Low Skilled Humans Need Not Apply: Exponential Job Disruption

I wish to emphasise before I begin that robots taking jobs is not the problem, the issue is the current government policies that are not ready to handle this disruption. I am not against automation, far from it, I want as much automation as possible but it would be naive to not consider any potential side effects with the way policies currently are and how slow government and culture can change regarding attitudes towards the most vulnerable in our society. The way the unemployed are treated and the government's acceptance of spiralling education costs, low social mobility and rising wealth/income inequality are difficult. but optimistically. not insurmountable obstacles.

In this first article I discuss the new technologies which will disrupt many popular occupations, particularly low skilled, as cautionary wake up call to better handle the transition that is inevitably coming our way within the next 20 years. We can prevent people from having to needlessly suffer and turn this coming disruption into a situation where we can all more equally enjoy the wealth the machines can bring us.

Ben Horowitz -“Technological change is largely inevitable and impossible to stop. Short of closing borders and halting communications, like North Korea, an economy cannot turn its back on progress.”

Mental Work

As much of the muscle work has been replaced by machines, the brain work is starting to face a similar fate by “intelligent” software bots. Cognitive computing like IBM’s Watson is giving computers the ability to “think”, which has the potential to disrupt a wide range of occupations.

Computer power will become exponentially more powerful and AI algorithms being fed Big Data from the Internet of Things will evolve from a smart assistants like Siri, into smart workers, smart bosses and even to smart teachers and doctors within the next 20 years. I discuss this thoroughly in my article Disruption Of Mental Work.

TechPro Research found that 71% of businesses are already automating IT work, or plan to Automation plays an ever-increasing role in IT and is impacting job and how business is done. Their latest survey digs into the subject and shows that 60% are already automating some IT jobs and another 11% plan to do so.

An example of an AI Assistant is Amelia, she can digest an oil-well centrifugal-pump manual in 30 seconds — and give instructions for repairs, do the job of a call-centre operator, a mortgage or insurance agent, even a medical assistant, with virtually no human help. She speaks more than 20 languages, and her core knowledge of a process needs only to be learned once for her to be able to communicate with customers in their language.

During trials, IPsoft found Amelia was able to progress from answering very few queries independently to 64% of queries within two months. Analyst house Gartner predicts that by 2017, autonomic and cognitive platforms such as Amelia will drive down the cost of services by 60%.

Erik Brynjolfsson — “The steam engine was a remarkable breakthrough and really set off the industrial revolution, but as we say in the book it doubled in power and efficiency approximately once every 70 years and quadrupled after 140 years. The computer processor doubles in power every 18 months, 10 times greater every five years, it’s a very different scale of advancement and it’s affecting a broader set of the economy than the steam engine did, in terms of all the cognitive tasks. It’s happening a lot faster and more pervasively than before.

There’s no economic law that says ‘You will always create enough jobs or the balance will always be even.’ It’s possible for a technology to dramatically favour one group and to hurt another group, and the net of that might be that you have fewer jobs,”

Mark Zuckerberg — “I think 10 years from now computers will be better than humans at reading, listening, talking, and other things. So we are developing this.”

Larry Page — “You can’t wish away these things from happening, they are going to happen, You’re going to have some very amazing capabilities in the economy. When we have computers that can do more and more jobs, it’s going to change how we think about work. There’s no way around that. You can’t wish it away.”

Newer generations of Watson are currently being trained in customer service as a support representative. Individual consumers can interact with Watson in plain English to get personalized responses to questions and receive actionable insight with supporting evidence and confidence to help create the experiences customers expect.

Kenneth Brant, research director at Gartner — “Many new combinations of technology — from intelligent software agents, expert systems and virtual reality assistants to software systems embedded in smart products and revolutionary new forms of robotics — will emerge and have great impacts in this decade. We won’t need to develop a full-functioning artificial brain by 2020 for smart machines to have radically changed our business models, workforce, cost structure and competitiveness.”

Yann LeCun — “The technology is still providing more accuracy and power in every area of AI where it has been applied. New ideas are needed about how to apply it to language processing, but the still-small field is expanding fast as companies and universities dedicate more people to it, that will accelerate progress.

