Forecasting Jobs and Skills 2030:

Melding Machine Learning and Participatory Foresight

What advice would you give to a child today about careers tomorrow?

What did you want to be when you grow up? Soccer player? Astronaut? Film star? Stockbroker? Nuclear physicist (my pick)? Are you worried about the jobs available for your children, and what skills they might need to succeed in the future? What advice would you give to your four-year-old?

You might say, learn to write code — learn to design robots. But more and more, programs are writing programs themselves. With the boom in smart infrastructure, the rise of smart cities, and the evolution of the Internet of Things, electrical engineers and mechanical engineers and robotics specialists may increasingly find that their inventions are helping each other in mutual redesign. Looking into the future, the job-seekers of tomorrow might think beyond the digital age to the emerging biological age: focus on biosciences, all you kindergarteners.

Too many changes to count…

Pearson asked Nesta UK and the University of Oxford to forecast which occupations may disappear in the future, and which may be in greater demand (see . Robots and smart things everywhere are supplanting human labour — how many occupations are actually in jeopardy? For example, will we still need chefs in the future? Or will nimble robots assemble raw ingredients into gourmet meals in gleaming restaurant kitchens? Does that only mean that restaurants no longer require line cooks, and that the chef’s key tasks — of designing the dishes and curating the meals, calculating the ingredients required and their most efficient use — and related skills will still be in high demand?

How can we even begin to think about what the future might bring?

We can’t go to the future, observe it, measure it, and bring back facts. We can look at what’s changing around us, think about the patterns of change we’ve seen in the past, and explore how change and its impacts might create many possible futures.

How can experts work together to help train an algorithm to forecast occupations?

Working with Pearson, Nesta UK and the University of Oxford designed a dialogue between human experts and an active learning algorithm — a bridge between intuitive extrapolation and quantitative forecasting. It’s a tricky bridge to build. Pearson and Nesta invited world class thinkers to discuss emerging changes and their potential effects on occupations to one workshop in Boston, and another in London. We wanted everyone to have a shared understanding of a broad array of changes that might affect the future of occupations. So we kicked off the workshop with a rapid review of various emerging changes that could transform economies and occupations over the next twenty years.

On a table-sized cartoon of a city landscape (office buildings, retail space, government buildings, arts and leisure, manufacturing facilities, transport infrastructure, agriculture, suburbs, etc.), we asked our experts to map where those emerging changes would hit hardest, using a key changes card deck. If they thought the change affected more than one economic activity, they could use coloured tape to connect their chosen change to other activities. They summarised their thinking by suggesting what jobs might decrease in the future — “fewer jobs in semi-skilled manufacturing; fewer driving jobs” — and what jobs might increase — “health & social care; designers & architects; negotiators & facilitators.”

After stretching their mental muscles with that exercise, we moved on to labelling 30 different government-categorised occupations with directional forecasts — “will this occupation increase or decrease in the next twenty years?” Participants referred to the trend deck for change ‘evidence’ to support their assumptions and forecasts.

We ended the workshop by asking our experts to brainstorm entirely novel occupations, or radical transformations in current occupations, based on the changes they had discussed. To capture those creative insights, participants scrawled their wild and divergent extrapolations of the occupations of the future on a wall mural with the key change clusters already posted.

My personal favourite? “Jellyfish sniper” — a future of global climate change with warming seas could see major blooms in jellyfish populations. These swarms of jellyfish could foul seawater intake valves on power plants, manufacturing plants, and other strategic facilities — prompting the encouragement of jellyfish eradication bounties and hunting bonuses.

Emergent forecasting… watch this space.

The research team has mapped the interesting changes, the experts have discussed the possible impacts, and the algorithm is computing. We are looking forward to the output: forecasts of potential future growth — and decline — in specific occupations in the UK and the USA and, more importantly, in the range of critical tasks and skills that may be required of the next generation of workers. Web pundits trumpet the erosion of employment due to increasing roboticisation and the emerging ecology of ‘smart everything’ (cities, factories, cars, toasters, you name it). Nesta have just demonstrated at least one instance where human intuition and machine learning can work hand in hand. Maybe that’s the best future we could hope for.

For more information on this project, see…

Pearson, Future Jobs: One of the most important conversations in learning

or

Nesta UK, Employment in 2030: Skills, Competencies, and the Implications for Learning

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