Creativity in Machines

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

team development in CSCL. Educational

Psychologist. 2013;**48**(1):9-24

Universiteit Nederland; 2002

[38] Johnson DW, Johnson RT.

[37] Dillenbourg P. Over-Scripting CSCL: The Risks of Blending Collaborative Learning with

Instructional Design. Heerlen: Open

Cooperation and Competition: Theory and Research. MN, US: Interaction Book Company; 1989;viii:253

[39] Johnson DW, Johnson RT, Smith KA. Cooperative learning returns to college what evidence is there that it works? Change: The Magazine of Higher Learning. 1998;**30**(4):26-35

[40] Resta P, Laferrière T. Technology in support of collaborative learning. Educational Psychology Review.

Donovan SS. Effects of small-group learning on undergraduates in science, mathematics, engineering, and

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[42] Wiske MS, Sick M, Wirsig S. New technologies to support teaching for understanding. International Journal of Educational Research.

[43] Kamga R, Romero M, Komis V, Mirsili A. Design requirements for educational robotics activities for sustaining collaborative problem solving. In: International Conference EduRobotics 2016; Springer, Cham;

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[41] Springer L, Stanne ME,

**52**

Chapter 5

Teboho Pitso

1. Introduction

55

Abstract

Shared Futures: An Exploration

of the Collaborative Potential of

Intelligent Machines and Human

This chapter reports on the exploratory study that aimed at better understanding the conditions under which the combined capabilities of intelligent technologies and human ingenuity could be harnessed to create new efficiencies. The study was conducted within a university setting as universities should model how future societies ought to look like and drive societal change. As the new digital society 5.0 takes shape, the time has come to critically probe one aspect of society 5.0, the leveraging of human-machine collaborations to generate unique ideas and convert them into tangible results. The sequential mixed methods' approach together with a sociocultural lens was used to investigate the ideal university conditions that could

foster human-machine collaborations in value cocreation. Nineteen Senior

Keywords: value cocreation, intelligent machines, human ingenuity,

human-machine collaboration, sociocultural perspective

Scandinavian and South African managers were interviewed to elicit their views on how human-machine collaborations could be harnessed to cocreate value within complex university settings. Entrenched cultures, policies, systems, and multiple stakeholder interests which complex into rules and routines mostly define university mores. These university mores are often impervious to rapid newness and radical change. Fifteen advanced undergraduates at one South African university also participated in a quasi-experimentation that investigated team formation and team development within the context of human-machine collaborations.

Intelligent technologies represent a major shift in the capabilities of computing machines from performing repetitive tasks within the mainly quiescent algorithmic problem-solving frameworks towards generating smarter solutions through the use of advanced heuristics and active interaction with humans. Algorithmic frameworks are considered quiescent when they rely on a specific set of instructions that totally reproduce expected outputs. The capabilities of intelligent technologies that are most likely to contribute in creative problem-solving, deep learning required in creativity and new discoveries with potential to create value require huge data processing, multiple iteration abilities and huge resource commitment. These three

Ingenuity in Cocreating Value

### Chapter 5

## Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines and Human Ingenuity in Cocreating Value

Teboho Pitso

### Abstract

This chapter reports on the exploratory study that aimed at better understanding the conditions under which the combined capabilities of intelligent technologies and human ingenuity could be harnessed to create new efficiencies. The study was conducted within a university setting as universities should model how future societies ought to look like and drive societal change. As the new digital society 5.0 takes shape, the time has come to critically probe one aspect of society 5.0, the leveraging of human-machine collaborations to generate unique ideas and convert them into tangible results. The sequential mixed methods' approach together with a sociocultural lens was used to investigate the ideal university conditions that could foster human-machine collaborations in value cocreation. Nineteen Senior Scandinavian and South African managers were interviewed to elicit their views on how human-machine collaborations could be harnessed to cocreate value within complex university settings. Entrenched cultures, policies, systems, and multiple stakeholder interests which complex into rules and routines mostly define university mores. These university mores are often impervious to rapid newness and radical change. Fifteen advanced undergraduates at one South African university also participated in a quasi-experimentation that investigated team formation and team development within the context of human-machine collaborations.

Keywords: value cocreation, intelligent machines, human ingenuity, human-machine collaboration, sociocultural perspective

### 1. Introduction

Intelligent technologies represent a major shift in the capabilities of computing machines from performing repetitive tasks within the mainly quiescent algorithmic problem-solving frameworks towards generating smarter solutions through the use of advanced heuristics and active interaction with humans. Algorithmic frameworks are considered quiescent when they rely on a specific set of instructions that totally reproduce expected outputs. The capabilities of intelligent technologies that are most likely to contribute in creative problem-solving, deep learning required in creativity and new discoveries with potential to create value require huge data processing, multiple iteration abilities and huge resource commitment. These three

study that is being reported in this chapter makes a modest contribution towards understanding the complexities involved in making society 5.0

Noted as the supersmart service society and still essentially human-centred, society 5.0 combines innovation, education and social action to generate new value using human-machine capabilities [4–6]. It leverages unprecedented progress in technological advances that allow for human-machine interaction and possible collaboration to cocreate new value propositions that disrupt the current societal and

In a powerful book called Futureproof, Minter and Storkey [7] identify 15 forces that will shape society 5.0 and disrupt current societal practices. Three of these forces relate to the mindset and the rest on technological advances. This emphasis on the mindset and technological savvy in shaping society 5.0 illuminates stronger synergistic relations between human psychology and advanced information technologies (IT) that will define society 5.0. Society 5.0 will not be defined by the dominance of intelligent machines over humans but will see greater humanmachine collaborations that deliver innovation that result in the creation of a supersmart service society and the galvanising of a quinary economic sector (Figure 1). A quinary economic sector is noted mainly for disrupting and

reorganising economic activities of the primary, secondary, tertiary and quaternary sectors [2], leveraging big data analytics and relying upon new technologies to create superior human conveniences. There is thus a legitimate need to work on the

Added to these ideologising concerns around technology is the general marginalisation of creativity, innovation and entrepreneurship in the core academic practices. The entrenched academic cultures tend to sideline human creativity, and the human-machine creativity would find it even harder to negotiate a space within the entrenched strategic core of university curricula. In this sense, there are strong indications that without appreciating the sociocultural aspect of enacting humanmachine creativity in universities and even other organisations, human-machine creativity would remain on the margins of such institutions or organisations. It is, in this sense, that the collaborative potential of intelligent machines and human ingenuity as mapping out within a university context was examined through perspectives of key role players in university innovation and entrepreneurship units. This collaborative potential was also examined in terms of the extent to which it impacted team formation and development. The variant of Tuckman's Stages of Team Development [3] as expounded by Crosta and McConnell [4] was used as the basis of analysis. The study is thus a subfield that falls somewhere between the emerging scholarship of artificial intelligence and human psychology within the socio-cognitivist traditions that recognise the value of teamwork in creativity. In the next section, an understanding of the historical trajectory of artificial intelligence and its recent forages into the hallowed spaces of human creativity is developed. Furthermore, understandings of the limits of creative machines which open up possibilities of human-machine collaborations are explored in ways that locate the study in these debates. These debates are then further processed within two main psychological concepts of sociocultural perspective and stages of team

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

possible.

DOI: http://dx.doi.org/10.5772/intechopen.85054

development.

2.1 Society 5.0

57

2. Framing the study

business practices plinth (Figure 1).

conditionalities of creative machines would enable these machines to generate advanced heuristics that can produce smarter solutions that might still lack formal proofs of their veracity and efficiency in practical situations. The testing of the correctness of the machine-generated solutions and their efficacy in resolving real, practical problems falls within the human realm. This is one area of collaboration between machines and humans. Other areas of collaboration between machines and humans relate to the inability of machines to adapt to real environmental changes, inability to frame and define complex problems as well as inability of machines to negotiate the complex sociocultural realities that can facilitate the adoption of machine-generated solutions in specific organisational contexts. The latter area of human-machine collaborations was the focus of the study that is reported in this chapter. The study sought to understand better the organisational conditions that could enable the adoption of machine-generated solutions and, by extension, those organisational conditions that could be inimical to the use of such solutions. Through the use of a sociocultural lens, the possibilities of human-machine collaborations are first explored through eliciting the perspectives and experiences of senior university managers in areas of innovation and entrepreneurship. Creativity was assumed, in the study, as the plinth of innovation and entrepreneurship; hence, focus was on the realities of key senior players in innovation and entrepreneurship as they actuate in real university spaces. Universities are considered as complex spaces where entrenched cultures that subsume taken-for-granted social mores, systems and policies as well as multiple stakeholder interests determine the activities and strategic directions of the university. Universities across the globe have already adopted technological solutions in varying degrees of sophistication, and some scholars have critiqued the fetishist and ideological manner of their adoption in universities [1]. Some of the major concerns include:


Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

study that is being reported in this chapter makes a modest contribution towards understanding the complexities involved in making society 5.0 possible.

Added to these ideologising concerns around technology is the general marginalisation of creativity, innovation and entrepreneurship in the core academic practices. The entrenched academic cultures tend to sideline human creativity, and the human-machine creativity would find it even harder to negotiate a space within the entrenched strategic core of university curricula. In this sense, there are strong indications that without appreciating the sociocultural aspect of enacting humanmachine creativity in universities and even other organisations, human-machine creativity would remain on the margins of such institutions or organisations. It is, in this sense, that the collaborative potential of intelligent machines and human ingenuity as mapping out within a university context was examined through perspectives of key role players in university innovation and entrepreneurship units. This collaborative potential was also examined in terms of the extent to which it impacted team formation and development. The variant of Tuckman's Stages of Team Development [3] as expounded by Crosta and McConnell [4] was used as the basis of analysis. The study is thus a subfield that falls somewhere between the emerging scholarship of artificial intelligence and human psychology within the socio-cognitivist traditions that recognise the value of teamwork in creativity. In the next section, an understanding of the historical trajectory of artificial intelligence and its recent forages into the hallowed spaces of human creativity is developed. Furthermore, understandings of the limits of creative machines which open up possibilities of human-machine collaborations are explored in ways that locate the study in these debates. These debates are then further processed within two main psychological concepts of sociocultural perspective and stages of team development.

### 2. Framing the study

### 2.1 Society 5.0

conditionalities of creative machines would enable these machines to generate advanced heuristics that can produce smarter solutions that might still lack formal proofs of their veracity and efficiency in practical situations. The testing of the correctness of the machine-generated solutions and their efficacy in resolving real, practical problems falls within the human realm. This is one area of collaboration between machines and humans. Other areas of collaboration between machines and humans relate to the inability of machines to adapt to real environmental changes, inability to frame and define complex problems as well as inability of machines to negotiate the complex sociocultural realities that can facilitate the adoption of machine-generated solutions in specific organisational contexts. The latter area of human-machine collaborations was the focus of the study that is reported in this chapter. The study sought to understand better the organisational conditions that could enable the adoption of machine-generated solutions and, by extension, those organisational conditions that could be inimical to the use of such solutions. Through the use of a sociocultural lens, the possibilities of human-machine collaborations are first explored through eliciting the perspectives and experiences of senior university managers in areas of innovation and entrepreneurship. Creativity was assumed, in the study, as the plinth of innovation and entrepreneurship; hence, focus was on the realities of key senior players in innovation and entrepreneurship as they actuate in real university spaces. Universities are considered as complex spaces where entrenched cultures that subsume taken-for-granted social mores, systems and policies as well as multiple stakeholder interests determine the activities and strategic directions of the university. Universities across the globe have already adopted technological solutions in varying degrees of sophistication, and some scholars have critiqued the fetishist and ideological manner of their adoption

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

in universities [1]. Some of the major concerns include:

56

• That university technological response tends to be framed in ways that endow technology with magical power that is capable of resolving protracted problems of academic practices. This framing remains undertheorised and mostly empirically undertested such that it assumes an ideological posture and a marketing-like puffery which attracts scholarly and intellectual critique.

• That technology tends to influence university policies and systems in ways that upset deeply entrenched academic cultures of autonomy and professional identities such that academic autonomy and professional identities get reduced to bureaucratic and technocratic logic [2]. Understood this way, academic autonomy and professional identities are positioned as subordinate to technology without substantial logical and empirical justifications. This subordinated positioning of academic shrines (autonomy and identity) mostly considered as sacrosanct in academia would most likely affect the smooth transition of universities to the cyber-physical spaces of society 5.0. Society 5.0 relies on greater convergence between virtual and real spaces such that a proper understanding of the sociocultural nature of the real spaces is essential in human progression towards society 5.0 which leverages closer collaborations between these cyber-physical spaces. While information society 4.0 relied on the cloud technologies facilitated through the internet to store, retrieve and analyse data, society 5.0 will depend on intelligent technologies to process and interpret big swathes of data elicited through sensors in the physical space which would then be used to suggest and help in cocreation of new value propositions. Knowledge and theorisation around the virtual and real spaces in terms of creating ideal conditions for greater synergy and collaborations between these spaces would be essential in the realisation of society 5.0. The

Noted as the supersmart service society and still essentially human-centred, society 5.0 combines innovation, education and social action to generate new value using human-machine capabilities [4–6]. It leverages unprecedented progress in technological advances that allow for human-machine interaction and possible collaboration to cocreate new value propositions that disrupt the current societal and business practices plinth (Figure 1).

In a powerful book called Futureproof, Minter and Storkey [7] identify 15 forces that will shape society 5.0 and disrupt current societal practices. Three of these forces relate to the mindset and the rest on technological advances. This emphasis on the mindset and technological savvy in shaping society 5.0 illuminates stronger synergistic relations between human psychology and advanced information technologies (IT) that will define society 5.0. Society 5.0 will not be defined by the dominance of intelligent machines over humans but will see greater humanmachine collaborations that deliver innovation that result in the creation of a supersmart service society and the galvanising of a quinary economic sector (Figure 1). A quinary economic sector is noted mainly for disrupting and reorganising economic activities of the primary, secondary, tertiary and quaternary sectors [2], leveraging big data analytics and relying upon new technologies to create superior human conveniences. There is thus a legitimate need to work on the

increasingly sought-after in business and industry as the post-workerist era became

A good starting point would be on whether university leadership is ready for this

mindset disruption and whether our students can cope with new projects that involve cocreating value with non-humans in the form of intelligent technologies. It was thus particularly important to tease out the readiness of university leadership

• Reimagining university and curricular processes in ways that leverage AI technologies. This would require that universities move away from fixed mindsets that see very little value in creative problem-solving. There is an absolute need for universities to prepare students to collaborate with smart machines to generate new and better ideas that can be converted to tangible results. For the purpose of this chapter, this was a major focus, but there are many areas of university setup that are ready to be disrupted in order for universities to move into society 5.0. Societies rely on universities to prepare them for the next order of things, and it is thus incumbent upon universities to discharge this mandate without fail. Classroom routines can be automated and thus free human teachers from such tedious work and allow them to set up more research projects that involve discrete students-machine teams that engage in creative activities. Known knowledge opens itself up for automation with robots, smartphones and virtual learning providing lessons wherever students are with less concerns to attend classes in physical spaces. Human teachers could become industry, government and community consultants as they prepare society for society 5.0 which could become a serious cultural shock and pose new risks such as cybersecurity and ethics of human/robot

to embrace the framework of society 5.0 in ways that compel:

behaviours that could be detrimental to humans.

59

a reality. University qualifications increasingly became the basis of securing employment with increased demand for 'fixed' graduate attributes that were purportedly sought-after by industry and business. While the third wave builds on the two waves, it offers an entirely new way of doing things. It leverages AI technologies and human ingenuity in such a way as to galvanise them into accessing realtime data to produce products and services that are highly individualised and optimised to meet human needs in the smartest way possible. It also extricates humans from tedious, standardised and routine work as robots can now assume that role which creates new roles for humans. The third wave thus sees the resurgence of human work albeit in new roles. These new human roles in industry and business will see greater collaboration between humans and smart machines as they collectively search, design, test and scale new or improved products and services, that is, engage actively in cocreation of value. These new work roles and human-machine collaborations will require an entirely new mindset and a new skills set. The 'fixed' mindset of the first and, to a certain extent, second wave will become redundant and obsolete in the next 5–25 years as new work roles emerge at an exponential pace. Dweck [8] argues that a growth-focused mindset thrives on challenges, persists in the face of formidable odds and embraces uncertainty as it innovates and adapts to changes on a continual basis. My strongest sense is that such a growthfocused mindset ought to constantly try out new things, experiment, fail, try again and be able to undertake research projects as it effectively works with highly discrete teams which also consist of non-humans. Society 5.0 will increasingly see the formation of such human-machine teams with AI technologies filtering and doing basic analysis of huge swathes of data and humans converting it into real value with benefits accruing to humans. This will require not only new sets of skills

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

but new ways of thinking and doing things.

DOI: http://dx.doi.org/10.5772/intechopen.85054

### Figure 1.

The evolution of the economic sectors [2].

mindset of people in order to substantially increase their awareness level with regard to the changing nature of the relation between humans and technologies from quiescent to intellectual exchanges. These intellectual exchanges will enhance both the human and machine intelligences. This awareness begins with understanding human-machine interaction within the framework of artificial intelligences and then followed by exploring possibilities of human-machine collaboration that result in innovative ideas. A more growth-focused mindset will be needed if people are to cope and thrive in society 5.0. There is thus a need to develop an understanding of how such a mindset can be cultivated in order to prepare people for the future in which people interact and collaborate with smart machines. A terse historical background is essential so we could put the growthfocused mindset into perspective. Humans are 'puzzles of needs', and every society and its economic activities have been organised around meeting and, in most cases, even creating these needs so as to meet them most conveniently. Humans and tools have been at the heart of figuring out these puzzles of needs and meeting them in the most efficient way. We have termed different stages of solving these 'puzzles of needs' industrial epochs with each epoch building on the previous one and providing a better and more efficient way of dealing with these 'puzzles of needs' through the use of evolving technological advances. The first wave of these technological advances thought to have started in early twentieth century saw the dominance of standardised, routine industrial processes that were organised around assembly line and powered by human muscle as a proxy for real robots. Efficiency was achieved through the measurability of each step of the assembly line and fixed tasks that were sufficiently easy as to be performed by semi-skilled workers. These semi-skilled workers required little formal education and represented, in form and substance, some kind of 'Homo sapiens robots'. The second wave is set to have started in the early 1970s and reached its apotheosis in the 1990s. It is noted for its reliance on advanced information technologies with computers as its key cynosure, large databases and the onset of automation. This economic epoch is also noted for big machines that replaced human muscle as human muscle started increasingly losing its relevance in economic activity, but human cognitive abilities became

### Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

increasingly sought-after in business and industry as the post-workerist era became a reality. University qualifications increasingly became the basis of securing employment with increased demand for 'fixed' graduate attributes that were purportedly sought-after by industry and business. While the third wave builds on the two waves, it offers an entirely new way of doing things. It leverages AI technologies and human ingenuity in such a way as to galvanise them into accessing realtime data to produce products and services that are highly individualised and optimised to meet human needs in the smartest way possible. It also extricates humans from tedious, standardised and routine work as robots can now assume that role which creates new roles for humans. The third wave thus sees the resurgence of human work albeit in new roles. These new human roles in industry and business will see greater collaboration between humans and smart machines as they collectively search, design, test and scale new or improved products and services, that is, engage actively in cocreation of value. These new work roles and human-machine collaborations will require an entirely new mindset and a new skills set. The 'fixed' mindset of the first and, to a certain extent, second wave will become redundant and obsolete in the next 5–25 years as new work roles emerge at an exponential pace. Dweck [8] argues that a growth-focused mindset thrives on challenges, persists in the face of formidable odds and embraces uncertainty as it innovates and adapts to changes on a continual basis. My strongest sense is that such a growthfocused mindset ought to constantly try out new things, experiment, fail, try again and be able to undertake research projects as it effectively works with highly discrete teams which also consist of non-humans. Society 5.0 will increasingly see the formation of such human-machine teams with AI technologies filtering and doing basic analysis of huge swathes of data and humans converting it into real value with benefits accruing to humans. This will require not only new sets of skills but new ways of thinking and doing things.

A good starting point would be on whether university leadership is ready for this mindset disruption and whether our students can cope with new projects that involve cocreating value with non-humans in the form of intelligent technologies. It was thus particularly important to tease out the readiness of university leadership to embrace the framework of society 5.0 in ways that compel:

• Reimagining university and curricular processes in ways that leverage AI technologies. This would require that universities move away from fixed mindsets that see very little value in creative problem-solving. There is an absolute need for universities to prepare students to collaborate with smart machines to generate new and better ideas that can be converted to tangible results. For the purpose of this chapter, this was a major focus, but there are many areas of university setup that are ready to be disrupted in order for universities to move into society 5.0. Societies rely on universities to prepare them for the next order of things, and it is thus incumbent upon universities to discharge this mandate without fail. Classroom routines can be automated and thus free human teachers from such tedious work and allow them to set up more research projects that involve discrete students-machine teams that engage in creative activities. Known knowledge opens itself up for automation with robots, smartphones and virtual learning providing lessons wherever students are with less concerns to attend classes in physical spaces. Human teachers could become industry, government and community consultants as they prepare society for society 5.0 which could become a serious cultural shock and pose new risks such as cybersecurity and ethics of human/robot behaviours that could be detrimental to humans.

mindset of people in order to substantially increase their awareness level with regard to the changing nature of the relation between humans and technologies from quiescent to intellectual exchanges. These intellectual exchanges will enhance both the human and machine intelligences. This awareness begins with understanding human-machine interaction within the framework of artificial intelli-

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

collaboration that result in innovative ideas. A more growth-focused mindset will be needed if people are to cope and thrive in society 5.0. There is thus a need to develop an understanding of how such a mindset can be cultivated in order to prepare people for the future in which people interact and collaborate with smart machines. A terse historical background is essential so we could put the growthfocused mindset into perspective. Humans are 'puzzles of needs', and every society and its economic activities have been organised around meeting and, in most cases, even creating these needs so as to meet them most conveniently. Humans and tools have been at the heart of figuring out these puzzles of needs and meeting them in the most efficient way. We have termed different stages of solving these 'puzzles of needs' industrial epochs with each epoch building on the previous one and providing a better and more efficient way of dealing with these 'puzzles of needs' through the use of evolving technological advances. The first wave of these technological advances thought to have started in early twentieth century saw the dominance of standardised, routine industrial processes that were organised around assembly line and powered by human muscle as a proxy for real robots. Efficiency was achieved through the measurability of each step of the assembly line and fixed tasks that were sufficiently easy as to be performed by semi-skilled workers. These semi-skilled workers required little formal education and represented, in form and substance, some kind of 'Homo sapiens robots'. The second wave is set to have started in the early 1970s and reached its apotheosis in the 1990s. It is noted for its reliance on advanced information technologies with computers as its key cynosure, large databases and the onset of automation. This economic epoch is also noted for big machines that replaced human muscle as human muscle started increasingly losing its relevance in economic activity, but human cognitive abilities became

gences and then followed by exploring possibilities of human-machine

Figure 1.

