CHAPTER 12

Emerging Futures

Introduction

Attempting to accurately predict which advances in science or technology will become the foundation upon which a new source of industrial wealth generation will be built is virtually impossible. Hence, all that any futurist can hope to achieve is to identify which fields of endeavor have the potential to provide the basis for disrupting existing organizations or creating a new to the world proposition, which will eventually evolve into a total new sector of industry. Nevertheless, what is feasible is to identify areas of science and technology that may offer the potential to support upgrading existing sectors or create entirely new sector as a result of the activities of technological entrepreneurs. Hence, the purpose of this final chapter is to review some of the opportunities and global problems requiring resolution, which may become the basis of significant wealth generation at some time in the 21st century (Barrett et al. 2015).

Global Warming

It could be reasonably argued that the greatest threat facing the human race in the 21st century is global warming. A reduction in the rate of global warming demands a reduction in the level of greenhouse gas emissions. Key opportunities in this area include the expanded use of renewable energy and an improvement in the processes for the storage and distribution of electricity generated by renewables. Progress in relation to the later issue is somewhat slower than in technological advances in relation to renewable energy. This is due to the fact that, in many countries, electrical grids are often fragmented and poorly suited to achieving distribution of renewable-generated electricity (Cohen 2015). Problems include traditional electrical systems being centralized with electricity being generated at a large-scale power plant and transmitted to customers. The alternative technological opportunity is to move to distributed generation from renewable sources at or near the point of consumption combined with advances in load management and energy storage systems. Such actions would reduce the amount of energy lost in transmitting electricity and reduce the size and number of power lines needed.

PricewaterhouseCoopers (2008) described cleantech as “not one tidy group, but rather an array of distinct sub-sectors: solar, wind, and geothermal energy generation, biofuels, energy storage (power supplies such as batteries and uninterruptible power supplies), nuclear, new pollution-abatement, recycling, clean coal, and water technologies.” The common thread across many cleantech applications is these subsectors represent technologies, services, or products aimed at reducing greenhouse gas emissions and other pollutants and promoting energy efficiency and the conservation of natural resources. Firms in the cleantech industry are dedicated to finding technological solutions to energy, ecological, and industrial processes while growing economies and improving environmental productivity. Energy-related companies make up the largest cleantech segment, with energy being broken down into supply-side and demand-side technologies. Energy generation is probably the most well known as a result of the emergence of new technologies in relation to products such as wind turbines, batteries, electric cars, and solar panels. Other areas within the cleantech taxonomy include commercial lighting, programmable thermostats, intelligent network devices, materials, recycling, water and air purification.

For existing organizations, legislation and individual commitment to reducing greenhouse gas emission opportunities do exist to exploit innovation. The primary focus is on revising or modernizing the production processes. In energy-intensive industries such as chemicals, mining, metals, utilities, and oil and gas, new energy-efficient technologies are being developed and implemented to achieve emission reductions (Kolk and Pinkse 2005). Companies have the option of drawing upon organizational capabilities as well by exploring new product or market combinations. One possible way to enter new markets is by becoming involved in a strategic alliance with other companies, such as that now occurring in oil and automobile companies in relation to the development of fuel cells. Another solution is to ensure that activities and sources of high emissions are carried out elsewhere in the supply chain.

Agriculture

Global warming is having a dramatic adverse impact on food production in areas such as East Africa. As a consequence, increasing emphasis is being given to the exploitation of new technology to enhance agricultural productivity. These activities involve moving beyond simple rain-fed farming techniques and harnessing water resources for food production through investment in technologies to store water, measure, and control flows for irrigation. One approach is known as “smart water management,” which focuses on exploiting new technologies to enhance the effectiveness and efficiency of crop irrigation systems (Kay 2011).

Much of the pioneering in smart water management is being undertaken in developed nations such as the United States and Israel. This technology is often quite expensive, and hence, at the moment, usage will tend to be restricted to farms generating a high value from crop production. As much as 50 percent of the water applied to crops by farmers may be lost by evaporation, wind drift, and run-off, or because too much water is applied and the water sinks below the level required by plants’ roots. To overcome these problems, U.S. irrigation equipment manufacturers, such as Lindsay Corporation, have developed smart irrigation systems, such as overhead water sprinklers, to reduce water loss (O’Driscoll 2012).

For hundreds of years, one way of improving crop yields has been to modify the genetic makeup of plants using techniques such as selective breeding and hybridization. This has led to the creation of “super-hybrids,” which has permitted the seed companies to offer farmers the opportunity to achieve greater productivity. More recently, advances in biotechnology have resulted in the creation of genetically modified (GM) crops using a laboratory process whereby the DNA of one species are extracted and artificially introduced into the genes of an unrelated plant. The foreign DNA may come from bacteria, viruses, insects, animals, or even humans (Qaim 2005).