Our relationship with the digital world will completely change due to intelligent agents you can interact with. There is the same phenomenon that we were observing just before 2012, things are starting to work, but the people doing more classical techniques are not convinced. Within a year or two it will be the end. The revolution is on the way”

AI expert LeCun guesses that virtual helpers with a mastery of language unprecedented for software will be available in just two to five years. He expects that anyone who doubts deep learning’s ability to master language will be proved wrong even sooner. He also predicts that computers may even begin to gain common sense one day.

Yann LeCun: I think a form of common sense could be acquired through the use of predictive unsupervised learning. For example, I might get the machine to watch lots of videos were objects are being thrown or dropped. The way I would train it would be to show it a piece of video, and then ask it, “What will happen next? What will the scene look like a second from now?” By training the system to predict what the world is going to be like a second, a minute, an hour, or a day from now, you can train it to acquire good representations of the world.

This will allow the machine to know about the constraints of the physical world, such as “Objects thrown in the air tend to fall down after a while,” or “A single object cannot be in two places at the same time,” or “An object is still present while it is occluded by another one.” Knowing the constraints of the world would enable a machine to “fill in the blanks” and predict the state of the world when being told a story containing a series of events.

Google’s secretive DeepMind startup(which it bought for $400 million) goal is “solving intelligence. In late 2014 they unveiled a prototype computer that attempts to mimic some of the properties of the human brain’s short-term working memory. The result is a computer that learns as it stores memories and can later retrieve them to perform logical tasks beyond those it has been trained to do. “Our experiments demonstrate that [our Neural Turing Machine] is capable of learning simple algorithms from example data and of using these algorithms to generalize well outside its training regime. That is an important step forward that has the potential to make computing machines much more brainlike than ever before. But there is significant work ahead.”

Jeremy Howard — “So we now know that computers can learn, and computers can learn to do things that we actually sometimes don’t know how to do ourselves, or maybe can do them better than us.

So I’m very excited about the opportunities. I’m also concerned about the problems. The problem here is that every area in blue on this map is somewhere where services are over 80 percent of employment. What are services?

These are services. These are also the exact things that computers have just learned how to do. So 80 percent of the world’s employment in the developed world is stuff that computers have just learned how to do. What does that mean?‘Well, it’ll be fine. They’ll be replaced by other jobs. For example, there will be more jobs for data scientists.’ Well, not really. It doesn’t take data scientists very long to build these things. For example, these four algorithms were all built by the same guy. So if you think, oh, it’s all happened before, we’ve seen the results in the past of when new things come along and they get replaced by new jobs, what are these new jobs going to be?

It’s very hard for us to estimate this, because human performance grows at this gradual rate, but we now have a system, deep learning, that we know actually grows in capability exponentially.

And we’re here. So currently, we see the things around us and we say, “Oh, computers are still pretty dumb.” Right? But in five years’ time, computers will be off this chart. So we need to be starting to think about this capability right now. ‘Well, computers can’t really think, they don’t emote, they don’t understand poetry, we don’t really understand how they work.’ So what? Computers right now can do the things that humans spend most of their time being paid to do, so now’s the time to start thinking about how we’re going to adjust our social structures and economic structures to be aware of this new reality.

Physical Work

Robots are approaching a technological inflection point that will let them operate more and more reliably in dynamic, unscripted environments because of improving capabilities in;

• Cognition: The robot’s ability to perceive, understand, plan, and navigate in the real world.
• Manipulation: Precise control and dexterity for manipulating objects in the environment.
• Interaction: The robot’s ability to learn from and collaborate with humans and make robots simpler to program and use.

Improvements in electromechanical design tools and manufacturing tools, electrical energy storage and power efficiency is driving helping to accelerate the advancement of robotics as well as the exponential growth in computing performance, global computation power, worldwide data storage including the scale and performance of the internet and expansion, availability and performance of local wireless digital communications.

PWC report on The new hire: How a new generation of robots is transforming manufacturing — Industrial robots are taking on more “human” capabilities and traits such as sensing, dexterity, memory, trainability, and object recognition. As a result, they’re taking on more jobs — such as picking and packaging, testing or inspecting products, or assembling minute electronics. In addition, a new generation of “collaborative” robots ushers in an era shepherding robots out of their cages and literally hand-in-hand with human workers who train them through physical demonstration.

As costs of advanced robotics continue to fall (from several hundreds of thousands of dollars now to tens of thousands) and applications widen, industries beyond automotive — such as food and beverage — are adding them to their ranks. One major robotics company refers to its new-generation robot as an “intelligent industrial work assistant.”

