As computers began to appear in offices and robots on factory floors,President Jhon F. Kennedy declared that the major domestic challenge of the 1960s was to ''maintain full employment at a time when automation...is re placing men.''
In a column in the Guardian, stephen Hawking wrote ''that the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining''.
He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years an decades.The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines.
A report put out in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation.
In the UK, 35% are.In China, it's a whopping 77%-while across the OECD it's an average of 57%.
Sitting in an office in San Francisco, Igor Barani calls up some medicals scans on his screen.He is the chief executive of Enlitic,one of a host of startups applying deep learning to medecine, starting with the analyses of images such as X-rays and CT scans.It is an obvious use of the technology.Deep learning is renowed for its superhuman prowess at data to crunch; and there is tremendous potential to make health care more accurate and efficient.
Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.Red blobs flicker on the screen as Elentic's deep-learning system examines and compares them to see if they are blood vessels, harmless imaging artefacts or malignant lung nodules.The system ends up highlighting a particular feature for futher investigation.In a test against three expert human radiologists working together, Enlitic's system was 50% better at classifying malignant tumours and had a false-negative rate(where a cancer is missed) of zero,compared with 7% of humans.Another Enlitic's systems, which examines X-rays to detect wrist fractures,also handily outperformed human experts.
The firm's technology is currently being tested in 40 clinics across Australia.
So which jobs are most vulnerable?In a widely noted study published in 2013,Carl Benedikt Frey and Michael Osborne examined the probability of computerisation for 702 occupations and found that 47% of workers in America had jobs at high risk of potential automation.In particular,they warned that most workers in transport and logistics(such as taxi and delivery drivers) and office support (such as receptionists and security guards) ''are likely to be substituted by computer capital'', and that many workers in sales and services (such as cashiers,counter and rental clercks,telemarketers and accountants) also faced a high risk of computerisation.
They concluded that ''recent developments in machine learning will put a substantial share of employment, across a wide range of occupations, at risk in the future''.Subsequent studies put the equivalent figure at 35% of the workforce for Britain (where more people work in creative field less susceptible to automation) and 49% for Japan.
Economists, are already worrying about ''job polarisation'', where middle skill jobs (such as those in manufacturing ) are declining but both low-skill and high-skill jobs are expanding.In effect,the workforce bifurcates into two groups doing non-routine work: highly paid, skilled workers (such as architects and senior managers) on the one hand and low-paid, unskilled workers (such as cleaners and burger-flippers) on the other.The stagnation of median wages in many western countries is cited as evidence that automation is already having an effect-though it is hard to disentangle the impact of offshoring, which has also moved many routine jobs (including manufacturing and call-centre work to low-wage countries in the developing world.Figures published by the federal Reserve Bank of St Louis show that in America, employment in non-routine cognitive and non-routine manual jobs has grown steadily since the 1980s, whereas employment in routine jobs has been broadly flat.As more jobs are automated, this trend seems likely to continue.
While it is easy to see fields in which automation might do away with the need for human labor, it is less obvious where technology might create new jobs.We can't predict what jobs will be created in the future,but it's always been like that,says Joel Mokyr,an economic historian at Northwestern University.Imagine trying to tell someone a century ago that her great-grandchildren would be video games designers or cybersecurity specialists he suggests.''These are jobs that nobody in the past would have predicted''.
Similarly just as people worry about the potential impact of self-driving vehicules today, a century ago there was much concern about the impact of the switch from horses to cars.Horse-related jobs declined, but entirely new jobs were created in the motel and fast-food industries that arose to serve motorists and truck drivers.As those industries decline,new ones will emerge.Self-driving vehicules will give people more time to consume goods and services, increasing demand elsewhere in the economy;and autonomous vehicules might greatly expand demand for products (such as food) delivered locally.
-Frédéric Betta-Akwa
In a column in the Guardian, stephen Hawking wrote ''that the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining''.
He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years an decades.The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines.
A report put out in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation.
In the UK, 35% are.In China, it's a whopping 77%-while across the OECD it's an average of 57%.
Sitting in an office in San Francisco, Igor Barani calls up some medicals scans on his screen.He is the chief executive of Enlitic,one of a host of startups applying deep learning to medecine, starting with the analyses of images such as X-rays and CT scans.It is an obvious use of the technology.Deep learning is renowed for its superhuman prowess at data to crunch; and there is tremendous potential to make health care more accurate and efficient.
Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.Red blobs flicker on the screen as Elentic's deep-learning system examines and compares them to see if they are blood vessels, harmless imaging artefacts or malignant lung nodules.The system ends up highlighting a particular feature for futher investigation.In a test against three expert human radiologists working together, Enlitic's system was 50% better at classifying malignant tumours and had a false-negative rate(where a cancer is missed) of zero,compared with 7% of humans.Another Enlitic's systems, which examines X-rays to detect wrist fractures,also handily outperformed human experts.
The firm's technology is currently being tested in 40 clinics across Australia.
So which jobs are most vulnerable?In a widely noted study published in 2013,Carl Benedikt Frey and Michael Osborne examined the probability of computerisation for 702 occupations and found that 47% of workers in America had jobs at high risk of potential automation.In particular,they warned that most workers in transport and logistics(such as taxi and delivery drivers) and office support (such as receptionists and security guards) ''are likely to be substituted by computer capital'', and that many workers in sales and services (such as cashiers,counter and rental clercks,telemarketers and accountants) also faced a high risk of computerisation.
They concluded that ''recent developments in machine learning will put a substantial share of employment, across a wide range of occupations, at risk in the future''.Subsequent studies put the equivalent figure at 35% of the workforce for Britain (where more people work in creative field less susceptible to automation) and 49% for Japan.
Economists, are already worrying about ''job polarisation'', where middle skill jobs (such as those in manufacturing ) are declining but both low-skill and high-skill jobs are expanding.In effect,the workforce bifurcates into two groups doing non-routine work: highly paid, skilled workers (such as architects and senior managers) on the one hand and low-paid, unskilled workers (such as cleaners and burger-flippers) on the other.The stagnation of median wages in many western countries is cited as evidence that automation is already having an effect-though it is hard to disentangle the impact of offshoring, which has also moved many routine jobs (including manufacturing and call-centre work to low-wage countries in the developing world.Figures published by the federal Reserve Bank of St Louis show that in America, employment in non-routine cognitive and non-routine manual jobs has grown steadily since the 1980s, whereas employment in routine jobs has been broadly flat.As more jobs are automated, this trend seems likely to continue.
While it is easy to see fields in which automation might do away with the need for human labor, it is less obvious where technology might create new jobs.We can't predict what jobs will be created in the future,but it's always been like that,says Joel Mokyr,an economic historian at Northwestern University.Imagine trying to tell someone a century ago that her great-grandchildren would be video games designers or cybersecurity specialists he suggests.''These are jobs that nobody in the past would have predicted''.
Similarly just as people worry about the potential impact of self-driving vehicules today, a century ago there was much concern about the impact of the switch from horses to cars.Horse-related jobs declined, but entirely new jobs were created in the motel and fast-food industries that arose to serve motorists and truck drivers.As those industries decline,new ones will emerge.Self-driving vehicules will give people more time to consume goods and services, increasing demand elsewhere in the economy;and autonomous vehicules might greatly expand demand for products (such as food) delivered locally.
-Frédéric Betta-Akwa
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