The range of desirable crop traits that could potentially be developed using biotechnology is very wide, ranging from biotic and abiotic stress resistances, higher yields, better nutrient efficiency, and the ability to farm new plants. As a consequence, GM crops have been seen as beneficial not just in developed nations, but even more importantly, as a vital way of upgrading food production in poorer nations across the world. So far, however, only very few GM crop strains have been commercialized. A key obstacle is biotechnology research plus the testing and approval procedures are expensive. This means large commercial markets are required to recover the initial investment. These tend to be restricted to major crops grown on farms in developed nations. The other obstacle to expansion of usage has been that concerns among the general public problems has led to restrictions or outright bans on the growing of GM crops or their use in the production of food products in some parts of the world such as the European Union (EU). The basis of these concerns is that certain methods used to transfer the genes of modified DNA of a genetically modified plant are imprecise and unpredictable. This possibly may lead to unintended changes, such as differences in a food’s nutritional values, toxic and allergic effects, lower crop yields, and unforeseen harm to the environment, that cannot be reversed (Legge Jr. and Durant 2010).

These factors mean the big multinationals have little incentive to develop GM crops for small or uncertain markets in developing nations or where poverty levels mean that farmers cannot afford to purchase GM seeds. As a consequence, farmers in developing nations are usually reliant upon GM plant research being undertaken in projects funded by their own governments. One such example is China where the government has funded research using rice genomic information to assist the conventional breeding process and directly applying genetic engineering technology to create new varieties. Successfully developed transgenic rice traits are insect- and disease-resistant aimed at overcoming the acute problems stemming from overuse of and/or heavy reliance on pesticides (Shen 2010).

Health Care

Shostak (2005) noted that since midway through the 20th century, the major multinational pharmaceutical companies have been very successful in developing small molecules that affect specific targets such as proteins or cells. In the small molecules model of drug development, a new formula is synthesized and tested in animal models and in clinical studies on human subjects. However, over the last two decades, there has been a growing interest in new forms of therapies based on stem cell technologies, biologics (i.e., larger molecules or aggregates of molecules), and the creation of new antibodies. The potential for these new forms of therapies is anchored in the understanding of the human genome. Skostak concluded, however, that the initial enthusiasm for the potential to develop therapies based on gene sequencing has been muted, reflecting the fact that identifying a connection between gene sequences and a specific medical disorder is a complex process.

Nevertheless, a new situation has emerged in the health care industry. This is because there has been a significant decline in the marginal return on investment in small molecule R&D, and the pharmaceutical industry has been forced to consider changes. Approaches such as new techno-scientific procedures involving stem cells, antibody, or biologics therapies are becoming established as complements to the small molecule therapies (Prainsack et al. 2008).

Understanding of the human genome has made valuable contributions to science, but produced only limited number of new therapies. Stem cell research is arguably capable of providing both new experimental methods and new forms of therapies in which cells are grown to replace poorly functioning or badly damaged cells in organs such as the eye or the liver. This and other areas of techno-scientific procedures have resulted in a shift in focus from “wet biology,” wherein in-vivo studies are increasingly substituted by bio-computation, and bio-informatics models describing biological systems are simulated using software tools (Thacker 2006).

Genome sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to genomic sequences. The aim of “functional genomics” is to turn the huge amount of data obtained by observation and experiments into knowledge about life and life functions, with a focus on how genomic sequences determine normal and abnormal cell functioning. As more genome sequencing projects are undertaken, this fuels more and more projects in the area of functional genomics.

A major focus in functional genomics is to build upon the knowledge that errors in gene sequencing in DNA or RNA can lead to mutations. Identification of errors in gene sequences provides the basis for knowledge that can permit identification of the possible causes behind certain medical conditions such as cancers. This understanding can provide the basis for undertaking gene splicing. This involves manipulation of the gene sequence to create a change in the behavior of a specific type of cell, which when re-introduced into the patient can lead to effective treatment of an identified medical condition (Werner 2010).

Robots

Twentieth-century science fiction writers have been extremely successful in describing new technology that has subsequently become reality. One area of such writings has been in relation to robots. These machines first appeared in real life in manufacturing environments such as car assembly plants. Their expansion into other roles was delayed, however, due to the need for advances in areas such as microchip memory storage capacity, more powerful software programs, and use of Artificial Intelligence (AI). These requirements are now being met, and as a consequence, robots now represent an area where emerging technology is resulting in machines offering significant potential as aids to human kind in a diversity of roles (Bibel 2014).

One important constraint in the process of developing more effective robots is these machines have tended to be solitary creatures, carrying out their allotted tasks with a single-minded purpose. This reflected by the fact that to-date, most robotics research has focused on building individual, autonomous machines. However, the era of the lone robot may be drawing to a close. This is because researchers have started to explore the possibilities of social machines capable of working together with minimal human supervision. In theory, collaborative robots hold enormous potential. They could augment human workers in high-risk situations such as firefighting or search and rescue or boost productivity in construction and manufacturing (Wright 2012).

A priority area of development is in using robots in the health care sector. This emphasis reflects factors such as the growing need to stabilize the costs of caring for the elderly in the face of population is aging, patient surgery, and patient recovery while in hospital. In relation to caregiving, researchers are developing social robotics to supplement or even replace human caregivers. These personal robots are created to act in any residential premise, such as at home and in nursing homes. Over time, robot carers can be expected to become part of standard health care service provision (Kachouie et al. 2014).