58

The evolution of the economic sectors [2].


Through interviews with university leadership in innovation hubs and entrepreneurship centres, I sought to better understand how universities in two different contexts reacted and prepared themselves for these mindset and operations disruptions. My sense was that how universities treated leadership in these university entities (hubs, centres) and how this leadership in university hubs and centres challenged entrenched university cultures would provide a preliminary framework of how university readied themselves for society 5.0 or, if you like, the age of AI. I also conducted the quasi-experimentation on how students related to smart machines that actively interact with them as equals. I used a simple AI technology version called Google Assistant mainly because it appeared to be a simple but more advanced interactive AI technology in comparison with a similar Apple assistant device called Siri. I opted for the simplest human-machine interaction because the purpose was more to determine the potential of human-machine collaboration especially the complexities of team development. This study was thus a baseline research on human-machine collaboration. It offers insights on how these possibilities of fusing human ingenuity with intelligent technologies could map out within a university setting. The study also sought to avoid presenting this chapter as a polemic for AI rather sought to provide a framework that could lead to theorisation around supercreativity as it pens out in a university setting. The realities of society 5.0 are already with us. The largest economy in the world, which is that of the USA, is already feeling the impact of society 5.0. Over the period between 1990 and 2007, the US manufacturing sector lost 670,000 jobs as a result of automation [9], and the picture looks bleak on a global scale as more than 6 million jobs have been lost to industrial robots and automation technologies, and as we approach society 5.0 realities, the picture of human-based jobs looks bleaker in the manufacturing and agricultural sectors. It is estimated that 73 million jobs are at risk of being automated in the next 5–10 years [10]. In the US agricultural sector and between 1990 and now, 41% of Americans were farmers, and today that number is around 2% [9] as smart agriculture takes effect and the traditional one declines more in society 5.0. It is important to note that automation, one of the defining features of society 5.0 that will grow exponentially, includes capital, software, smart machinery, robots and artificial intelligences (AI), and its impact is often invisible and requires astute leadership. It is, however, a misnomer and a false narrative to assume that automation and digitisation technologies only lead to job losses. New technologies disrupt traditional work patterns but create new opportunities for new kinds of work and new roles for humans in the workplace. For instance, in the UK, research on impact of new technologies on the work market shows that by 2037, new technologies will create more work than it sheds. It is estimated that the healthcare sector will create more than 1 million jobs and other sectors with growth prospects include law, accounting, advertising, cybersecurity, robot technicians and education if it invests now on developing fusion skills (human-machine capabilities) via multiple platforms and accessing requisite expertise across the globe through optimal use of new technologies. These issues form a backdrop of the study that was undertaken within the university setting on teasing out the humanmachine collaborations for cocreating new value propositions and possibly compelling a rethink of how universities should prepare and ready themselves for the inevitabilities and disruptions of society 5.0 (Figure 2).

2.2 The creative potential of intelligent technologies

Figure 2.

61

Society 5.0).

The ubiquity and power of computational capabilities increased substantially in information society 4.0 and are set to exponentially grow in the digital society 5.0 albeit in ways never imagined before. Big data analysis and interpretation by intelligent technologies, internet of things, robotics and other new technologies will, in the society 5.0, exceed human capabilities and generate new value propositions in areas of mobility, agriculture, health, energy and all aspects of human needs. For instance, diverse data from automobiles, weather forecasts, traffic, accommodations, tourist attractions and personal preferences would be recombined and reconfigured by intelligent technologies in ways that benefit the tourism industry. Mobility of the elderly and physically impaired will be substantially improved with

Societal evolutions (adopted from: 2017 Conference Proceedings on Future Services and Societal Systems in

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

DOI: http://dx.doi.org/10.5772/intechopen.85054

advanced and smart wheelchairs. We will, in society 5.0, talk about smart manufacturing that employs intelligent technologies for interplant coordination that produce greater efficiencies that were never imagined before and smart healthcare that use intelligent devices in storing, retrieving and interpreting physical and medical data as well as use drones to provide on-time delivery of medications. New value creations will even reach the agriculture sector resulting in automated tractors, automated water management and self-driving delivery cars. These new value creation opportunities would not have been possible without

shifts and advances in technological capabilities. In the past, smart machines required that they be programmed and reprogrammed in order to perform specific tasks. This is what I call operating within quiescent algorithmic frameworks

which, in the past, reduced machines to complimentary but generally passive tools. More than two decades ago, advances in technologies offered new possibilities in the interface between humans and machines. Technologies, especially computer technologies, had so advanced as to allow them to contribute in aiding cognitive processing, anchor intellectual performance and enrich human intellect. The shift was on effects and capabilities that technologies had on humans and moved from effects of technology to effects with and of a technology. Salomon et al. [11] define this distinction thus 'effects with technology occur when people work in partnership with machines, whereas effects of technology occur when such a partnership with machines have subsequent cognitive spin-off effects for humans working away from machines'. These crucial and early scholarly rumblings on the relationship between machines and humans focused largely on the implications of such technological advances on human cognition with some scholars arguing that this new partnership

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

Figure 2.

• Establishing groundwork for human-machine collaborations that could help

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

• Rethinking the business of human-business relations in ways that ethically optimise redistribution of wealth, eliminate inequality and harmonise race

Through interviews with university leadership in innovation hubs and entrepreneurship centres, I sought to better understand how universities in two different contexts reacted and prepared themselves for these mindset and operations disruptions. My sense was that how universities treated leadership in these university entities (hubs, centres) and how this leadership in university hubs and centres challenged entrenched university cultures would provide a preliminary framework of how university readied themselves for society 5.0 or, if you like, the age of AI. I also conducted the quasi-experimentation on how students related to smart machines that actively interact with them as equals. I used a simple AI technology version called Google Assistant mainly because it appeared to be a simple but more advanced interactive AI technology in comparison with a similar Apple assistant device called Siri. I opted for the simplest human-machine interaction because the purpose was more to determine the potential of human-machine collaboration especially the complexities of team development. This study was thus a baseline research on human-machine collaboration. It offers insights on how these possibilities of fusing human ingenuity with intelligent technologies could map out within a university setting. The study also sought to avoid presenting this chapter as a polemic for AI rather sought to provide a framework that could lead to theorisation around supercreativity as it pens out in a university setting. The realities of society 5.0 are already with us. The largest economy in the world, which is that of the USA, is already feeling the impact of society 5.0. Over the period between 1990 and 2007, the US manufacturing sector lost 670,000 jobs as a result of automation [9], and the picture looks bleak on a global scale as more than 6 million jobs have been lost to industrial robots and automation technologies, and as we approach society 5.0 realities, the picture of human-based jobs looks bleaker in the manufacturing and agricultural sectors. It is estimated that 73 million jobs are at risk of being automated in the next 5–10 years [10]. In the US agricultural sector and between 1990 and now, 41% of Americans were farmers, and today that number is around 2% [9] as smart agriculture takes effect and the traditional one declines more in society 5.0. It is important to note that automation, one of the defining features of society 5.0 that will grow exponentially, includes capital, software, smart machinery, robots and artificial intelligences (AI), and its impact is often invisible and requires astute leadership. It is, however, a misnomer and a false narrative to assume that automation and digitisation technologies only lead to job losses. New technologies disrupt traditional work patterns but create new opportunities for new kinds of work and new roles for humans in the workplace. For instance, in the UK, research on impact of new technologies on the work market shows that by 2037, new technologies will create more work than it sheds. It is estimated that the healthcare sector will create more than 1 million jobs and other sectors with growth prospects include law, accounting, advertising, cybersecurity, robot technicians and education if it invests now on developing fusion skills (human-machine capabilities) via multiple platforms and accessing requisite expertise across the globe through optimal use of new technologies. These issues form a backdrop of the study that was undertaken within the university setting on teasing out the humanmachine collaborations for cocreating new value propositions and possibly compelling a rethink of how universities should prepare and ready themselves for the

usher in the super smart service society.

inevitabilities and disruptions of society 5.0 (Figure 2).

60

relations.

Societal evolutions (adopted from: 2017 Conference Proceedings on Future Services and Societal Systems in Society 5.0).

### 2.2 The creative potential of intelligent technologies

The ubiquity and power of computational capabilities increased substantially in information society 4.0 and are set to exponentially grow in the digital society 5.0 albeit in ways never imagined before. Big data analysis and interpretation by intelligent technologies, internet of things, robotics and other new technologies will, in the society 5.0, exceed human capabilities and generate new value propositions in areas of mobility, agriculture, health, energy and all aspects of human needs. For instance, diverse data from automobiles, weather forecasts, traffic, accommodations, tourist attractions and personal preferences would be recombined and reconfigured by intelligent technologies in ways that benefit the tourism industry. Mobility of the elderly and physically impaired will be substantially improved with advanced and smart wheelchairs. We will, in society 5.0, talk about smart manufacturing that employs intelligent technologies for interplant coordination that produce greater efficiencies that were never imagined before and smart healthcare that use intelligent devices in storing, retrieving and interpreting physical and medical data as well as use drones to provide on-time delivery of medications. New value creations will even reach the agriculture sector resulting in automated tractors, automated water management and self-driving delivery cars.

These new value creation opportunities would not have been possible without shifts and advances in technological capabilities. In the past, smart machines required that they be programmed and reprogrammed in order to perform specific tasks. This is what I call operating within quiescent algorithmic frameworks which, in the past, reduced machines to complimentary but generally passive tools. More than two decades ago, advances in technologies offered new possibilities in the interface between humans and machines. Technologies, especially computer technologies, had so advanced as to allow them to contribute in aiding cognitive processing, anchor intellectual performance and enrich human intellect. The shift was on effects and capabilities that technologies had on humans and moved from effects of technology to effects with and of a technology. Salomon et al. [11] define this distinction thus 'effects with technology occur when people work in partnership with machines, whereas effects of technology occur when such a partnership with machines have subsequent cognitive spin-off effects for humans working away from machines'. These crucial and early scholarly rumblings on the relationship between machines and humans focused largely on the implications of such technological advances on human cognition with some scholars arguing that this new partnership

do not harm humans in anyway. Other studies on trust issues between humans and machines focus on experimentations and simulations to measure how trust impact overall tasks completions and performance in organisations that employ humanmachine collaborations [5, 19, 20]. Other studies on collaborations have signified the role of trust in team formation and development. These studies are equally important in human-machine collaborations as they go into the heart of organizational culture and how it could be affected by human-machine collaborations. The way human-machine collaborations could affect organizational culture and illuminate factors such as trust or mistrust of technological advances was measured in this study through the use of qualitative and quantitative measures. Similar to Xu and Dudek [18] observation and my own study that is reported in this chapter, trust studies on human-machine collaborations highlight the reality that organisational culture could torpedo the good intentions of human-machine partnerships. While studies that investigate trust relations between human-machine interactions focus on achievement of optimal performance by paying attention to delivering suitable and practical measures of trust variables that can be harnessed for high performance, a modicum of attention is put on the role of organisational culture in ensuring the successful use of human-machine collaborations. Freedy et al. [5] study on trust variables regarding human-machine collaborations developed and experimentally tested trust variables within the mixed initiative team performance assessment system (MITPAS) using simulations. The testing was based on the degree to which the levels of robot autonomy as well as its adaptive automation enhance soldiers' teleoperation and limit the continued use of such human-based task within the framework of trust. In other words, how far should technology go in terms of automating this human function without alienating humans which could potentially affect task accomplishment and the success of the mission. The results show that while teleoperations could be fully automated, critical performance factors of human teams such as information exchange gleaned from intelligence, coordinated communication, expected soldier behaviours in such missions and team leadership remain central to the successful mission accomplishment. Although automation via robots took away aspects of human tasks in a mission, it accentuated other aspects of human abilities as harnessed through teams such as the degree of predictability of each stage of the mission, leadership and risk assessment. This way,

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

DOI: http://dx.doi.org/10.5772/intechopen.85054

the findings show, the human-machine collaboration became effective.

that get filtered and analysed in ways that can lead to reorganisation,

63

When applied within the creative design where value creation becomes key, Pu and Lalanne [21] identify complex cognitive processes, artistic intuition and a rich repertory of knowledge and experiences as exclusive domains of humans that make exploration of new possibilities probable through targeting current imperfections in the world. Humans will therefore, according to the authors, play the role of framing the exploration, while intelligent technologies will provide big data analysis and processing. Their study focused on developing an architectural method of harnessing human-machine partnering for designs that target newness or higher designs of existing things. The results show that semi-automation and human collaboration are likely to harness the capabilities of human = machine collaborations. These conceptions of human-machine collaborations occurred at the time when intelligent technologies were still moving into the deep learning mode. Currently, these machines are capable of deep learning and thus can adapt to different tasks with little or no human effort. It is this ability of smart machines to adapt and learn deeply that has opened possibilities for these technologies to attempt generating creative ideas, concepts or models. As a result of the confluence of three main factors, the AI capabilities have been profoundly enhanced to a point of considering them for providing creative solutions. The first factor involves swathes of big data

between humans and smart technologies would lead to reexamination of prevailing conceptions of intelligence and ability [11–13]. The questions revolved around intellectual property ownership in terms of whether the intellectual benefits that accrue from the human-smart technologies collaborations should be attributed to humans or whether they must be acknowledged as joint ownership with the status of smart technologies ownership posing a complex conundrum. This conundrum was, however, not new especially in education and human skilling as Pea [14] and Papert [15] raised the issue almost three decades ago in relation to ordinary and scientific calculators' role in human thinking and learning processes especially the resultant cognitive residue attribution. There was going to be an inevitable attribution effect and opened a research gap on the relationship between humans and intelligent technologies. With the advent of expanded intelligent technologies which now includes AI capabilities, the conundrum would be even more pronounced given the huge resource commitment that comes with the use of AI capabilities. This conundrum would extend to the human-machine collaborations for cocreation of value with the questions arising as to who becomes the owner of the innovative idea or new products. This matter is relevant to this chapter because human-machine partnerships for value cocreation include issues of not only intellectual property rights but also the commercialization of the generated creative ideas. For instance, within the university and developing countries context, these AI capabilities will most likely be accessed via universities by the share weight of their costs and opportunities to use these human-machine collaborations, for value cocreation could only happen in these spaces. The question of the ownership of the generated creative idea and its commercialization would naturally develop into a conflict and clashes with established cultures in universities and developing countries. In developed countries such as in Scandinavia, such ownership of new ideas and accruing commercialization benefits go to the generator of the innovation as clearly articulated in their national innovation strategies [6, 16, 17]. Even when that is the case, data collected in selected Scandinavian universities show that the university cultures have ensured general marginalisation of such practices. Universities generally play a minimal role in such activities because very little incentives accrue to the university as all costs of the innovation centres, while located within universities, are met by the government including staff salaries, office space and the whole administrative shebang.

Partnerships between humans and machines would become even more acute when humans realise that automation poses a threat to their well-being and unless clear protocols of use in the production system and innovation are clarified. Scholarly work has been done on the trust levels between humans and machines which demonstrates that lack of clarity on the roles of intelligent technologies in productivity and performance could be counterproductive. It is not difficult to discern that the following five benefits will accrue to companies and industries that leverage intelligent technologies capabilities that include AI. These benefits are increased flexibility of the work, speed of task completion, scale of productivity, and quick and superior decision-making processes based on big data interpretations that smart machines make possible. The companies, according to Xu and Dudek [18], that harness the collaborative and combined intelligence capabilities of both humans and smart machines are likely to be highly effective and competitive. According to Xu and Dudek [18], smart machines expand human abilities in three ways through amplifying humans' cognitive strengths, automating routine tasks and freeing humans to focus on innovation and other tasks the smart machines cannot perform. However, they argue that in order to optimise human-machine collaborations and increase trust between humans and machines, humans ought to perform three tasks such as training machines to perform certain tasks, explain the outcomes of those tasks and ensure the sustainability of machines in ways that ensure that machines

### Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

do not harm humans in anyway. Other studies on trust issues between humans and machines focus on experimentations and simulations to measure how trust impact overall tasks completions and performance in organisations that employ humanmachine collaborations [5, 19, 20]. Other studies on collaborations have signified the role of trust in team formation and development. These studies are equally important in human-machine collaborations as they go into the heart of organizational culture and how it could be affected by human-machine collaborations. The way human-machine collaborations could affect organizational culture and illuminate factors such as trust or mistrust of technological advances was measured in this study through the use of qualitative and quantitative measures. Similar to Xu and Dudek [18] observation and my own study that is reported in this chapter, trust studies on human-machine collaborations highlight the reality that organisational culture could torpedo the good intentions of human-machine partnerships. While studies that investigate trust relations between human-machine interactions focus on achievement of optimal performance by paying attention to delivering suitable and practical measures of trust variables that can be harnessed for high performance, a modicum of attention is put on the role of organisational culture in ensuring the successful use of human-machine collaborations. Freedy et al. [5] study on trust variables regarding human-machine collaborations developed and experimentally tested trust variables within the mixed initiative team performance assessment system (MITPAS) using simulations. The testing was based on the degree to which the levels of robot autonomy as well as its adaptive automation enhance soldiers' teleoperation and limit the continued use of such human-based task within the framework of trust. In other words, how far should technology go in terms of automating this human function without alienating humans which could potentially affect task accomplishment and the success of the mission. The results show that while teleoperations could be fully automated, critical performance factors of human teams such as information exchange gleaned from intelligence, coordinated communication, expected soldier behaviours in such missions and team leadership remain central to the successful mission accomplishment. Although automation via robots took away aspects of human tasks in a mission, it accentuated other aspects of human abilities as harnessed through teams such as the degree of predictability of each stage of the mission, leadership and risk assessment. This way, the findings show, the human-machine collaboration became effective.

When applied within the creative design where value creation becomes key, Pu and Lalanne [21] identify complex cognitive processes, artistic intuition and a rich repertory of knowledge and experiences as exclusive domains of humans that make exploration of new possibilities probable through targeting current imperfections in the world. Humans will therefore, according to the authors, play the role of framing the exploration, while intelligent technologies will provide big data analysis and processing. Their study focused on developing an architectural method of harnessing human-machine partnering for designs that target newness or higher designs of existing things. The results show that semi-automation and human collaboration are likely to harness the capabilities of human = machine collaborations. These conceptions of human-machine collaborations occurred at the time when intelligent technologies were still moving into the deep learning mode. Currently, these machines are capable of deep learning and thus can adapt to different tasks with little or no human effort. It is this ability of smart machines to adapt and learn deeply that has opened possibilities for these technologies to attempt generating creative ideas, concepts or models. As a result of the confluence of three main factors, the AI capabilities have been profoundly enhanced to a point of considering them for providing creative solutions. The first factor involves swathes of big data that get filtered and analysed in ways that can lead to reorganisation,

between humans and smart technologies would lead to reexamination of prevailing conceptions of intelligence and ability [11–13]. The questions revolved around intellectual property ownership in terms of whether the intellectual benefits that accrue from the human-smart technologies collaborations should be attributed to humans or whether they must be acknowledged as joint ownership with the status of smart technologies ownership posing a complex conundrum. This conundrum was, however, not new especially in education and human skilling as Pea [14] and Papert [15] raised the issue almost three decades ago in relation to ordinary and scientific calculators' role in human thinking and learning processes especially the resultant cognitive residue attribution. There was going to be an inevitable attribution effect and opened a research gap on the relationship between humans and intelligent technologies. With the advent of expanded intelligent technologies which now includes AI capabilities, the conundrum would be even more pronounced given the huge resource commitment that comes with the use of AI capabilities. This conundrum would extend to the human-machine collaborations for cocreation of value with the questions arising as to who becomes the owner of the innovative idea or new products. This matter is relevant to this chapter because human-machine partnerships for value cocreation include issues of not only intellectual property rights but also the commercialization of the generated creative ideas. For instance, within the university and developing countries context, these AI capabilities will most likely be accessed via universities by the share weight of their costs and opportunities to use these human-machine collaborations, for value cocreation could only happen in these spaces. The question of the ownership of the generated creative idea and its commercialization would naturally develop into a conflict and clashes with established cultures in universities and developing countries. In developed countries such as in Scandinavia, such ownership of new ideas and accruing commercialization benefits go to the generator of the innovation as clearly articulated in their national innovation strategies [6, 16, 17]. Even when that is the case, data collected in selected Scandinavian universities show that the university cultures have ensured general marginalisation of such practices. Universities generally play a minimal role in such activities because very little incentives accrue to the university as all costs of the innovation centres, while located within universities, are met by the government

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

including staff salaries, office space and the whole administrative shebang.

62

Partnerships between humans and machines would become even more acute when humans realise that automation poses a threat to their well-being and unless clear protocols of use in the production system and innovation are clarified. Scholarly work has been done on the trust levels between humans and machines which demonstrates that lack of clarity on the roles of intelligent technologies in productivity and performance could be counterproductive. It is not difficult to discern that the following five benefits will accrue to companies and industries that leverage intelligent technologies capabilities that include AI. These benefits are increased flexibility of the work, speed of task completion, scale of productivity, and quick and superior decision-making processes based on big data interpretations that smart machines make possible. The companies, according to Xu and Dudek [18], that harness the collaborative and combined intelligence capabilities of both humans and smart machines are likely to be highly effective and competitive. According to Xu and Dudek [18], smart machines expand human abilities in three ways through amplifying humans' cognitive strengths, automating routine tasks and freeing humans to focus on innovation and other tasks the smart machines cannot perform. However, they argue that in order to optimise human-machine collaborations and increase trust between humans and machines, humans ought to perform three tasks such as training machines to perform certain tasks, explain the outcomes of those tasks and ensure the sustainability of machines in ways that ensure that machines

recombination and reinterpretation of data, concepts and ideas such that unique, unexpected ideas or patterns could emerge heuristically. However, the current deep learning models of smart machines rely on massive datasets that must still be labelled by humans so that the system could understand what each piece of data represents. This is what is called supervised learning that depends on humans for data labelling which is quite tedious and laborious. The data labelling can also open itself to human bias and thus compromise the quality of such learning. If deep learning models are going to be more efficient in creating value and generating useful ideas, then these models are going to require scaling-up across complex and highly diversified tasks and shift towards small datasets. For smart machines to generate real value then attempts will be required to:

the next two to three decades. In this sub-section, I attempt to look at possible areas of these human-machine collaborations. I have already pointed to those areas of collaboration and only seek to make them more logical and clearer. Areas for

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

• Use of machines to source, analyse and interpret large volumes of data which

• Humans adapt unique ideas or concepts generated by smart machines to real

• Digital simulations allow for the design and testing of virtual prototypes which

The capabilities of smart machines to analyse, interpret, reorganise, recombine and reinterpret data allow humans to improve existing products and services so as to increase their salience and efficiency and thus both cocreate real value. These human-machine collaborative capabilities also provide for the development of

• Machines identify previously invisible inefficiencies through sensors in complex industrial and logical systems, and humans develop means of

humans can refine and adapt in order to create real value.

products and services that are disruptive of existing order of things.