Cloud Robotics is posed to revolutionize robotics by allowing robots to sharing experiences and knowledge they gain, simulations in computers to allow machines to learn from Imagination(Google has built a “Matrix-style” digital simulation of the entire Californian road system in which it is testing its self-driving cars). Another possibility is to learn from people by watching them perform actions.

Dr. S.K. Gupta — “Perhaps most exciting for the future of cloud computing in robotics is when one robot can impart something it perceives or learns instantaneously to other robots. This sharing could have a catalytic effect on the capabilities of robots, particularly in unstructured environments.”

Robots are taking on tasks that once relied on humans’ manual dexterity, cognition and good eyesight. They are becoming easier to train and cheaper to buy and gaining mobility. Service robots are taking on more “human” capabilities and traits opening new contexts for productivity gains beyond what industrial robots have done, I discuss this in my article on Physical Work Disruption.

Jeremy Howard in 2014 — “I think that on the software side, in 3 to 5 years we could be at a point where robots could operate in fairly unstructured environments, in a fairly autonomous way — based on the recent progress in machine vision and reinforcement learning. Job sectors relying primarily on perception will be the first and hardest hit, since perception is what computers are most rapidly improving at thanks to deep learning”

Scott Phoenix — A human brain, for example, has about a thousand times as many neurons as a frog brain. Whereas it took evolution about 250 million years to achieve a thousand time increase in processing power, our computers improve a thousand times every 10 years or so.

UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence. Professor Pieter Abbeel says. “We still have a long way to go before our robots can learn to clean a house or sort laundry, but our initial results indicate that these kinds of deep learning techniques can have a transformative effect in terms of enabling robots to learn complex tasks entirely from scratch. In the next five to 10 years, we may see significant advances in robot learning capabilities through this line of work.”

Hod Lipson — “It all boils down to machine learning. Most of the automation will be driven by software that learns from its own experience, as it learns, it gets better. Not just that specific instance of the software gets better, but all instances learn from each other’s experiences. This compounding effect means that there is tremendous leverage.

In a relatively short while, the driverless car’s AI will have accumulated a billion hours of driving experience — more than a thousand human lifetimes. That’s difficult to beat. And it’s the same situation for medical diagnostics, strategic investment, farming, pharmacy. The AI doctor that sees patients will have quickly seen millions of patients and encounter almost all possible types of problems — more than even the most experienced doctor will see in her lifetime.”

A startup called Clarifai is offering a service that uses deep learning to understand video. The company says its software can rapidly analyze video clips to recognize 10,000 different objects or types of scene. The software can analyze video faster than a human could watch it; in the demonstration, the 3.5 minute clip was processed in just 10 seconds.

Ian Goodfellow — “I expect within five years, we will have neural networks that can summarize what happens in a video clip, and will be able to generate short videos. Neural networks are already the standard solution to vision tasks. I expect they will become the standard solution to NLP and robotics tasks as well.”

Jeremy Howard — “We are seeing order-of-magnitude improvements( in computer vision) every few months, similar leaps are starting to appear in computers’ ability to understand written text. In five years’ time, a single computer could be hundreds or thousands of times better at that task than humans, combine it with other computers on a network, and the advantage becomes even more pronounced. Probably in your lifetime, certainly in your kids’ lifetime computers will be better than humans at all these things, and within five years after that, they will be 1,000 times better.”

Compliant robots like Baxter, Sawyer , KUKA , YuMi , UR3 or Justin , which are safe to work along humans and that even children can program, have just entered the market targeting low and medium end manufacturing. Baxter, which costs $25,000, has now the option to be given wheels for mobility. Perhaps the best example of a mobile robot is the self driving car.

Google — “Given the time we’re spending on busy streets, we’ll inevitably be involved in collisions; sometimes it’s impossible to overcome the realities of speed and distance. Thousands of minor accidents happen every day on typical American streets, 94% of them involving human error, and as many as 55% of them go unreported. (And we think this number is low; for more, see here.) In the six years of our project, we’ve been involved in 15 minor accidents during more than 1.9 million miles of autonomous and manual driving combined. Not once was the self-driving car the cause of the accident.”

