizations, discussion of whether something is risky or simply uncertain is almost “certainly” going to drift quickly towards the semantic. We should start training ourselves and our organizations to talk more about the level of evidence required (not to mention whether proof is even attainable) and level of confidence, and less about how risky something seems. With this approach, we might actually be able to start thriving in a world that is increasingly uncertain.

 

Source: Huffington Post-Don’t Confuse Uncertainty with Risk

How to Win with Automation (Hint: It’s Not Chasing Efficiency)

In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.

In the decades to come, we are likely to see similar shifts. Today, just like then, many people’s jobs will be taken over by machines and many of the jobs of the future haven’t been invented yet. That inspires fear in some, excitement in others, but everybody will need to plan for a future that we can barely comprehend today.

This creates a dilemma for leaders. Clearly, any enterprise that doesn’t embrace automation won’t be able to survive any better than a farmer with a horse-drawn plow. At the same time, managers need to continue to motivate employees who fear their jobs being replaced by robots. In this new era of automation, leaders will need to identify new sources of value creation.

Identify Value At A Higher Level

It’s fun to make lists of things we thought machines could never do. It was said that that only humans could recognize faces, play chess, drive a car, and do many other things that are automated today. Yet while machines have taken over tasks, they haven’t actually replaced humans. Although the workforce has doubled since 1970, unemployment remains fairly low, especially among those that have more than a high school level of education. In fact, overall labor force participation for working age adults has risen from around 70% in 1970 to over 80% today.

Once a task becomes automated, it also becomes largely commoditized. Value is then created on a higher level than when people were busy doing more basic things. The value of bank branches, for example, is no longer to manually process deposits, but to solve more complex customer problems like providing mortgages. In much the same way, nobody calls a travel agency to book a simple flight anymore. They expect something more, like designing a dream vacation. Administrative assistants aren’t valuable because they take dictation and type it up on a typewriter, but because they serve as gatekeepers who prioritize tasks in an era of information overload.

So the first challenge for business leaders facing a new age of automation is not try to simply to cut costs, but to identify the next big area of value creation. How can we use technology to extend the skills of humans in ways that aren’t immediately clear, but will seem obvious a decade from now? Whoever identifies those areas of value first will have a leg up on the competition.

Innovate Business Models

Amazon may be the most successfully automated company in the world. Everything from its supply chain to its customer relationship management are optimized through its use of big data and artificial intelligence. Its dominance online has become so complete that during the most recent Christmas season it achieved a whopping 36.9% market share in online sales.

So a lot of people were surprised when it launched a brick and mortar book store, but as Apple has shown with its highly successful retail operation, there’s a big advantage to having stores staffed with well trained people. They can answer questions, give advice, and interact with customers in ways that a machine never could.

Notice as well that the Apple and Amazon stores are not your typical mom-and-pop shops, but are largely automated themselves, with industrial age conventions like cash registers and shopping aisles disappearing altogether. That allows the sales associates to focus on serving customers rather than wasting time and energy managing transactions.

Redesign Jobs

When Xerox executives first got a glimpse of the Alto, the early personal computer that inspired Steve Jobs to create the Macintosh, they weren’t impressed. To them, it looked more like a machine that automated secretarial work than something that would be valuable to executives. Today, of course, few professionals could function without word processing or spreadsheets.

We’re already seeing a similar process of redesign with artificially intelligent technologies. Scott Eckert, CEO of Rethink Robotics, which makes the popular Baxter and Sawyer robots told me, “We have seen in many cases that not only does throughput improve significantly, but jobs are redesigned in a way that makes them more interesting and rewarding for the employee.” Factory jobs are shifting from manual tasks to designing the work of robots.

Lynda Chin, who co-developed the Oncology Expert Advisor at MD Andersonpowered by IBM’s Watson, believes that automating cognitive tasks in medicine can help physicians focus more on patients. “Instead of spending 12 minutes searching for information and three with the patient, imagine the doctor getting prepared in three minutes and spending 12 with the patient,” she says.

“This will change how doctors will interact with patients.” she continues. “When doctors have the world’s medical knowledge at their fingertips, they can devote more of their mental energy to understanding the patient as a person, not just a medical diagnosis. This will help them take lifestyle, family situation and other factors into account when prescribing care.”