3. Human-machine supercreativity in complex university settings

the heart of this quote is the need to develop a deeper understanding of

survive and thrive within a university setting.

65

organisational culture and a sociocultural analysis which becomes crucial in trying to understand how change can be effected in any organisation. Given that creativity has had a difficult relationship with faculty, curriculum and pedagogy [1] and technology use within universities has been criticised for its undertheorisation and fetishistic implementation [1], supercreativity, as others prefer to call the humanmachine collaboration to cocreate new or improved value, would find a generally hostile university environment. Adapting the model developed by Daugherty and Wilson called MELDS, and incorporating aspects of a sociocultural perspective, I attempt to better understand the conditions under which supercreativity could

Mindset (Meds): Universities are large complex systems that have developed certain entrenched social processes that translate into deep-seated cultures. These

Universities have traditionally been designed to conduct research and teach. Overtime, universities have become implicated in the resolution of protracted societal problems but have also been experiencing high-level and high-stakes evaluations in the form of university rankings and strategic planning which were

attempting to alter the very plinth of what a university is meant to be so they could function as quasi-businesses. In the South African context, universities have been given an added burden of resolving historical inequality and poverty. These profound and sustained strategic onslaughts on the university have, however, failed to fundamentally change the culture of university as academic autonomy and professional identities remain deeply ingrained. This issue demonstrates that change strategy alone is not enough to change cultures and mindset. There is a need for something more than a change strategy to affect mindset shift and significantly change a culture. As Peter Trucker once stated 'culture eats strategy for lunch'. At

possible human-machine collaborations include:

DOI: http://dx.doi.org/10.5772/intechopen.85054

situations to create real value.

eliminating these inefficiencies.

humans use to resolve real, protracted problems.


The second variable in the AI growth equation entails the graphic processing units (GPUs) which allows for complex computations.

The third of such factors relates to the re-emergence of old AI computation model that makes deep learning possible. However, as indicated earlier, more effort will be required to push towards unsupervised learning, and AI computation is insufficient as new algorithms and possibly even more advanced hardware will be necessary to grow AI into deep reasoning spaces.

With considerable effort, the combined capabilities of data, GPUs and deep learning could facilitate greater AI growth and efficiency in creating value and constant generation of useful ideas that can be translated into tangible results. Current machine capabilities require human effort to function optimally and are also still limited in terms of executing common-sense activities and improvising in order to adapt to real-life complexities. This state of affairs allow for humanmachine collaborations in generation of useful ideas and translating them into real value. In summing up this sub-section, it is important to point out that there is a tendency to limit the meaning of creativity to disrupting established patterns through reorganising, recombining and reinterpreting data, ideas and concept. While these issues form part of creativity, creativity is more than just the generation of unique or unexpected ideas. When those ideas, despite their statistical rarity, do not lead to usefulness or human conveniences (social impact) then such ideas lack proper salience and cannot lead to real value.

### 2.3 The collaborative potential of human-machine partnership in value cocreation

Until such time that machine learning could be unsupervised such that these systems could use raw, unlabelled small data to generate reasoning capabilities that allow machines to function optimally across broader swath of applications and in real, complex situations using even common-sense capabilities, then humanmachine collaborations will become the order of the day in value cocreation over

### Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

the next two to three decades. In this sub-section, I attempt to look at possible areas of these human-machine collaborations. I have already pointed to those areas of collaboration and only seek to make them more logical and clearer. Areas for possible human-machine collaborations include:


The capabilities of smart machines to analyse, interpret, reorganise, recombine and reinterpret data allow humans to improve existing products and services so as to increase their salience and efficiency and thus both cocreate real value. These human-machine collaborative capabilities also provide for the development of products and services that are disruptive of existing order of things.

### 3. Human-machine supercreativity in complex university settings

Universities have traditionally been designed to conduct research and teach. Overtime, universities have become implicated in the resolution of protracted societal problems but have also been experiencing high-level and high-stakes evaluations in the form of university rankings and strategic planning which were attempting to alter the very plinth of what a university is meant to be so they could function as quasi-businesses. In the South African context, universities have been given an added burden of resolving historical inequality and poverty. These profound and sustained strategic onslaughts on the university have, however, failed to fundamentally change the culture of university as academic autonomy and professional identities remain deeply ingrained. This issue demonstrates that change strategy alone is not enough to change cultures and mindset. There is a need for something more than a change strategy to affect mindset shift and significantly change a culture. As Peter Trucker once stated 'culture eats strategy for lunch'. At the heart of this quote is the need to develop a deeper understanding of organisational culture and a sociocultural analysis which becomes crucial in trying to understand how change can be effected in any organisation. Given that creativity has had a difficult relationship with faculty, curriculum and pedagogy [1] and technology use within universities has been criticised for its undertheorisation and fetishistic implementation [1], supercreativity, as others prefer to call the humanmachine collaboration to cocreate new or improved value, would find a generally hostile university environment. Adapting the model developed by Daugherty and Wilson called MELDS, and incorporating aspects of a sociocultural perspective, I attempt to better understand the conditions under which supercreativity could survive and thrive within a university setting.

Mindset (Meds): Universities are large complex systems that have developed certain entrenched social processes that translate into deep-seated cultures. These

recombination and reinterpretation of data, concepts and ideas such that unique, unexpected ideas or patterns could emerge heuristically. However, the current deep learning models of smart machines rely on massive datasets that must still be labelled by humans so that the system could understand what each piece of data represents. This is what is called supervised learning that depends on humans for data labelling which is quite tedious and laborious. The data labelling can also open itself to human bias and thus compromise the quality of such learning. If deep learning models are going to be more efficient in creating value and generating useful ideas, then these models are going to require scaling-up across complex and highly diversified tasks and shift towards small datasets. For smart machines to

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

generate real value then attempts will be required to:

units (GPUs) which allows for complex computations.

necessary to grow AI into deep reasoning spaces.

lack proper salience and cannot lead to real value.

reasoning.

cocreation

64

• Find ways of training systems to function on small datasets.

• Develop means for these systems to achieve symbolic reasoning

• Develop capabilities that allow these systems to learn in an unsupervised

cars. There is still a lot of work yet to be done to achieve system's deep

The second variable in the AI growth equation entails the graphic processing

The third of such factors relates to the re-emergence of old AI computation model that makes deep learning possible. However, as indicated earlier, more effort will be required to push towards unsupervised learning, and AI computation is insufficient as new algorithms and possibly even more advanced hardware will be

With considerable effort, the combined capabilities of data, GPUs and deep learning could facilitate greater AI growth and efficiency in creating value and constant generation of useful ideas that can be translated into tangible results. Current machine capabilities require human effort to function optimally and are also still limited in terms of executing common-sense activities and improvising in order to adapt to real-life complexities. This state of affairs allow for humanmachine collaborations in generation of useful ideas and translating them into real value. In summing up this sub-section, it is important to point out that there is a tendency to limit the meaning of creativity to disrupting established patterns through reorganising, recombining and reinterpreting data, ideas and concept. While these issues form part of creativity, creativity is more than just the generation of unique or unexpected ideas. When those ideas, despite their statistical rarity, do not lead to usefulness or human conveniences (social impact) then such ideas

2.3 The collaborative potential of human-machine partnership in value

Until such time that machine learning could be unsupervised such that these systems could use raw, unlabelled small data to generate reasoning capabilities that allow machines to function optimally across broader swath of applications and in real, complex situations using even common-sense capabilities, then humanmachine collaborations will become the order of the day in value cocreation over

manner, that is, be able to use raw, unlabelled data to generate real value with little or no human effort. There are currently important pointers towards teaching systems to reason albeit in narrow applications such as in self-driving social processes and university cultures privilege certain mindsets and displace the others. Most universities subscribe to the notion of Magna Charta Universitatum that European universities have formalised in a document. The charter recognises and makes sacrosanct academic freedom and formation of professional identities. These identities form over time and are often driven by a strong scholarship and values. Some of the key academic values that shape cultures of universities subsume openness to ideas and multiple if not opposing perspectives, deep awareness of own beliefs and their limitations, a non-judgemental attitude that makes academics to be slow to judge and wait for evidence and outcomes of critical analysis, a cognitive flexibility that remains open to new possibilities as well as adaptability to newness. This academic mindset allows universities to be open systems that are presumably malleable to newness, but as Becher and Trowler states in Academic Tribes and Territories [22], professional identities can lead to narrowness, group myopia and defence in ways that could make universities inimical to external change initiatives. It is particularly important to appeal to the malleable aspect of the academic mindset and that requires working within the framework academics better understand which is that of research and rigorous theorisation. Part of what posed resistance to technology by academics was its enactment in technocratic ways that insidiously encroached on their academic practices and professional identities [21]. As a way of negotiating an academic space for supercreativity, there is a need to work on the mindset of academics through their own research and theorisation framework. In the next sub-section, I provide and elaborate on this framework as a way of providing a model for changing academic mindsets.

Leadership (meLds): University leadership has a responsibility to prepare universities for the next wave of new technologies that will alter the business of universities in very extraordinary ways in the next 5–25 years. There is an urgent need to revisit all university policies and align them to the realities of society 5.0 so universities could help communities of commerce, industry, retail, politics and ordinary local communities to adapt to society 5.0 realities or risk irrelevance which is worse than death. University leadership needs to change the entire university business plinth as expounded earlier and rally it around the joint capabilities of new intelligent technologies and human ingenuity. The time to craft a new university strategy around AI and other new technologies as well as around human ingenuity is now. Universities that remain stuck to traditional modes and business plinth may need to learn lessons of the manufacturing sector and realise that education and work will need to be reimagined in the age of society 5.0. The study that is reported in this chapter makes an extremely modest contribution to that debate. In fact, it is only scratching the surface but provides a starting point to initiate a new narrative within a university setting, one that takes the sociocultural realities of a university into account in matters of crafting smart strategies for the university. Smart strategies will have to shift focus away from traditional task-oriented operations towards

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

DOI: http://dx.doi.org/10.5772/intechopen.85054

investing heavily in human-machine collaborations and activities of

ing, connecting and correlating these data with value creation.

Skills (meldS): Society 5.0 renders traditional skills inadequate but creates new opportunities for fusion skills. While the concept of fusion skills is relatively new especially when used within the university context and requires better understanding so it could be integrated into courses, graduate attributes and form part of core curricula. Fusion skills serve as a collective concept for the effects of digital disrup-

• Smart team formation and leveraging of human-machine capabilities to create

tion, that is, these kinds of skills have the capability to fundamentally alter workflows, business models and relationships of value creation such as, in the university context, strategy, PQM, curriculum, pedagogy as well as research and scholarship. For the purpose of this chapter, fusion skills are understood as creative

• Development of smart innovation and technopreneurship

Data (melDs): Universities have always been driven by big data and have historically struggled to manage it. With new technologies such as Hadoop, storing big data has become quite a cinch. The critical issue and of relevance to this chapter is what to do with these big nuggets of textual and numerical data sourced in multiple ways and through all types of formats including sensors, RFID tags and smart monitoring most of which are either structured or unstructured. There is a need to develop some form of organising these big data. This can be organised in terms of the time or period when such big data is available which is termed 'periodic peaks'. The organising of such data could also be done in terms of relevance to a particular aspect of university business (strategy, PQM, curriculum, pedagogy, registration and censors, security including cybersecurity) and more importantly on how it helps universities to drive supercreativity, smart innovation and technopreneurship. A smart scoping review of these big data could make these data relevant to creativity, innovation and entrepreneurship through cleansing, connecting and correlating such data with cocreation of new or improved value. A smart scoping review combines the human-machine capabilities for accessing stored data, cleans-

supercreativity.

skillsets that support:

67

new or improved value propositions

Experimentation (mELds): If universities are to adapt to the realities of society 5.0, then they need to reimagine and rework its entire university plinth (strategy, PQM, curriculum, research, pedagogy, community engagement) around artificial intelligence (AI) technologies, automate repetitive lecture sessions based on known knowledge and experiments and access expertise throughout the globe in real time using advanced technologies. This adaptation to the disruptions of traditional university plinth would require reimagining the entire university business. The new university plinth could involve virtual lecturing (lecture sessions), on-time access to expertise across the globe, new modules around human-machine collaborations, and super-creativity delivered on an international platform. This international platform could use multiple accreditation mechanisms that enhance students brand (nothing wrong with a certificate that bears emblems of more than one knowledge institution preferably university-university, university-industry or universityspecialised colleges accreditations). Joint student-staff research projects on supercreativity, innovation that leverages digital simulations and supercreativitydriven entrepreneurship using multiple platforms and accessing expertise globally will become normal in society 5.0. These are hugely experimentation precincts and they require urgent adoption. However, their adoption needs to be done in ways that do not alienate academics through undermining their academic autonomy; rather a deep commitment to incentive schemes that encourage change processes in research, teaching and curriculum would most likely nudge academics to realities of society 5.0. When Research Directorates incentive grants favour joint research undertaken with students on supercreativity, innovation based on digital simulations and supercreativity-based entrepreneurship, then chances of success increase and traditional identities based on group loyalties could exponentially vitiate. When teaching grants favour the use of virtual learning, access to expertise on a global scale and in all sectors of society through the use of technologies as well as joint research projects with students, commerce, industry, retail and local communities, then positive adoption could occur.

### Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

Leadership (meLds): University leadership has a responsibility to prepare universities for the next wave of new technologies that will alter the business of universities in very extraordinary ways in the next 5–25 years. There is an urgent need to revisit all university policies and align them to the realities of society 5.0 so universities could help communities of commerce, industry, retail, politics and ordinary local communities to adapt to society 5.0 realities or risk irrelevance which is worse than death. University leadership needs to change the entire university business plinth as expounded earlier and rally it around the joint capabilities of new intelligent technologies and human ingenuity. The time to craft a new university strategy around AI and other new technologies as well as around human ingenuity is now. Universities that remain stuck to traditional modes and business plinth may need to learn lessons of the manufacturing sector and realise that education and work will need to be reimagined in the age of society 5.0. The study that is reported in this chapter makes an extremely modest contribution to that debate. In fact, it is only scratching the surface but provides a starting point to initiate a new narrative within a university setting, one that takes the sociocultural realities of a university into account in matters of crafting smart strategies for the university. Smart strategies will have to shift focus away from traditional task-oriented operations towards investing heavily in human-machine collaborations and activities of supercreativity.

Data (melDs): Universities have always been driven by big data and have historically struggled to manage it. With new technologies such as Hadoop, storing big data has become quite a cinch. The critical issue and of relevance to this chapter is what to do with these big nuggets of textual and numerical data sourced in multiple ways and through all types of formats including sensors, RFID tags and smart monitoring most of which are either structured or unstructured. There is a need to develop some form of organising these big data. This can be organised in terms of the time or period when such big data is available which is termed 'periodic peaks'. The organising of such data could also be done in terms of relevance to a particular aspect of university business (strategy, PQM, curriculum, pedagogy, registration and censors, security including cybersecurity) and more importantly on how it helps universities to drive supercreativity, smart innovation and technopreneurship. A smart scoping review of these big data could make these data relevant to creativity, innovation and entrepreneurship through cleansing, connecting and correlating such data with cocreation of new or improved value. A smart scoping review combines the human-machine capabilities for accessing stored data, cleansing, connecting and correlating these data with value creation.

Skills (meldS): Society 5.0 renders traditional skills inadequate but creates new opportunities for fusion skills. While the concept of fusion skills is relatively new especially when used within the university context and requires better understanding so it could be integrated into courses, graduate attributes and form part of core curricula. Fusion skills serve as a collective concept for the effects of digital disruption, that is, these kinds of skills have the capability to fundamentally alter workflows, business models and relationships of value creation such as, in the university context, strategy, PQM, curriculum, pedagogy as well as research and scholarship. For the purpose of this chapter, fusion skills are understood as creative skillsets that support:


social processes and university cultures privilege certain mindsets and displace the others. Most universities subscribe to the notion of Magna Charta Universitatum that European universities have formalised in a document. The charter recognises and makes sacrosanct academic freedom and formation of professional identities. These identities form over time and are often driven by a strong scholarship and values.

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

subsume openness to ideas and multiple if not opposing perspectives, deep awareness of own beliefs and their limitations, a non-judgemental attitude that makes academics to be slow to judge and wait for evidence and outcomes of critical analysis, a cognitive flexibility that remains open to new possibilities as well as adaptability to newness. This academic mindset allows universities to be open systems that are presumably malleable to newness, but as Becher and Trowler states in

narrowness, group myopia and defence in ways that could make universities inimical to external change initiatives. It is particularly important to appeal to the malleable aspect of the academic mindset and that requires working within the framework academics better understand which is that of research and rigorous theorisation. Part of what posed resistance to technology by academics was its enactment in technocratic ways that insidiously encroached on their academic practices and professional identities [21]. As a way of negotiating an academic space for supercreativity, there is

Experimentation (mELds): If universities are to adapt to the realities of society 5.0, then they need to reimagine and rework its entire university plinth (strategy, PQM, curriculum, research, pedagogy, community engagement) around artificial intelligence (AI) technologies, automate repetitive lecture sessions based on known knowledge and experiments and access expertise throughout the globe in real time using advanced technologies. This adaptation to the disruptions of traditional university plinth would require reimagining the entire university business. The new university plinth could involve virtual lecturing (lecture sessions), on-time access to expertise across the globe, new modules around human-machine collaborations, and super-creativity delivered on an international platform. This international platform could use multiple accreditation mechanisms that enhance students brand (nothing wrong with a certificate that bears emblems of more than one knowledge institution preferably university-university, university-industry or universityspecialised colleges accreditations). Joint student-staff research projects on supercreativity, innovation that leverages digital simulations and supercreativitydriven entrepreneurship using multiple platforms and accessing expertise globally will become normal in society 5.0. These are hugely experimentation precincts and they require urgent adoption. However, their adoption needs to be done in ways that do not alienate academics through undermining their academic autonomy; rather a deep commitment to incentive schemes that encourage change processes in research, teaching and curriculum would most likely nudge academics to realities of society 5.0. When Research Directorates incentive grants favour joint research undertaken with students on supercreativity, innovation based on digital simulations and supercreativity-based entrepreneurship, then chances of success increase and traditional identities based on group loyalties could exponentially vitiate. When teaching grants favour the use of virtual learning, access to expertise on a global scale and in all sectors of society through the use of technologies as well as joint research projects with students, commerce, industry, retail and local communities,

Some of the key academic values that shape cultures of universities

Academic Tribes and Territories [22], professional identities can lead to

a need to work on the mindset of academics through their own research and theorisation framework. In the next sub-section, I provide and elaborate on this framework as a way of providing a model for changing academic mindsets.

then positive adoption could occur.

66

• Broad and smart collaborations that disrupt traditional modes of partnerships in the classroom that is based on in-house expertise towards smart collaborations with experts all over the globe and machine-based expertise (Siri, Bixby, Google Assistant and videoconferencing to state the easy-to-access intelligent technologies)

done by and is available in the 2017 Clarivate Analytics Report. All these three universities are research-intensive. The Directors of Innovation Hubs, Technology Transfer units or similar units dealing with innovation within these universities were interviewed. All in all, 6 Directors were interviewed totalling 18 research

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

Digital artificial intelligence (AI) assistant tools: Given that the main purpose of the experiment was to better understand the conditions under which humans and machines could interact and possibly collaborate in the creation of new value propositions, I sought a more advanced but simple digital AI assistant tool that is easily available and easy to use. The digital AI tool could be available on any computer or mobile devices. Three such latest and smartest AI tools that facilitate human-machine interactions are Google Assistant, Apple Siri and Samsung Bixby. The Google Assistant has proved to be the most advanced tool in this area of

• Showing photos and diagrams that are taken within few weeks and within specific locations. This capability can facilitate human-machine collaboration in generation of ideas especially when baseline graphic information is required

• Providing instance (and contextual) answers from the web and is capable of responding more specifically to most questions posed to it. This ability comes handy in terms of saving time for humans in searching for answers in huge swathes of information. It is a technological ability that could assist humans in identifying different categories of information and ideas and help with sound judgement. It also helps humans on scoping reviews of existing ideas, concepts, products and services given that creativity is about finding ideas and concepts

• Developing a memory of information and knowledge that you have previously searched. It makes it possible to ask follow-up questions and receive sensible

• Reading poetry, telling a joke and translating foreign phrases. The activity of generating new or improved ideas requires multiple perspectives and

this technological capability brings these possibilities to the fore.

• Handling complete conversations with its users, and its protocols and

• Collecting feedback and comments on collaborated creative designs.

combinations, recombinations and reorganisation as well as reinterpretation of information, ideas, concepts, art and so on and in whatever language as such

heuristics are open to third party developers which allows for own application which can be deployed to the Google Assistant across the globe and thus making collaboration on such a scale possible. It can thus be used to create diverse teams that can collaborate on the same Google Assistant mock-ups.

Its Botsociety API allows changes in the design and multiple iterations such that a design could be refined and be ready for prototyping via digital

Based on these benefits, Google Assistant was preferred and selected over Siri

participants.

technology. It is capable of:

to trigger idea generation.

DOI: http://dx.doi.org/10.5772/intechopen.85054

with statistical rarity.

responses.

simulations.

69

and Bixby in the experiment.

### 4. The research design

### 4.1 Context and purpose of study

This study was undertaken within two different geographical contexts—Scandinavia and South Africa—in order to make a comparative case of how creativity and innovation are handled in these spaces. As earlier stated, universities feel the urge to protect academic autonomy and professional identities, and if these two factors are ignored, then mindset shifts towards society 5.0 could be significantly delayed. In Scandinavia, attempts are made to drive creativity and innovation from the national government level through setting their agenda and strong financing, yet these activities remain on the margins of the core university activities because the National Innovation Strategy did not take into account the sociocultural aspects of the universities. Given that society 5.0 somehow demands that universities ought to embrace and leverage more fervently and passionately the benefits of combined capabilities of intelligent technologies as made possible by artificial intelligence (AI) advances and human ingenuity, then a more measured approach, that is, one that accounts for the entrenched institutional culture is most likely to help universities to ease into society 5.0. South African universities have an added burden of resolving social ills of poverty, unemployment and inequality, yet they have mostly and obdurately sought to emulate strategies, PQMs, curricula, pedagogy and research of developed countries as they chase the mirage of top rankings. The very notion that universities are ranked on the basis of research outputs with inadequate additional indicators on impact of such research on society and its future prospects is problematic. These extra indicators in league tables could help nudge universities into society 5.0. In both contexts, there is a strong call for universities to drive creativity and innovation, yet these activities remain largely outside the core university plinth. Annual reports of these universities paint a clearer picture of this general marginalisation. The purpose of the study was thus to understand better the conditions under which those that drive innovation and entrepreneurship in universities operate and how supercreativity could possibly negotiate a space in these complex university spaces and help drag universities into society 5.0.

