The fears of the likelihood that technologies probably take human’s jobs and spark off mass unemployment are not a new idea (Anslow, 2016), it can be traced to the 18th century, which grew over the impact of machinery on jobs, especially in Great Britain as the pioneer of the Industrial Revolution. While automation technologies, which was defined as a process or procedure that performing without human assistance, can improve the speed, quality, and cost of available goods and services, they may also replace large-scale human labours (Mikell, 2003). The same fear has been persistent, the phenomenon was popularised as the term “technological unemployment” by the economist John Maynard Keynein the 1930s. Besides some warn of technological unemployment, some other experts disagreed on the influence that automation technologies might cause (West, 2015), they pointed out that the new technology may create corresponding job categories, which will employ those displaced workers. The argument about the extent of the technology would impact on employment is ever-present, and kept being redefined with the evolution of machines.

Nowadays, the advancement of technology is entirely different from the mechanization of the industrial revolution. With the onward pace of artificial intelligence applications, robotics and other forms of automation, the significance of technological unemployment was emphasised again as machines begin to reach and even beyond the ability provided by some segment of the workforce. Martin Ford, futurist and author of Rise of the Robots: Technology and the Threat of a Jobless Future, stated the jobs that are most at risk are those which “are on some level routine, repetitive and predictable”. In 2013, a highly cited research by Oxford Martin School named The Future of Employment revealed that employees engaged in “tasks following well-defined procedures that can easily be performed by sophisticated algorithms” are at risk of displacement (Frey & Osborne, 2013). The study examined 702 common occupations and found that both skilled and unskilled work and both high and low-paying occupations are affected by automation. In contrast with the past, automation is “blind to the colour of your collar (Kaplan, 2015).”  Even knowledge-based occupations, especially entry-level jobs, will be increasingly susceptible to automation via expert systems, machine learning and other AI-enhanced applications.

Then what type of job will safe from automation? The jobs that involve “genuine creativity”, like artists, scientists, position as developing a new business strategy must be in the first place. “For now, humans are still best at creativity but there’s a caveat there.” Ford notes, “I can’t guarantee you that in 20 years a computer won’t be the most creative entity on the planet. There are already computers that can paint original works of art. So, in 20 years who knows how far it’s going to go?” Creativity is thought of something so incorporeal, those creative class – who imagine art, innovations, words, novel strategies for companies and products that change the world – their jobs have always been considered outside the reach of the automated algorithm.

For the distance between creation and automation, generative art, the art created by an autonomous system that non-human and can determine features independently, might be the first step in this experimental field. Johann Philipp Kirnberger’s musical dice game “Musikalisches Würfelspiel” in 1757 is considered an early example of a generative system based on randomness. Dice were used for selecting musical sequences from a numbered pool of previously composed phrases. This system provided a balance between order and disorder. The structure was based on an element of order on one hand, and disorder on the other. Generative art also refers to algorithmic art, focusing on the computer-generated artwork that is determined algorithmically (Verostko, 1994). Algorithm a set of executable formal instructions for the computer, the algorithmic instruction code can be “generative” if it uses computers for computation, not only as storage and transmission media (Cramer, 2002).

But algorithm art is still far away from the attempt that creativity is taught and imitated by the machine, many features in the creative process are hard to algorithmize. Music, for instance, is regarded as one of the more unlikely areas for algorithm invasion. It is the art reflects the beating creativity of the human soul. We can have difficulty describing how exactly music affects us, changes our mood, and alters our consciousness. Those who make music can barely explain how inspiration strike. However, these assumptions are challenged now while the emergent of artificial intelligence and machine learning, while algorithm can be taught to evaluate the quality and originality of creations, even generate their own creations. In the following chapter, the case studies in current English-speaker pop music industry sphere will explore the consequences result from the algorithm and artificial intelligence.



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Groover, M. (2003). WIE Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. John Wiley & Sons.

West, D. M. (2015). What happens if robots take the jobs? The impact of emerging technologies on employment and public policy. Centre for Technology Innovation at Brookings, Washington DC.

Frey, C. and Osborne, M. (2013). ‘The future of employment: How susceptible are jobs to computerisation?’ Technological Forecasting and Social Change, 114, pp.254-280.

Kaplan, J. (2015). Humans need not apply: A guide to wealth and work in the age of artificial intelligence. New Haven, CT: Yale University Press.

Cramer, F. (2002) Concepts, notations, software. Available at: notations software art.html (Accessed 23 Nov. 2017).

Verostko, R. (1994). Algorithmic Art: Composing the Score for Fine Art. Available at: (Accessed 23 Nov. 2017).