The beginning of this year, and the new decade, has been marred by the outbreak of a new coronavirus, which has sent the world into panic mode as fears of the spread of the epidemic heighten. Reminiscent of the SARS epidemic a few years ago, the virus has spread quickly, with approximately 20,629 cases confirmed so far.
Already, the deaths have outnumbered those caused by SARS. Yet, amid this global health crisis, proponents of artificial intelligence (AI) have been swift to act. The use of AI in combating the coronavirus has ranged from robotic cleaners spraying disinfectant at segregated wards to AI voice assistants calling people to advise on home-quarantine, to AI-powered infrared sensors that detect body temperatures on the foreheads of moving passengers. The autonomous robots have replaced human cleaners, which has reduced infection rates and are also able to work nonstop for more than three hours.
The voice robot is based on big data and can check information such as personal identity, location and health condition. It can then categorise information and produce daily reports, and this has aided in monitoring the spread of the virus. As the Chinese company Baidu’s founder Robin Li put it in a letter to employees earlier in February, “Big data and AI are not only instrumental in increasing city management efficiency and healthcare breakthroughs during public emergency events but can also empower all industries and become a driving force.”
These developments translate into changes in the kind of skills that are required and how work is organised. This is just a single instance of the impact the technologies of the Fourth Industrial Revolution (4IR) can have. What is clear about this response to the coronavirus outbreak is that 4IR has undoubtedly arrived. This, of course, also provides a window into the possibilities if we are to tap into 4IR. If this is merely one response to one event, what else is possible? The 4IR is the confluence of people and technology, which has inextricably linked the physical, digital and biological worlds.
AI, in particular, is fundamentally built on mathematical principles such as calculus, statistics and linear algebra. Much of these have been met with fear – particularly with regard to job losses. In a 2018 report, the World Economic Forum predicted that the Fourth Industrial Revolution will create massive job losses, but will simultaneously pave the way for new occupations, especially in areas such as data analysis, computer science and engineering. It is envisaged that the demand will be for professionals who have a blend of digital and science, technology, engineering and mathematics skills with traditional subject knowledge. It is estimated that by 2022 more than 75 million jobs will have disappeared and be replaced by 133 million new types of jobs.
The mathematics landscape in South Africa is fraught with inadequacy. Mathematics teaching in schools here is said to be among the worst in the world. Following the release of the matric results earlier this year, education standards monitor Umalusi said it had noticed a worrying trend in mathematics, as the subject was not progressing in tandem with cognate disciplines in terms of learner performances. The numbers are startling.
The number of students writing mathematics dropped from 270,516 in 2018 to 222,034 in 2019 while only 54% of the pupils who wrote the exam passed it – particularly concerning given that the pass mark is only 30%. The concern is that mathematics is a gateway subject which is vital for any country’s economic growth and development, yet the numbers are in direct contradiction to the needs of 4IR. This is becoming increasingly apparent. Skills in mathematics and statistics will be more vital than ever as we navigate 4IR.
In fact, at university level, we are actively promoting the importance of these subjects in a challenging and ever-changing context. To succeed in the 4IR, we ought to refine problem-solving skills, deepen computational abilities, engage in multi-disciplinary thinking, think systematically and most importantly, master the social world. These are skills that equip people with the ability to measure, analyse, design and advance.
A study by the Smithsonian Institute found that in 2018, 2.4 million science, engineering and technology jobs in the US went unfilled. Yet, this problem is compounded because it is not mathematics or statistics in the traditional sense that we are looking at. Many computers can perform mathematical equations with far more accuracy than mathematicians. If we look at mathematics in a conventional sense, it is based on a problem and formulae to solve it, which give the exact solution. This is not the case in 4IR. The skills needed are far more complex than this. Can we take a problem which may not be well-defined or have an exact solution and progress enough with it that we can come up with multiple and imprecise answers to it?
We are not looking at variants of algebraic equations, but at real-world problems in a world that is not as neat and tidy as those in mathematics or statistics. Perhaps the US astronomer Maria Mitchell predicted this in the 19th century when she said, “We especially need imagination in science. It is not all mathematics, nor all logic, but it is somewhat beauty and poetry.”
Few would solve the coronavirus with calculus, but mathematical thinking can equip people with problem-solving techniques. If a machine can complete equations more accurately than the most accomplished mathematician or statistician, then the advantage of people is limited to their ability to creatively solve problems. This goes beyond numbers and symbols but talks to an understanding of the language of mathematics and statistics. In science-fiction, along with stories of robots taking over, there is an emphasis placed on mathematics.
For instance, in Foundation by Isaac Asimov, the protagonist is a mathematician named Hari Seldon, who develops a new and useful mathematical sociology called psychohistory. Using statistical laws of mass action, it can predict the future of large populations which Seldon uses to foresee the imminent fall of the Empire.
Arguably, we now need more of an interdisciplinary lens to look through. What is the role that mathematics and statistics can play in the social sciences? What can proponents from these fields learn from each other? To adapt to this rapidly changing context, you need a combination of these skills. According to the global consultancy McKinsey & Company, you will need to excel at social and emotional, technological, and higher cognitive abilities. Keith Devlin, from Stanford University, has written extensively on mathematical thinking. As he explains, each concept in mathematics or statistics gives you tools to think in this way because you are taught to think in patterns. Arithmetic and number theory study the trends of number and counting, while geometry studies the patterns of shape.
This, of course, is changing the way we package programmes at universities. For instance, there is a move towards new, flexible, often multidisciplinary curricula that move away from the traditional focus on predefined categories and types of learning. This requires reliable and robust conversations on research: what are the questions that should keep us on edge, what are the focus areas for a university, how do we reorganise ourselves? The current packaging of knowledge into modules and qualifications and the way this is taught and learnt also has to be revisited.
In part, this is because we are preparing current students for a different work environment, but we are also a platform for people to revisit their skills. Arguments are also made for multidisciplinary curricula. Part of the solution then is to shift the focus from teaching to learning, with emphasis on real-world problem-solving abilities. The American scholar Steven G Krantz once said, “It is becoming increasingly evident that the delineations among ‘engineer’ and ‘mathematician’ and ‘physicist’ are becoming ever more vague.”
This perhaps rings truer now than ever. The crucial questions we should ask are whether early career mathematicians and statisticians are sufficiently empowered to rise to these challenges, and how can we properly equip the science system? I would argue that we have many of the tools at our disposal, but the focus must be on learning how to use those tools outside of a whiteboard and marker, to move past the equations and into solving problems that do not necessarily have exact solutions.
As Microsoft’s former chief technology officer Nathan Myhrvold explained, “It turns out that human intelligence is not just one trick or technique – it is many.” Given the observation of Myhrvold, we will thrive in the 4IR if we do not lose focus on blending science, engineering and technology with human and social sciences. DM