9 minute read
AI Automation, Human Resourcing and the Future
by Eldon Marks
Innovation, automation and labour displacement have been occurring in cycles since the first industrial revolution. Three centuries later, we now face our fourth iteration (the 4IR), but it is far different this time around. Artificial Intelligence (AI) and the Information Age have changed the rate of emergence of newer jobs and the decline of older jobs, a rate which is causing some degree of concern about what the future of work will look like. In this article we take a journey into understanding AI, the cycle of innovation and resourcing through the ages, a data-driven view of AI automation on industries and the unique advantage we have in the region to prepare ourselves for the 4IR.
The promise of AI
When broken down, AI can be thought of as a collection of data and “tools”, which when combined, can produce even more sophisticated tools which process information in interesting ways. Technically speaking, AI is a branch of computing encompassing algorithms, models and datasets, which are architected to emulate or surpass the way humans process information. These tasks include but are not limited to interpreting visual information, natural language understanding and synthesizing new information with phenomenal efficiency.
Over the last few years, the typical AI stack (a combination of “tools” used to build AI applications), has evolved into one that is more complex and capable of deep learning and reinforcement learning. What this means is that in addition to being able to teach these algorithms and models to perform human-level tasks well, they are also able to teach themselves at a superhuman pace. This makes this modern AI stack extraordinarily powerful.
With this extraordinarily powerful AI stack, we see real-world examples of AI making promising contributions to modern science. One such example is augmenting the diagnostic capabilities of medical doctors in detecting the presence and spread of cancer and tumors and predicting heart attacks. Simply by being fed labeled training data in the form of patient scans, these AI models are able to visually pick up on the most subtle abnormalities that typically escape human eyes and make inferences based on large, detailed datasets which are beyond the processing capabilities of your typical human MD. We see AI in self-driving cars significantly reduce the likelihood of traffic collisions because they never get tired, drive drunk or fail to respond in time. With these advances alone, it is safe to say that AI has the potential to save millions of lives.
Our method of interfacing with services online, in our home or over the phone has been transformed by AI based systems like chatbots, virtual assistants and virtual assistant-powered Internet of things (IoT) devices. We are now even closer to commonplace “human in the room” type conversations with machines that take our drive-thru orders, answer our frequently asked questions, manage our banking transactions and personal finances and more. Conversational AI makes this possible and with further advances in the field, we won’t be able to tell the difference between interacting with humans or machines, whether online, through IoT devices in our homes or over the phone.
Concerns of Growing AI Capability
These promising advances in AI are all happening at such a rapid pace as we approach artificial general intelligence, where AI becomes more capable of learning and mastering just about anything a human can. Although we are several years away from this eventuality, we should however, ensure that we recognise how important it is to approach this future with ample preparation and strategy.
Even now, there are growing concerns with regards to the state of our preparedness to face the evolving implications of AI in this, our information age. Central to these concerns is the reality that our physical world, quite frankly, depends on, and is intertwined with this digital one that we’ve built using the same stuff that AI so masterfully manipulates - data.
Among the significant objects of our concerns related to AIbased innovation such as data privacy and data legitimacy is the potential threat to human resourcing in the future of AI automation.
AI Automation and the Job Market
Thanks to AI automation, we are experiencing increased rates of innovation, productivity gains and resourcing in this, our fourth industrial revolution (4IR) era. To understand why this is different in the 4IR and properly place AI automation and the job market into perspective, we need to understand the cycle of innovation, productivity and resourcing over the ages.
Before the first industrial revolution, history tells us that we were predominantly farmers in an agrarian and handicraft era. Harnessing the power of steam shifted us into our first industrial revolution which lasted from the 18th to the mid 19th century. Automation started here where we innovated machines which were no longer powered by man, but by steam. The advent of electric power brought us into the second industrial revolution during the mid 19th - 20th century and gave way to internal combustion engines, electric motors, telephones and radio, among other advancements. It was our shift to the third industrial revolution which ushered in what we currently refer to as the Information Age. During the early periods of the Information Age, electronics, information technology and the Internet influenced the rapid increase in the rate of technological innovation and notable leaps in productivity levels. As innovations continued apace, the field of computing saw advancements in processing capability as well as practical applications of Artificial Intelligence. AI and AI-based automation characterizes our fourth and current industrial revolution (the 4IR).
What we see is that over the ages, we have incrementally discovered newer and more improved ways of doing things which require less resources including human labour. As part of this process, we cyclically underwent rounds of transformative innovation which included some degree of automation and gains in productivity. With each round of innovation and the elevated productivity that came with it, we altered our reliance on older, more manual jobs and these would either be adapted or became obsolete altogether. During these shifts, however, there would be newer and better jobs which would be created for the next generation of workers based on newer technologies. The abundance of newer and better jobs mitigated the degree of disruption created in the wake of the waning need for older jobs. This was maintained primarily because the rate of innovation and increases in productivity (where we would do more with less) was relatively sustainable. At the start of the Information Age, however, older jobs became obsolete at a faster pace than the creation of newer and better jobs. The rate of innovation assumed a different pace and we began requiring less resources, human and other, to do more in less time. To illustrate this, it has been recorded that in 1979, before improvements in business and personal computing power began their influence, General Motors employed more than 800,000 workers and made 11B USD. In contrast, however, Google, by 2012, made 14B USD with a workforce of just 58,000 employees.
In this most recent leg of our current industrial revolution we are bound to experience increases in the automation potential of existing occupations as advances in computing continue to occur at its current pace. This observation has contributed to concerns about disruptions in the labour market, raising worrisome questions about the negative disruption automation may have on the labour market. A study by the US Bureau of Labour Statistics depicts an empirical view of the automation potential of occupations. The data gathered suggest that less than 5% of occupations are fully automatable at present; however, 60% of occupations have at least 30% of their activities that may be automated through the application of technology. The fully automatable activities fall under predictable physical activities, data processing and data collection. Added to the reality that the adoption of automated methods is subject to technical, economic and social feasibility, the anticipated transition towards fully realising the automation potential of occupations is likely to take some time. In spite of this, the progression is underway and it would be prudent to implement creative ways of adapting to our inevitable future.
Our Regional Advantage and Preparing for the Future
From a regional perspective, we possess a different profile of technical, economic and social factors which ultimately grants us a slower pace of adoption of automated methods compared to the more developed world. It is our developing status within the region, particularly with regard to technological adoption, that presents a unique advantage. It affords us more time to prepare for the impacts of automation on human resourcing. In our preparation we must, however, recognise that our position in this globalized world grants us a prime vantage point to look outwards into our future, engage and learn from more developed economies, and leapfrog in our regional development.
When we look outwards, we can take note that new, innovative industries born within this Information Age, present the greatest opportunities for the creation of new occupations with low automation potential and a high relevance in the future of work. Google is a prime example of how new and innovative technology-driven industries can be created and contribute to the creation of these new occupations. What began as an Internet search engine evolved into a technology conglomerate employing over 135,000 persons as of 2020 and spanning a diverse array of industries including AI, robotics, consumer electronics, telecommunications, life sciences and healthcare, to name a few. Other notable innovative industries include game development and extended reality (XR), which are set to gross over 268 billion USD and 393 billion USD by 2025, respectively (according to Statista).
To begin our leapfrog and strategically prepare for the future of work within the region, it is imperative that we recognise the importance of innovative, technology-driven industries and work towards upskilling our human capital to export technology related services and products as we seed new innovative industries of our own.
Eldon Marks is the CEO of V75 Inc and the Founding Director of NeXus Hub Inc. He is a former lecturer of the University of Guyana, a tech entrepreneur and innovator who is on a personal mission to create enabling environments for tech innovation and tech industry growth in Guyana. Email Eldon at: eldon.marks@v75inc.com
Eldon Marks, Founder & CEO, V75 Inc.
