Half Of All Jobs Will Be Automated By 2034

47% Of All Jobs Will Be Automated By 2034

47% Of All Jobs Will Be Automated By 2034

Almost half of all jobs could be automated by computers within two decades and “no government is prepared” for the tsunami of social change that will follow, according to the Economist.

The magazine’s 2014 analysis of the impact of technology paints a pretty bleak picture of the future.

It says that while innovation (aka “the elixir of progress”) has always resulted in job losses, usually economies have eventually been able to develop new roles for those workers to compensate, such as in the industrial revolution of the 19th century, or the food production revolution of the 20th century.

But the pace of change this time around appears to be unprecedented, its leader column claims. And the result is a huge amount of uncertainty for both developed and under-developed economies about where the next ‘lost generation’ is going to find work.

It quotes a 2013 Oxford Martin School study that estimates 47% of all jobs could be automated in the next 20 years:

“Our findings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills,” that study says.

The Economist also points out that current unemployment levels are startlingly high, but that “this wave of technological disruption to the job market has only just started”.

Specifically the Economist points to new tech like driverless cars, improved household gadgets, faster and more efficient online communications and ‘big data’ analysis to areas that humans are quickly being superceded. And while new start-ups are raising billions, they employ few people – Instagram, sold to Facebook in 2012 for $1 billion, employed just 30 people at the time.

Those conclusions are echoed elsewhere. Another study (‘Are You Ready For #GenMobile?’), to be released in full on 21 January by Aruba Networks, points out just how fast traditional working models are changing.

It says that 72% of British people now believe they work more efficiently at home, and that 63% need a WiFi network to complete their tasks – not bad for a technology that was barely standardised 10 years ago.

Meanwhile in ‘The Second Machine Age’, out this week, Erik Brynjolfsson and Andrew McAfee argue workers are under unprecedented pressure by the automation of skilled and unskilled jobs.

In a recent Salon interview Brynjolfsson said: “technology has always been destroying jobs, and it’s always been creating jobs, and it’s been roughly a wash for the last 200 years. But starting in the 1990s the employment to population ration really started plummeting and it’s now fallen off a cliff and not getting back up. We think that it should be the focus of policymakers right now to figure out how to address that.”

The BBC also produced a report earlier this month which claimed, in stark tones, that “the robots are coming to steal our jobs”.

“AI’s are embedded in the fabric of our everyday lives,” head of AI at Singularity University, Neil Jacobstein, told the Beeb.

“They are used in medicine, in law, in design and throughout automotive industry.”

That report too pointed out the change will affect jobs of all kinds – from a Chinese factory Hon Hai which has announced plans to replace 500,000 workers with robots in three years, to lawyers, surgeons and public sector workers.

Opinions remain divided on the impact and future of technological innovation on the jobs market, and wealth inequality. The Economist leader argues that governments have a responsibility to innovate in education, taxation and embracing progress, though the solutions are by no means obvious or without uncertainty.

If only we could automate the process of making and implementing those political decisions – now that would really be something.

 

Source:  huffingtonpost.co.uk

Your brain works like a dictionary

Why your brain may work like a dictionary:

Why your brain may work like a dictionary

Why your brain may work like a dictionary

DOES your brain work like a dictionary? A mathematical analysis of the connections between definitions of English words has uncovered hidden structures that may resemble the way words and their meanings are represented in our heads.

“We want to know how the mental lexicon is represented in the brain,” says Stevan Harnad of the University of Quebec in Montreal, Canada.

As every word in a dictionary is defined in terms of others, the knowledge needed to understand the entire lexicon is there, as long as you first know the meanings of an initial set of starter, or “grounding”, words. Harnad’s team reasoned that finding this minimal set of words and pinning down its structure might shed light on how human brains put language together.

The team converted each of four different English dictionaries into a mathematical structure of linked nodes known as a graph. Each node in this graph represents a word, which is linked to the other words used to define it – so “banana” might be connected to “long”, “bendy”, “yellow” and “fruit”. These words then link to others that define them.

This enabled the team to remove all the words that don’t define any others, leaving what they call a kernel. The kernel formed roughly 10 per cent of the full dictionary – though the exact percentages depended on the particular dictionary. In other words, 90 per cent of the dictionary can be defined using just the other 10 per cent.

But even this tiny set is not the smallest number of words you need to produce the whole dictionary, as many of these words can in turn be fully defined by others in the kernel. This is known as the minimal grounding set (MGS), which Harnad explores in his most recent work. Unlike the kernel, which forms a unique set of words for each dictionary, there are many possible word combinations that can be used to create an MGS – though it is always about half the size of the kernel.

What’s more, the kernel has a deeper structure. The team found that half of its words made up a core group in which every word connects to every other via a chain of definitions. The other half was divided into satellite groups that didn’t link to each other, but did connect with the core (see diagram).

And this structure seems to relate to meaning: words in the satellites tend to be more abstract than those in the core, and an MGS is always made up of words from both the core and satellites, suggesting both abstract and concrete words are needed to capture the full range of meaning.

So what, if anything, can this tell us about how our brains represent words and concepts? To find out, Harnad’s team looked at data on how children acquire words and found a pattern: as you move in from the full dictionary towards the kernel and finally the MGS, words tend to have been acquired at a younger age, be used more frequently, and refer to more concrete concepts (arxiv.org/abs/1308.2428). “The effect gets stronger as you go deeper into the kernel,” Harnad says.

That doesn’t mean children learn language in this way, at least not exactly. “I don’t really believe you just have to ground a certain number of things and from then on close the book on the world and do the rest by words alone,” says Harnad. But the correlation does suggest that our brains may structure language somewhat similarly to a dictionary. To learn more, the team has created an online game that asks players to define an initial word, then define the words in those definitions. The team then compares whether their mental dictionaries are similar in structure to actual ones.

Phil Blunsom at the University of Oxford isn’t convinced word meanings can be reduced to a chain of definitions. “It’s treating words in quite a symbolic fashion that is going to lose a lot of the meaning.” But Mark Pagel of the University of Reading, UK, expects the approach to lead to new insights – at least for adult brains. “This will be most useful in giving us a sense of how our minds structure meaning,” he says. For example, one question raised by the relatively small size of the MGS is why we burden ourselves with so much extraneous vocabulary.

No profit for workers

Corporate Margins and Profits are Increasing, But Workers’ Wages Aren’t:

Wage Slavery

Wage Slavery

As we’ve been noting, corporate profits have made it back to their pre-recession heights (even if corporate tax revenue hasn’t followed suit). In fact, in 2011, corporate profits hit their highest level since 1950. But as Bloomberg News noted today, this hasn’t translated into wage growth or more purchasing power for workers:

Companies are improving margins and generating profits as wage growth for the American worker lags behind the prices of goods and services…While benefiting the bottom line for businesses, the decline in inflation-adjusted wages bodes ill for the sustainability of economic growth as consumers may eventually be forced to cut back. […]

Of the 394 companies in the Standard & Poor’s 500 Index that have reported since Jan. 9, earnings for the quarter ended Dec. 31 increased 5.1 percent on average and beat analyst estimates by 3.2 percent. Some 70 percent of the companies have posted better-than-projected results.

This pattern has become all too familiar during the slow economic recovery. In fact, real wages fell in 2011, despite record corporate profits. “There’s never been a postwar era in which unemployment has been this high for this long,” explained labor economist Gary Burtless. “Workers are in a very weak bargaining position.”  Between 2009 and 2011, 88 percent of national income growth went to corporate profits, while just 1 percent went to wages, a stat that is “historically unprecedented.”