### 4.2 Sampling and selection

Universities: Five Scandinavian universities were selected via an exponential nondiscriminatory snowballing technique. I linked up with my network in one of the Swedish universities who arranged that I become a guest researcher in their Centre for Engineering Education for 3 months. He also arranged the first interview with the Deputy-Dean for Innovation and Collaborations who then pointed me to the Directors in Innovation Hubs and Centres for Entrepreneurship. These Directors, in turn, suggested names of Directors of other universities. I was able to interview 13 of these Directors from five different Scandinavian universities.

The three South African universities were selected on the basis that they were considered as the top three innovative universities in South Africa. This ranking was

### Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

done by and is available in the 2017 Clarivate Analytics Report. All these three universities are research-intensive. The Directors of Innovation Hubs, Technology Transfer units or similar units dealing with innovation within these universities were interviewed. All in all, 6 Directors were interviewed totalling 18 research participants.

Digital artificial intelligence (AI) assistant tools: Given that the main purpose of the experiment was to better understand the conditions under which humans and machines could interact and possibly collaborate in the creation of new value propositions, I sought a more advanced but simple digital AI assistant tool that is easily available and easy to use. The digital AI tool could be available on any computer or mobile devices. Three such latest and smartest AI tools that facilitate human-machine interactions are Google Assistant, Apple Siri and Samsung Bixby. The Google Assistant has proved to be the most advanced tool in this area of technology. It is capable of:


Based on these benefits, Google Assistant was preferred and selected over Siri and Bixby in the experiment.

• Broad and smart collaborations that disrupt traditional modes of partnerships

collaborations with experts all over the globe and machine-based expertise (Siri, Bixby, Google Assistant and videoconferencing to state the easy-to-access

This study was undertaken within two different geographical contexts—Scandinavia and South Africa—in order to make a comparative case of how creativity and innovation are handled in these spaces. As earlier stated, universities feel the urge to protect academic autonomy and professional identities, and if these two factors are ignored, then mindset shifts towards society 5.0 could be significantly delayed. In Scandinavia, attempts are made to drive creativity and innovation from the national government level through setting their agenda and strong financing, yet these activities remain on the margins of the core university activities because the National Innovation Strategy did not take into account the sociocultural aspects of the universities. Given that society 5.0 somehow demands that universities ought to embrace and leverage more fervently and passionately the benefits of combined capabilities of intelligent technologies as made possible by artificial intelligence (AI) advances and human ingenuity, then a more measured approach, that is, one that accounts for the entrenched institutional culture is most likely to help universities to ease into society 5.0. South African universities have an added burden of resolving social ills of poverty, unemployment and inequality, yet they have mostly and obdurately sought to emulate strategies, PQMs, curricula, pedagogy and research of developed countries as they chase the mirage of top rankings. The very notion that universities are ranked on the basis of research outputs with inadequate additional indicators on impact of such research on society and its future prospects is problematic. These extra indicators in league tables could help nudge universities into society 5.0. In both contexts, there is a strong call for universities to drive creativity and innovation, yet these activities remain largely outside the core university plinth.

Annual reports of these universities paint a clearer picture of this general

university spaces and help drag universities into society 5.0.

of these Directors from five different Scandinavian universities.

4.2 Sampling and selection

68

marginalisation. The purpose of the study was thus to understand better the conditions under which those that drive innovation and entrepreneurship in universities operate and how supercreativity could possibly negotiate a space in these complex

Universities: Five Scandinavian universities were selected via an exponential nondiscriminatory snowballing technique. I linked up with my network in one of the Swedish universities who arranged that I become a guest researcher in their Centre for Engineering Education for 3 months. He also arranged the first interview with the Deputy-Dean for Innovation and Collaborations who then pointed me to the Directors in Innovation Hubs and Centres for Entrepreneurship. These Directors, in turn, suggested names of Directors of other universities. I was able to interview 13

The three South African universities were selected on the basis that they were considered as the top three innovative universities in South Africa. This ranking was

in the classroom that is based on in-house expertise towards smart

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

intelligent technologies)

4.1 Context and purpose of study

4. The research design

### 4.3 Research methods

### 4.3.1 Interviews

Eighteen Directors of innovation hubs and entrepreneurship centres from Scandinavian universities and three from South African universities were interviewed on:

• Situatedness of their entities within the university, that is, whether they formed part of the strategic core of the university or remained on the margins

5. Findings and explanations

DOI: http://dx.doi.org/10.5772/intechopen.85054

space in faculties:

innovation'.

The Scandinavian Directors indicated that governments mainly drove innova-

'some of our entrepreneurship programmes tend to be removed in preference of more traditional courses…but we keep trying to secure room for our programmes'. There is a general acceptance of the digital tools mainly as quiescent platforms

The Directors also share the view that if creativity is generally marginalised on the core undergraduate curriculum, then human-machine creativity may struggle even more to find space. These Directors also indicated that Swedish universities are co-signatories of the Magna Charta Universitatum that defends academic freedom, and thus external change efforts may struggle to gain traction under these entrenched university cultures. Innovation Hubs tend to mostly use the NABC model to determine the ideas pitch and provide little training in terms of generation of novel ideas. In South Africa, the positioning of innovation units similar to Scandinavia remains generally outside the core university units and serves as support structures rather than as core academic activity. Entrepreneurship is mostly located

The results of the experiment that was undertaken with students, while preliminary and quite precarious, suggest the following team formation framework as also

There is a strong view coming from the research participants via interviews that

• Developing a common understanding as human teams prior to engaging with Google Assistant. The main challenge here was that teams were formed in terms of their diversity, that is, in relation to the courses they were doing rather than on the projects they were currently running. For example, a team would consist of advanced engineering undergraduate student, a computer science student, an HR student and a humanities student. Only two teams were

kept homogeneous and with their current project. The students believe developing a common ground on what to explore as a new or improved idea while trying to harness diverse knowledge bases require more time. There are just too many variables to manage, and this is seen as counterproductive from the perspectives of the research participants, 'we spent more time arguing over what our project should involves (sic) and how we could use Google Assistant to help us'. While this is seen as a constraint, it is equally an important aspect of

becoming creative, and while it was a major source of frustration, it

tion and entrepreneurship within universities through generous funding that includes fully furnished offices, salaries and small seed-funds. They also confirmed that critical and creative thinking were not explicitly taught within the core university curriculum and even within their units. Centres for Entrepreneurship offered both contact and online formal entrepreneurship programmes up to doctoral degree but their undergraduate entrepreneurship programmes have tended to struggle for

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

for online offerings, desktop research and as part of university operations: 'we have really not started to appreciate the huge potential of AI in entrepreneurship as an ally and to come to your question, we have not even started to explore the human-machine collaboration in driving creativity and

in Business Schools of these three South African universities.

they needed to be properly prepared for the activity in terms of:

extrapolated from Costa and McConnell study [4]: Emerging contours of smart team formation:

1. Pre-connectivity

71


Students that participated in the experiment were also interviewed with focus on:


### 4.3.2 Quasi-experimentation

Four teams of mostly advanced undergraduate students were involved in this project pulled from a database of students who have already submitted their innovation projects to the university for possible assistance. Projects included the use of waste to produce electricity and web application development for selling second-hand books, an application for Smart Logistics. Teams had a simple task of using Google Assistant to scope the statistical rarity of their project idea, that is, whether no or very few people or businesses have already set up such a product or service. There had to be clear evidence of such statistical rarity. They also had to be clear about the need they are creating and attempting to meet and use Google Assistant to filter and analyse multiple nuggets of information pulled from Google Assistant. Google Assistant is capable of building a memory and history of number of times visited on a similar topic, periodic peaks as well as trajectory and nature of visits. Teams also had to demonstrate how Google Assistant helped them shape their approach in creating and meeting a need including competitors (statistical rarity of idea/approach) and possible benefits that will accrue to the customers/ target market. Observation of teams was done with two research assistants and we compared notes. These research assistants are postgraduate students in IT and programming. They helped retrieve evidence on the interaction of students with Google Assistant.

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

### 5. Findings and explanations

4.3 Research methods

Eighteen Directors of innovation hubs and entrepreneurship centres from Scandinavian universities and three from South African universities were

they formed part of the strategic core of the university or remained on the

• Whether critical and creative thought was explicitly taught within the core

artificial intelligence (AI) capabilities and contribute in shaping society 5.0 and its 15 forces of disruption [5] and factors inimical to university or entity

1. Their expectation on interacting with Google Assistant and whether they have interacted with any of these AI technologies before such as Siri, Bixby or any

2. How they were coping with interacting with Google Assistant (ease of use, confidence, helpfulness in finding answers, reliability of answers, what can be

Four teams of mostly advanced undergraduate students were involved in this

done to optimise the interaction) during and after the experiment

project pulled from a database of students who have already submitted their innovation projects to the university for possible assistance. Projects included the use of waste to produce electricity and web application development for selling second-hand books, an application for Smart Logistics. Teams had a simple task of using Google Assistant to scope the statistical rarity of their project idea, that is, whether no or very few people or businesses have already set up such a product or service. There had to be clear evidence of such statistical rarity. They also had to be clear about the need they are creating and attempting to meet and use Google Assistant to filter and analyse multiple nuggets of information pulled from Google Assistant. Google Assistant is capable of building a memory and history of number of times visited on a similar topic, periodic peaks as well as trajectory and nature of visits. Teams also had to demonstrate how Google Assistant helped them shape their approach in creating and meeting a need including competitors (statistical rarity of idea/approach) and possible benefits that will accrue to the customers/ target market. Observation of teams was done with two research assistants and we compared notes. These research assistants are postgraduate students in IT and programming. They helped retrieve evidence on the interaction of students with

• Situatedness of their entities within the university, that is, whether

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

• The state of readiness for their entities and universities to embrace

Students that participated in the experiment were also interviewed with

university curriculum or in their entities

advanced intelligent technologies

4.3.2 Quasi-experimentation

Google Assistant.

70

4.3.1 Interviews

interviewed on:

margins

readiness

focus on:

The Scandinavian Directors indicated that governments mainly drove innovation and entrepreneurship within universities through generous funding that includes fully furnished offices, salaries and small seed-funds. They also confirmed that critical and creative thinking were not explicitly taught within the core university curriculum and even within their units. Centres for Entrepreneurship offered both contact and online formal entrepreneurship programmes up to doctoral degree but their undergraduate entrepreneurship programmes have tended to struggle for space in faculties:

'some of our entrepreneurship programmes tend to be removed in preference of more traditional courses…but we keep trying to secure room for our programmes'.

There is a general acceptance of the digital tools mainly as quiescent platforms for online offerings, desktop research and as part of university operations:

'we have really not started to appreciate the huge potential of AI in entrepreneurship as an ally and to come to your question, we have not even started to explore the human-machine collaboration in driving creativity and innovation'.

The Directors also share the view that if creativity is generally marginalised on the core undergraduate curriculum, then human-machine creativity may struggle even more to find space. These Directors also indicated that Swedish universities are co-signatories of the Magna Charta Universitatum that defends academic freedom, and thus external change efforts may struggle to gain traction under these entrenched university cultures. Innovation Hubs tend to mostly use the NABC model to determine the ideas pitch and provide little training in terms of generation of novel ideas. In South Africa, the positioning of innovation units similar to Scandinavia remains generally outside the core university units and serves as support structures rather than as core academic activity. Entrepreneurship is mostly located in Business Schools of these three South African universities.

The results of the experiment that was undertaken with students, while preliminary and quite precarious, suggest the following team formation framework as also extrapolated from Costa and McConnell study [4]:

Emerging contours of smart team formation:

### 1. Pre-connectivity

There is a strong view coming from the research participants via interviews that they needed to be properly prepared for the activity in terms of:

• Developing a common understanding as human teams prior to engaging with Google Assistant. The main challenge here was that teams were formed in terms of their diversity, that is, in relation to the courses they were doing rather than on the projects they were currently running. For example, a team would consist of advanced engineering undergraduate student, a computer science student, an HR student and a humanities student. Only two teams were kept homogeneous and with their current project. The students believe developing a common ground on what to explore as a new or improved idea while trying to harness diverse knowledge bases require more time. There are just too many variables to manage, and this is seen as counterproductive from the perspectives of the research participants, 'we spent more time arguing over what our project should involves (sic) and how we could use Google Assistant to help us'. While this is seen as a constraint, it is equally an important aspect of becoming creative, and while it was a major source of frustration, it

demonstrated the difficulty research participants have in shifting their mindsets that was the primary purpose of the experiment. The homogeneous teams seemed to have already overcome some of these initial team challenges. technologies than those who are using them for the first time. More

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

eliminated.

5. Maturing

allies in creative problem-solving.

6.Deep learning

73

4.Intense interactivity

DOI: http://dx.doi.org/10.5772/intechopen.85054

experimentation will provide evidence of whether this stage is necessary or can be

Following from the previous stage, the degree of interaction with Google Assistant increased substantially once its potential benefits in helping teams to work on their projects increased. As stated earlier, research participants who have beyond level 1 proficiency in working with these technologies would most likely show intense

understanding how the technology works to being able to interact and possibly even collaborate with it as efforts of cocreation of value increase. There is also a need to conduct a rigorous experimentation to determine whether this stage can be retained. There are strong indications that this stage can survive the rigour of research.

Similar to the norming stage of Tuckman's Stages of Team Development, this

stage appeared to focus teams towards the project, and the specific pieces of information and knowledge that teams required from Google Assistant tended to be more targeted to specific aspects of the project. For teams that had no clear project, this stage tended to narrow down areas that might be pursued as possible projects. For teams that started off with a clear project, this stage deals with pieces of information and knowledge that progress resolution of some aspects of the project. It is also important to note that proficiency in use of these technologies provides a basis of how teams mature into real interactions with intelligent technologies as

The human-machine collaboration will work even better when intelligent technologies move away from hard-coded knowledge and can extract patterns from raw data which means functioning in an unsupervised way. This is what is called machine-learning capability because it allows for tackling problems that entail knowledge of the real world which is informal, intuitive and subjective. Creative problem-solving goes beyond formal knowledge (known, established) and includes intersubjective knowledge that can be contextual and unique to certain people. Deep learning would require intelligent machines that can transform such input data that can be esoteric and informal into a slightly more abstract and composite representation in ways that could lead to the development of concepts hierarchies. Given that humans rely mostly on informal and intersubjective knowledge in problem-solving, the point of deep learning with smart machines would most likely occur at the intersection where smart machines can reason about statements in the informal, subjective language as humans bring their own informal knowledge into the equation. This will be the point where humans and smart machines generate intersubjective knowledge that allows for reimagination, which is a crucial element of creativity. Currently, smart machines can reason using logical inference rules or on the basis of the knowledge base approach which relies on formal knowledge; hence such learning is considered superficial because it is based on known,

established knowledge. Deep learning, as an important stage in team development, would be achieved and observed, I posit, when the human-machine collaboration leads to reimagination. Value is often cocreated when things are reimagined. This is probably the most important stage of human-machine team development. As AI

interaction with these technologies to a point where learning goes beyond

• Most heterogeneous teams failed to go through the first stage of the experiment, that is, agreeing on the project. Two of the teams were from engineering, and we kept it as intact because they were already working on some innovation projects that focused on turning organic waste into energy. The projects of these two teams had already advanced to how the waste bins in restaurants and hotels could be developed to become the first stage of transforming waste, that is, mixing this waste and how urine could be harnessed to generate energy. Access to Google Assistant helped them gain knowledge such as waste mixing for energy yield which takes almost 30 days to be ready for the next stage of transformation, and Google Assistant also pointed the team to some relevant videos.

### 2. Connecting/connectivity

There are obvious challenges when teams attempt to link up with intelligent technologies such as Google Assistant. These challenges appear to develop into a typology of apprehension, doubt and cynicism when teams have not resolved what their project is about and how the intelligent technologies could help the team to shape the project. Teams that have a clear project tend to embrace the interaction with intelligent technologies better than those with a vague project and demonstrate less apprehension, less doubt about the efficiency of the team-Google Assistant interactions. Given that teams actually connected with Google Assistant in computer centres that all students used meant that each team member would have to use earphones so they could not disturb other students. There was no attempt, at this stage, to develop virtual teams that could enable cross interaction between human teams and human-machines teams within the digital space which meant that human-machine interaction occurred at an individual level. Team members then met to share information generated via interactions with Google Assistant. This led to some early superficial learning, more like sharing notes. Team members still had to agree on which information to pursue further with Google Assistant. Given the limited time I had for this experiment and indeed the experimentation is ongoing, the next stages of team development are really hypothetical and will still undergo rigorous experimentation including the identified preliminary stages of team development. My informed conjecture is that beyond the connectivity stage, early superficial learning will follow.

### 3. Early superficial learning

Once teams recognise the value of intelligent technologies such as Google Assistant in their projects, an exploration of what such smart machines can offer tended to follow. At this stage, learning is more focused on testing the potential and limits of the smart machine. This learning is superficial because it does not contribute directly to resolving issues in the project. Few of our research participants have not been actively using any of these intelligent technologies which might explain this exploration. There is a need to determine whether this stage may not be redundant under conditions where students have a regular use of Siri, Bixby and Google Assistant. The additional condition could be to determine the level of proficiency in using these technologies. Those research participants that have gone beyond level 1 of these technologies would most likely have a better use of these

technologies than those who are using them for the first time. More experimentation will provide evidence of whether this stage is necessary or can be eliminated.

### 4.Intense interactivity

demonstrated the difficulty research participants have in shifting their mindsets that was the primary purpose of the experiment. The homogeneous teams seemed to have already overcome some of these initial team challenges.

• Most heterogeneous teams failed to go through the first stage of the experiment, that is, agreeing on the project. Two of the teams were from engineering, and we kept it as intact because they were already working on some innovation projects that focused on turning organic waste into energy. The projects of these two teams had already advanced to how the waste bins in

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

restaurants and hotels could be developed to become the first stage of transforming waste, that is, mixing this waste and how urine could be harnessed to generate energy. Access to Google Assistant helped them gain knowledge such as waste mixing for energy yield which takes almost 30 days to be ready for the next stage of transformation, and Google Assistant also

There are obvious challenges when teams attempt to link up with intelligent technologies such as Google Assistant. These challenges appear to develop into a typology of apprehension, doubt and cynicism when teams have not resolved what their project is about and how the intelligent technologies could help the team to shape the project. Teams that have a clear project tend to embrace the interaction with intelligent technologies better than those with a vague project and demonstrate less apprehension, less doubt about the efficiency of the team-Google Assistant interactions. Given that teams actually connected with Google Assistant in

computer centres that all students used meant that each team member would have to use earphones so they could not disturb other students. There was no attempt, at this stage, to develop virtual teams that could enable cross interaction between human teams and human-machines teams within the digital space which meant that human-machine interaction occurred at an individual level. Team members then met to share information generated via interactions with Google Assistant. This led to some early superficial learning, more like sharing notes. Team members still had to agree on which information to pursue further with Google Assistant. Given the limited time I had for this experiment and indeed the experimentation is ongoing, the next stages of team development are really hypothetical and will still undergo rigorous experimentation including the identified preliminary stages of team development. My informed conjecture is that beyond the connectivity stage, early

Once teams recognise the value of intelligent technologies such as Google Assistant in their projects, an exploration of what such smart machines can offer tended to follow. At this stage, learning is more focused on testing the potential and

participants have not been actively using any of these intelligent technologies which might explain this exploration. There is a need to determine whether this stage may not be redundant under conditions where students have a regular use of Siri, Bixby and Google Assistant. The additional condition could be to determine the level of proficiency in using these technologies. Those research participants that have gone beyond level 1 of these technologies would most likely have a better use of these

limits of the smart machine. This learning is superficial because it does not contribute directly to resolving issues in the project. Few of our research

pointed the team to some relevant videos.

2. Connecting/connectivity

superficial learning will follow.

72

3. Early superficial learning

Following from the previous stage, the degree of interaction with Google Assistant increased substantially once its potential benefits in helping teams to work on their projects increased. As stated earlier, research participants who have beyond level 1 proficiency in working with these technologies would most likely show intense interaction with these technologies to a point where learning goes beyond understanding how the technology works to being able to interact and possibly even collaborate with it as efforts of cocreation of value increase. There is also a need to conduct a rigorous experimentation to determine whether this stage can be retained. There are strong indications that this stage can survive the rigour of research.

### 5. Maturing

Similar to the norming stage of Tuckman's Stages of Team Development, this stage appeared to focus teams towards the project, and the specific pieces of information and knowledge that teams required from Google Assistant tended to be more targeted to specific aspects of the project. For teams that had no clear project, this stage tended to narrow down areas that might be pursued as possible projects. For teams that started off with a clear project, this stage deals with pieces of information and knowledge that progress resolution of some aspects of the project. It is also important to note that proficiency in use of these technologies provides a basis of how teams mature into real interactions with intelligent technologies as allies in creative problem-solving.

### 6.Deep learning

The human-machine collaboration will work even better when intelligent technologies move away from hard-coded knowledge and can extract patterns from raw data which means functioning in an unsupervised way. This is what is called machine-learning capability because it allows for tackling problems that entail knowledge of the real world which is informal, intuitive and subjective. Creative problem-solving goes beyond formal knowledge (known, established) and includes intersubjective knowledge that can be contextual and unique to certain people. Deep learning would require intelligent machines that can transform such input data that can be esoteric and informal into a slightly more abstract and composite representation in ways that could lead to the development of concepts hierarchies. Given that humans rely mostly on informal and intersubjective knowledge in problem-solving, the point of deep learning with smart machines would most likely occur at the intersection where smart machines can reason about statements in the informal, subjective language as humans bring their own informal knowledge into the equation. This will be the point where humans and smart machines generate intersubjective knowledge that allows for reimagination, which is a crucial element of creativity. Currently, smart machines can reason using logical inference rules or on the basis of the knowledge base approach which relies on formal knowledge; hence such learning is considered superficial because it is based on known, established knowledge. Deep learning, as an important stage in team development, would be achieved and observed, I posit, when the human-machine collaboration leads to reimagination. Value is often cocreated when things are reimagined. This is probably the most important stage of human-machine team development. As AI

capabilities develop to make it possible for smart machines to function unsupervised, that is, on the basis of processing raw data and in similar fashion as humans who rely on informal, subjective knowledge and knowledge developed, over time, with others in particular contexts (intersubjective) to resolve problems, then deep learning that lead to creativity will be possible. Creativity is not merely about creating concepts hierarchies, frameworks and models for understanding reality. It is not also about making some ontological commitments rather is about reimagining and altering naturalistic and authentic contexts. It is about trying out things through multiple iterations, testing and refining. It is even about defying logic as its primary purpose is to seek pragmatic solutions to real problems and impact societal practices in ways that advance human conveniences. It requires smart machines with deep learning capabilities not as currently understood but as yet to be imagined.

### 7. Resolution

Given that the possibilities of a real deep learning between humans and smart machines remain constraint, this stage of team development remains imagined.

