Wednesday, May 27, 2020

Only two years ago, technology company Cisco and economic forecasting agency Oxford Economics reported in a study that a fifth of the local workforce will have their jobs displaced by 2028 as artificial intelligence and machine learning draw greater industrial application. This is the highest in the region, with Vietnam, Thailand and the Philippines trailing behind. This statistic indicates that Singapore will face the largest mismatch between skills and jobs.

Before understanding how artificial intelligence and machine learning will disrupt the future workforce, here is a quick summary of what these two terms mean.

What is artificial intelligence or AI?

Artificial intelligence is the overarching concept of incorporating human intelligence into machines. Simply put, it refers to the science of training machines to perform tasks which are performed by humans. AI-powered machines can be classified into two categories - narrow and general. Narrow AI machines are made to perform specific tasks very well. In our everyday lives, we can refer to Siri, Snapchat filters and self-driving cars as examples. General AI machines are those that can intelligently solve problems, apply skills and knowledge in different contexts. It does not specialise in any task but rather knows how to do everything. While there are no solid examples to illustrate the application of general AI machines in our everyday lives, one could imagine a machine that replicates human intelligence and behaves as any human would in any environment, practises common sense, works efficiently with many humans and has a state of consciousness. Scientists are already working on making this possible.

Artificial intelligence, as we have stated above, is a science. Machine learning, a term which it is often used interchangeably with, is a subset of artificial intelligence.

What is machine learning?

If artificial intelligence allows machines to learn human capabilities, machine learning is the method through which artificial intelligence is realised. Machine learning basically teaches machines how to learn or in other words, how to be artificially intelligent. One of the most popular data science methodologies to date, machine learning trains algorithms to learn specific sets of instructions to perform a particular task. To dig deeper, one would come across the term ‘deep learning’. A subset of machine learning, deep learning makes use of artificial neural networks, a set of algorithms that are modelled loosely on the human brain and designed to imitate the way our brains make decisions. It trains the machine to perform a specific task automatically as any human would in a given situation.

Based on our aforementioned explanation of artificial intelligence and machine learning, we can conclude that the key enabler of the technology is machines. Whether virtual or physical, machines are the actors that drive the disruptions in our future workforce - the first being the need for effective human-machine collaboration.

1. Human-machine collaboration

In working environments with high levels of repeatability, automation underpinned by AI-driven robots and computers will take over. However, not all jobs can be replaced by automation. A 2017 report by McKinsey Global Institute revealed that only 5% of occupations can be fully automated but almost every occupation can be partially automated. What this means is that humans will now begin to work closely with machines as artificial intelligence and machine learning begin to penetrate across industries. We need people who can interact with these machines actively, understand their underlying technologies, can troubleshoot and solve problems when they occur. Our ‘humanness’ remains a competitive advantage when we work with complex systems - we develop and maintain these systems. Our human behaviours still supersede that of any present and future technologies.

The future job workforce will thus require the combination of human and technological capabilities that embraces an AI-driven world. Where the Singapore job market is concerned, we can already see the demand for tech-savvy candidates by corporations who have harnessed digitalisation in the spirit of the country’s Smart Nation initiative.

2. Growth of new industries

As artificial intelligence and machine learning begin to automate and create new jobs across industries, we can potentially see the rise of new industries in the years to come. We say this because this is the power technology brings to economies. For instance, with the advent of the Internet, we now have the e-commerce and digital marketing industries. The introduction of the global positioning satellite or GPS gave rise to private-hire industries with the creation of Uber and Grab, not forgetting the food delivery sectors driven by popular players like FoodPanda and Deliveroo. With artificial intelligence and machine learning embedding into more factions of our everyday lives (at work and at home), we can anticipate new sectors dominating global market share in the next few decades. As an example, we can look at how the smart home industry is expanding capabilities with artificial intelligence. Some of these include smart thermostats as well as facial and voice recognition security systems. The birth of new industries means new job categories will emerge - it is in our hands to leverage the opportunities and keep up with the evolving world.

3. Focus on support for older workers

As machines begin to take over roles or enhance job functions, a sector of workers will find the change hard to adapt to. We are referring to our older workers who may not only struggle with such technological applications across industries but also fear losing their jobs for being less tech-savvy. As we earlier discussed, the workforce will require humans who can work with machines effectively. This means undergoing training on using a range of software and hardware, and understanding how to maintain these systems so they operate at their optimal level all the time. For our older workforce to thrive in such an environment, they must be willing to learn. However, this can only be made possible if the younger generation steps up to provide a supportive environment for these individuals. Anybody can be taught - what only differs is the pace and method of teaching. If we want the local workforce to harness the power of artificial intelligence and machine learning, we need to create a safe space for older workers to keep learning. They can then take advantage of such opportunities and continue to contribute to the economy.

Artificial intelligence and machine learning create limitless opportunities in various fields, including cybersecurity, project management and cloud computing. Find out how COMAT can help develop your IT career with such capabilities!