Toyota's latest strategy has two main aspects. First, it wants to make sure that workers truly understand the work they're doing instead of feeding parts into machines and being helpless when one breaks down. Second, it wants to figure out ways to make processes higher quality and more efficient in the long run. The company worries that automation means it has too many average workers and not enough craftsmen and masters.
So far, people taking back work done by robots at over 100 workspaces reduced waste in crankshaft production by 10%, and helped shorten the production line. Others improved axel production and cut costs for chassis parts.
"We cannot simply depend on the machines that only repeat the same task over and over again," project lead Mitsuru Kawai told Bloomberg. "To be the master of the machine, you have to have the knowledge and the skills to teach the machine."
The workers that machines threaten to displace cover a wide range of office work. Smart digital assistants, for instance, could stand in for many types of support staff – or, by making the ones who remain more productive, greatly reduce their numbers.
The jobs of many analysts and researchers could also be in the line of fire. Advances in machine learning and natural language systems make it easier to interrogate large amounts of data and to derive smarter answers in more intelligible forms.
Even highly paid professionals with specialist expertise are not immune. In fields such as law and medicine, machines are likely to produce “generally better answers” than humans, who struggle to keep up with the latest knowledge in their fields, says James Manyika, a director at McKinsey Global Institute.
In the chart you can see that in the US in the 1980s there was strong growth in high-skill jobs and a reduction in low-skill jobs, in the 1990s there was massive growth in high-skill jobs and a little growth in low-skill jobs, and in 1999-2007 there was high growth in low-skill jobs and a little growth in high-skill jobs.
At no point was there growth in middle-skilled jobs.
Why Everyone Will Have to Become an Entrepreneur (Infographic)
It used to be that entrepreneurs were the renegade cowboys out in Silicon Valley. Nowadays, you have to be an entrepreneur just to get and hold a job.
Consultants and freelancers are cheaper than full-time staffers with benefits, software developers overseas cost a fraction of what they cost in the U.S. and, by 2030, robots will be able to perform most manual labor, according to an infographic (below) from San Francisco-based startup organization Funders and Founders. Even employees who are employed in large corporations are encouraged to be "intrapreneurs," meaning that they are in many cases given company time to come up with disruptive ways of thinking about corporate organization and practices.
"That’s all pretty abstract. Let’s take a specific example: Google’s self-driving cars. What happens when they finally make their way onto American highways en masse? (Which, to be fair, Kurzweil predicted for 2019 back in 1999.) What happens if and when it turns out that they’re much safer than human drivers? Insurance costs will make human driving very expensive, and fewer vehicles will be sold–partly because cars will last longer, partly because fractional ownership of a pool of self-driving vehicles will make more economic sense than having your own.
Improved safety, lower insurance overheads, more efficiency–that’s all great, right? Sure! Of course it is! …Unless you’re one of the more than 2 million truck and taxi drivers out of work.
Self-driving cars are a striking example of software eating jobs, but far from the only one. Almost every job, in every field, probably including yours, will increasingly be threatened by obsolescence and/or automation. That’s a simple and inevitable corollary of software eating the world and the concomitant increasing rate of change. As that rate accelerates, technology will soon start destroying jobs faster than it creates them…if it isn’t already."
"Year after year, the software that runs computers and an array of other machines and devices becomes more sophisticated and powerful and capable of doing more efficiently tasks that humans have always done. For decades, science fiction warned of a future when we would be architects of our own obsolescence, replaced by our machines; an Associated Press analysis finds that the future has arrived."
"The most short-sighted business decision in history: out-sourcing
Changes in relative economics are part of the reasons for the change. Oil prices are three times what they were in 2000. Natural gas in the US is a quarter of what it is in Asia. Chinese wages are five times what they were in 2000 and are expected to keep rising rapidly. And labor is a steadily decreasing percentage of the cost of manufacturing.
But even more important than these shifts in prices is the growing realization that the massive international outsourcing that took place over the past few decades didn’t make sense in the first place.
“Just five years ago, not to mention 10 or 20 years ago,” writes Fishman, “the unchallenged logic of the global economy was that you couldn’t manufacture much besides a fast-food hamburger in the United States… There was no reason design and marketing could not take place in one country while production, from the start, happened half a world away.”
A factory is a laboratory
But a funny thing happened, writes Fishman, when GE decided to bring manufacturing of its innovative GeoSpring water heater back from the “cheap” Chinese factory to the “expensive” Kentucky factory.