Google, Apple, Uber and Tesla and many other car manufacturers are working on self driving cars which will happily drive 24/7, every day of the year with no breaks, holidays, sick days, lateness, distractions, tiredness, hangovers, complaints, strikes, wage rises, overtime pay, training or retraining, compensation, severance pay, contract negotiations, bonuses, maternity leave, require pensions, crash less and only demand a ‘salary’ equal to the cost of energy if they are electric. They will disrupt many occupations, this is discussed in my article titled Self Driving Cars Disruption.

Advanced robotics could generate a potential economic impact of between $1.9tn and $6.4tn (£1.3tn to £4.4tn) per year by 2025 according to the McKinsey Institute. I discuss how collaborative robots combined with self driving cars and other tech will disrupt many occupations in my article How Self Driving Cars Will Disrupt Retail.

Is This Time Different? — This general pattern has repeated itself in many sectors of the economy over the last two centuries despite warnings every few decades that automation was about to cause mass unemployment. However, this time may be different. When robot capabilities evolve very rapidly, robots may displace a much greater proportion of the workforce in a much shorter time than previous waves of technology. Increased robot capabilities will lower the value of human labor in many sectors.

Human abilities as suppliers, even in highly educated societies, evolve slowly. In other words, the increase in robot capabilities may be so rapid that many human workers may find themselves with little to sell. In the longer run, the diversity and scale of human demand for goods and services has seemed insatiable — so that the labor demanded by the economy did not diminish. But as robot capabilities improve beyond a growing range of human capabilities, will this pattern continue to hold true?

One can imagine a future in which many of the material goods that most people want are produced at low cost by advanced robots. Such an economy could evolve in a number of ways. But one possible outcome is that robots may do to many sectors of the economy what the Internet has done to the music business — that is, lead to an economy that pays superstar wages to a small number of exceptionally talented people while paying only a low level of income to many others.

What could this mean for jobs?

We have not seen an exponential technology like AI in past 140 years, trying to use it interchangeably with past technology is a mistake and is why this time, it is different. Ray Kurzweil’s Law of accelerating returns predicts “As exponential growth continues to accelerate into the first half of the twenty-first century, It will appear to explode into infinity, at least from the limited and linear perspective of contemporary humans.”

Jeremy Howard — “In the Industrial Revolution, we saw a step change in capability thanks to engines. The thing is, though, that after a while, things flattened out. There was social disruption, but once engines were used to generate power in all the situations, things really settled down.

The Machine Learning Revolution is going to be very different, it never settles down. The better computers get at intellectual activities, the more they can build better computers to be better at intellectual capabilities, so this is going to be a kind of change that the world has actually never experienced before, so your previous understanding of what’s possible is different.”

On its own, each technology has the capacity to change business activity. Taken together, they have the potential to radically reshape society, businesses, the workforce and the economy within the next 10 years.

A study by 2 researchers at Oxford found nearly half of U.S. jobs, and 70% of low-skill jobs, could be susceptible to computerization over the next two decades. It found that occupations within the service industry are highly susceptible. While the original study assumes that in the US, 47% of all jobs are at risk, it is at 59% for Germany according to a calculation of the economists of the bank ING-Diba.

A study by Gartner predicts one in three US jobs will be converted to software, robots and smart machines by 2025. Cognitive capability in software will extend to other areas, including financial analysis, medical diagnostics and data analytic jobs of all sorts, says Gartner.“Knowledge work will be automated, new digital businesses require less labor; machines will be make sense of data faster than humans can.”

An analysis from the Committee of Economic Development of Australia warns more than five million jobs, almost 40% of jobs, could disappear in the next 10 to 15 years because of technological advancements, it found that technology is set to wipe out 60 per cent of rural jobs. Nobel prize-winning economist Joe Stiglitz has a NBER paper out that comes to a conclusion that the robots really are coming for your job.

Deloitte, the Big Four accountancy firm, and the University of Oxford found that 35% of existing UK jobs at high risk of replacement in next twenty years, and that lower-paid jobs over five times more likely to be replaced than higher-paid. Angus Knowles-Cutler, London senior partner at Deloitte, said: “Unless these changes coming in the next two decades are fully understood and anticipated by businesses, policy makers and educators, there will be a risk of avoidable unemployment and under-employment. A widening gap between ‘haves’ and ‘have nots’ is also a risk as lower skill jobs continue to disappear.”

