Artificial Intelligence (AI) and automation portend huge disruptions to work and lifestyle — impacting the social and psychological well-being of humans in many ways. This paper is to discuss the areas of impact in specific terms with use cases and means to leverage on the opportunities presented, at the global, organisational and personal levels. Automation is not new but the increased scale and capability of the technology portend huge opportunities and challenges for our generation and the generations to come (Choy, 2019).
The context is the future especially with education and training requiring decades of investment before our current and next generation of workers can be equipped with the right disciplines and competences to excel in the A2 (Artificial Intelligence and Automation-based) economy. It will also take time for legislature and societal contracts between government and people to be established. Riding on our current surplus of ‘social credits’ where there is sufficient trust among people groups and government, it is critical to prepare for the transformations to work and jobs as the upcoming waves of technological and industrial disruptions can be difficult and upsetting to many.
Overview to Paper
We will begin by examining the key characteristics of what constitutes an A2 economy and the scenarios to drive home the point how A2 disruptions to the society will be wide-ranging and deep, impacting many areas of work and family life. Following which, we will look at what the A2 economy will mean to us and the interventions we can take to mitigate the risk and leverage on the opportunities. This paper will review the following topics:
Section 1: How will Work and Learning be Disrupted?
What Work will be Impacted
Characteristics of A2 — the 4Ps
4 Warnings about A2 economy
· Section 2: Peering into the AI-Enabled Future
Examining 7 Scenarios for A2 disruptions
What do these Scenarios Mean for Us?
· Section 3: What Can We Do About the A2 Economy?
5 Recommendations to Implement for the A2 Economy
What will the disruptions mean to us?
What can we do about these disruptions?
Section 1: How Will Work and Learning Be Disrupted?
What Work Will Be Impacted
The types of work which could be impacted by AI and Automation include:
1. routinized tasks involving both psychomotor and rule-based cognition e.g. transportation, sorting of objects and quality check of components
2. visual recognition tasks e.g. security access involving facial recognition of personnel and staff
3. semantic analysis and generation of texts e.g. analysing legal documents for keywords and meaning, and crafting of articles; answering emails based on schedule and requester
4. basic levels of communication with humans or machines (involving needs and basic requests)
While the above tasks may seem piecemeal and nondescript, there are serious implications when they are viewed collectively. For example, a radiographer identifying tumours in scans, writing reports based on the diagnosis, and subsequently arranging for appointments to meet the patient and caregivers can be performed by AI subsequently. All of these tasks may constitute 80% of the radiographer’s job role on a typical day.
Characteristics of A2 Economy — the 4 Ps
We can tell that the A2 economy has superseded our current (or ‘traditional’) economic model when AI and automation take on these characteristics (denoted by the 4Ps) in the future:
A2 will grow in all of these 4Ps over time depending on how we manage the rise of the technology. The challenge is that a lot of the growth is occurring organically across different domains and driven by many stakeholders from wannabe entrepreneurs to small enterprises, MNCs and governments. We just don’t know where and when the ‘next big thing’ will come. These innovations are likely to keep coming in waves riding on major breakthroughs every few years.
Simply put, we cannot and will not be able to ignore the rise of the A2 economy. We will have to leverage on the opportunities and mitigate the potential risks. What are these risks? Let’s examine a few.
4 Warnings about A2 Economy
If we see these warning signs appearing in our economy, we should do well to take note and navigate them carefully. These are ominous signs indicative that our economy and society are not doing well. We should prepare and pre-empt a future as described in these 4 warnings. Let’s take a look.
Warning 1: Losing Control of Knowledge Domains
As intelligent machines take over key knowledge tasks (paralegal research, medical prescription, academic literature research, scheduling etc.) as well as some creative tasks (e.g. writing, painting, architectural design, curriculum design, dance choreography), the value of humans learning these tasks will diminish over time. Humans will question the need to learn these domains of knowledge since they will rarely if ever apply them to either their work or family. Machines will perform most of these tasks. If humans cede their control over knowledge domains to intelligent machines, our next generation of humans will lose their foundational grasp of the sciences and arts. This weakens our autonomy over machines and eventually, there is little humans can do without knowledge as we slip back into the ‘dark ages of knowledge illiteracy’.