### 6. Recommendations and future direction of research

Given that university cultures remain rooted in practices and activities that are task-oriented and output-driven, investment on human and machine thinking would remain a major challenge. This challenge is exacerbated by the general marginalisation of creativity in the university plinth. It is thus suggested that more research be conducted which illuminate the potential benefits of mainstreaming human-human and human-machine creativity. More experiments with more advanced intelligent technologies would help shape the team development stages suggested in this chapter. The following areas of research are worthy of consideration:


Author details

Vaal University of Technology, Vanderbijlpark, South Africa

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

DOI: http://dx.doi.org/10.5772/intechopen.85054

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: biki@vut.ac.za

provided the original work is properly cited.

Teboho Pitso

75


### Acknowledgements

The role and contribution of the Research Directorate at Vaal University of Technology particularly Dr. Speech Nelana and Ms. Chantelle Sonnekus in funding my Scandinavian research visit and pay for this platform are truly appreciated. The author would like to thank Ms. Gape Motswana in the Office of the Vice-Chancellor and Principal, Professor Gordon Zide, for both adding the magic touch to this scholarly journey.

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

### Author details

capabilities develop to make it possible for smart machines to function

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

yet to be imagined.

7. Resolution

consideration:

machine collaborations

realities of society 5.0

4.University investment on advanced IT and AI

formation stages

Acknowledgements

scholarly journey.

74

unsupervised, that is, on the basis of processing raw data and in similar fashion as humans who rely on informal, subjective knowledge and knowledge developed, over time, with others in particular contexts (intersubjective) to resolve problems, then deep learning that lead to creativity will be possible. Creativity is not merely about creating concepts hierarchies, frameworks and models for understanding reality. It is not also about making some ontological commitments rather is about reimagining and altering naturalistic and authentic contexts. It is about trying out things through multiple iterations, testing and refining. It is even about defying logic as its primary purpose is to seek pragmatic solutions to real problems and impact societal practices in ways that advance human conveniences. It requires smart machines with deep learning capabilities not as currently understood but as

Given that the possibilities of a real deep learning between humans and smart machines remain constraint, this stage of team development remains imagined.

Given that university cultures remain rooted in practices and activities that are task-oriented and output-driven, investment on human and machine thinking would remain a major challenge. This challenge is exacerbated by the general marginalisation of creativity in the university plinth. It is thus suggested that more research be conducted which illuminate the potential benefits of mainstreaming human-human and human-machine creativity. More experiments with more advanced intelligent technologies would help shape the team development stages suggested in this chapter. The following areas of research are worthy of

1. More research on university cultures and their relationship with the

development of staff and student creativity within the framework of human -

2. University management mindset shift towards AI and appreciating the future

3.More experiments to clarify and possibly refine the human-machine team

The role and contribution of the Research Directorate at Vaal University of Technology particularly Dr. Speech Nelana and Ms. Chantelle Sonnekus in funding my Scandinavian research visit and pay for this platform are truly appreciated. The author would like to thank Ms. Gape Motswana in the Office of the Vice-Chancellor and Principal, Professor Gordon Zide, for both adding the magic touch to this

6. Recommendations and future direction of research

Teboho Pitso Vaal University of Technology, Vanderbijlpark, South Africa

\*Address all correspondence to: biki@vut.ac.za

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### References

[1] Torgilsson P. A critical reflection on the hegemony of technology and possibilities of including ethics in public consumption [Master's degree dissertation]. Lund, Sweden: Lund University; 2015

[2] Kuzmin O, Pyrog O, Melnik L. Transformation of development model of national economies as conditions of postindustrial society. ECONTECHMOD: An International Quarterly Journal. 2014; 3(2):41-45

[3] Tuckman B. Developmental sequence in small groups. Psychological Bulletin. 1965;63:384-399

[4] Crosta L, McConnell D. Challenging the traditional theorisation on group development. An international online perspective. In: The 7th International Conference on Networked Learning. 2010. pp. 3-4

[5] Freedy A, McDonough J, Freedy E, Jacobs R, Thagels S, Weltman G. A Mixed Initiative Team Performance Management Assessment System (MITPMA) for use in Training and Operational Environments. SBIR, Phase 1 Report, Constract No. N61339-04-c-0020, Perceptronics Solutions. 2004

[6] Karvonius V. National Innovation Policies in Finland. Helsinki, Finland: Ministry of Employment and the Economy Press; 2013

[7] Minter D, Storkey C. Futureproof: How to Get Ready for the Next Disruption. New York: Pearson; 2017

[8] Dweck C. Mindset: The New Psychology of Success. New York: Ballantine Books; 2007

[9] Acemoglu D, Restrepo P. Robots and Jobs: Evidence from US Labor Markets. NBER Working Paper No. w23285. 2017. Available from SSRN: https://ssrn.com

[10] Harvey L. Transitions from Higher Education to Work 2003. Available from: http://www.shu.ac.uk/research/c re/publications

simulated multi-tasking environment.

DOI: http://dx.doi.org/10.5772/intechopen.85054

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines…

Ergonom. 2009;52(8):907-920

automation: Implications for understanding autonomy in future systems. Human Factors. 2016;58(3):

377-400

Lausanne; 1996

Press; 2011

77

[20] Kristin E, Chen J, Szalman J, Hancock P. A meta-analysis of factors influencing the development of trust in

[21] Pu P, Lalanne D. Human and Machine Collaboration in Creative Design: Technical Report. Switzerland: Ecole Polytechnique Federale de

[22] Becher T, Trowler P. Academic Tribes and Territories. Buckingham, UK: The Society for Research into Higher Education and Open University

[11] Salomon G, Perkins D, Globerson T. Partners in cognition: Extending human intelligence with intelligent technologies. Educational Research. 1990;20(3):2-9

[12] Mandinach E. Model-building and the use of computer simulation of dynamic systems. Journal of Educational Computing Research. 1989;5:221-243

[13] Mitchel W. Introduction: A new agenda for computer-aided design in the electronic design studio. In: Mitchell W, McColough C, editors. The Electronic Design Studio. Cambridge: MIT Press; 1990

[14] Pea R. Distributed intelligence and education. In: Paper Presented at the Annual Meeting of the Social Science Research Council on Computers and Learning, British Virgin Islands. 1989

[15] Papert S. Computer criticism vs. technocentric thinking. Educational Research. 1987;17(1):22-30

[16] Loof A. The Swedish Innovation Strategy. Stockholm, Sweden: Ministry of Enterprise; 2017

[17] OECD. OECD Reviews of Innovation Policy, Norway. Norway: OECD Press; 2017

[18] Xu A, Dudek G. Online Probabilistic Trust Inferences Model for Asymmetric Human Robot Collaborations. 2007. DOI: 10.1145/26964542896429

[19] Chen J, Terrence P. Effects of imperfect automation and individual differences in concurrent performance of military and robotics tasks in a

Shared Futures: An Exploration of the Collaborative Potential of Intelligent Machines… DOI: http://dx.doi.org/10.5772/intechopen.85054

simulated multi-tasking environment. Ergonom. 2009;52(8):907-920

References

University; 2015

3(2):41-45

2010. pp. 3-4

[1] Torgilsson P. A critical reflection on the hegemony of technology and possibilities of including ethics in public [10] Harvey L. Transitions from Higher Education to Work 2003. Available from: http://www.shu.ac.uk/research/c

[11] Salomon G, Perkins D, Globerson T. Partners in cognition: Extending human

technologies. Educational Research.

[12] Mandinach E. Model-building and the use of computer simulation of dynamic systems. Journal of Educational Computing Research. 1989;5:221-243

[13] Mitchel W. Introduction: A new agenda for computer-aided design in the electronic design studio. In: Mitchell W, McColough C, editors. The Electronic Design Studio. Cambridge: MIT Press;

[14] Pea R. Distributed intelligence and education. In: Paper Presented at the Annual Meeting of the Social Science Research Council on Computers and Learning, British Virgin Islands. 1989

[15] Papert S. Computer criticism vs. technocentric thinking. Educational

[16] Loof A. The Swedish Innovation Strategy. Stockholm, Sweden: Ministry

[18] Xu A, Dudek G. Online Probabilistic Trust Inferences Model for Asymmetric Human Robot Collaborations. 2007. DOI: 10.1145/26964542896429

[19] Chen J, Terrence P. Effects of imperfect automation and individual differences in concurrent performance of military and robotics tasks in a

Research. 1987;17(1):22-30

[17] OECD. OECD Reviews of Innovation Policy, Norway. Norway:

of Enterprise; 2017

OECD Press; 2017

intelligence with intelligent

re/publications

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human…

1990;20(3):2-9

1990

consumption [Master's degree dissertation]. Lund, Sweden: Lund

[2] Kuzmin O, Pyrog O, Melnik L. Transformation of development model of national economies as conditions of postindustrial society. ECONTECHMOD: An International Quarterly Journal. 2014;

[3] Tuckman B. Developmental

Bulletin. 1965;63:384-399

sequence in small groups. Psychological

[4] Crosta L, McConnell D. Challenging the traditional theorisation on group development. An international online perspective. In: The 7th International Conference on Networked Learning.

[5] Freedy A, McDonough J, Freedy E, Jacobs R, Thagels S, Weltman G. A Mixed Initiative Team Performance Management Assessment System (MITPMA) for use in Training and Operational Environments. SBIR, Phase 1 Report, Constract No. N61339-04-c-0020, Perceptronics Solutions. 2004

[6] Karvonius V. National Innovation Policies in Finland. Helsinki, Finland: Ministry of Employment and the

[7] Minter D, Storkey C. Futureproof: How to Get Ready for the Next Disruption. New York: Pearson; 2017

[9] Acemoglu D, Restrepo P. Robots and Jobs: Evidence from US Labor Markets. NBER Working Paper No. w23285. 2017. Available from SSRN: https://ssrn.com

[8] Dweck C. Mindset: The New Psychology of Success. New York:

Economy Press; 2013

Ballantine Books; 2007

76

[20] Kristin E, Chen J, Szalman J, Hancock P. A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors. 2016;58(3): 377-400

[21] Pu P, Lalanne D. Human and Machine Collaboration in Creative Design: Technical Report. Switzerland: Ecole Polytechnique Federale de Lausanne; 1996

[22] Becher T, Trowler P. Academic Tribes and Territories. Buckingham, UK: The Society for Research into Higher Education and Open University Press; 2011

**79**

Section 3

Creativity and Human -

Machine Collaborations

### Section 3

## Creativity and Human - Machine Collaborations

**81**

**Chapter 6**

**Abstract**

dependable results.

**1. Introduction**

of Achinivu,

*form.*

Diginalysis: The Man-Machine

Collaboration in Music Analysis

The digital technology of the twenty-first century has put man and machine in the center stage where electronic generation, production and manipulation of the musical sound are the norm. The dynamics of the century have made time more elusive and patience more diminutive. Time and patience are vital for any form of successful exercise in music analysis. The intricacies of applying logic to resolve complex musical structures, facts, propositions, and concepts into their elements demand more than technical know-how; they demand a lot of time and patience. With the continued fleeing of time and patience, mechanical accuracy in music analysis would need a full-blown computer-driven "diginalysis." However, inherent limitations of the computer in music analysis, such as decoding the composer's ideologies, necessitate human-machine collaboration. An in-depth descriptive survey has shown that this effective collaboration between man and machine will collapse time and energy by providing immediate feedback, technical accuracy and

**Keywords:** music, analysis, digital, collaboration, computer, software

Analysis is considered the resolution, by application of logic, of complex structures, facts, propositions and concepts into their elements. By extension, it is the tracing of things to their source and the resolution of knowledge into its original principles, the discovery of general principles underlying concrete phenomena. Music analysis, therefore, is the dissection of the musical composition to separate the component parts of the whole in order to take a proper examination of the nature, function, connotations, compatibility, complementary and unitary contributions of these components. This exercise will, among other things, offer the analyst a chance for proper appraisal of the effects of different compositional and performance techniques on the consumers of the musical product. It will also ensure personal and institutional in-depth studies of the composition. In the words

*Through analysis, the various elements of musical architecture become less technical and less dry to music students. Conversely, by their application of the knowledge they have of musical elements and concepts in the analysis of a piece of music, they obtain greater insights into and understanding of musical design and content of* 

*Ikenna Emmanuel Onwuegbuna*

### **Chapter 6**

## Diginalysis: The Man-Machine Collaboration in Music Analysis

*Ikenna Emmanuel Onwuegbuna*

### **Abstract**

The digital technology of the twenty-first century has put man and machine in the center stage where electronic generation, production and manipulation of the musical sound are the norm. The dynamics of the century have made time more elusive and patience more diminutive. Time and patience are vital for any form of successful exercise in music analysis. The intricacies of applying logic to resolve complex musical structures, facts, propositions, and concepts into their elements demand more than technical know-how; they demand a lot of time and patience. With the continued fleeing of time and patience, mechanical accuracy in music analysis would need a full-blown computer-driven "diginalysis." However, inherent limitations of the computer in music analysis, such as decoding the composer's ideologies, necessitate human-machine collaboration. An in-depth descriptive survey has shown that this effective collaboration between man and machine will collapse time and energy by providing immediate feedback, technical accuracy and dependable results.

**Keywords:** music, analysis, digital, collaboration, computer, software

### **1. Introduction**

Analysis is considered the resolution, by application of logic, of complex structures, facts, propositions and concepts into their elements. By extension, it is the tracing of things to their source and the resolution of knowledge into its original principles, the discovery of general principles underlying concrete phenomena. Music analysis, therefore, is the dissection of the musical composition to separate the component parts of the whole in order to take a proper examination of the nature, function, connotations, compatibility, complementary and unitary contributions of these components. This exercise will, among other things, offer the analyst a chance for proper appraisal of the effects of different compositional and performance techniques on the consumers of the musical product. It will also ensure personal and institutional in-depth studies of the composition. In the words of Achinivu,

*Through analysis, the various elements of musical architecture become less technical and less dry to music students. Conversely, by their application of the knowledge they have of musical elements and concepts in the analysis of a piece of music, they obtain greater insights into and understanding of musical design and content of form.*

In the recent applications of the theory of observational learning, terms such as *mastery learning*, *teaching machines*, *programmed instruction*, *computer-based training* (CBT), *computer-aided instruction* (CAI) and *audiovisual education* have found their place in the center stage of the twenty-first century educational procedures.

Sidney Leavitt Pressey had, in the 1920s, designed the first set of teaching machines, which provided immediate feedback for multiple-choice tests. In using the machine, the learners had the advantage of correcting their errors immediately. This immediate feedback system enabled the learners to work at the test items until their answers were correct. Improving on the efforts of Pressey, B. F. Skinner, in 1954—exploring the possibilities of his operant conditioning, developed his own version of teaching machine that became known as *programmed instruction*. The basis of Skinner's programming includes simple principles, namely, presentation of information in small steps called frames, immediate confirmation of the learner's response, active responding to induce sustained activity, self-pacing and dual evaluation of learner's progress by both learner and teacher [1–4].

The application of the programmed-learning theory in analyzing music in the twenty-first century, obviously, engages the computer as an inseparable aid. The elements and items for analysis are codified and, thereby, reduced to icons which are packaged in music software (programmed). The programme then becomes the model to be observed and interacted with by the analyst, in a multimedia of presentation. Sociocultural, ideological and historical issues in music—through a human and machine collaboration—can equally, and easily, be reduced into electronic forms for analysis in the same interactive manner as in musical issues [5].

### **2. Approaches to music analysis**

In trying to dissect music, to separate the component parts of the whole in order to take a proper examination of the nature, function, connotations, compatibility, complementary and unitary contributions of these components, the scholar has already embarked on an analytical assignment that would stretch his/her studies into other disciplines than music. Such studies, whether carried out by an individual or a team, would demand the application of knowledge from at least such academic disciplines as sociology, history, anthropology, semiology, linguistics, economics and philosophy. Because of these interpretative demands, there is a need to engage with music analysis from various approaches.

### **2.1 Musical approach**

Certain elements are globally accepted as intrinsic commonalities in the phenomenon of sound. Such elements as rhythm, pitch, timbre and duration when consciously or subconsciously manipulated distinguish the musical sound from the rest. Analyzing music along the lines of its sonic elements, exposing the inherent stylistic features, conventions and idioms is basically in the domain of systematic musicology. This approach tends to describe 'the over-all structure of a piece of music, and … the interrelationships of its various sections. In most cases, indeed, it is the fitting of this structure into a preconceived mode' [6].

For instance, 'form', as a basic element in music, refers to the structural make-up of a musical composition. It exposes the basic shape of the composition that gives it its distinctive character. Musical form, as one of the characteristic elements of music, is the bases of the systematic and coherent arrangement of the structural design of a musical composition. Apel, therefore, expresses the fact that:

**83**

*Diginalysis: The Man-Machine Collaboration in Music Analysis*

*imagine a procedure by which it could be avoided [7].*

form, tempo, metre, timbre, intensity and texture.

expression and the functionality of music in society.

**2.2 Sociocultural approach**

**2.3 Ideological approach**

**2.4 Historical approach**

*Music, like all art, is not a chaotic conglomeration of sounds, but...it consists of sounds arranged in orderly manner according to numerous obvious principles as well as to a still greater number of subtle and hidden relationships which evade formulation. In this meaning, form is so essential to music that it is difficult to* 

The musical approach to analysis exposes the stylistic features of the piece, the conventions and the exceptions in the application of those features by the composer and the performers of the piece. In this approach, the analyst is trying to appreciate the composer's application of expressive variables in music—like tonality, rhythm,

In the sociocultural approach, music is considered not just as a sonic material but also a symbolical representation of entities, deities, communities, age-grades, generations, classes, races, norms and societies. Analysis under this approach must expose and explain the determinate associations that are implied in the musical

The sociocultural issues in music—especially the 'popular' genre—are implicated more in the processes and negotiated decisions that lead to the creation and consumption of the musical product, than in the textual pronouncements that make up the lyrics of the song, those belong to the ideological angle of the piece. Other sociocultural-related issues in popular music include recording/performance contracts, copyright protection, signing on a record label, publicity, promotion, marketing, publishing, artiste-patron agreements, collaborations, public performance and broadcasting rights and hiring the services of an entertainment law attorney.

Personal opinions held by individual composers and other stakeholders in the musical enterprise, expressed in the textual materials and the musical product, form the bulk of the ideological stance of the music. These opinions could be philosophical, religious, spiritual, political, interpersonal relationships and the total world-view of the composers, which are perceptible, not just in the lyrics but also in the CD sleeves, video clips, interviews, press releases, personality image of the

In the historical approach, the analyst embarks on a retrospective study of schemata of music and how they have developed over time. He/she studies the major stylistic features that characterize each particular period and relate them to parallel developments in other forms of the arts and sciences of the same period, and how each individual composer has interpreted the dominating music of his/her own time. In addition, she/he exposes the practices that marked the points of transition from one era to the different practices of another era, thereby establishing the

The computer technology which saw its modest beginnings in the 1960s and, within a decade of its development, succeeded in turning the world into a global

artistes and their style of usage of metalanguage and polyglottism.

trends that distinguish one period from another.

**3. Computer-aided music analysis**

*DOI: http://dx.doi.org/10.5772/intechopen.84355*

*Diginalysis: The Man-Machine Collaboration in Music Analysis DOI: http://dx.doi.org/10.5772/intechopen.84355*

> *Music, like all art, is not a chaotic conglomeration of sounds, but...it consists of sounds arranged in orderly manner according to numerous obvious principles as well as to a still greater number of subtle and hidden relationships which evade formulation. In this meaning, form is so essential to music that it is difficult to imagine a procedure by which it could be avoided [7].*

The musical approach to analysis exposes the stylistic features of the piece, the conventions and the exceptions in the application of those features by the composer and the performers of the piece. In this approach, the analyst is trying to appreciate the composer's application of expressive variables in music—like tonality, rhythm, form, tempo, metre, timbre, intensity and texture.

### **2.2 Sociocultural approach**

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

In the recent applications of the theory of observational learning, terms such as *mastery learning*, *teaching machines*, *programmed instruction*, *computer-based training* (CBT), *computer-aided instruction* (CAI) and *audiovisual education* have found their place in the center stage of the twenty-first century educational

Sidney Leavitt Pressey had, in the 1920s, designed the first set of teaching machines, which provided immediate feedback for multiple-choice tests. In using the machine, the learners had the advantage of correcting their errors immediately. This immediate feedback system enabled the learners to work at the test items until their answers were correct. Improving on the efforts of Pressey, B. F. Skinner, in 1954—exploring the possibilities of his operant conditioning, developed his own version of teaching machine that became known as *programmed instruction*. The basis of Skinner's programming includes simple principles, namely, presentation of information in small steps called frames, immediate confirmation of the learner's response, active responding to induce sustained activity, self-pacing and dual evalu-

The application of the programmed-learning theory in analyzing music in the twenty-first century, obviously, engages the computer as an inseparable aid. The elements and items for analysis are codified and, thereby, reduced to icons which are packaged in music software (programmed). The programme then becomes the model to be observed and interacted with by the analyst, in a multimedia of presentation. Sociocultural, ideological and historical issues in music—through a human and machine collaboration—can equally, and easily, be reduced into electronic forms for analysis in the same interactive manner as

In trying to dissect music, to separate the component parts of the whole in order to take a proper examination of the nature, function, connotations, compatibility, complementary and unitary contributions of these components, the scholar has already embarked on an analytical assignment that would stretch his/her studies into other disciplines than music. Such studies, whether carried out by an individual or a team, would demand the application of knowledge from at least such academic disciplines as sociology, history, anthropology, semiology, linguistics, economics and philosophy. Because of these interpretative demands, there is a need to engage

Certain elements are globally accepted as intrinsic commonalities in the phenomenon of sound. Such elements as rhythm, pitch, timbre and duration when consciously or subconsciously manipulated distinguish the musical sound from the rest. Analyzing music along the lines of its sonic elements, exposing the inherent stylistic features, conventions and idioms is basically in the domain of systematic musicology. This approach tends to describe 'the over-all structure of a piece of music, and … the interrelationships of its various sections. In most cases, indeed, it

For instance, 'form', as a basic element in music, refers to the structural make-up of a musical composition. It exposes the basic shape of the composition that gives it its distinctive character. Musical form, as one of the characteristic elements of music, is the bases of the systematic and coherent arrangement of the structural

ation of learner's progress by both learner and teacher [1–4].

**82**

procedures.

in musical issues [5].

**2.1 Musical approach**

**2. Approaches to music analysis**

with music analysis from various approaches.

is the fitting of this structure into a preconceived mode' [6].

design of a musical composition. Apel, therefore, expresses the fact that:

In the sociocultural approach, music is considered not just as a sonic material but also a symbolical representation of entities, deities, communities, age-grades, generations, classes, races, norms and societies. Analysis under this approach must expose and explain the determinate associations that are implied in the musical expression and the functionality of music in society.

The sociocultural issues in music—especially the 'popular' genre—are implicated more in the processes and negotiated decisions that lead to the creation and consumption of the musical product, than in the textual pronouncements that make up the lyrics of the song, those belong to the ideological angle of the piece. Other sociocultural-related issues in popular music include recording/performance contracts, copyright protection, signing on a record label, publicity, promotion, marketing, publishing, artiste-patron agreements, collaborations, public performance and broadcasting rights and hiring the services of an entertainment law attorney.