A study by Deloitte found a massive impact is unavoidable for Canada. The way Canadians live and work is about to change profoundly. For the past year we have studied the Canadian economy in depth and surveyed over 700 business leaders across the country. “Rapid advances in technology are poised to disrupt Canada’s economy, and our businesses aren’t prepared for it and the damage could be catastrophic. Only 13% of firms are highly prepared, excelling in all four key areas of preparedness, Disruption isn’t going to happen in the distant future — it’s happening now.” — CIO at Deloitte Canada.

Andrew Ng — “I do think there’s a significant risk of technological unemployment over the next few decades, many people are doing routine, repetitive jobs. Unfortunately, technology is especially good at automating routine, repetitive work. It’s also not just about full automation. For example, if 50 percent of a radiologist’s job can be automated, this will put pricing pressure on their salaries.

There will be AI that gradually learns to do everything we do. And when a machine can do almost everything better than almost everyone, our social structure will begin to unravel. And that’s something we need to prepare for.”

We are already seeing less demand for low skilled people and more demand for high skilled people reflected in earnings, this will increase due to low skilled work being the easiest to computerize.

Riccardo Campa — Technological unemployment limits the demand for unskilled labor. It will also further centralize capital because the owners of the robots will be able to extract more profit from their machines in comparison to production based on human labor where profits need to be shared with workers.

However, the demand for skilled maintenance staff will increase, as will their salaries. This will divide society into a small group of owners of the means of production, the specialists in the realm of technology development and maintenance, the class servicing these groups, and the majority of population — those who cannot sell their labor on the labor market.

The BLS predicts services to be 80.9% of the labour force in 2022. Humans now have less to offer in making goods compared to machines, whenever you manufacture something you think can a machine do this better/cheaper, could the same happen to providing services? There is no law of economics that says a product or service must require human labor.

Is This Time Different? — In pre-mechanized economies, human beings were born with innate capital for producing economically valuable goods — their bodies. When technology lowered the value of mechanical labor, the economic value of bodies declined but the intrinsic capital value of human brains increased.

The machines of that time could not reason, compare, compute, read, smell, sense, hear, or make snap decisions. However, if artificial intelligence and robotics continue on their present trend, future machines will be able to carry out these human capabilities, at least in certain contexts and to a certain extent. If brains go the way of bodies, what inherent value will human beings have? Thus, it seems frighteningly plausible that this time will be different, and large sections of the labor market will be dislocated or hollowed out”

What is occurring is that the entire labor complexity spectrum is moving in the direction of increasing complexity. Accordingly, our definition of ‘mundane’ and ‘menial’ is also shifting. As we are able to engineer better and better machines to replace jobs on the menial end of the spectrum, more and more people are having to push the boundaries on the complex end of the spectrum. A high school education used to be enough for someone, that is no longer the case.

Andrew McAfee — One thing I am pretty sure about though is that the economic consequences of digital progress — both the benefits and the challenges — are also coming with surprising speed. The benefits are the same ones we’ve always received from tech progress: more and better goods and services, of higher quality and greater variety, available at lower prices to more people. The challenges, though, are new, and they stem from the fact that, while previous major waves of technology were complements to human labour (serving to increase wages and the number of jobs for workers), the AI included in this wave appears to be acting as a substitute for labour.

Franklin D. Roosevelt — “What does the country ultimately gain if we encourage businessmen to enlarge the capacity of American industry to produce unless we see that the income of our working population actually expands to create markets to absorb that increased production.”

People say why would a company automate and displace workers, then they won’t have money to buy what they makes and produces or purchase the services they offer. True, but a single firm isn’t going to consider the decreased number of low-income consumers on an aggregate level, they will simply do what is individually rational (profit maximising) for them.

A single firm won’t consider the long term effects 10–20 years down the line when quarterly reports are whats important. And why should a company or country not be allowed to more productive by using technology?

Vivek Wadhwa — 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 age lasted a century, and its consequent changes have happened over generations. During the industrial revolution, it was the younger generations who were trained — not the older workers. Now we have startups in Silicon Valley shaking up bedrock industries such as cable and broadcasting, hotels, and transportation.

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. How are policy makers going to grapple with entire industries’ disruptions in periods that are shorter than election cycles? The only certainty is that much change lies ahead that no one really knows how to prepare for.