As robots take on human appearance, differentiating them from humans may be critical, to ensure that humans continue to be the ruling race. However, over time, as human body parts are replaced with specific machine parts, there is a high likelihood that people become part human and part machine and the need to recognise what makes humans different from machines become critical. The danger of not addressing this issue of human identity is that the human race will gradually be assimilated into the intelligent machine population and we will cede our domination over this planet.
Warning 3: Being Numerically Overwhelmed by Intelligent Machines
It is not an issue if humans are always superior to intelligent machines either by default (due to our superior intellect) or through a built in mechanism to make machines subservient to humans. However, if either of these two conditions are not met, humans will one day be overwhelmed by the sheer number of intelligent machines. Control of these autonomous machines cannot be guaranteed if the engineering of these machines did not provide a back-key (or ‘kill switch’) to manage the machines in the event of unforeseen circumstances.
Warning 4: Either We Undertake or We Undergo the Transformation
As the A2 economy gains traction, certain services (e.g. employment agencies, educational institutions and community services) could be overwhelmed as the various industries are progressively impacted by technology and disruption. It is likely that enterprises will keep their most capable staff (across operational, middle and senior management levels) to manage the transformation and implement the automation. These ‘best in class’ practitioners and experts will also be tasked to drive innovation and pre-empt errors which may arise over time.
The remaining (say, 80% of the) staff could be redeployed to other work (if the automation affects only certain departments e.g. the factory floor), given short-term contracts or retrenched with benefits. Typically, newly unemployed people do not look for retraining immediately. Their primary preoccupation is to look for another job. If it is still at the start of the disruption cycle, there may still be jobs available to soak up the displaced workers. However, as the waves of the A2 economy hit the workforce, jobs will become scarcer and of ‘lower’ quality, leading to more unemployment or underemployment.
Currently, it takes between 3 to 6 months to get a person reskilled properly in a new role, including some form of coaching in the new workplace. Assuming that there are sufficient new jobs to absorb the newly unemployed, we will need:
· rapid selection processes (e.g. AI-based recruitment) to match these newly unemployed to new jobs
· training organisations or enterprise L&Ds to rapidly create new training programmes to develop these skills
· equip trainers (either human or AI) with the know-how to train these skills
· conduct the training for large masses of people in a very short time
· move these new employees into their new roles quickly and picking up any who drop out
We had some experience moving people into short-term training during the Great Recession. It took the Singapore Workforce Development Agency (now known as SkillsFuture Singapore and Workforce Singapore) a very short period of time to get the training sector to ramp up their programmes to provide training places for the couple of hundred thousand adult learners. Credit to the government then, in 2009, to respond so quickly and effectively. However, the programme to save jobs took a fair amount of effort and resources to mitigate the impact of the increased unemployment. The danger with the upcoming wave of technological disruption is that unemployment will take place very quickly (imagine self-driving cars replacing taxi drivers, truckers, van drivers, delivery goods drivers within 3 to 6 months), across many more sectors and most importantly, it will be unclear what new jobs there are for these unemployed to be trained in. In addition, new training programmes can take up to a year to be developed. New trainers will need 3 to 6 months to gain the necessary experience in the new job role before taking on the training assignment. We don’t know if our training institutions are agile enough to produce and implement new curricula within a very short span of time. If we look at this issue at the macro level, we may need another ‘training’ system to keep people ‘self-pre-occupied’ meaningfully for a period of time while waiting for new training programmes to transit these unemployed to new jobs. One possible alternative training system can be in the form of ‘maker space’ education and we will discuss this in a later part of this paper.
The potential for societal unrest is present if people’s needs remain unmet for a sustained period of time. Rather than wait for the A2 economy to hit our shores, we can undertake the transformation in an efficient and managed manner. There will still be some fallout but these can be managed assuming that countries have sufficient resources to ride this next transformational wave. If we can do it well, those countries that can get their act together will be in the driver’s seat to become some of the most advanced and wealthiest nations globally.
In the next section, we will peer into AI-Enabled Future, looking specifically at the changes expected across 7 sectors.