Humanity Is Becoming The Scarce Resource

Before the industrial revolution, most people earned their living through physical labor. Much like today, many tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.

Today we’re seeing a similar shift from cognitive skills to social skills. When we all carry supercomputers in our pocket that can access the collective knowledge of the world in an instant, skills like being able to retain information or manipulate numbers are in less demand, while the ability to collaborate, with humans and machines, are rising to the fore.

There are, quite clearly, some things machines will never do. They will never strike out in Little League, get their heart broken, or worry about how their kids are doing in school. These limitations mean that they will never be able to share human experiences or show genuine empathy. We will always need humans to collaborate with other humans.

As the futurist Dr. James Canton put it to me, “It is largely a matter of coevolution. With automation driving down value in some activities and increasing the value of others, we redesign our work processes so that people are focused on the areas where they can deliver the most value by partnering with machines to become more productive.”

So the key to winning in the era of automation, where robots do jobs formerly performed by humans, is not simply more efficiency, but to explore and identify how greater efficiency creates demand for new jobs to be done.

 

Source: Harvard Business Review -How to Win with Automation (Hint: It’s Not Chasing Efficiency)

Please Don’t Hire a Chief Artificial Intelligence Officer

Every serious technology company now has an Artificial Intelligence team in place. These companies are investing millions into intelligent systems for situation assessment, prediction analysis, learning-based recognition systems, conversational interfaces, and recommendation engines. Companies such as Google, Facebook, and Amazon aren’t just employing AI, but have made it a central part of their core intellectual property.

As the market has matured, AI is beginning to move into enterprises that will use it but not develop it on their own. They see intelligent systems as solutions for sales, logistics, manufacturing, and business intelligence challenges. They hope AI can improve productivity, automate existing process, provide predictive analysis, and extract meaning from massive data sets. For them, AI is a competitive advantage, but not part of their core product. For these companies, investment in AI may help solve real business problems but will not become part of customer facing products. Pepsi, Wal-Mart and McDonalds might be interested in AI to help with marketing, logistics or even flipping burgers but that doesn’t mean that we should expect to see intelligent sodas, snow shovels, or Big Macs showing up anytime soon.

As with earlier technologies, we are now hearing advice about “AI strategies” and how companies should hire Chief AI Officers. In much the same way that the rise of Big Data led to the Data Scientist craze, the argument is that every organization now needs to hire a C-Level officer who will drive the company’s AI strategy.

I am here to ask you not to do this. Really, don’t do this.

It’s not that I doubt AI’s usefulness. I have spent my entire professional lifeworking in the field. Far from being a skeptic, I am a rabid true believer.

However, I also believe that the effective deployment of AI in the enterprise requires a focus on achieving business goals. Rushing towards an “AI strategy” and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.

This is not what you’ll get if you hire a Chief AI Officer. The very nature of the role aims at bringing the hammer of AI to the nails of whatever problems are lying around. This well-educated, well-paid, and highly motivated individual will comb your organization looking for places to apply AI technologies, effectively making the goal to use AI rather than to solve real problems.

This is not to say that you don’t need people who understand AI technologies. Of course you do. But understanding the technologies and understanding what they can do for your enterprise strategically are completely different. And hiring a Chief of AI is no substitute for effective communication between the people in your organization with technical chops and those with strategic savvy.

One alternative to hiring a Chief AI Officer is start with the problems. Move consideration of AI solutions into the hands of the people who are addressing the problems directly. If these people are equipped with a framework for thinking about when AI solutions might be applicable, they can suggest where those solutions are actually applicable. Fortunately, the framework for this flows directly from the nature of the technologies themselves. We have already seen where AI works and where its application might be premature.

The question comes down to data and the task.

For example, highly structured data found in conventional databases with well-understood schemata tend to support traditional, highly analytical machine learning approaches. If you have 10 years of transactional data, then you should use machine learning to find correlations between customer demographics and products.

In cases where you have high volume, low feature data sets (such as images or audio), deep learning technologies are most applicable. So a deep learning approach that uses equipment sounds to anticipate failures on your factory floor might make sense.