Another point is the goods and service drop in price allowing more spending power, though I’m not sure how a person with no source of income can find this comforting. Economists believe that rising incomes have allowed consumers to spend more on personal services, which used to be true but incomes have been flat for many years, they have not reflected any of the increased productivity of workers.

Many of the essentials that people need to live have not been dropping in price(education, health care, housing), they have been rising much faster than wages causing deteriorating living standards and suffering for people as they try to make ends meet and provide for their families. One wage used to provide for a family now not even 2 does, raising children contributes to 43% of American Poverty.

As services get taken over by machines less people will have income to buy services which decrease service jobs. With companies offering services now making less money they will cut costs which will probably mean replacing the costly humans with cheaper machines that have now become available, a cyclic pattern man emerge.

Riccardo Campa — Services requiring efficiency rather than creativity will also be performed by robots. Will we all be assigned to frivolous or useless services? It could be one solution, but it would seem that even this road is blocked. The ‘service economy’ functions today because many humans willing to buy services work in the primary industries, and so return money to the service providers, who in turn use it to buy life’s essentials. As the pool of humans in the primary industries evaporates, the return channel chokes off; efficient, no-nonsense robots will not engage in frivolous consumption.

Money will accumulate in the industries, enriching any people still remaining there, and become scarce among the service providers. Prices for primary products will plummet, reflecting both the reduced costs of production, and the reduced means of the consumers. In the ridiculous extreme, no money would flow back, and the robots would fill warehouses with essential goods which the human consumers could not buy.

Technology could very well much exacerbate wealth/income inequality by allowing the owners of capital(robots) to get higher and higher returns compared to what people can offer as labor.

Federico Pistono — “The old jobs are not coming back. The new jobs will be highly sophisticated, technically and creatively challenging jobs, and only a handful of them will be needed. The question is simple: what will the unskilled workers of today do? So far, nobody has been able to answer that question. The reason for this, I think, is because there is no answer. Not in this system, not in the way it is designed to work.”

Jobs are also increasingly at risk from outsourcing to countries with lower wages due to a rising globally educated workforce. By 2020, China expects to have nearly 195 million community college and university graduates — compared with no more than 120 million in the United States. Accenture predicts that Brazil will increase its engineering graduates by 68% by 2015 and will produce more PhD engineers than the US by 2016.

Thomas Friedman — “Forget about “outsourcing.” In today’s hyperconnected world, there is no “in” and no “out.” There’s only “good, better and best,” and if you don’t assemble the best team you can from everywhere, your competitor will. In the past, workers with average skills, doing an average job, could earn an average lifestyle. But, today, average is officially over. Being average just won’t earn you what it used to. It can’t when so many more employers have so much more access to so much more above average cheap foreign labor, cheap robotics, cheap software, cheap automation and cheap genius.

The rapid rise in a globalized educated workforce means this historic strength is being eroded, the increasing numbers of highly educated people in the world will inevitably increase the international competition for the goods and services they produce.

Low skilled people may not be valued in a future economy. How many low skill laborers in countries with poor education infrastructure can adapt fast enough to a high skill job? Even though about ten million new college educated people per year will be added from China and India, they still have many people who leave the education system with low skills.

Bloomberg article on Robots in India — “India’s largely uneducated labor force and broken educational system aren’t ready for the more complex jobs that workers need when their low-skilled roles are taken over by machines. Meanwhile, nations employing robots more quickly, such as China, are becoming even more competitive. “The need for unskilled labor is beginning to diminish, whatever education we’re putting in and whatever skill development we’re potentially trying to put out — does it match where the industry will potentially be five to 10 years hence? That linkage is reasonably broken in India.”

India will struggles as robots take over the more low skilled jobs, the country is failing to educate its illiterate 287 million — greater than the population of every other country except China and the U.S. — to do much more than that. The average Indian adult has been schooled for only 4.4 years, the worst among Asia’s major developing economies, according to United Nations data. Tharman Shanmugaratnam, chairman of the International Monetary Fund’s policy advisory committee says “Time is not on India’s side, I give 10 years for labor-intensive manufacturing to survive in its present form before machines take over.”

Advanced robotics will be especially bad for the Chinese economy which is largely dependent on manufacturing jobs. The Financial Times reported that analysts are forecasting the payback period for industrial robots with a life cycle of 10 years has shortened to 1.7 years in 2015 from 11.8 years in 2008 and will likely shorten again to 1.3 years in 2016. The prospect of being able to pay off the cost of a robot in slightly more than a year, Goldman Sachs says, has brought industrial automation to within the reach of China’s millions of small and medium-sized manufacturers, creating the conditions for a productivity surge.