Robots designed as caregivers are required to have the ability to interact like humans with their patients. Robots for elderly people can be broadly categorized into two groups (Carrera et al. 2011). One group is the “rehabilitation robots” that focus on physical assistive technology and are principally not communicative. Examples include smart wheel-chairs, advanced artificial limbs, and exoskeletons. The second group is “assistive social robots” that can be divided into two subgroups, namely service robots and companion robots. Service robots are used to support basic tasks of independent living, such as eating and bathing, mobility, navigation, or patient monitoring.

Another area attracting interest is enhancing safety and improved medical outcomes from surgical treatments. A recognized aspect of surgical treatments is that errors do occur within the operating theater. Causes of such errors include team instability due to lack of familiarity between nurses and surgeons, a lack of resources, distractions, and poor communication. These factors increase the likelihood of instrument-count discrepancies caused by retention of surgical instruments in a patient’s body along with disposables, such as sponges and towels, most common. Robotic scrub nurses under development are able to deliver surgical instruments to the surgeon by being able to understand the hand gestures and verbal requests from humans. These robots can also reduce the possibility of surgical instruments being retained within the patient’s body by undertaking an accurate, thorough, and timely tracking of instruments in use during the operation (Mithun et al. 2013).

Robots are also perceived as having an important role in the transportation sector. Driverless trains have been in use for some years to link passenger terminals in a number of the world’s airports. This is a relatively simple environment because the train is moving along a fixed track, and intervention, when necessary, can be based on using simple automated signaling systems. Once the concept of the driverless vehicle is extended to roads, the technological problems in areas such navigation, data collection, and decision making, become significantly much more complex. It was the highly entrepreneurial Google Corporation that decided to embark on the years of research to validate the viability of the driverless car. Having fully validated the technology, the car industry is finally accepting the market potential for the driverless car and is either investing in their own research projects or forming technological partnerships with high-tech companies such as Apple and Google. This growing interest has also prompted expanding the focus of robotic transportation systems to include other vehicles such as trucks and construction equipment (Blau 2015).

AI

In 1950, the British academic Alan Turing proposed that a machine’s ability to exhibit intelligent behavior could be tested to determine whether the activities of the machine are indistinguishable from that of a human (Muggleton 2014). The ability of a machine to exhibit intelligence has since become known as AI. Machines utilizing AI are able to competently perform or mimic the cognitive functions that traditionally have been associated with humans. Modern examples of AI include computers that can beat professional players at games such as Chess and Go and self-driving cars.

Autor et al. (2006) opined that one possible impact as computer technologies such as AI spread across developed economies is employment opportunities will be clustered at the top of the market based around high-wage or high-education jobs and at the bottom in low-wage jobs requiring little education. Nevertheless, lower-skilled jobs such as those in retailing are also likely to be impacted. Already self-service checkout lanes in supermarkets are becoming increasingly common and with mobile apps available to support all aspects of the product purchase decision. This situation implies that the need for staff in terrestrial outlets may, over time, be reduced.

Ford (2015) noted that, in the past, many low-wage jobs have been protected from automation because humans are extremely good at tasks requiring mobility, dexterity, and hand–eye coordination. However, these advantages can be expected to diminish as more affordable robots utilizing AI software become available, which can mimic humans in the fulfillment of various job roles. It is unlikely that ongoing advances in AI will lead to immediate job destruction and rapidly rising unemployment. Nevertheless, as with the two previous Industrial Revolutions, in the current third Industrial Revolution, the structure and nature of job markets will change with opportunities in some sectors significantly diminished, while hopefully new opportunities will arise elsewhere within nations’ economies.

The Internet of Things

The British entrepreneur Kevin Ashton is attributed to have coined the term the “Internet of Things” (or IoT). This area of technology is an open and partially standardized technological infrastructure that consists of unique identification devices (e.g., radio frequency identification or RFID devices) and sensors (e.g., to assess temperature, location, and vibration) embedded in everyday objects. These everyday objects are, in turn, embedded in a larger computer network and are often connected to servers and combined with other existing technologies in a modular manner. Data are communicated wirelessly. Taken together, IoT can be seen as distinctively different from the PC paradigm. This is because computing capabilities are not restricted to servers, fixed PCs, and laptops, but instead are distributed across devices embedded in everyday objects (Boos et al. 2013).

IoT offers the potential to improve activities in areas such as control of household appliances, supply chain management, health and safety management, and in retailing. In the case of retailing, IoT offers the potential for improved stock and asset management, reduced materials handling, greater information sharing, and better product tracking (Bose 2009). The retail example illustrates that most current IoT applications are mainly seen as allowing automation of data capture, thereby making manual intervention in data capture unnecessary. Within the supply chain, IoT technologies make it possible to automatically scan goods entering a warehouse and to update the information stored in a management information system in real time. This again results in the intervention of human actors becoming unnecessary. Some IoT applications can not only take over existing activities, but are also capable of supporting new functions, such as the complete and more accurate monitoring of the transportation path of goods (Bendavid and Cassivi 2010).