If you are wondering how jobs, work, education, training, social policy and business are changing worldwide because of technology, automation and what I call the ’10 ations’, you really should download read this brilliant new report.
A quick comment: I would add that I think we need to look at lot more towards what I call the humarithms to define new, future jobs (yes, as opposed to algorithms). Sure, jobs in IT, health and STEM in general will certainly grow, as well, but many of them will sooner or later be done by machines, as well. We need to look more towards to HUMAN-ONLY jobs (and educate our kids accordingly), i.e. those jobs that are based on creativity, imagination, compassion, emotion etc. So, not just STEM but also HECI (humanity, ethics, creativity, imagination) See the images below.
Some best-of snippets (high-lights by me)
“A number of forecasts suggest good opportunities for future job creation in the information technology, industrial (i.e. robot engineers and technicians) and green sectors. In addition, the health sector is set to create the largest job openings, estimated at more than 4 million new jobs in the US from 2012 to 2022. This is not surprising given that many people in advanced countries are living for longer.
Looking at the macroeconomic and microeconomic effects of technological change, we find further evidence that the pace, scope and benefits of this change will be a challenge to societies. The remainder of the report focuses on potential policy options. The chapter ‘Response: Policy and Job Risk from Automation’ details active labour market policies which could help people find jobs: from training, to earned income tax credits (EITCs) or lowering tax wedges, to incentives to support self-employment. We believe EITCs are more appropriate than current moves to raise minimum wages given the latter could lead to higher unemployment. If self- employment is becoming a new norm, this also has policy implications — 34% of the total US workforce, or 53 million people, are currently freelancers (see Figure 34). Some argue a basic income or benefits (healthcare, housing, pensions) should be provided, yet we fear changes in taxation that may be required to pay for solutions will impede an effective policy response.
With technological change a key driver for the demand of both the level and type of skills needed ahead, it is no surprise that the number one policy suggestion in our client survey was for increased investment in education. Although it is clear that more information & communication technology (ICT) skills will be needed, significant differences currently exist in IT skills between countries, with the US and the UK lagging behind other countries on some measures. While many argue science, technology, engineering & math (STEM) skills are needed, intermediate level skills in STEM seem to be a riskier educational investment. Non-cognitive skills can be increasingly important, but malleability of these skills should not be assumed.
The expanding scope of automation mainly relates to advances in Machine Learning technology, including Data Mining, Machine Vision, Computational Statistics and other sub-fields of Artificial Intelligence (AI), turning a wide range of knowledge work into well-defined problems. This is, in turn, made possible by the provision of relevant data. For example, companies such as Work Fusion sell software to automate non-routine tasks, previously performed by office workers. Specifically, the software divides the job into smaller tasks, automates the routine work and then recruits freelance workers through crowdsourcing platforms to perform the non-routine work. As the software monitors the workers it learns from them, meaning that over time it can automate more of the non-routine tasks. In other words, the freelance workers train the system to replace them.
Similarly, surgical robots have become significantly more flexible and with a greater range of motion now perform more operations, while commercial service robots are now able to perform more complex tasks in elderly care, food preparation, and cleaning. In Japan, some hotels already use robots for check-in and to guide guests to their rooms. The robotic receptionist can also speak different languages depending on the preferences of the guest. Thus, in short, while sophisticated algorithms for big data can perform more knowledge work, the potential scope for the automation of physical labour is expanding in tandem.
Creative intelligence, social intelligence, and perception and manipulation are three potential bottlenecks to expanding the scope of automation.
