IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their “Introduction to Artificial Intelligence” course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Mr Thrun’s was completed by 23,000 people; Mr Ng’s by 13,000.

Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs). In 2012 Mr Thrun founded an online-education startup called Udacity, and Mr Ng co-founded another, called Coursera. That same year Harvard University and the Massachusetts Institute of Technology got together to form edX, a non-profit MOOC provider, headed by Anant Agarwal, the head of MIT’s artificial-intelligence laboratory. Some thought that MOOCs would replace traditional university teaching. The initial hype around MOOCs has since died down somewhat (though millions of students have taken online courses of some kind). But the MOOC boom illustrated the enormous potential for delivering education online, in bite-sized chunks.

The fact that Udacity, Coursera and edX all emerged from AI labs highlights the conviction within the AI community that education systems need an overhaul. Mr Thrun says he founded Udacity as an “antidote to the ongoing AI revolution”, which will require workers to acquire new skills throughout their careers. Similarly, Mr Ng thinks that given the potential impact of their work on the labour market, AI researchers “have an ethical responsibility to step up and address the problems we cause”; Coursera, he says, is his contribution. Moreover, AI technology has great potential in education. “Adaptive learning”—software that tailors courses for each student individually, presenting concepts in the order he will find easiest to understand and enabling him to work at his own pace—has seemed to be just around the corner for years. But new machine-learning techniques might at last help it deliver on its promise.

Adapt and survive

At the moment, adaptive-learning techniques work best in areas where large numbers of pupils have to learn the same material and a lot of data can be collected, says Mr Ng. Geekie, a Brazilian adaptive-learning startup, guides pupils through the high-school syllabus in thousands of the country’s schools. Other startups working in this area include Knewton, Smart Sparrow and DreamBox. Education giants are also paying attention. McGraw-Hill bought ALEKS, another adaptive-learning system, in 2013; Pearson recently announced an expansion of its partnership with Knewton. In a report published in February, Pearson suggests that AI could make learning “more personalised, flexible, inclusive and engaging”. Such systems do not replace teachers, but allow them to act as mentors rather than lecturers.

Even outside the AI community, there is a broad consensus that technological progress, and artificial intelligence in particular, will require big changes in the way education is delivered, just as the Industrial Revolution did in the 19th century. As factory jobs overtook agricultural ones, literacy and numeracy became much more important. Employers realised that more educated workers were more productive, but were reluctant to train them themselves because they might defect to another employer. That prompted the introduction of universal state education on a factory model, with schools supplying workers with the right qualifications to work in factories. Industrialisation thus transformed both the need for education and offered a model for providing it. The rise of artificial intelligence could well do the same again, making it necessary to transform educational practices and, with adaptive learning, offering a way of doing so.

“The old system will have to be very seriously revised,” says Joel Mokyr of Northwestern University. Since 1945, he points out, educational systems have encouraged specialisation, so students learn more and more about less and less. But as knowledge becomes obsolete more quickly, the most important thing will be learning to relearn, rather than learning how to do one thing very well. Mr Mokyr thinks that education currently treats people too much like clay—“shape it, then bake it, and that’s the way it stays”—rather than like putty, which can be reshaped. In future, as more tasks become susceptible to automation, the tasks where human skills are most valuable will constantly shift. “You need to keep learning your entire life—that’s been obvious for a long time,” says Mr Ng. “What you learn in college isn’t enough to keep you going for the next 40 years.”

Education will therefore have to be interwoven with full-time work. “People will have to continuously learn new skills to stay current,” says Mr Thrun. Hence his firm’s focus on “nanodegrees” which can be completed in a few months, alongside a job. Studying for a nanodegree in, say, data science or website programming costs $200 a month, but students who complete a course within 12 months get a 50% refund. A host of websites now offer courses in all kinds of skills, from user-experience design to project management to leadership. Some, like Udacity, charge by the course; others, like Lynda.com, which is owned by LinkedIn, a business-networking site, charge a monthly fee for access to all courses. (It is not difficult to imagine LinkedIn comparing the skill sets of its users against those required to apply for a particular job—and then offering users the courses necessary to fill the gaps.) Users and their potential employers sometimes find it difficult to tell which ones offer good value. More co-operation between government, training providers and employers over certification would help.

America and other developed countries should also put more emphasis on vocational and technical education, as Germany does, rather than encouraging everyone to go to university, says David Autor at MIT. But that does not simply mean offering more apprenticeships, which typically involve five to seven years of training. “That doesn’t make sense if the skills you need are changing every three to five years,” says James Bessen at the Boston University School of Law. So the traditional apprenticeship model will have to be tweaked. Community colleges are setting up all kinds of schemes that combine education with learning on the job, says Mr Bessen. For example, Siemens, a German industrial giant, has launched a four-year “earn and learn” programme for apprentices at its wind-turbine factory in Charlotte, North Carolina. Apprentices graduate with a degree in mechatronics from a local community college, certification from the local department of labour—and no student debt.