Yann LeCun — Whether or not we produce intelligent robots, a solution to the problem of growing wealth inequality will have to be found. Wealth inequality is not solely the result of technological progress, but of the combination of technological progress and the concentration of powers (particularly when wealth buys you political power to an unreasonable degree, as in the US).

Perhaps the acceleration of technological progress will require new models of political organization which do not leave so many people behind, as our current system does (at least in the US). People in some European countries seem to be considerably more worried about this than Americans. One thing about the advent of intelligent robots is that it will reduce the economic advantage of countries with low labor costs.

Robots recently replaced 90 per cent of humans in a Chinese factory — productivity increased 162 per cent Instead of 650 human employees, there are now only 60, with general manager Luo Weiqiang telling Chinese newspaper the People’s Daily that the aim is to cut this further to just 20 staff.

Vivek Wadhwa — “Even if the Chinese automate their factories with AI-powered robots and manufacture 3D printers, it will no longer make sense to ship raw materials all the way to China to have them assembled into finished products and shipped back to the U.S. Manufacturing will once again become a local industry with products being manufactured near raw materials or markets. So China has many reasons to worry.”

Conclusion

Bill Gates — “Software substitution, whether it’s for drivers or waiters or nurses … it’s progressing, technology over time will reduce demand for jobs, particularly at the lower end of skill set. 20 years from now, labor demand for lots of skill sets will be substantially lower. I don’t think people have that in their mental model.”

A 2014 Pew Report Some 1,896 experts responded to the following question: Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?

It found these reasons to be concerned about it this time;

1. Impacts from automation have thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well.
2. Certain highly-skilled workers will succeed wildly in this new environment — but far more may be displaced into lower paying service industry jobs at best, or permanent unemployment at worst.
3. Our educational system is not adequately preparing us for work of the future, and our political and economic institutions are poorly equipped to handle these hard choices.

Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers — with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.

The fact it’s only looking 10 years ahead and almost 50% of experts are concerned I think is astounding, I believe if the question was asked in 20 years instead of 10, I expect a greater percentage of concerned experts. The key reasons experts found to be hopeful for the future were;

1. Advances in technology may displace certain types of work, but historically they have been a net creator of jobs.
2. We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities.
3. Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way.
4. Ultimately, we as a society control our own destiny through the choices we make.

I will discuss in the next two articles why an optimistic outlook(1.&2.) of job growth to replace the amount of low skill jobs disrupted with new low skills that aren't suspect to outsourcing or automation, and to increase for a growing population is, within a 20 year time frame, is difficult to have an optimistic outlook for.

I also show how there is little evidence of 3. taking place with current laws and cultural attitudes in the fourth article. I agree with the optimistic point 4. but I don’t feel our government’s response time will be quick enough partly because I feel any change will be reactive rather than preventative. In the next article I discuss the predictions of job growth and the quality/skill level for new jobs using current trends in the economy.

Jeremy Howard — I think that this standard economic thought is an oversimplification. It relies entirely on extrapolating from history. The argument is simply “in the past new employment has followed from new technology, therefore it will happen this time too.” CPG Grey makes a good analogy in Humans need not apply where he points out that horses may have felt the same way at the start of the 20th century, if they applied the same arguments as economists do today. But at that time, for horses, it turned out to be true that “this time it’s different”.

At some point, historical patterns break down — if the underlying causality that resulted in these patterns changes. I think that just assuming that everything will be the same as before is intellectually lazy. It’s not necessarily wrong, but it ought to be justified using logic, not just through extrapolating previous trends. Otherwise, the result of any significant structural change will, by definition, be missed.

It seems to me that humans can provide three basic inputs into processes: energy, perception, and judgement. The Industrial Revolution removed the need for humans to provide energy inputs into processes, and therefore in the developed world today nearly all jobs are in the service sector. Computers are now approaching or passing human capability in perception, and in the areas where the bulk of the employment exists also surpass human capabilities in judgement (because computers can processed more data in a less biased way).

So if economists are going to claim that there will be new industries which require human input, I think they need to explain exactly what the human will be doing, and why the computer would not be able to do it at least as well.