### **2.3 Ideological approach**

Personal opinions held by individual composers and other stakeholders in the musical enterprise, expressed in the textual materials and the musical product, form the bulk of the ideological stance of the music. These opinions could be philosophical, religious, spiritual, political, interpersonal relationships and the total world-view of the composers, which are perceptible, not just in the lyrics but also in the CD sleeves, video clips, interviews, press releases, personality image of the artistes and their style of usage of metalanguage and polyglottism.

### **2.4 Historical approach**

In the historical approach, the analyst embarks on a retrospective study of schemata of music and how they have developed over time. He/she studies the major stylistic features that characterize each particular period and relate them to parallel developments in other forms of the arts and sciences of the same period, and how each individual composer has interpreted the dominating music of his/her own time. In addition, she/he exposes the practices that marked the points of transition from one era to the different practices of another era, thereby establishing the trends that distinguish one period from another.

### **3. Computer-aided music analysis**

The computer technology which saw its modest beginnings in the 1960s and, within a decade of its development, succeeded in turning the world into a global village, has its impact felt in music production. From the introduction and advancement of music synthesizers and other complementary devices, the once dominating analogue audio recording devices have progressively and dexterously been replaced by digital equivalents [8]. The introduction of computer technology, therefore, started a radical turning point in audio production. This turning point has finally eclipsed the analogue system of recording, giving way to the more efficient, realtime and almost real-life digital system [9–11].

An audio recording in which the raw sounds emanating from the initial sources are represented by the spacing between pulses (bits) rather than by waves, thereby making the sounds less susceptible to degradation, is known as digital recording. In digital recording, computer programmes are used to manipulate the audio data stored in the form of alphanumeric codes. This manipulation is done through mathematical processes [8, 10, 12]. The process involves 'the description of a sound waveform as sequence of numbers representing the instantaneous amplitudes of the wave over small successive intervals of time' [9]. Some of the advantages of the digital technique, according to Salt (as cited in [13]), are:

*In digital recording systems, many of the distortions are removed because the continuously varying sound signal is transformed into a digital signal (a sequence of binary values, or a series of bits), by a process called quantizing or quantization, as soon as it is captured. This enables the stored sound data to be checked and processed so that it can, in theory, be reproduced exactly as it was recorded.*

The basic advantage of digital storage of the musical sound is the ease of processing, manipulation and analyzing of the data. This flexibility of the digital data has made it a nearly effortless task to create sound effects, enhance quality and ease editing of the recorded sound. This flexibility makes it possible for the analyst to not only engage but also interact with the digitized items. However, the challenge lies in the reversibility of such digitized items.

The creative and production processes involve computer synthesis in digital recording—starting from the generation of audio samples from analogue sources to conversion to digital equivalents through series of voltage steps, electronic means of creating, filtering and modifying sound—mediated via special interfaces such as effects boxes, tone generators, MIDI, drumulator, vocoder and keyboard sampler.

Through the use digital audio software such as Cakewalk, Cubase, Sonar, Nuendo, Adobe Audition and Fruity Loops, among others, audio projects ranging from sampling, sequencing, quantizing, voicing, boosting, compressing, mixing, recording, re-mixing, etc. are successfully delivered. Music analysis is greatly favored by the instant generation of notated music scores by these audio production music software.

In the application of the computer as the analytical tool, the musical elements are codified and, thereby, reduced to icons which are packaged in music software (programmed). The programme then becomes the model to be observed and interacted with by the analyst, in a multimedia of presentation. The reduction of the elements into electronic forms is the major duty of the computer programmers. The analyst, working with professional computer programmers who are adepts in computer programming language, reduces the issues and elements in music into icons for which the options for digitized items are only a click away.

In analyzing the musical elements of tonality, rhythm, form, tempo, metre, timbre, intensity, texture, vocal/performance techniques and orchestration, among others, the items are reduced to icons backed up with motion pictures, simulations, musical examples, sound clips, diagrams, graphs and charts, all of which are activated as soon as the right icon is clicked at. By engaging the computer programmes,

**85**

**5. Conclusion**

*Diginalysis: The Man-Machine Collaboration in Music Analysis*

any analyst can dissect a piece of music by selecting and clicking at the right icons to access and interact with the compositional rationalizations of the music composer. Sociocultural, ideological and historical issues in music can equally, and easily, be reduced to electronic forms for analysis in the same interactive manner as in musical issues. In this multimedia formats, computer-aided music analysis encourages interactive relationships between the analyst and the models through the use of images (still and motion), animations, speeches, sounds, figures and, mostly, music. It is advantageous that the analyst can quickly access information, get immediate feedback, move at his/her own pace, monitor his/her progress, motivate

In this era of digital technology, the prospects of computer-aided music analysis have inspired computer programmers to create many programmes with different capabilities and limitations. Some of the programmes are the Digital Alternative Representation of the Musical Score (DARMS), Humdrum, Finale, Sibelius, Lemon and Studio 4. Others with some specialization in audio analysis include Fourier,

Sociocultural issues in music are implicated more in the processes and negotiated decisions that lead to the creation and consumption of the musical product than in the textual pronouncements that make up the lyrics of the song. Here music is considered not just as a sonic material but also a symbolical representation of entities, deities, communities, age-grades, generations, classes, races, norms and societies. Analysis must expose and explain the determinate associations that are implied in the musical expression. The functionality of music in society becomes the main focus of the analyst. Is the purpose for music-making self-fulfilling or group-fulfilling? Is it to train, to communicate, to enlighten, to worship, to praise, to heal, to supplicate, to mourn, to mock, to invoke, to curse, to defy, to survive or

Whether on a live stage or an electronic stage, one observes that the emotions expressed by music performers are not always felt by the artistes; sometimes they are feigned to create a contingent, a utilitarian or an esthetic value. The simulated emotions are constructively packaged by the producers to disguise the commercial intent, thereby succeeding in presenting the art as necessary, useful or entertaining in itself. The stochastic nature of the foregoing makes it difficult for the computer to detect or decode the creative intent of the composers of such musical phenomena and activities. This limitation also applies to the subject matter encoded in CD sleeves, video clips, interviews, press releases, personality image of the artistes and

The foregoing makes the human-machine collaboration imperative. While the computer analyzes the machine-modifiable music notations, simulations, animations and icons, the rest of the variables that are largely psychological, sociological and philosophical are humanly analyzed to make up for the limitations of the machine. This model of collaboration therefore bestrides the music domain and other related disciplines including visual arts, architecture, design and film-making and editing.

The chapter has proposed the effective collaboration of human and the machine in analyzing music—especially in this twenty-first century where the computer

*DOI: http://dx.doi.org/10.5772/intechopen.84355*

him/herself and learn independently [14–17].

SoundEdit, AudioSculpt, SARA and Lemur [18–20].

what? And what social events are they linked with?

their style of usage of metalanguage and polyglottism.

**4. The collaboration**

### *Diginalysis: The Man-Machine Collaboration in Music Analysis DOI: http://dx.doi.org/10.5772/intechopen.84355*

any analyst can dissect a piece of music by selecting and clicking at the right icons to access and interact with the compositional rationalizations of the music composer.

Sociocultural, ideological and historical issues in music can equally, and easily, be reduced to electronic forms for analysis in the same interactive manner as in musical issues. In this multimedia formats, computer-aided music analysis encourages interactive relationships between the analyst and the models through the use of images (still and motion), animations, speeches, sounds, figures and, mostly, music. It is advantageous that the analyst can quickly access information, get immediate feedback, move at his/her own pace, monitor his/her progress, motivate him/herself and learn independently [14–17].

In this era of digital technology, the prospects of computer-aided music analysis have inspired computer programmers to create many programmes with different capabilities and limitations. Some of the programmes are the Digital Alternative Representation of the Musical Score (DARMS), Humdrum, Finale, Sibelius, Lemon and Studio 4. Others with some specialization in audio analysis include Fourier, SoundEdit, AudioSculpt, SARA and Lemur [18–20].

### **4. The collaboration**

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

time and almost real-life digital system [9–11].

lies in the reversibility of such digitized items.

digital technique, according to Salt (as cited in [13]), are:

village, has its impact felt in music production. From the introduction and advancement of music synthesizers and other complementary devices, the once dominating analogue audio recording devices have progressively and dexterously been replaced by digital equivalents [8]. The introduction of computer technology, therefore, started a radical turning point in audio production. This turning point has finally eclipsed the analogue system of recording, giving way to the more efficient, real-

An audio recording in which the raw sounds emanating from the initial sources are represented by the spacing between pulses (bits) rather than by waves, thereby making the sounds less susceptible to degradation, is known as digital recording. In digital recording, computer programmes are used to manipulate the audio data stored in the form of alphanumeric codes. This manipulation is done through mathematical processes [8, 10, 12]. The process involves 'the description of a sound waveform as sequence of numbers representing the instantaneous amplitudes of the wave over small successive intervals of time' [9]. Some of the advantages of the

*In digital recording systems, many of the distortions are removed because the continuously varying sound signal is transformed into a digital signal (a sequence of binary values, or a series of bits), by a process called quantizing or quantization, as soon as it is captured. This enables the stored sound data to be checked and processed so that it can, in theory, be reproduced exactly as it was recorded.*

The basic advantage of digital storage of the musical sound is the ease of processing, manipulation and analyzing of the data. This flexibility of the digital data has made it a nearly effortless task to create sound effects, enhance quality and ease editing of the recorded sound. This flexibility makes it possible for the analyst to not only engage but also interact with the digitized items. However, the challenge

The creative and production processes involve computer synthesis in digital recording—starting from the generation of audio samples from analogue sources to conversion to digital equivalents through series of voltage steps, electronic means of creating, filtering and modifying sound—mediated via special interfaces such as effects boxes, tone generators, MIDI, drumulator, vocoder and keyboard

Through the use digital audio software such as Cakewalk, Cubase, Sonar, Nuendo,

Adobe Audition and Fruity Loops, among others, audio projects ranging from sampling, sequencing, quantizing, voicing, boosting, compressing, mixing, recording, re-mixing, etc. are successfully delivered. Music analysis is greatly favored by the instant generation of notated music scores by these audio production music software. In the application of the computer as the analytical tool, the musical elements are codified and, thereby, reduced to icons which are packaged in music software (programmed). The programme then becomes the model to be observed and interacted with by the analyst, in a multimedia of presentation. The reduction of the elements into electronic forms is the major duty of the computer programmers. The analyst, working with professional computer programmers who are adepts in computer programming language, reduces the issues and elements in music into

icons for which the options for digitized items are only a click away.

In analyzing the musical elements of tonality, rhythm, form, tempo, metre, timbre, intensity, texture, vocal/performance techniques and orchestration, among others, the items are reduced to icons backed up with motion pictures, simulations, musical examples, sound clips, diagrams, graphs and charts, all of which are activated as soon as the right icon is clicked at. By engaging the computer programmes,

**84**

sampler.

Sociocultural issues in music are implicated more in the processes and negotiated decisions that lead to the creation and consumption of the musical product than in the textual pronouncements that make up the lyrics of the song. Here music is considered not just as a sonic material but also a symbolical representation of entities, deities, communities, age-grades, generations, classes, races, norms and societies. Analysis must expose and explain the determinate associations that are implied in the musical expression. The functionality of music in society becomes the main focus of the analyst. Is the purpose for music-making self-fulfilling or group-fulfilling? Is it to train, to communicate, to enlighten, to worship, to praise, to heal, to supplicate, to mourn, to mock, to invoke, to curse, to defy, to survive or what? And what social events are they linked with?

Whether on a live stage or an electronic stage, one observes that the emotions expressed by music performers are not always felt by the artistes; sometimes they are feigned to create a contingent, a utilitarian or an esthetic value. The simulated emotions are constructively packaged by the producers to disguise the commercial intent, thereby succeeding in presenting the art as necessary, useful or entertaining in itself.

The stochastic nature of the foregoing makes it difficult for the computer to detect or decode the creative intent of the composers of such musical phenomena and activities. This limitation also applies to the subject matter encoded in CD sleeves, video clips, interviews, press releases, personality image of the artistes and their style of usage of metalanguage and polyglottism.

The foregoing makes the human-machine collaboration imperative. While the computer analyzes the machine-modifiable music notations, simulations, animations and icons, the rest of the variables that are largely psychological, sociological and philosophical are humanly analyzed to make up for the limitations of the machine. This model of collaboration therefore bestrides the music domain and other related disciplines including visual arts, architecture, design and film-making and editing.

### **5. Conclusion**

The chapter has proposed the effective collaboration of human and the machine in analyzing music—especially in this twenty-first century where the computer

age has expanded the frontiers of the audiovisual creativity—via the system of computer-aided music analysis.

Resources for the composition and performance of electronic music have recently been broadened considerably through the introduction and use of the Musical Instrument Digital Interface (MIDI). The MIDI, as a remarkable system, enables composers to manage quantities of complex information and allow computers, synthesizers, sound modules, drum machines and other electronic devices from many manufacturers to communicate with each other. Originally of interest only to a few so-called serious composers, today MIDI-based systems, are used to analyze and teach music, write and perform film scores, create rhythm tracks for popular music and provide music for computer games. Also with the MIDI, the numbers of ways in which the electronic synthesizer may serve composers seem limited only by the boundaries of human initiative and perception [21, 22].

Music, bestriding art and science, affects a zone where emotion intersects with processes taking place at a corporeal level and is capable of producing tactile, sensuous and involuntary reactions. The musical sound has the ability to change the emotional and physical states of people and could equally alter one in many ways, depending on the composer's manipulation of musical elements and the producer's manipulation of post-production sonic enhancements [23].

By acknowledging this protean nature of music, the chapter has identified the limitations of a single mode of analysis and therefore recommends the dual mode of man-machine collaboration in 'diginalysis'. In this effective collaboration, the computer analyzes the machine-modifiable music notations, simulations, animations and icons, while the human handles the psychological, sociological and philosophical elements of music. While the utilitarian value of this effective collaboration collapse time and energy by providing immediate feedback, technical accuracy and dependable results, the contingent will benefit other related disciplines including visual arts, architecture, design and film-making and editing.

### **Author details**

Ikenna Emmanuel Onwuegbuna Department of Music, University of Nigeria, Nsukka, Enugu, Nigeria

\*Address all correspondence to: ikenna.onwuegbuna@unn.edu.ng

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**87**

*Diginalysis: The Man-Machine Collaboration in Music Analysis*

[13] Onwuegbuna IE. Digitizing the analogue: The creative and economic impacts of the Nigerian audio engineer. Journal of the Association of Nigerian

Musicologists. 2011;**5**:68-79

Corporation; 2009

[14] Arnold DN. Computer-based training. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft

[15] Kelly SN. A sociological basis for music education. International Journal of Music Education. 2002;**39**:40-49

[16] Lebler D. Student-as-master? Reflections on a learning innovation in popular music pedagogy. International

[17] Portowitz A, Klein PS. MISC-MUSIC: A music program to enhance cognitive processing among children with learning difficulties. International

[18] Brown AR. Music Education and Computers: Amplifying Musicality.

Journal of Music Education.

Journal of Music Education.

New York: Routledge; 2007

of Computing Science; 2002

[19] Gerhard D. Computer Music Analysis, Technical Report CMPT TR 97-13. Simon Fraser University, School

[20] Meredith D, editor. Computational Music Analysis. New York: Springer; 2016

[22] Rowe R. Interactive Music Systems.

Psychophysically faithful methods for extracting pitch. In: IJCAI Workshop on Computational Auditory Scene Analysis. Montreal, Quebec; 1995. pp. 19-25

[21] Rumsey F. MIDI Systems and Control. Toronto: Focal Press; 1994

Cambridge: MIT Press; 1993

[23] Meddis R, O'Mard L.

2007;**25**(3):205-221

2007;**25**(3):259-271

*DOI: http://dx.doi.org/10.5772/intechopen.84355*

[1] Chauhan SS. Advanced Educational Psychology. New Delhi: Vikas; 1987

[2] Ittelson JC. Audiovisual education. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[3] Tiemann PW, Markle SM. Teaching machines. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft

[5] Onwuegbuna IE. Pop music analysis in the 21st century: An adaptation of the Pressey-Skinner programmed-learning theory. Awka Journal of Research in Music and the Arts. 2009;**6**:90-104

[6] Nettl B. Theory and Method in Ethnomusicology. New York: The Free

[7] Apel W. The Harvard Dictionary of Music. London: Heinemann; 1964

[8] Fuertes C. Computer Music. 2006. Available from http://www.pie.xtec.es

[9] Moog RA. The electronic music synthesizer. In: Encyclopaedia Britannica. Ultimate Reference Suite. Chicago: Encyclopaedia Britannica; 2009

[10] Norman K. Electronic music. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[11] Salt B. Sound recording and reproduction. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft

[12] Machlis J, Forney K. The Enjoyment of Music. 7th ed. New York: W.W. Norton; 1995

Corporation; 2009

[Retrieved: July 23, 2006]

[4] Achinivu AK. Teaching and understanding musical elements and concepts through analysis. Awka Journal of Research in Music and the Arts.

**References**

Corporation; 2009

2003;**1**:54-68

Press; 1963

*Diginalysis: The Man-Machine Collaboration in Music Analysis DOI: http://dx.doi.org/10.5772/intechopen.84355*

### **References**

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

the boundaries of human initiative and perception [21, 22].

manipulation of post-production sonic enhancements [23].

visual arts, architecture, design and film-making and editing.

computer-aided music analysis.

age has expanded the frontiers of the audiovisual creativity—via the system of

Resources for the composition and performance of electronic music have recently been broadened considerably through the introduction and use of the Musical Instrument Digital Interface (MIDI). The MIDI, as a remarkable system, enables composers to manage quantities of complex information and allow computers, synthesizers, sound modules, drum machines and other electronic devices from many manufacturers to communicate with each other. Originally of interest only to a few so-called serious composers, today MIDI-based systems, are used to analyze and teach music, write and perform film scores, create rhythm tracks for popular music and provide music for computer games. Also with the MIDI, the numbers of ways in which the electronic synthesizer may serve composers seem limited only by

Music, bestriding art and science, affects a zone where emotion intersects with processes taking place at a corporeal level and is capable of producing tactile, sensuous and involuntary reactions. The musical sound has the ability to change the emotional and physical states of people and could equally alter one in many ways, depending on the composer's manipulation of musical elements and the producer's

By acknowledging this protean nature of music, the chapter has identified the limitations of a single mode of analysis and therefore recommends the dual mode of man-machine collaboration in 'diginalysis'. In this effective collaboration, the computer analyzes the machine-modifiable music notations, simulations, animations and icons, while the human handles the psychological, sociological and philosophical elements of music. While the utilitarian value of this effective collaboration collapse time and energy by providing immediate feedback, technical accuracy and dependable results, the contingent will benefit other related disciplines including

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Department of Music, University of Nigeria, Nsukka, Enugu, Nigeria

\*Address all correspondence to: ikenna.onwuegbuna@unn.edu.ng

**86**

**Author details**

Ikenna Emmanuel Onwuegbuna

provided the original work is properly cited.

[1] Chauhan SS. Advanced Educational Psychology. New Delhi: Vikas; 1987

[2] Ittelson JC. Audiovisual education. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[3] Tiemann PW, Markle SM. Teaching machines. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[4] Achinivu AK. Teaching and understanding musical elements and concepts through analysis. Awka Journal of Research in Music and the Arts. 2003;**1**:54-68

[5] Onwuegbuna IE. Pop music analysis in the 21st century: An adaptation of the Pressey-Skinner programmed-learning theory. Awka Journal of Research in Music and the Arts. 2009;**6**:90-104

[6] Nettl B. Theory and Method in Ethnomusicology. New York: The Free Press; 1963

[7] Apel W. The Harvard Dictionary of Music. London: Heinemann; 1964

[8] Fuertes C. Computer Music. 2006. Available from http://www.pie.xtec.es [Retrieved: July 23, 2006]

[9] Moog RA. The electronic music synthesizer. In: Encyclopaedia Britannica. Ultimate Reference Suite. Chicago: Encyclopaedia Britannica; 2009

[10] Norman K. Electronic music. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[11] Salt B. Sound recording and reproduction. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[12] Machlis J, Forney K. The Enjoyment of Music. 7th ed. New York: W.W. Norton; 1995 [13] Onwuegbuna IE. Digitizing the analogue: The creative and economic impacts of the Nigerian audio engineer. Journal of the Association of Nigerian Musicologists. 2011;**5**:68-79

[14] Arnold DN. Computer-based training. In: Microsoft® Student [DVD]. Redmond, WA: Microsoft Corporation; 2009

[15] Kelly SN. A sociological basis for music education. International Journal of Music Education. 2002;**39**:40-49

[16] Lebler D. Student-as-master? Reflections on a learning innovation in popular music pedagogy. International Journal of Music Education. 2007;**25**(3):205-221

[17] Portowitz A, Klein PS. MISC-MUSIC: A music program to enhance cognitive processing among children with learning difficulties. International Journal of Music Education. 2007;**25**(3):259-271

[18] Brown AR. Music Education and Computers: Amplifying Musicality. New York: Routledge; 2007

[19] Gerhard D. Computer Music Analysis, Technical Report CMPT TR 97-13. Simon Fraser University, School of Computing Science; 2002

[20] Meredith D, editor. Computational Music Analysis. New York: Springer; 2016

[21] Rumsey F. MIDI Systems and Control. Toronto: Focal Press; 1994

[22] Rowe R. Interactive Music Systems. Cambridge: MIT Press; 1993

[23] Meddis R, O'Mard L. Psychophysically faithful methods for extracting pitch. In: IJCAI Workshop on Computational Auditory Scene Analysis. Montreal, Quebec; 1995. pp. 19-25

**89**

**Chapter 7**

**Abstract**

**1. Introduction**

Innovation

*Neta Kela-Madar and Itai Kela*

The Machine-Human

Collaboration in Healthcare

cross industry innovation, research, development, and implementation.

2010 to 1.9% in 2018, the lowest levels the industry has seen in 9 years [1].

**Keywords:** artificial intelligence, healthcare, biopharma industry, personalized medicine, big data, digital transformation, machine learning, R&D, innovation

The biopharma industry is facing significant challenges reflected by unsustainable research and development (R&D) costs. This challenge is seen in several ways. First, aggressive pricing pressure has led to an increase in the cost needed to bring products to market—from \$1.188 billion in 2010 to a record level of \$2.168 billion in 2018. A second major reason is the threat of patent expirations on numerous blockbuster drugs. As a result, biopharma companies experienced record low R&D returns in 2018—10.1% in

In parallel to biopharma challenges, the healthcare system is having a crisis due to the prevalence of chronic diseases and increased life expectancy, the main causes for skyrocketing healthcare costs (in US, the health share of GDP is 18% and expected to reach 19.6% by 2014) [2]. Today, 50% of the entire US population is considered chronic patients, which accounts for 85% of the overall cost of healthcare [3]. Fortunately, the majority of chronic diseases can be prevented or delayed until significantly later stages in life due to successful medical interventions.