If all you have is text, the technologies of data extraction, sentiment analysis and Watson-like approaches to evidence-based reasoning will be useful. Automating intelligent advice based on HR best practice manuals could fit into this model.

And if you have data that is used to support reporting on the status or performance of your business, then natural language generation is the best option. It makes no sense to have an analyst’s valuable time dedicated to analyzing and summarizing all your sales data when you can have perfectly readable English language reports automatically generated by a machine and delivered by email.

If decision-makers throughout your organization understand this, they can look at the business problems they have and the data they’re collecting and recognize the types of cognitive technologies that might be most applicable.

The point here is simple. AI isn’t magic. Specific technologies provide specific functions and have specific data requirements. Understanding them does not require that you hire a wizard or unicorn to deal with them. It does not require a Chief of AI. It requires teams that know how to communicate the reality of business problems with those who understand the details of technical solutions.

The AI technologies of today are astoundingly powerful. As they enter the enterprise, they will change everything. If we focus on applying them to solve real, pervasive problems, we will build a new kind of man-machine partnership that empowers us all to work at the top of our game and realize our greatest potential.

Source: Harvard Business Review-Please Don’t Hire a Chief Artificial Intelligence Officer

From Bot Hype To Reality: 3 Keys to Success For Intelligent Automation by Enterprises

With the rapid adoption of messaging and artificial intelligence hitting the mainstream, it is ‘go’ time for enterprises to modernize and meet their customers where they want to be met: in mobile chat. Remember what email did to the fax machine? It won’t take long for email to meet a similar plight with messaging usurping its pole position in B2C communications.

In 2016, we saw the rise of chatbots. You couldn’t read a reputable editorial outlet without the term ‘chatbot’ appearing somewhere on the first page. But the hype quickly turned to a sad reality as many bots on Facebook, KiK, WeChat and other platforms failed to deliver on their promise. But then again, what was their promise? Do consumers really want to ‘chat’ with brands and have relatively meaningless ‘conversations’? I say no, and as a result, pragmatic AI is winning the day.

Pragmatic AI is the key to enterprise transformation in 2017 and beyond. It is the idea that machines can interact with humans through messaging conversations to resolve an issue quickly, efficiently and securely. Consumers are busy people. When they need something from a business, they want it immediately. Pragmatic AI doesn’t put you on hold, it doesn’t give you the wrong answer and it is always available – 24/7/365.

So, with this in mind, here are 3 ways enterprises can cut through the hype and modernize for the next generation of consumers:

 

1. Choosing the right AI

 

There are two flavors of AI: Open and Pragmatic.

Open AI – like the large-scale cognitive services with high-end AI capabilities – is the kind we’re accustomed to seeing in the headlines. But for the enterprise, this type of AI is often too ambitious to be put to any good use beyond data analytics. It lacks the performance-based capabilities and transactional components that are needed for day-to-day enterprise applications. It is extremely costly and requires a small army of system integrators to install and operate it.

Pragmatic AI, as defined above, works on a functional level. It takes IVR, call center and other scripts to create decision trees, and plugs into various backend APIs to execute a myriad of business processes. From changing passwords, to canceling accounts, binding policies and tracking claims, if a human can do it, Pragmatic AI can do it too.

We see the fallacy around deep learning and open AI catch up with many enterprises who are sometimes six to 12 months in on deployment (after feeling the pressure to adopt AI). These companies see no real solution in sight. Roughly 80% of call center inquiries don’t require cognitive services and deep learning. You have to start small, be practical and use bots that are nimble and functional. If you do this properly, your bots can also proactively engage consumers and replace email and social media as the primary channel for revenue-driving promotions and marketing initiatives.

 

2. Increasing loyalty by enabling transactions through automation

 

Enterprises exist in a world filled with a need to serve and deliver on consumer demands. Consumers are transaction-driven – when they want something, they want it instantaneously. So, when enterprises expand their communication strategies to explore new channels – such as chatbot-powered messaging – they need to ensure the new channels support an even greater level of functionality than all their other existing channels.