The reason tasks requiring social intelligence are difficult to automate is largely similar: social intelligence requires a wealth of tacit knowledge, about social and cultural contexts, and thus involve a wider range of subtleties that are difficult to specify and often give rise to misinterpretations, even among humans. The state-of- the-art of automation technology in the context of social interactions is best captured by Eugene Goostman, a software chatterbot, managing to convince a third of judges on a panel that it was human based on five minutes of text communication. Nevertheless, it did so by pretending to be a 13-year old boy using English as a second language. Thus, more challenging forms of social intelligence, including coordination, teaching, negotiation and mentoring, is today far from automatable”
And some more bullets from the PDF:
- Jobs in transportation and logistics as well as office and administration support are among the most susceptible to automation
- The pace of displacement in service occupations is also expected to increase
- Low-skilled jobs are the ones that are most susceptible to automation in the future
- The divergence in penetration rates of technology adoption can account for 82% of the increase in income gap across the globe
- Due to an increase in automation in manufacturing, low income countries will not achieve rapid growth by shifting workers from agriculture to factory jobs
- The susceptibility to automation across developing countries ranges from 55% in Uzbekistan to 85% in Ethiopia
- The best hope for developing and emerging nations is to upskill their workforce
- Robots are estimated to perform about 40- 45% of production tasks
- According to Boston Consulting Group projections, at least 85% of the production tasks in these robotic-intensive industries (under SITC code 7) can be automated.
- Korea, Germany, China, Japan, and the US are expected to obtain large competitive gains from the use of robotics
- The largest number of job opportunities in the US is expected to be in the health sectors
- 24% of the respondents to Citi’s survey stated that the IT sector was the most promising sector for jobs
- Linkedln’s job site has nearly 39,000 data scientist job openings
- Autonomous vehicles could indirectly create new jobs as users can now spend time working, relaxing or accessing entertainment via mobile Internet instead of driving
- There is a skills mismatch between current worker skills and future job skill requirements
- With the rise of the internet, employment in the publishing business has decreased
- With the increase of machines used for loading, transporting etc. there could be a near zero or zero human work needed in these areas
“Other studies have highlighted the importance of adapting our current skills for future employment. One such study is the work that has been done by the Institute for Future Work, which identified ten skills as being important and most relevant to the workforce of the future. These include:
• Sense-making – the ability to determine the deeper meaning of significance of what is being expressed,
• Social intelligence
• Novel and adapting thinking
• Cross-cultural competency
• Computational thinking
• New Media Literacy
• Transdisciplinarity – literacy in and the ability to understand concepts across multiple disciplines
Yanis Varoufakis (the former finance minister of Greece) views technology and automation in a different way to Hassabis. In an interesting discussion with the music producer Brian Eno, he stated that the issue with automation is ownership, and whether the ownership of the machinery will get even more unequal: “The way I try to express my own fear of, and hope for, the future is that we have our choice, which is between Star Trek and the Matrix. Star Trek is this: we’re all sitting around having philosophical conversations like in the ancient Agora in Athens and the slaves are not human. There are holes in the walls on the Starship Enterprise; you ask for something and it comes up. Fantastic. So then you can explore the universe and talk to Klingons. That’s one choice — the utopia. The dystopia is The Matrix, where the machines are being fed by our own energy. We are plugged into a false consciousness that the machines have been created to keep us happy. We think we are leading a perfectly normal life, but all along we are the slaves of the machines. So these are the two extremes. And the choice whether we go to Star Trek or The Matrix is ours. It’s a political choice.”
Living in a Star Trek world, as described in the previous chapter, could lead to a world with more leisure time. Keynes in 1930 had already predicted that ongoing technological advances could displace workers. He viewed this as a huge opportunity rather than a concern. He looked at technological unemployment as a way of mankind solving its economic problem and in his essay called ‘Economic Possibilities for our Grandchildren’156 predicted that by 2030 the average work week would shrink to 15 hours per week allowing humans to enjoy more leisure time. “Thus for the first time since his creation man will be faced with his real, his permanent problem-how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely and agreeably and well.”
In the words of the famous author Elbert Hubbard:
“One machine can do the work of fifty ordinary men, No machine can do the work of one extraordinary man”
Here is a gallery of the best images from the report: