**2. From the 4th industrial revolution to industry and society 5.0**

Over the last thirty years, the concept of *industrial revolution* has been elevated from its academic origins in literature addressing the economics, history, sociology and politics of technological change into mainstream media discussions and debates about the future trajectory of direction of societies. The concept that has generated most discussion in the last decade is the *4th Industrial Revolution* (4IR). The 4IR is an "umbrella" [6] concept, in other words, it packages together a number of technological developments, including recent and expected advances in machine learning (ML), artificial intelligence (AI), robotics, 3-D printing and the Internet of Things (IoT), to forecast the future direction of economic, social and technological development in the 21st century. Part of the reason the 4IR has become a commonplace term and a feature of the popular, policy and research vocabulary across the globe as a result of its promotion by the World Economic Forum [7]. The WEF – a not-for-profit organisation – is chaired by Founder and Executive Chairman Professor Klaus Schwab and is guided by a Board of Trustees made up of global leaders from business, politics, academia and civil society. It defines its mission as "committed to improving the state of the world by engaging business, political, academic, and other leaders of society to shape global, regional, and industry agendas" [8]. In the context of its mission statement, one of the WEF's concerns is to serve as a global platform for interaction, insight and impact on the scientific and technological changes that are changing the way we live, work and relate to one another.

To advance and popularise this concern, Schwab wrote in 2017 the first book to be published with the title *The 4th Industrial Revolution* [9]. Drawing, lightly, on the well-established tradition of the historical chronology of the invention of technological tools and techniques [10, 11], Schwab presents a compelling narrative about technological change. He argues it is possible to identify four distinctive phases of technological change or in his more flamboyant term "revolutions." They are summarised as follows [9]:


The Fourth is however, according to Schwab ([9], p. 1–2) very different, because it is "characterised by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, collectively referred to as cyber-physical systems". This fusion or blurring is occurring as a result of technological breakthroughs, such as artificial intelligence, nanotechnology, biotechnology and robotics, becoming firstly, commercialised via additive manufacture/3D printing and autonomous transport and secondly, interconnected through the Internet of Things underpinned by fifth-generation wireless technologies (5G) (**Figure 1**).

There are two discernible perspectives – promise and threats – on the 4IR.

## **2.1 The promise of the 4IR**

The underpinning assumption of the promise perspectives is that all the technological developments associated with the 4IR have one key feature in common Schwab ([9] p. 1–3): they are underpinned by cumulative and exponential developments in digitization and computer science impacting on their own development (i.e. continuous development of the next generation of algorithms and technological artefacts and services) as much as on the material and biological worlds.

The main systemic development enabled by the 4IR is the Internet of Things (IoT), that is, a network comprised of machine-to-machine communication empowered by computers that can gather and interpret information [13]. In its simplest form, the IoT will, as a result of convergence of multiple technologies such as real-time analytics, machine learning, commodity sensors and embedded systems, "connect everything with everyone in an integrated global network. People, machines, natural resources, production lines, logistics networks, consumption habits, recycling flows, and virtually every other aspect of economic and social life will be linked by sensors and software to the IoT platform, continually feeding Big Data to every node – businesses, homes, vehicles – moment to moment, in real time" ([13] p. 11). Rifkin's somewhat Panglossian vision of the IoT can be illustrated through reference to the role of 3D printing. This form of printing, which is sometimes called additive manufacturing employs, as Ford ([14], p. 171) explains, "a computer-controlled print head that fabricates solid objects by repeatedly depositing thin layers of material." Depending on the object to be created, 3D printing

#### **Figure 1.**

The fourth industrial revolution *– Created by William Genovese on behalf of the Chinese telecommunication company Huawei [12].*

starts with a decision about which material will be used and then proceeds to builds an object into a three-dimensional shape using a digital template. Currently, 3D printing is primarily limited to applications in the automotive, aerospace and medical industries, where it is being "integrated with traditional manufacturing" ([14], p. 173). Looking to the future, it is anticipated that as size, cost and speed constraints are reduced, 3D printing will become "more pervasive to include integrated electronic components such as circuit boards and even human cells and organs" ([9], p. 17).