A major problem we’re seeing in the industry is enterprises deploying bots on 3rd-party channels that lack basic transactional functionality – whether that be payment processing, scheduling, file transfer and storage, or authentication. The resulting experience is usually a negative one for both the customer and the enterprise.

The technology exists to support rich customer interactions over messaging. After all, it is the next frontier for enterprise communication. Enterprise platforms are meant for enterprises. Social platforms are meant for socializing. Let’s keep business with business and pleasure with pleasure; mixing the two can result in major repudiation and fraud issues through identity theft.

 

3. Protecting customer data through an end-to-end solution

 

Right up there on the ‘mission critical list’ of every CIO is data privacy and protection. Mobile messaging is generating newfound challenges for businesses as consumers flock to apps that are unsecure and can’t support the needs of enterprise communication. This means when businesses add social messaging apps into their communication mix, they can’t provide the functionality for customers to do anything more than merely ‘chat’. The result is poor customer experiences and lost revenue. The same is true for bots. To avoid potential security risks and wasted investment, businesses need to ensure the platform they intend to use meets the desired requirements so they can adequately serve their customers.

Enterprises in the healthcare, financial services and insurance industries face significant challenges in this respect. Whether it is HIPAA, FISMA, FINRA or other, these enterprises need to meet the various state, federal and international regulatory criteria. A poorly devised automation and bot strategy where one vendor’s bots are bolted onto another vendor’s messaging system almost guarantees compliance failure and legal recourse.

Find an end-to-end solution where the automation, messaging, transactions and consumer experience are all one and the same, built around compliance, privacy, scalability and security.

 

Driving customer satisfaction and cost savings for the enterprise

 

There’s been enough hype about chatbots and AI to make a portion of consumers and enterprises a little disillusioned with the technology’s promise. Skeptics begin to question the practicality of bots. But it’s more a case of a tradesman blaming his tools than the tools letting him down. With a strategic and carefully planned approach to bots and automation, the results can transform any enterprise, driving up NPS and dramatically reducing costs. These are just three examples of how enterprises can launch their own thorough and ROI-driven automation strategies to connect with consumers in new and engaging ways.

 

Source: Huffington Post-From Bot Hype To Reality: 3 Keys to Success For Intelligent Automation by Enterprises

Blue Prism Software Which Makes Bots Productive Will Now Run in the Cloud

The tech world is besotted with bots. This technology, also known as chatbots, provides automated but theoretically human-like responses to a user request. If you’ve clicked on a customer service button on your bank’s site, you’ve dealt with a bot. Businesses see bots as a great way to help people buy products or receive support on products they’ve already bought.

But the bots themselves typically act as the front door to a world of processes happening behind the scenes. If you ask a bot for help, your request kicks off activities to get the job done.

“We sit between the chat bots at the front end and the infrastructure and business processes on the back end. We view ourselves as the operating system for this digital workforce,” Blue Prism chief executive Alistair Bathgate told Fortune.

On Wednesday, the company is announcing a new version of its software that will run on public clouds like Amazon Web Services, Microsoft Azure, and GoogleCloud Platform. Blue Prism’s software until now has typically run on customers’ own servers.

“If you call your bank to report a lost or stolen credit card, that starts as a five minute conversation followed by 25 minutes of administrative processes, where someone has to cancel the card, add a new card, initiate anti-fraud procedures. We automate that 25-minute piece,” he s said

By opening up its technology to run on massive public cloud data centers, Blue Prism can also take advantage of all the artificial intelligence and other services those clouds offer, Bathgate said.

In this arena, which techies call robotic process automation or RPA, Blue Prism competes with companies like UIPath and Automation Anywhere. The big cloud providers, which are pouring billions into AI and other services, could enter the fray on their own as well.

Source: Fortune-Blue Prism Software Which Makes Bots Productive Will Now Run in the Cloud

Will automation take away all our jobs?

Here’s a paradox you don’t hear much about: despite a century of creating machines to do our work for us, the proportion of adults in the US with a job has consistently gone up for the past 125 years. Why hasn’t human labor become redundant and our skills obsolete? In this talk about the future of work, economist David Autor addresses the question of why there are still so many jobs and comes up with a surprising, hopeful answer.

Source: TED-Will automation take away all our jobs?