Challenges of automation - how can learning help?


(Photo by Franck V. on Unsplash)

Does artificial intelligence really have the ability to perform human-like tasks, or is it all just hype? Why is automation bad for emerging economies, and could education be a part of the solution? Will automation result in net gains in opportunities for people, or losses?

This week's focus is squarely on technology and automation, what the research indicates what's happening right now, and suggests what might happen in the future, specifically:

1. Why artificial intelligence might only be able to perform specific, highly-specialised tasks and why dynamic learning environments may be too much for it to cope with.

2. Why 'premature industrialisation' is a serious threat to emerging economies due to automation, and how education may be part of the solution.

3. How one recent report sees a net jobs and opportunities gain across the United Kingdom thanks to automation, and why students need to be involved in this conversation.

4. However the current reality appears to be slightly different - in some industries productivity, output and efficiency have never been higher yet jobs and pay continue to stagnate or decline? The problem? Automation. So how could learning help?

Despite the emerging power of artificial intelligence and its potential to transform how we work and learn, there appears to be a long way to go before it is capable of tackling the diverse tasks commonly encountered by a human being in a typical day.

In this article's example, if an AI is asked to analyse cake recipes and then create a recipe from what it understands, it can do a sort-of-ok, if incorrect job of producing a set of instructions that a person can follow to bake something. However, if the AI is asked to create any type of recipe from a diverse range of cookbooks, the results are comical.

Engineers are finding out that building an AI to cope with general tasks is an extremely difficult thing to do, and more success is being achieved through AI that specialises in specific tasks that it can do extremely well. Instead of an all-powerful general intelligence, AI is likely to be a series of highly specialised tools, perhaps talking and sharing information with each other and monitored.

So what does this mean for learning? AI is already proving useful for identifying how well students respond to routine tasks on a reading or maths learning programme and modifying content to suit their needs, along with planning out timed repetition so new knowledge is revisited. It's importance is growing in the personalised (of some types of) learning domain, and this will likely continue.

AI may be some time away from engaging in the dynamic learning environments that will provide today's students with the competencies they need for tomorrow's workplace. We will follow this closely, and promise to treat any announcement of a 'wonder AI that will transform learning' with caution

The effects of automation on jobs will be highest in developing countries that rely heavily on semi-skilled labour in repetitive work roles. Some industries are more at risk than others and today we are looking at textiles, which is a major employer and is among the industries most at risk from automation. For example, new robots halve the time it takes to make a t-shirt, and they are being deployed at a rapid pace.

Some welcome the coming demise of 'sweatshops' and their long hours, low pay and generally poor labour conditions, but the reality is more complex. These jobs are important not only for the workers, but also their families and communities. It's the first step on the ladder of work for millions of people, and job losses to automation means losing the opportunity to work and having few viable alternatives.

Money from wages will no longer be distributed to poor families and communities, and the country (in this case Cambodia) loses jobs before it's had the opportunity to develop other industries. The term is 'premature deindustrialization', and it places emerging economies globally at serious risk. So what are some possible solutions? And who's responsible?

One suggestion in the article is that companies and governments share responsibility for any workforce transitions and improvement of working conditions. Are these governments in a position to regulate and enforce? Will companies that have manufacturing based in developing countries with a close eye on profitability invest in workers if regulation and enforcement is non-existent? For the most part, we suspect not.

Could the solution lie in learning, and if so what would it look like? Accelerated post-school training schemes for new skills? A rebooted national curriculum? Leave your thoughts in the comments.

Every industry will be affected by automation over the next 20 years, with many jobs likely to be lost in transport, supply chain, manufacturing and administration tasks. However according to a recent report from Price Waterhouse Coopers, all is not lost.

There is expected to be a net jobs gain across the United Kingdom thanks to changing demographics, growth in the economy and new opportunities that are expected to emerge as a result of new technologies such as Artificial Intelligence. Total numbers of professional positions are likely to increase in healthcare, scientific and technical occupations, along with education.

The growth in new opportunities in which there will also be income growth will be the domain of the highly skilled, and those who have positioned and prepared to take advantage of the coming opportunities. There are also geographical considerations: London is expected to continue gaining as an economic powerhouse where the opportunities are, while other areas with a large number of declining industries will see fewer opportunities emerge.

Do students have the opportunity to look through the research and discuss how this might affect them? Are they able to make choices about what they learn that might help them prepare? Are the people responsible for their learning informed?

Total manufacturing output in the United States has never been higher, yet manufacturing jobs continue to decline. New oil rigs are being built, but oil jobs are disappearing. Mines are extracting more ore from the ground than ever, but again, fewer jobs. Every new industrial robot replaces 5.7 jobs. These trends are accelerating.

What's happening?

Automation is happening. Jobs are already being lost to machines at an ever-increasing rate, and despite the optimism of the report above those jobs are not being replaced. A large number of laid-off workers are taking longer to find new jobs, and these are not as well-paid, are lower-skilled and have fewer benefits. Net discretionary income per household is declining rapidly. People are working two jobs and longer hours, and/or in the gig economy with all of its uncertainty. The middle class is disappearing, and yet humanity has never had so much wealth.

This is a well researched, written and thoughtful article that argues that while we want automation to happen, the benefits of the massive productivity and wealth generated must, as an imperative, be spread across all of our society. Taxpayers deserve to benefit from the state-funded research and development into the the technologies driving the automation revolution, and as a society we need to decouple income from work.

Interesting yes, but how does learning fit into all of this? Perhaps we need to look at automation and transformation of work trends with our students? Maybe we could (again) look at trends and projected skill shortages with our community members and collectively talk about our children's and communities' futures? Do we need to re-examine how we can best learn for the future? How can learning help us be prepared to thrive?

Thank you for joining us this week - for regular updates and articles subscribe and follow our Facebook Page. Please don't forget to comment on our articles and posts - we want to share ideas, critical thought and constructive feedback.

Sean

#Technology #ArtificialIntelligence #Work #Automation #NewJobs #AI #Robots

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