Today, the healthcare industry is seeing an integration of novel genetic and digital technologies that help identify and cope with the complexity of chronic diseases and their often "silent" transition from healthy status to an active disease with a

The biopharma industry is in crisis, demonstrated by unsustainable research and development (R&D) costs. In parallel, the healthcare system suffers from skyrocketing costs, driven by the prevalence of chronic diseases and increased life expectancy. Innovative technologies have the potential to alleviate challenges both in the biopharma R&D model and in healthcare. This chapter considers how Big Data analysis based on artificial intelligence and machine learning offer opportunities to drive greater efficiency across the entire R&D value chain, enhance the quality of assets produced, and improve the time and cost to bring products to market. We also consider the unique challenges that arise with the integration of these fields into healthcare and medicine, specifically, the initially high costs when new medical and healthcare technologies are brought to the marketplace; widening socioeconomic health inequalities due to high marketplace costs; and unique methodological challenges presented by

### **Chapter 7**

## The Machine-Human Collaboration in Healthcare Innovation

*Neta Kela-Madar and Itai Kela*

### **Abstract**

The biopharma industry is in crisis, demonstrated by unsustainable research and development (R&D) costs. In parallel, the healthcare system suffers from skyrocketing costs, driven by the prevalence of chronic diseases and increased life expectancy. Innovative technologies have the potential to alleviate challenges both in the biopharma R&D model and in healthcare. This chapter considers how Big Data analysis based on artificial intelligence and machine learning offer opportunities to drive greater efficiency across the entire R&D value chain, enhance the quality of assets produced, and improve the time and cost to bring products to market. We also consider the unique challenges that arise with the integration of these fields into healthcare and medicine, specifically, the initially high costs when new medical and healthcare technologies are brought to the marketplace; widening socioeconomic health inequalities due to high marketplace costs; and unique methodological challenges presented by cross industry innovation, research, development, and implementation.

**Keywords:** artificial intelligence, healthcare, biopharma industry, personalized medicine, big data, digital transformation, machine learning, R&D, innovation

### **1. Introduction**

The biopharma industry is facing significant challenges reflected by unsustainable research and development (R&D) costs. This challenge is seen in several ways. First, aggressive pricing pressure has led to an increase in the cost needed to bring products to market—from \$1.188 billion in 2010 to a record level of \$2.168 billion in 2018. A second major reason is the threat of patent expirations on numerous blockbuster drugs. As a result, biopharma companies experienced record low R&D returns in 2018—10.1% in 2010 to 1.9% in 2018, the lowest levels the industry has seen in 9 years [1].

In parallel to biopharma challenges, the healthcare system is having a crisis due to the prevalence of chronic diseases and increased life expectancy, the main causes for skyrocketing healthcare costs (in US, the health share of GDP is 18% and expected to reach 19.6% by 2014) [2]. Today, 50% of the entire US population is considered chronic patients, which accounts for 85% of the overall cost of healthcare [3]. Fortunately, the majority of chronic diseases can be prevented or delayed until significantly later stages in life due to successful medical interventions.

Today, the healthcare industry is seeing an integration of novel genetic and digital technologies that help identify and cope with the complexity of chronic diseases and their often "silent" transition from healthy status to an active disease with a

late onset of symptoms. The challenge is to move medical interventions upstream to the pre-disease state, during which symptoms are cheaper and easier to treat. Significant change must be made to the current pharma R&D model, if productivity and profitability are ever to be restored and maximized. The view today is that a complete digital transformation is what is needed to achieve these goals and deliver the next generation of scientific breakthroughs.

Big Data analysis based on artificial intelligence (AI) and machine learning (ML) offer opportunities to address some of these challenges expected to drive greater efficiency across the entire R&D value chain, and eventually improve the quality of the assets produced, as well as the time and cost it takes to bring them to the market. The change is already beginning to take place. In fact, most of the big pharma companies (such as Novartis, Roche, Pfizer, Merck, AstraZeneca, GlaxoSmithKline, Sanofi, Abbvie, Bristol-Myers Squibb, Johnson & Johnson, etc.) are already on the road to taking advantage of AI innovation (healthcareweekly. com, online) as it becomes a driving force in the innovation of medicine and healthcare.

AI is a growing industry of personalized health technology, or personalized medicine, which will have tremendous effect on healthcare management. The key idea behind the technological personalization of medicine and healthcare is to capture, analyze, and utilize individual patient characteristics, such as biomarkers, then to base medical decisions on these individual characteristics rather than on population averages. Another direct application is the technological development of assisted devices that augment traditional medical practice and healthcare, such as the broad use of robotics as well as patient-worn devices ("wearables") that optimize care. This chapter reviews leading publications in these areas and outlines major advantages and also methodological and clinical weak points that need to be addressed in order for personalized medicine to realize its potential.

### **2. Toward personalized medicine age**

Advances in technology are shifting the practice of medicine from anecdotal to data-driven. Due to this shift, improvement in screening, prediction, diagnosis, and the treatment of disease has increased the quality of medical care worldwide and cost effectively ([4]: p. 139). Personalized medicine is generally recognized as promising and advantageous in several important ways. It can improve the efficacy of medication as treatments become better matched to patients; when patients are better matched to treatments, ineffective treatments and their accompanying harmful side effects are avoided; healthcare costs are driven down as a result of better use of therapies; diseases are detected sooner or even anticipated so care is shifted from detection to prevention, thereby avoiding late-care, less effective, and more costly treatment; disease management is more effective through wearable patient technology; and clinical trials can be more accurate as patient selection becomes more precise ([5]: pp. 1-2).

Despite these apparent advantages, the technological personalization of medicine brings numerous challenges that must be addressed in order to harness its full potential. When healthcare and medical technologies first enter the marketplace, for example, they are often initially more expensive, as the companies that develop these products need to recoup high expenses from R&D. As a result, personalized health technologies are utilized first by the more affluent, driving an even larger wedge between affluent populations and marginalized ones. This serves to broaden the already wide socioeconomic gap in health inequalities in the short term ([5]: p. 2).

**91**

*The Machine-Human Collaboration in Healthcare Innovation*

**3. Challenges of human-machine innovation**

borne out as solutions that impact clinical practice.

large amounts of already available biomedical data ([6]: p. 13).

Solutions must be found to provide for diverse socioeconomic patient access to personalized medicine, so that its benefits reach all populations. This is especially important as marginalized and disadvantaged populations are precisely the ones least likely to access and utilize these products, but typically the very populations that would disproportionally benefit from them. Early disease diagnosis and management provided by advances in personalized medicine are especially needed in these populations and innovating for these populations is crucial in order for personalized health technology to reach its public health potential. For this, creative, strategic health initiatives must be developed that aim to lower costs while

The challenge in personalized medicine is methodological and inherent in crossindustry innovation itself—the ways in which different technologies are utilized for healthcare and medicine. While machine learning techniques can process complex and large data and provide accurate predictions based on this analysis, they are unable to provide a deeper understanding of phenomena ([6]: p. 5). In this way, Data Science and AI do not replace classical research. As a result, there remains a gap between the potential of personalized medicine and its realized application

One foreseeable way to bridge this gap is to push for a better coordinated interdisciplinary effort. Scientists, physicians, patients and their advocates, regulatory agencies, and health insurance providers need to create a healthcare system that can learn and adapt as it develops ([6]: p. 12). In short, technology is not meant to replace physicians. Rather, the idea is to provide physicians with a tool that supports their decisions based on the accurate processing, understanding, and analysis of

Another way to understand this difficulty is that personalized medicine is "underpinned" by convergent, cross-industry innovation. This naturally results in complexity, and uncertainty in terms of organization ([7]: p. 44). The question becomes how best to innovate given this challenge of cross-industry integration. The two dominant forms of organizational learning aim for simplification and specialization. This is especially so in the context of uncertainty and complex integration issues that arise from innovation in an emerging cross-industry ecosystem. However, new research suggests a need to face this complexity via an adaption of a multitude of approaches, recognizing that uncertainty and risk are part and parcel

In this context, the management of risk might best be replaced with addressing uncertainty, understanding that in an emerging ecosystem of convergent innovation, comprehensive understanding is lacking. Approaches that embrace complexity rather than just managing it might prove more effective, specifically by adopting numerous measures to address the divergent factors in cross-industry innovation

As a case in point, consider the impact of AI in cardiology and cardiac imaging. Machine learning and the "deep" neural networks used for this purpose hold great promise when applied to medical imaging. Improving the identification accuracy in patients at risk for cardiovascular events is critical, as well as patients who are not at

**4. AI and digital healthcare case study: AI for cardiac patients**

*DOI: http://dx.doi.org/10.5772/intechopen.88951*

expanding access ([6]: pp. 2–4).

of the very nature of innovation.

([7]: pp. 51–52).

*The Machine-Human Collaboration in Healthcare Innovation DOI: http://dx.doi.org/10.5772/intechopen.88951*

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

the next generation of scientific breakthroughs.

healthcare.

late onset of symptoms. The challenge is to move medical interventions upstream to the pre-disease state, during which symptoms are cheaper and easier to treat. Significant change must be made to the current pharma R&D model, if productivity and profitability are ever to be restored and maximized. The view today is that a complete digital transformation is what is needed to achieve these goals and deliver

Big Data analysis based on artificial intelligence (AI) and machine learning (ML) offer opportunities to address some of these challenges expected to drive greater efficiency across the entire R&D value chain, and eventually improve the quality of the assets produced, as well as the time and cost it takes to bring them to the market. The change is already beginning to take place. In fact, most of the big pharma companies (such as Novartis, Roche, Pfizer, Merck, AstraZeneca, GlaxoSmithKline, Sanofi, Abbvie, Bristol-Myers Squibb, Johnson & Johnson, etc.) are already on the road to taking advantage of AI innovation (healthcareweekly. com, online) as it becomes a driving force in the innovation of medicine and

AI is a growing industry of personalized health technology, or personalized medicine, which will have tremendous effect on healthcare management. The key idea behind the technological personalization of medicine and healthcare is to capture, analyze, and utilize individual patient characteristics, such as biomarkers, then to base medical decisions on these individual characteristics rather than on population averages. Another direct application is the technological development of assisted devices that augment traditional medical practice and healthcare, such as the broad use of robotics as well as patient-worn devices ("wearables") that optimize care. This chapter reviews leading publications in these areas and outlines major advantages and also methodological and clinical weak points that need to be

Advances in technology are shifting the practice of medicine from anecdotal to data-driven. Due to this shift, improvement in screening, prediction, diagnosis, and the treatment of disease has increased the quality of medical care worldwide and cost effectively ([4]: p. 139). Personalized medicine is generally recognized as promising and advantageous in several important ways. It can improve the efficacy of medication as treatments become better matched to patients; when patients are better matched to treatments, ineffective treatments and their accompanying harmful side effects are avoided; healthcare costs are driven down as a result of better use of therapies; diseases are detected sooner or even anticipated so care is shifted from detection to prevention, thereby avoiding late-care, less effective, and more costly treatment; disease management is more effective through wearable patient technology; and clinical trials can be more accurate as patient selection becomes

Despite these apparent advantages, the technological personalization of medicine brings numerous challenges that must be addressed in order to harness its full potential. When healthcare and medical technologies first enter the marketplace, for example, they are often initially more expensive, as the companies that develop these products need to recoup high expenses from R&D. As a result, personalized health technologies are utilized first by the more affluent, driving an even larger wedge between affluent populations and marginalized ones. This serves to broaden the already wide socioeconomic gap in health inequalities in the

addressed in order for personalized medicine to realize its potential.

**2. Toward personalized medicine age**

more precise ([5]: pp. 1-2).

short term ([5]: p. 2).

**90**

Solutions must be found to provide for diverse socioeconomic patient access to personalized medicine, so that its benefits reach all populations. This is especially important as marginalized and disadvantaged populations are precisely the ones least likely to access and utilize these products, but typically the very populations that would disproportionally benefit from them. Early disease diagnosis and management provided by advances in personalized medicine are especially needed in these populations and innovating for these populations is crucial in order for personalized health technology to reach its public health potential. For this, creative, strategic health initiatives must be developed that aim to lower costs while expanding access ([6]: pp. 2–4).

### **3. Challenges of human-machine innovation**

The challenge in personalized medicine is methodological and inherent in crossindustry innovation itself—the ways in which different technologies are utilized for healthcare and medicine. While machine learning techniques can process complex and large data and provide accurate predictions based on this analysis, they are unable to provide a deeper understanding of phenomena ([6]: p. 5). In this way, Data Science and AI do not replace classical research. As a result, there remains a gap between the potential of personalized medicine and its realized application borne out as solutions that impact clinical practice.

One foreseeable way to bridge this gap is to push for a better coordinated interdisciplinary effort. Scientists, physicians, patients and their advocates, regulatory agencies, and health insurance providers need to create a healthcare system that can learn and adapt as it develops ([6]: p. 12). In short, technology is not meant to replace physicians. Rather, the idea is to provide physicians with a tool that supports their decisions based on the accurate processing, understanding, and analysis of large amounts of already available biomedical data ([6]: p. 13).

Another way to understand this difficulty is that personalized medicine is "underpinned" by convergent, cross-industry innovation. This naturally results in complexity, and uncertainty in terms of organization ([7]: p. 44). The question becomes how best to innovate given this challenge of cross-industry integration.

The two dominant forms of organizational learning aim for simplification and specialization. This is especially so in the context of uncertainty and complex integration issues that arise from innovation in an emerging cross-industry ecosystem. However, new research suggests a need to face this complexity via an adaption of a multitude of approaches, recognizing that uncertainty and risk are part and parcel of the very nature of innovation.

In this context, the management of risk might best be replaced with addressing uncertainty, understanding that in an emerging ecosystem of convergent innovation, comprehensive understanding is lacking. Approaches that embrace complexity rather than just managing it might prove more effective, specifically by adopting numerous measures to address the divergent factors in cross-industry innovation ([7]: pp. 51–52).

### **4. AI and digital healthcare case study: AI for cardiac patients**

As a case in point, consider the impact of AI in cardiology and cardiac imaging. Machine learning and the "deep" neural networks used for this purpose hold great promise when applied to medical imaging. Improving the identification accuracy in patients at risk for cardiovascular events is critical, as well as patients who are not at risk but suffer from misdiagnosis and are given unnecessary and sometimes harmful treatments with negative side effects. The importance of improving the accuracy in detection and diagnosis is thus monumental given that cardiovascular disease is leading cause of death worldwide ([4]: p. 139).

The use of AI in cardiology has increased dramatically in the past 5 years. Machine learning algorithms now outperform many traditional algorithms, including the established risk prediction algorithm used by the American College of Cardiology (ACC)/American Heart Association (AHA), performing with a 3.6% predictive accuracy improvement over the ACC/AHA algorithm ([4]: p. 139).

Still major challenges lie ahead. Before AI can reliably be utilized by any field of medicine let alone realize its potential for cardiac patients, the neural networks necessary for its application require constant and extremely timeconsuming expansion and revision. Key difficulties are (i) the extremely large amount of training data required by neural networks; (ii) the need to annotate (label) any dataset used for the training of a neural network; (iii) creating an understating of what computers learn given that the patterns and knowledge gained by a network are contained in the weights of the nodes of the network; and (iv) the risk of "overfitting" the training data when designing and training a neural network.

In other words, better efficiency of machine learning, together with improved accuracy with less training and data necessary, are all needed in order to approximate the efficiency of human learning and bring its relevance to a clinical setting.

Given these challenges, AI in cardiac CT angiography has made tremendous gains in the past 10 years, and over the next 10 years, the expectation is that we will see more AI software development and use in cardiac imaging than in the past 50 years ([4]: p. 139).

### **5. Decision-making tools for practitioners**

A recent study revealed that one in every 71 cases from 6000 tissue samples of cancer patients across the US was misdiagnosed and up to one in five were misclassified. This same study reviewed 25 years of US malpractice claims and concluded that diagnostic errors were the cause of the most severe patient harm. According to the National Academies' Institute of Medicine, 10% of patient deaths and as much as 17% of hospital complications are a result of diagnostic errors ([8]: p. 1).

What is more, it was not primarily the physicians who were the cause of most diagnostic errors. Instead, the study found, the fault lies primarily in substandard collaboration and synthesis of information in the healthcare system as well as communication gaps, and that the healthcare system as a whole failed to effectively support the diagnostic process ([8]: p. 1).

Now let us consider the application of AI to address the need for collaboration and integration in healthcare to improve the diagnostic process. Optum, a leading company providing these solutions for the healthcare industry, developed a program called Care Coordination Platform. It processes vast amounts of data and provides a comprehensive overview of every patient's full medical history, allowing healthcare providers an immediate, complete picture of each patient. The platform suggests the most appropriate and cost-effective treatment options; identifies highrisk patients before symptoms occur; and has adaptive algorithms that incorporate clinical data, claims, and socioeconomic figures ([8]: p. 2).

**93**

*The Machine-Human Collaboration in Healthcare Innovation*

required are more complex and have a higher patient variance.

answering some of life's most difficult and challenging questions.

streams of business based on its cutting-edge technologies [10].

**7. Robotics and advanced medical devices**

clinical trials in the United States using CRISPR technology are underway.

daunting challenges before its application can be realized clinically. One such hurdle, if not the most significant one, is regulation. Personalized medicine, including gene editing technologies, is involved in a regulatory business, involving peer-reviewed, published papers and clinical trials. Even in cooperation with the FDA, for example, it could still take a new technology 20 years to be approved.

timely manner to be of relevance for physician use.

**6. Advanced genetic technologies**

computer-generated clinical knowledge and patient-related information, were studied. When such data are filtered and made available at appropriate times, it was shown to enhance patient care. CDSS can also send reminders, warnings, test results, check for drug interactions, dosage errors, contraindications, and list patients eligible for specific interventions such as immunizations and

The study found that CDSSs that require large amounts of data entry adversely affect physician satisfaction and use of the system. When large amounts of data required for the CDSS to be effective are incomplete, diagnoses will be less accurate, or it will take longer to complete the data, resulting in delays in the CDSS to accurately deliver advice. Anticoagulant-prescribing CDSSs are a case in point; the data

CDSSs requiring limited number of patient data items for input were the most used and clinically successful. Examples include preventative care reminder systems

The study concluded that CDSSs become more effective as they become more specified and sensitive in their levels of advice but at the same time the manual input of data needs to be minimized, and the CDSS advice needs to be available in a

Personalized medicine is making an impact in advanced genetic technologies as well. Genome modulation (modifications), in particular, has an array of applications, from energy, food, and industrial to medical. Researchers are turning to genome modulation with the hope that it will provide the key to understanding and

Genome modulation applied medically has been known as gene therapy, but with new technologies, has evolved into the science of gene editing. At the forefront of this technology is what is now known as Clustered Regularly Interspaced Short Palindromic Repeats, or CRISPR. Experts claim CRISPR has brought with it new

One recent example of CRISPR technology application is the correction of blood clotting problems in newborn and adult mice, with marked success. The aim is to cure the majority of patients with hemophilia B with CRISPR-based gene targeting [10]. Growing interest in CRISPR technology is speeding its transition to research, clinical trials, and applications in humans, and it was recently tested on a human being for the first time. In China, a patient diagnosed with terminal lung cancer was treated with CRISPR gene editing therapy as part of a clinical trial. Meanwhile,

CRISPR technology has opened channels for business, but there are still many

Another way in which personalized medicine is driven by advancements in technology is in healthcare robotics. The introduction of robotics in healthcare is

for routine tasks such as blood pressure tests, pap smears, vaccinations, etc.

*DOI: http://dx.doi.org/10.5772/intechopen.88951*

follow-ups [9].

A recent study examined the effects of clinical decision-support systems (CDSSs) on practitioner performance and patient outcomes. Clinical decision-support tools made available to practitioners and patients, such as *The Machine-Human Collaboration in Healthcare Innovation DOI: http://dx.doi.org/10.5772/intechopen.88951*

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

leading cause of death worldwide ([4]: p. 139).

**5. Decision-making tools for practitioners**

support the diagnostic process ([8]: p. 1).

clinical data, claims, and socioeconomic figures ([8]: p. 2).

neural network.

50 years ([4]: p. 139).

risk but suffer from misdiagnosis and are given unnecessary and sometimes harmful treatments with negative side effects. The importance of improving the accuracy in detection and diagnosis is thus monumental given that cardiovascular disease is

The use of AI in cardiology has increased dramatically in the past 5 years. Machine learning algorithms now outperform many traditional algorithms, including the established risk prediction algorithm used by the American College of Cardiology (ACC)/American Heart Association (AHA), performing with a 3.6% predictive accuracy improvement over the ACC/AHA algorithm ([4]: p. 139). Still major challenges lie ahead. Before AI can reliably be utilized by any field of medicine let alone realize its potential for cardiac patients, the neural networks necessary for its application require constant and extremely timeconsuming expansion and revision. Key difficulties are (i) the extremely large amount of training data required by neural networks; (ii) the need to annotate (label) any dataset used for the training of a neural network; (iii) creating an understating of what computers learn given that the patterns and knowledge gained by a network are contained in the weights of the nodes of the network; and (iv) the risk of "overfitting" the training data when designing and training a

In other words, better efficiency of machine learning, together with improved accuracy with less training and data necessary, are all needed in order to approximate the efficiency of human learning and bring its relevance to a clinical setting. Given these challenges, AI in cardiac CT angiography has made tremendous gains in the past 10 years, and over the next 10 years, the expectation is that we will see more AI software development and use in cardiac imaging than in the past

A recent study revealed that one in every 71 cases from 6000 tissue samples of cancer patients across the US was misdiagnosed and up to one in five were misclassified. This same study reviewed 25 years of US malpractice claims and concluded that diagnostic errors were the cause of the most severe patient harm. According to the National Academies' Institute of Medicine, 10% of patient deaths and as much

What is more, it was not primarily the physicians who were the cause of most diagnostic errors. Instead, the study found, the fault lies primarily in substandard collaboration and synthesis of information in the healthcare system as well as communication gaps, and that the healthcare system as a whole failed to effectively

Now let us consider the application of AI to address the need for collaboration and integration in healthcare to improve the diagnostic process. Optum, a leading company providing these solutions for the healthcare industry, developed a program called Care Coordination Platform. It processes vast amounts of data and provides a comprehensive overview of every patient's full medical history, allowing healthcare providers an immediate, complete picture of each patient. The platform suggests the most appropriate and cost-effective treatment options; identifies highrisk patients before symptoms occur; and has adaptive algorithms that incorporate

as 17% of hospital complications are a result of diagnostic errors ([8]: p. 1).