Turning our attention to the 4IR's potential through the use of technologies and intelligent systems design to not only restore and regenerate our natural environment, but also support a "great reset' [15] after Covid we encounter the promotion of a new natural and social Panglossian vision. At its heart is a tantalising suggestion that the 4IR can be harnessed to "build entirely new foundations for our economic and social systems [15]. This great reset would, according to Schwab, have three main components. The first would steer the market toward fairer outcomes. To this end, governments should improve coordination (for example, in tax, regulatory, and fiscal policy), upgrade trade arrangements, and create the conditions for a "stakeholder economy." The second component of a Great Reset agenda would ensure that investments, especially in AI, advance shared goals, such as equality and sustainability. Here, the large-scale spending programs that many governments are implementing, for example, the "Biden" plan, represent a major opportunity for progress. One way is to ensure funds are used to create a more resilient, equitable, and sustainable society by using AI to assist with, for example, building "green" urban infrastructure and creating incentives for industries to improve their track record on environmental, social, and governance metrics. The third and final priority is to harness the innovations of the Fourth Industrial Revolution to support the public good, especially by addressing health and social challenges. During the COVID-19 crisis, companies, universities, and others have joined forces to develop diagnostics, therapeutics, and possible vaccines; establish testing centers; create mechanisms for tracing infections; and deliver telemedicine. Imagine what could be possible if similar concerted efforts were made in every sector.

#### **2.2 The threat posed by the 4IR**

Alongside the above Panglosian vision of the 4IR, its market-focused advocates also acknowledge the possibility that it might result in a world without work. Reports from global professional service companies, such as Deloitte, Forbes McKinsey, PEW and Price Waterhouse Coopers, all contain sections contrasting the impact of emerging technologies on the labour market. At the heart of this dystopian view of about the potential outcomes of the 4IR lies the issue of *automation.* The threat that the development of new technology might pose to employment has been a subject of debate in History of Technology, Labour Economics, and Political Economy for many decades (see [16] for a recent overview). The scene was set however for the current debate among think tanks, professional service firms and researchers about the effects of automation on employment by the report [17]. Their report has achieved near totemic status as regards the forms of employment 'at risk' of automation issue because, as Frey and Osborne ([17], p. 5) note, they forecast before more or less any other researchers "what recent technological progress is likely to mean for the future of employment." **Figure 2**, using data from the Data from McKinsey Global Institute [18] gives an indication of the scale of the shift required, predicting that up to 800 million workers worldwide, approximately 30% of the workforce, may be impacted with up to 375 million needing to change occupation category as a consequence.

*Fusion Skills and Industry 5.0: Conceptions and Challenges DOI: http://dx.doi.org/10.5772/intechopen.100096*

**Figure 2.**

*The impact and threat of 4IR on employability and jobs by 2030. Data from McKinsey global institute analysis [18].*

They achieved this goal by focusing on the susceptibility of jobs to computerisation in the following way. Selecting the technological advances in Machine Learning (ML) and Mobile Robotics (MR), Frey and Osborne demonstrated the ways in which such technologies are now able to perform tasks which have until recently been considered genuinely human and this state of affairs is escalating rapidly. Moreover, Frey and Osborne concluded based on this possibility and their prediction employers would automate work processes that this enhanced technological performance was no longer confined to routine tasks as has been the assumption of most studies in labour economics in the past decade (see [19] and [20] for reviews of the literature). It is increasingly the case that machines are capable of performing non-routine cognitive tasks such as driving or legal writing. Frey and Osborne noted that advances in the field of ML facilitated the automation of cognitive tasks, the only exception to this threat was "Engineering Bottlenecks" ([17], p. 33), in other words, tasks related to perception and manipulation that, at present, cannot be substituted by machines since they cannot be defined in terms of codifiable rules and thus algorithms.

Subsequent research has also produced equally eye-catching, albeit slightly different, forecasts about the threat of job loss. One notable example is the report from the Brookings Institute – "What jobs are affected by AI?" The report argues the reason it has been difficult to "get a specific read" on AIs implications for work is because "the technologies have not yet been widely adopted" ([21], p. 3). Consequently, analyses from "Oxford (i.e. [17]), OECD, and McKinsey have had to rely either on case studies or subjective assessments by experts to determine which occupations might be susceptible to an AI takeover" ([21], p. 4). The report also points out that none of these analyses focused solely and specifically on AI, mainly concentrating on an "undifferentiated array" of automation technologies including robotics, software, and AI all at once. In contrast, the Brookings Report claims that it is drawing on a "new approach … [based on]… quantifying the overlap between the text of AI patents and the text of job descriptions … to identify the kinds of tasks and occupations likely to be affected by particular AI capabilities ([21], p. 4). The former provide a way to predict the commercial relevance of specific technological applications, for example, applicants willingness to pay nontrivial fees to file them is a proxy measure of patents likely uptake, and the latter because they provide a textured insight into economic activities at the scale of the whole economy. Using this method, the Brooking team undertook a granular, statistical analysis of the specific documented task content of occupations in a number of sectors, that are, potentially, exposed to emerging AI capabilities, for example, agriculture, finance etc., and drew the following conclusions: AI could affect work in virtually every occupational group and that better-paid, white collar occupation may be most exposed to AI, with business, technology and finance being particularly vulnerable (**Figure 3**).