“The material cost went down. The labor required to make it went down. The quality went up. Even the energy efficiency went up. GE wasn’t just able to hold the retail sticker to the ‘China price.’ It beat that price by nearly 20 percent. The China-made GeoSpring retailed for $1,599. The Louisville-made GeoSpring retails for $1,299.
“Time-to-market has also improved, greatly. It used to take five weeks to get the GeoSpring water heaters from the factory to U.S. retailers—four weeks on the boat from China and one week dockside to clear customs. Today, the water heaters—and the dishwashers and refrigerators—move straight from the manufacturing buildings to Appliance Park’s warehouse out back, from which they can be delivered to Lowe’s and Home Depot. Total time from factory to warehouse: 30 minutes.”"
I love it when others summarize my work:
"The Change in Work - It's not just factory workers but even Doctors that are going to be automated or outsourced. So how will you make a living? Only truly creative work will pay.
So what is Creative Work? - It is not just design etc but will include making valuable things and even growing food - and new sites such as Etsy enable you to find a market
The Industrial World Deskilled work - It all became assembly - Anything like this can be automated and will be
The jobs cannot come back
Training works well when you want to learn how to drive a car - you can train to be a carpenter but making the shift to be creative or to stand for themseleves - you cannot train for that
What is the new?
So what helps you be this new person?
Apprenticing - complex things cannot be learned except by shared experience
The crafts communities have never lost this - learn the rules and then learn how to break them - look at studios - very little teaching - mainly doing
Then you have to get connected to your community
All sorts of studios will emerge that will help you where clusters of people who know aggregate
The Knowledge Artisans have to take charge of themselves
What about advice for you?
Learn REAL skills - not just how to make it in an organization
Learn how to have a network - in the job world we don't have them - many of us don't know anything about this if we have had a job - so start now
This must be diverse and be about your interests
Put yourself OUT THERE
You are as good as your network
Think of yourself as a Freelancer for Life - and so always nuture your network no matter what - avoid getting lulled into a sense of false security
His advice to his kids
Find the sweet spot (Dave Pollard) Find out your passion, what you are good at and what people will pay you for
You have to have all three"
"Anything that can be reduced to a flowchart will be automated"
"As I often say, in a connected world, unless your skills are world-class, you are a commodity.
However there are three domains in which individuals and organizations can transcend commoditization and push their value creation to the other end of the spectrum, where they can command their price and choose their work.
The three domains are:
EXPERTISE. As more information is available, deep specialist world-class expertise that can be applied to create value for clients is the heart of much global value creation. That expertise must be broad-based, up-to-the-minute, and directly relevant to real-world issues.
RELATIONSHIPS. Expertise in isolation is not useful. The rich sets of relationships that form networks are at the heart of value creation. Those who can connect expertise and facilitate the co-creation of value in relationships will be at the heart of the economy.
INNOVATION. Innovation stems from connecting expertise, ideas, insights, and experience. Those who have original perspectives or can elicit new frames by bringing together diversity can capture and share extraordinary pools of value.
The future is stark. There will be a large and increasing divide between those who have one or more of these core strengths, and those who do not and whose livelihoods are on an ongoing path of commoditization."
"The Jobs Of Yesteryear: Obsolete Occupations
As computers and automated systems increasingly take the jobs humans once held, entire professions are now extinct. Click through the gallery below to see examples of endangered professions, from milkman to telegrapher, and hear from people who once filled those oft-forgotten jobs."
"The falling costs and growing sophistication of robots have touched off a renewed debate among economists and technologists over how quickly jobs will be lost. This year, Erik Brynjolfsson and Andrew McAfee, economists at the Massachusetts Institute of Technology, made the case for a rapid transformation. "The pace and scale of this encroachment into human skills is relatively recent and has profound economic implications," they wrote in their book, "Race Against the Machine."
In their minds, the advent of low-cost automation foretells changes on the scale of the revolution in agricultural technology over the last century, when farming employment in the United States fell from 40 percent of the work force to about 2 percent today. The analogy is not only to the industrialization of agriculture but also to the electrification of manufacturing in the past century, Mr. McAfee argues.
"At what point does the chain saw replace Paul Bunyan?" asked Mike Dennison, an executive at Flextronics, a manufacturer of consumer electronics products that is based in Silicon Valley and is increasingly automating assembly work. "There's always a price point, and we're very close to that point.""
