On the first 2 days of November, the biennial Adult Learning Symposium 2018 was held at Marina Bay Sands, Singapore. Organised by Institute of Adult Learning (which was where I got certified as a Trainer, by the way), it was one of the main events for professionals in the Continuing Education sector.
My last attendance was in 2014, when the inaugural symposium was held at Raffles City. As a newly-minted ACTA trainer, I was fascinated by the promise that technology, in the forms of AR/VR/MR, would bring about a revolution in the way we train adults in Singapore.
This year, the symposium, opened by Mr Chee Hong Tat, was made up of a series of Keynote and Concurrent sessions based on the themes Seize, Adapt and Transform.
My most memorable concurrent session was presented by IAL researchers, Mr Sheng Yee Zher and Ms Chia Ying, as they presented the first survey on lifelong learning in Singapore, aptly named, “Measuring Lifelong Learning in Singapore”. Building upon Jacques Delors’ lifelong learning framework, they took the pillars of the framework (Formal Learning, Workplace Learning, Personal Learning and Social Learning), added 2 pillars (Technologies for Learning, Learning to Learn) and used them as metrics for the survey.
Most of the survey outcomes confirms what I observed in my past 10 years in people development: when it comes to making use of technology for learning, Singaporeans who are senior in age, or had lower academic qualifications, or both, are most at risk of missing out; the reason for them not adopting technology for learning was not included in the survey. Also, when I probed if the technology was for self-directed, online learning, as opposed to technology used in classrooms attended by these high-risk individuals (let’s say AR goggles are already included in the curriculum, to be used by seniors in the classroom setting), the researchers confirmed that the technology was for the former.
The new information coming out the survey was the fact that work-related non-formal education participation rate in Singapore was higher than compared to other countries, pointing to a rather learning-receptive culture. This was in contrast to my current (but proven erroneous) understanding that Singaporeans would rather spend their money on travel and food, rather than for training that is not supported (financially) by their companies.
On another note, Singaporeans also scored low on personal learning; when it comes to reading and going to museums, Singaporeans scored very low in comparison. My friends were not surprised, though, as they quickly pointed out the boring curation at local museums. I thought museums like the Art Science Museum that has been bringing modern exhibitions into Singapore, or our newly opened National Gallery Singapore fared rather well in terms of entertainment and education. Perhaps we have too few of them and that we need more museums like Deutsches Museum that incorporates engaging exhibitions with education elements that resonates with our young people. Will this survey help our our museum management in revamping our collection of museums?
Other notable concurrent sessions that I sat in included “How can Artificial Intelligence Change the Way We Work and Learn” by Dioworks on a DIY chatbot that trainers can use to provide on-demand training for their learners, and “Self-Awareness – The #1 Superskill for the Modern Career” by Forest Wolf, which was about the importance of mindfulness and empathy, and the speakers also went through concrete steps on how to become more mindful about our daily actions and how to find a way towards empathising others.
A common theme that ran throughout the 2 days of sessions was how technology through automation and AI are putting jobs at risk of being replaced in the near future.
The founder of Unlisted Collection, Mr Loh Lik Peng, started the series of keynotes by showing the participants some current and successful examples of automation taking place inside and outside of hospitality, his main domain of expertise, and how those initiatives successfully helped enterprises cut manpower and become more efficient.
Ms Jane Hart, Founder of Centre for Learning and Performance Technologies then gave us a peek of the future of the workplaces and workforce. She highlighted that knowledge has been doubling in the recent years, and yet the half life of knowledge is reducing, which made it more important for workers to be ardent learners, so that we can keep up with the changing knowledge and apply them in the workplace. More importantly, it is becoming more apparent that “job for life” is giving way to “life of jobs”, where a fresh graduate now is expected to hold more than 15 jobs in 5 industries over the course of their careers.
The topic of AI and automation had been the mainstay of my daily conversations as most of my professional time had been spent on interacting with professors in these 2 areas. Organisations we spoke to also wanted to know how AI could help them in the operations. However, just like how not all “digital transformation” is useful to a company, the “rise of AI” that replaces humans must not be generalised easily.
News of how IBM’s Deep Blue won Garry Kasporov, the top chess player in the world or how autonomous vehicles will run our roads should pique our interests, yet we should be aware of the constraints of AI and automation: they run on rules and are incapable of human touches.
Keynote speaker, Mr Klaus D Wittkuhn used a joke to epitomise the rule-based nature of AI:
A Physicist, Engineer and a Statistician went hunting. When they encountered a deer, the Physicist, using his knowledge of projectile and motion, fired the first shot, and missed. The Engineer highlighted that the hunting ground was not a closed environment used in Physics experiments and proceeded to fire his shot after incorporating the effect of wind. He too, missed his shot. The Statistician, noting that the engineer missed by 5m to the left, while the Physicist missed by 5m to the right, proclaimed, “By average, you guys are right!”
Mr Klaus pointed out that data science, the foundation of AI, presents a model using an analysis of a series of events – the events in themselves are not perfect, as they are by nature supposed to deviate about a “true” event and all data science did was to aggregate all these events and find that “true” event.
Also, current machines that beat world champions, despite being the top in that area, are only (luckily) capable of doing things in that area only. Our top chess players, other than being good in playing chess, are capable of being loving fathers or helping elderly cross the road, among a whole spectrum of capabilities. Therefore, it would take many machines, each being good in one area, to totally take over the world a la The Matrix style.
Even at a more personal level, there is definitely still a long way to go before machines can understand human behaviour and react accordingly, making personal services, like attending to elderly, less permeable by the machines.
Or as Mr Eddy Lee, founder of Coffee Ventures, put it, as machines and AIs take over current jobs, jobs are created in other forms and industries, if we take our past experiences in Industrial Revolution and the Digital Revolution into account.
Peeling away all the fear of doom and gloom that robots and AIs will take over humans, the fundamental message was clear: employment as we know it will change. In fact, we should be aware that it has been been changing.
The new machines created as a result of Industrial Revolution freed up manpower from the farms and allowed people to migrate to the cities to take up administrative jobs, which by the way, is a rather modern invention. Education systems as we know it are also new, as they had been set up to respond to the changes in the workplace; the new administrative jobs then needed more labour who are literate, which in turn spurred demand for a more “efficient” way to train the workforce – the earliest form of training focused a lot on rote training and the signature of it lived on in even Singapore’s pre-employment training. As technology effected changes in the industry, the industry changed its demand for the competencies required from the workforce, which in turn changed the way training is conducted. The current “revolution” is just another phase in a continual wave of changes. Every time such a change takes place, humans have shown resilience to adapt and change, so instead of being afraid, we should embrace the change.
Though Thomas Friedman, in his book, “Thank You for Being Late”, noted that human adaptation to technology advances has been lagging since 2007 (that was the year when iPhone, Facebook and many other revolutionary technology came to market), this phenomenon also meant that those who can rise above the adaption curve will be able rise above competition; it doesn’t matter if he didn’t manage to overcome the technological advances like humans did pre-2007, because just being able to adapt better than the others will put him in a more advantageous position.
That was the basic takeaway Mr Klauss gave us when he listed some of the new foundational literacy required of the modern learner (or worker, in the adult learning aspect) – the literacy has more to do with being culturally sensitive and tech-savvy than excelling in examinations.
Quoting from Forest Wolf, “To manage Deep Tech, we have to be Deep Human” – and that is the way we should evolve when it comes to adapting to the new workplace of the future.
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