A recent study examined the effects of clinical decision-support systems (CDSSs) on practitioner performance and patient outcomes. Clinical decision-support tools made available to practitioners and patients, such as

**92**

computer-generated clinical knowledge and patient-related information, were studied. When such data are filtered and made available at appropriate times, it was shown to enhance patient care. CDSS can also send reminders, warnings, test results, check for drug interactions, dosage errors, contraindications, and list patients eligible for specific interventions such as immunizations and follow-ups [9].

The study found that CDSSs that require large amounts of data entry adversely affect physician satisfaction and use of the system. When large amounts of data required for the CDSS to be effective are incomplete, diagnoses will be less accurate, or it will take longer to complete the data, resulting in delays in the CDSS to accurately deliver advice. Anticoagulant-prescribing CDSSs are a case in point; the data required are more complex and have a higher patient variance.

CDSSs requiring limited number of patient data items for input were the most used and clinically successful. Examples include preventative care reminder systems for routine tasks such as blood pressure tests, pap smears, vaccinations, etc.

The study concluded that CDSSs become more effective as they become more specified and sensitive in their levels of advice but at the same time the manual input of data needs to be minimized, and the CDSS advice needs to be available in a timely manner to be of relevance for physician use.

### **6. Advanced genetic technologies**

Personalized medicine is making an impact in advanced genetic technologies as well. Genome modulation (modifications), in particular, has an array of applications, from energy, food, and industrial to medical. Researchers are turning to genome modulation with the hope that it will provide the key to understanding and answering some of life's most difficult and challenging questions.

Genome modulation applied medically has been known as gene therapy, but with new technologies, has evolved into the science of gene editing. At the forefront of this technology is what is now known as Clustered Regularly Interspaced Short Palindromic Repeats, or CRISPR. Experts claim CRISPR has brought with it new streams of business based on its cutting-edge technologies [10].

One recent example of CRISPR technology application is the correction of blood clotting problems in newborn and adult mice, with marked success. The aim is to cure the majority of patients with hemophilia B with CRISPR-based gene targeting [10].

Growing interest in CRISPR technology is speeding its transition to research, clinical trials, and applications in humans, and it was recently tested on a human being for the first time. In China, a patient diagnosed with terminal lung cancer was treated with CRISPR gene editing therapy as part of a clinical trial. Meanwhile, clinical trials in the United States using CRISPR technology are underway.

CRISPR technology has opened channels for business, but there are still many daunting challenges before its application can be realized clinically. One such hurdle, if not the most significant one, is regulation. Personalized medicine, including gene editing technologies, is involved in a regulatory business, involving peer-reviewed, published papers and clinical trials. Even in cooperation with the FDA, for example, it could still take a new technology 20 years to be approved.

### **7. Robotics and advanced medical devices**

Another way in which personalized medicine is driven by advancements in technology is in healthcare robotics. The introduction of robotics in healthcare is driven by the desire to improve quality, safety, and control expenditure. Surgical robots, service robots, companion robots, cognitive therapy robots, robotic limbs and exoskeletons, humanoids, and rehabilitation robots are just a few applied areas already making use of this technology.

Despite clear advantages and a promising, growing future of robotics in healthcare and in medical devices, there is a need for a robotics strategy that addresses concerns and challenges. Patient and cultural perceptions, liability rules, and ethical debates present challenges to the integration and development of robotics in healthcare.

A recent study suggested that a deliberative approach is needed to find a balance between developing overarching rules in this industry and allowing innovation to flourish, and that robots and robotic devices should be viewed as "augmenting human capabilities and empowering professionals in their role" so that patients would have a more positive perception of robotics in their healthcare settings [11].

Another recent study suggests that robotics lags behind its healthcare potential primarily because the industry has yet to live up to a primary principle of Cybernetics. According to this theory, robots and robotic devices should have a high level of adaptation and reaction to environments, resulting in complete interaction between humans and robots [12]. In this study, robotics-assisted surgery, rehabilitation, prosthetics, and companion systems were analyzed.

In all areas, the study concluded, for one, that the real potential of robotics in these fields requires a much greater degree of customization. Customization is defined as the robotic technology's adaptation to clinicians and patients, and the authors argue that existing robotic systems are limited in their ability for customization, which greatly limits its practical use in healthcare. The idea is that technology should adapt to users, rather than forcing users to adapt to technology.

Despite implicit or even explicit claims of the superiority of robotic systems for healthcare, when compared to more traditional methods, the clear advantage of these systems is currently unproven and highly dependent on the skills of the users. Therefore, the success of such technologies is still heavily dependent on adequate training and experience.

### **8. 3D printing drugs**

As our last example in this chapter of the impact of technology in medicine, consider the "3D" printing (3DP) of oral drugs. While it may sound novel and revolutionary, drug manufacturing using 3DP technology is actually a combination of well-established technologies first developed to meet the needs of engineering prototypes [13], namely building objects by creating sequentially added layers.

There are a few driving forces behind the 3DP of oral drugs: personalization, on-demand capability, and the ability to manufacture drugs in new, decentralized locations. A recent study suggests that the key to the success of utilizing 3DP technology for healthcare and medicine is to maximize patient benefits while providing production efficiency, and that 3DP has a proven track record. As such, they argue, its future is clearly viable in three fields, namely preclinical, within a pharmaceutics framework; innovative drug delivery concepts; and decentralizing the drug manufacturing process [13].

Although this technology is currently niche and not an alternative to mainstream mass production processes, there is a clear place for 3DP in healthcare and medicine and its role will be more clearly defined in the future by incorporating

**95**

change.

behavioral economics.

*The Machine-Human Collaboration in Healthcare Innovation*

live, and thus adapt personalized treatment accordingly.

lifestyles changes needed to effectively manage the disease.

tive activities management is done mostly by individuals themselves.

research in social psychology and behavioral economics.

considerations such as ideal population product profile, drug formulation, and engineering, as well as the management of regulatory and supply chain factors [13].

Due to the technological advances described above along with the growing need for smarter, preventive, more accurate, and effective medicine, the healthcare industry is advancing into the digital age—the digital health revolution. The digital health revolution is made possible by advances in medical information technologies—information storage, data analysis, mobile, sensors, and genetic information. All this will enable the capture and analysis of vast amounts of information about patients, populations, environments, and the lifestyle in which they

The technological advances facilitating personalized medicine enable the capture of major challenges of the health system and chronic diseases [3]. The reason that chronic diseases are the major financial burden on the healthcare system is because most chronic disorders develop outside healthcare settings, and patients with these conditions require continuous interventions to make behavioral and

The challenge with chronic diseases is the transition from health to disease with late-onset symptoms that can be irreversible. Coincidentally, the majority of chronic diseases can be prevented or delayed in life through interventions as described above, which results in an extended health span (the duration of individual life spent in a state of wellness, free of disease). Current chronic disease management is characterized by fragmented interventions and communication and recommendations from specialists, becoming constitutive only following the onset of disease symptoms. At the stage where an individual is free of symptoms, preven-

Due to the growing evidence that links patients' activation, defined as the patients' willingness and ability to take independent actions to manage their health and care, to their health and cost outcomes, methods and tools need to be developed to increase patient activation and engagement to accelerate the needed behavior

Encompassing both the design thinking approach and behavioral economics can motivate people to change their current behavioral health-related habits to improve their health. This underscores the need to devise a personalized, preventive medical infrastructure with recommendations and motivation mechanisms taken from

Behavioral economics aims at realizing the human irrational decision process underpinning suboptimal outcomes, which in our context translates to unhealthy behavior patterns. In recent years, government agencies around the world have been employing behavioral economics models and methods as complementing means to standard public-policy tools that are implemented by decision-makers. These measures, based on the "Nudge" theory [14], are used for preventing policyimplementation failures and positively impacting motivation and decision-making by individuals and groups. Thus far, this theory has inspired a variety of applications in areas such as education, health, safety and environment. Extensive applied research, performed in the UK by the Department for Environment, Food & Rural Affairs [15], has outlined nine principles influencing human behavior, based on

Integrating elements from persuasive technologies for supporting extrinsic motivation factors stemming from communication and social aspects, such as

**9. How to engage the patient to the human-machine innovation?**

*DOI: http://dx.doi.org/10.5772/intechopen.88951*

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

already making use of this technology.

healthcare.

care settings [11].

training and experience.

**8. 3D printing drugs**

facturing process [13].

driven by the desire to improve quality, safety, and control expenditure. Surgical robots, service robots, companion robots, cognitive therapy robots, robotic limbs and exoskeletons, humanoids, and rehabilitation robots are just a few applied areas

Despite clear advantages and a promising, growing future of robotics in healthcare and in medical devices, there is a need for a robotics strategy that addresses concerns and challenges. Patient and cultural perceptions, liability rules, and ethical debates present challenges to the integration and development of robotics in

A recent study suggested that a deliberative approach is needed to find a balance between developing overarching rules in this industry and allowing innovation to flourish, and that robots and robotic devices should be viewed as "augmenting human capabilities and empowering professionals in their role" so that patients would have a more positive perception of robotics in their health-

Another recent study suggests that robotics lags behind its healthcare potential primarily because the industry has yet to live up to a primary principle of Cybernetics. According to this theory, robots and robotic devices should have a high level of adaptation and reaction to environments, resulting in complete interaction between humans and robots [12]. In this study, robotics-assisted surgery, rehabilita-

In all areas, the study concluded, for one, that the real potential of robotics in these fields requires a much greater degree of customization. Customization is defined as the robotic technology's adaptation to clinicians and patients, and the authors argue that existing robotic systems are limited in their ability for customization, which greatly limits its practical use in healthcare. The idea is that technol-

Despite implicit or even explicit claims of the superiority of robotic systems for healthcare, when compared to more traditional methods, the clear advantage of these systems is currently unproven and highly dependent on the skills of the users. Therefore, the success of such technologies is still heavily dependent on adequate

As our last example in this chapter of the impact of technology in medicine, consider the "3D" printing (3DP) of oral drugs. While it may sound novel and revolutionary, drug manufacturing using 3DP technology is actually a combination of well-established technologies first developed to meet the needs of engineering prototypes [13], namely building objects by creating sequentially added

There are a few driving forces behind the 3DP of oral drugs: personalization, on-demand capability, and the ability to manufacture drugs in new, decentralized locations. A recent study suggests that the key to the success of utilizing 3DP technology for healthcare and medicine is to maximize patient benefits while providing production efficiency, and that 3DP has a proven track record. As such, they argue, its future is clearly viable in three fields, namely preclinical, within a pharmaceutics framework; innovative drug delivery concepts; and decentralizing the drug manu-

Although this technology is currently niche and not an alternative to mainstream mass production processes, there is a clear place for 3DP in healthcare and medicine and its role will be more clearly defined in the future by incorporating

ogy should adapt to users, rather than forcing users to adapt to technology.

tion, prosthetics, and companion systems were analyzed.

**94**

layers.

considerations such as ideal population product profile, drug formulation, and engineering, as well as the management of regulatory and supply chain factors [13].

### **9. How to engage the patient to the human-machine innovation?**

Due to the technological advances described above along with the growing need for smarter, preventive, more accurate, and effective medicine, the healthcare industry is advancing into the digital age—the digital health revolution. The digital health revolution is made possible by advances in medical information technologies—information storage, data analysis, mobile, sensors, and genetic information. All this will enable the capture and analysis of vast amounts of information about patients, populations, environments, and the lifestyle in which they live, and thus adapt personalized treatment accordingly.

The technological advances facilitating personalized medicine enable the capture of major challenges of the health system and chronic diseases [3]. The reason that chronic diseases are the major financial burden on the healthcare system is because most chronic disorders develop outside healthcare settings, and patients with these conditions require continuous interventions to make behavioral and lifestyles changes needed to effectively manage the disease.

The challenge with chronic diseases is the transition from health to disease with late-onset symptoms that can be irreversible. Coincidentally, the majority of chronic diseases can be prevented or delayed in life through interventions as described above, which results in an extended health span (the duration of individual life spent in a state of wellness, free of disease). Current chronic disease management is characterized by fragmented interventions and communication and recommendations from specialists, becoming constitutive only following the onset of disease symptoms. At the stage where an individual is free of symptoms, preventive activities management is done mostly by individuals themselves.

Due to the growing evidence that links patients' activation, defined as the patients' willingness and ability to take independent actions to manage their health and care, to their health and cost outcomes, methods and tools need to be developed to increase patient activation and engagement to accelerate the needed behavior change.

Encompassing both the design thinking approach and behavioral economics can motivate people to change their current behavioral health-related habits to improve their health. This underscores the need to devise a personalized, preventive medical infrastructure with recommendations and motivation mechanisms taken from behavioral economics.

Behavioral economics aims at realizing the human irrational decision process underpinning suboptimal outcomes, which in our context translates to unhealthy behavior patterns. In recent years, government agencies around the world have been employing behavioral economics models and methods as complementing means to standard public-policy tools that are implemented by decision-makers. These measures, based on the "Nudge" theory [14], are used for preventing policyimplementation failures and positively impacting motivation and decision-making by individuals and groups. Thus far, this theory has inspired a variety of applications in areas such as education, health, safety and environment. Extensive applied research, performed in the UK by the Department for Environment, Food & Rural Affairs [15], has outlined nine principles influencing human behavior, based on research in social psychology and behavioral economics.

Integrating elements from persuasive technologies for supporting extrinsic motivation factors stemming from communication and social aspects, such as

incentives and norms, will have a great impact on the implantation and engagement of the patient. These technologies provide effective means for supporting the operationalization of "Nudge" theory, for example by producing email messages for raising awareness regarding fulfillment of required assignments, delivering informative messages related to the performance of these assignments, and promoting a climate that reflects social norms within online social networks. Studies have shown that nudging could also incorporate various approaches that focus on changing physical or social environments to increase the likelihood of certain behaviors. This could include the provision of social norm feedback, which will increase the likelihood of healthy behaviors, altering the defaults surrounding how food and drinks are served, or even changing the layout of buildings to encourage physical activity ([16]: p. 263). Nudging focuses on a set of simple and low-cost remedies that may not require any legislation and can be used to solve most of the problems emanating from human contact. On the other hand, nudging could also enhance behaviors that may worsen the health of individuals ([16]: p. 264). For instance, food products may be labeled as healthy, hence causing consumers to ignore the energy content, which may lead to excessive consumption of such products.

The value of technologies that increase patient activation and engagement is paramount due to the increasing incidence of chronic diseases. Therefore, developing "patient-centered" technologies will increase adoption and diffusion of these technologies.

### **Author details**

Neta Kela-Madar1 \* and Itai Kela2

1 The Sami Shamoon College of Engineering, Ashdod, Israel

2 Israel Innovation Authority, Jerusalem, Israel

\*Address all correspondence to: neta.kela@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**97**

*The Machine-Human Collaboration in Healthcare Innovation*

practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings. Journal of the American Medical Informatics

[10] Stangelli J. CRISPR Tech Heralds Hype, Hope, and Hurdles for Gene-Based Therapeutics. 2016. Available from: http://www.bio-itworld.com/2016/12/12/ crispr-tech-heralds-hype-hope-andhurdlesfor-gene-based-therapeutics-orhow-to-cure-cancer-and-aids-with-thisone-weirdtrick.aspx [Accessed: Dec. 20,

Association. 2011;**18**:327-334

2016, in Bio-ITWorld: Online]

Sheikh A. Health care robotics: qualitative exploration of key challenges and future directions. Journal of Medical Internet Research. 2018;**20**(7):e10410. DOI: 10.2196/10410

Signal Processing and Control.

10.1080/17425247.2017.1371698

[14] Thaler RH, Sunstein CB. Nudge. New Haven, CT: Yale University Press;

[15] Department for Environment, Food, and Rural Affairs. A Framework for Proenvironmental Behaviours. 2008. Available from: www.defra.gov.uk

[16] Roland M, Kelly PM, Suhrcke M. Judging nudging: Can nudging improve population health? BMJ.

2011;**342**:263-265

2014;**10**:65-78

2008

[11] Cresswell K, Cunningham-Burley S,

[12] Andrade AO, Pereira AA, Walter S, Almeida R, Loureiro R, Compagna D, et al. Bridging the gap between robotic technology and health care. Biomedical

[13] Hsiao WK, Lorber B, Reitsamer H, Khinast J. 3D printing of oral drugs: a new reality or hype? Expert Opinion On Drug Delivery. 2018;**15**(1):1-4. DOI:

*DOI: http://dx.doi.org/10.5772/intechopen.88951*

[1] Embracing the future of work to unlock R&D productivity. Deloitte. 2018. Available from: https:// www2.deloitte.com/uk/en/pages/ life-sciences-and-healthcare/ articles/measuring-return-frompharmaceutical-innovation.html

[2] Sagner M, McNeil A, Puska P, et al. The P4 health spectrum—A predictive, preventive, personalized and participatory continuum for promoting healthspan. Progress in Cardiovascular Diseases.

[3] Kvedar JC. Digital medicine's march on chronic disease. Nature Biotechnology. 2016;**34**:239-246

[4] Dilsizian ME, Siegel EL. Machine meets biology: A primer on artificial intelligence in cardiology and cardiac imaging. Current Cardiology Reports.

[5] Frölich H et al. From Hype to Reality: Data Science Enabling Personalized Medicine. 2018. DOI: 10.1186/s12916-

[7] Phillips MA, Harrington TS, Srai JS. Convergent innovation in emerging healthcare technology ecosystems: Addressing complexity and integration. Technology and Innovation Management Review.

Misdiagnosis: Using AI to Solve a Quiet Crisis in Healthcare. USA: Aibusiness,

**References**

2016;**59**(5):506-521

2018;**20**:139

2017;**7**(9):44

Texas; 2018. p. 1

018-1122-7 [Online]

[6] Allen LN, Christie GP. The emergence of personalized health technology. Journal of Medical Internet

Research. 2016;**18**(5):e99

[8] Ainslie J. An Epidemic of

[9] Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on *The Machine-Human Collaboration in Healthcare Innovation DOI: http://dx.doi.org/10.5772/intechopen.88951*

### **References**

*Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human...*

incentives and norms, will have a great impact on the implantation and engagement of the patient. These technologies provide effective means for supporting the operationalization of "Nudge" theory, for example by producing email messages for raising awareness regarding fulfillment of required assignments, delivering informative messages related to the performance of these assignments, and promoting a climate that reflects social norms within online social networks. Studies have shown that nudging could also incorporate various approaches that focus on changing physical or social environments to increase the likelihood of certain behaviors. This could include the provision of social norm feedback, which will increase the likelihood of healthy behaviors, altering the defaults surrounding how food and drinks are served, or even changing the layout of buildings to encourage physical activity ([16]: p. 263). Nudging focuses on a set of simple and low-cost remedies that may not require any legislation and can be used to solve most of the problems emanating from human contact. On the other hand, nudging could also enhance behaviors that may worsen the health of individuals ([16]: p. 264). For instance, food products may be labeled as healthy, hence causing consumers to ignore the energy content,

The value of technologies that increase patient activation and engagement is paramount due to the increasing incidence of chronic diseases. Therefore, developing "patient-centered" technologies will increase adoption and diffusion of these

**96**

**Author details**

technologies.

Neta Kela-Madar1

\* and Itai Kela2

2 Israel Innovation Authority, Jerusalem, Israel

provided the original work is properly cited.

\*Address all correspondence to: neta.kela@gmail.com

1 The Sami Shamoon College of Engineering, Ashdod, Israel

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

which may lead to excessive consumption of such products.

[1] Embracing the future of work to unlock R&D productivity. Deloitte. 2018. Available from: https:// www2.deloitte.com/uk/en/pages/ life-sciences-and-healthcare/ articles/measuring-return-frompharmaceutical-innovation.html

[2] Sagner M, McNeil A, Puska P, et al. The P4 health spectrum—A predictive, preventive, personalized and participatory continuum for promoting healthspan. Progress in Cardiovascular Diseases. 2016;**59**(5):506-521

[3] Kvedar JC. Digital medicine's march on chronic disease. Nature Biotechnology. 2016;**34**:239-246

[4] Dilsizian ME, Siegel EL. Machine meets biology: A primer on artificial intelligence in cardiology and cardiac imaging. Current Cardiology Reports. 2018;**20**:139

[5] Frölich H et al. From Hype to Reality: Data Science Enabling Personalized Medicine. 2018. DOI: 10.1186/s12916- 018-1122-7 [Online]

[6] Allen LN, Christie GP. The emergence of personalized health technology. Journal of Medical Internet Research. 2016;**18**(5):e99

[7] Phillips MA, Harrington TS, Srai JS. Convergent innovation in emerging healthcare technology ecosystems: Addressing complexity and integration. Technology and Innovation Management Review. 2017;**7**(9):44

[8] Ainslie J. An Epidemic of Misdiagnosis: Using AI to Solve a Quiet Crisis in Healthcare. USA: Aibusiness, Texas; 2018. p. 1

[9] Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings. Journal of the American Medical Informatics Association. 2011;**18**:327-334

[10] Stangelli J. CRISPR Tech Heralds Hype, Hope, and Hurdles for Gene-Based Therapeutics. 2016. Available from: http://www.bio-itworld.com/2016/12/12/ crispr-tech-heralds-hype-hope-andhurdlesfor-gene-based-therapeutics-orhow-to-cure-cancer-and-aids-with-thisone-weirdtrick.aspx [Accessed: Dec. 20, 2016, in Bio-ITWorld: Online]

[11] Cresswell K, Cunningham-Burley S, Sheikh A. Health care robotics: qualitative exploration of key challenges and future directions. Journal of Medical Internet Research. 2018;**20**(7):e10410. DOI: 10.2196/10410

[12] Andrade AO, Pereira AA, Walter S, Almeida R, Loureiro R, Compagna D, et al. Bridging the gap between robotic technology and health care. Biomedical Signal Processing and Control. 2014;**10**:65-78

[13] Hsiao WK, Lorber B, Reitsamer H, Khinast J. 3D printing of oral drugs: a new reality or hype? Expert Opinion On Drug Delivery. 2018;**15**(1):1-4. DOI: 10.1080/17425247.2017.1371698

[14] Thaler RH, Sunstein CB. Nudge. New Haven, CT: Yale University Press; 2008

[15] Department for Environment, Food, and Rural Affairs. A Framework for Proenvironmental Behaviours. 2008. Available from: www.defra.gov.uk

[16] Roland M, Kelly PM, Suhrcke M. Judging nudging: Can nudging improve population health? BMJ. 2011;**342**:263-265

### *Edited by Sílvio Manuel Brito*

What is super creativity? From the simple creation of a meal to the most sophisticated artificial intelligence system, the human brain is capable of responding to the most diverse challenges and problems in increasingly creative and innovative ways. This book is an attempt to define super creativity by examining creativity in humans, machines, and human–machine interactions. Organized into three sections, the volume covers such topics as increasing personal creativity, the impact of artificial intelligence and digital devices, and the interaction of humans and machines in fields such as healthcare and economics.

Published in London, UK © 2020 IntechOpen © StationaryTraveller / iStock

Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human - Machine Collaborations

Toward Super-Creativity

Improving Creativity in Humans, Machines,

and Human - Machine Collaborations

*Edited by Sílvio Manuel Brito*