"Teaching assistant -> Educational technologist
While public university systems in many countries are plagued by inadequate funding, higher education as a whole is one of the fastest-growing sectors: 170 million people were enrolled in higher ed in 2009, a 160 percent increase from 1990. And online education, once derided as correspondence classes for those who couldn't get into a four-year college, is booming. Software coders and curriculum developers will be needed to design online courses that deliver memorable learning in a new virtual medium. On the heels of Udacity and MIT's OpenCourseWare, new educational platforms have emerged that require the virtual curation of online, collaborative student groups, facilitating a multidirectional learning process. Rejoice! The days of tweed-jacketed professors droning on in lecture halls are nearly over.
Visionary: Salman Khan, founder of the popular, free online education platform Khan Academy, which features more than 3,000 videos teaching everything from basic algebra to the French Revolution."
In previous times, increasing earnings resulted in the hiring of more people. Thus both the Democratic approach (add money to the economy to increase earnings that way) or the Republican approach (cutting taxes or regulations) could work.
But not this time. The Bush Tax cuts had little effect on job creation. And the stimulus may have kept things from getting worse but do not seem to be providing the long lasting benefit they have in the past.
Neither approach works like it did in the past.
It may really be different this time. What worked in the 20th century may not work in the 21st.
I expect the reason is that technological solutions are simply replacing the need for more people as companies expand profits. Moore's law is now making an indelible impact on the job market. Technology does the jobs of people much too well.
In my own field, a robot can do 100 times the work of technicians. So what happens to those 100 technicians that had been paid a pretty good wage?
Technology is not only driving down the price to do things. It is driving down the cost of labor to the point where many people may simply never find a job that pays a living wage.
The manufacturing jobs that pay best today look a lot more like knowledge work than traditional factory work. In fact, high-paid manufacturing work - guiding and maintaining advanced machinery, engaging in problem solving, and continuous improvement with other workers and engineers - increasingly is knowledge work.
Paul Krugman points out that "the idea that modern technology eliminates only menial jobs, that well-educated workers are clear winners, may dominate popular discussion, but it's actually decades out of date." The Economist takes this thought experiment further, exploring the upward squeeze of losing jobs to automation, eventually taking all profits from now-unemployed workers and designers, leaving them to accrue in the accounts of the industry owners (note to self: buy stock in robotics companies). In this view, automation has the potential to not only massively concentrate wealth and undermine labor rights, but also to undermine our current concepts of value.
But this prognosis is a bit too bleak. We feel it would be possible to stabilize our economy around a concept where there are no working class poor, where there is a baseline wealth for all, and menial, dangerous, and deforming labor is performed by automations. The question is just one of how painful it would be to adapt to such a system. As Adam Smith pointed out in The Wealth of Nations, "the propensity to truck, barter, and exchange one thing for another is common to all." His insight is exemplified in modern times around the emerging notion of the sharing economy in which capital is removed from the equation and markets operate in their most basic definition as "places of exchange." From an automation perspective this means that even if we substitute aspects of our labor force with robots, our natural proclivity towards exchange will ensure that people are re-distributed in new sectors--though this may take some time.
Gizmodo's Matt Buchanan admits that for now, Google Wallet is a novelty. "But not for long. Wallet will fix a lot of things, perhaps sooner than you'd expect, even given how slow as the financial industry moves. But eventually it's going to wash over everything like a wave. It'll be on lots of phones. It'll work with lots of cards and lots of banks. It'll be in lots of stores. And then it'll be just as natural as pulling out a card and swiping. Maybe more, since I have my phone out all the time anyway. Besides, it's obvious this is just the beginning for Google. Google doesn't just want to replace your credit cards-there's a reason they're calling it Google Wallet, not Google Money or Google Cards."
Automation of higher-level jobs is accelerating because of progress in computer science and linguistics. Only recently have researchers been able to test and refine algorithms on vast data samples, including a huge trove of e-mail from the Enron Corporation.
"The economic impact will be huge," said Tom Mitchell, chairman of the machine learning department at Carnegie Mellon University in Pittsburgh. "We're at the beginning of a 10-year period where we're going to transition from computers that can't understand language to a point where computers can understand quite a bit about language."
Nowhere are these advances clearer than in the legal world.
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