As on-the-job skills come and go, having a solid foundation of basic literacy and numeracy skills will become even more vital. But teaching “soft” skills, too, will be increasingly important. In a paper published in 2013, James Heckman and Tim Kautz of America’s National Bureau of Economic Research argue for more emphasis on “character skills” such as perseverance, sociability and curiosity, which are highly valued by employers and correlate closely with employees’ ability to adapt to new situations and acquire new skills. Character is a skill, not a trait, they say, and schemes that teach it are both lasting and cost-effective.

Basic attraction

Concerns about AI and automation have also led to calls for a stronger safety net to protect people from labour-market disruption and help them switch to new jobs. In particular, many AI commentators support the idea of a universal basic income: a dramatic simplification of the welfare system that involves paying a fixed amount (say, $10,000 a year) to everyone, regardless of their situation, and doing away with all other welfare payments. Similar ideas were touted during the Industrial Revolution by Thomas Paine and John Stuart Mill, among others. Its chief merit, say its supporters, is that people who are not working, or are working part-time, are not penalised if they decide to work more, because their welfare payments do not decline as their incomes rise. It gives people more freedom to decide how many hours they wish to work, and might also encourage them to retrain by providing them with a small guaranteed income while they do so. Those who predict apocalyptic job destruction see it as a way to keep the consumer economy going and support the non-working population. If most jobs are automated away, an alternative mechanism for redistributing wealth will be needed.

Compared with the complexity of overhauling the education system, a basic income appears to offer a simple, attractive and easily understood solution. The idea enjoys broad support within the technology industry: Y Combinator, a startup incubator, is even funding a study of the idea in Oakland, California. Sam Altman, its president, argues that in a world of rapid technological change, a basic income could help ensure “a smooth transition to the jobs of the future”. The idea seems to appeal to techie types in part because of its simplicity and elegance (replacing existing welfare and tax systems, which are like badly written programming code, with a single line) and in part because of its Utopianism. A more cynical view is that it could help stifle complaints about technology causing disruption and inequality, allowing geeks to go on inventing the future unhindered. Mr Altman says that in his experience the techies who support basic income do so for “fairly charitable reasons”.

Automation could have a much bigger impact in developing economies than in rich ones

Though it is an attractive idea in principle, the devil is in the details. A universal basic income that replaced existing welfare budgets would be steeply regressive. Divide existing spending on social, pension and welfare schemes (excluding health care) equally, and each citizen would get a basic income of around $6,000 a year in America and $6,200 in Britain, for example (at purchasing-power parity). Compared with existing welfare schemes, that would reduce income for the poorest, while giving the rich money they do not need. But means-testing a basic income risks undermining its simplicity, and thus its low administrative cost. Funding a basic income that would provide a reasonable living would require much higher taxes than at present. Negative income taxes, or schemes such as earned-income tax credits, might be a less elegant but more practical approach.

Many countries, notably Finland and the Netherlands, are planning to experiment with limited forms of basic income next year. A big concern among economists is that a basic income could actually discourage some people from retraining, or indeed working at all—why not play video games all day?—though studies of previous experiments with a basic income suggest that it encourages people to reduce their working hours slightly, rather than giving up work altogether. Another problem is that a basic income is not compatible with open borders and free movement of workers; without restrictions on immigration or entitlement it might attract lots of freeloaders from abroad and cause domestic taxpayers to flee.

This points to another area where policymakers may have to grapple with the impact of advancing automation: its geopolitical implications as it benefits people in some countries more than others. Automation could have a much bigger impact in developing economies than in rich ones, says Mr Autor, because much of what they provide is essentially embodied labour: cheap goods made by low-wage workers, cheap services such as operating call-centres, or doing domestic and construction work overseas. If automation makes rich countries more self-sufficient in these areas, they will have less need for the products and services that have been driving exports and growth in the developing world. Automation could “erode the comparative advantage of much of the developing world”, says Mr Autor. Another worry, he says, is that rich countries own the technologies and patents associated with robots and AI, and stand to benefit if they cause a surge in productivity. For the developing world, “it’s not clear that they are on the winning side of the bargain” if machines end up outperforming humans in a wide range of activities.

The risk is that automation could deny poorer countries the opportunity for economic development through industrialisation. Economists talk of “premature deindustrialisation”; Dani Rodrik of Harvard University notes that manufacturing employment in Britain peaked at 45% just before the first world war, but has already peaked in Brazil, India and China with a share of no more than 15%. This is because manufacturing is much more automated than it used to be. China recently overtook America as the largest market for industrial automation, according to a report by Citi, a bank, and Oxford University’s Martin School. Industrial automation may mean that other emerging economies, such as those in Africa and South America, will find it harder to achieve economic growth by moving workers from fields to factories, and will need to find new growth models. Without manufacturing jobs to build a middle class, observes Tyler Cowen, an economist at George Mason University, such countries “may have high income inequality baked into their core economic structures”.

During the Industrial Revolution, John Stuart Mill wrote that “there cannot be a more legitimate object of the legislator’s care” than looking after those whose livelihoods are disrupted by machines. At the moment it is mostly rich countries that worry about the effects of automation on education, welfare and development. But policymakers in developing countries will increasingly need to consider them too.

Read on: Ethical and safety concerns >>