When innovation disrupts an established industry, the capacity of jobs, or crafts, within that industry changes. Traditional, often century-old tasks or skills disappear or shift into other completely new or transformed ones. The essence or nature of a job, the work processes involved, and the way the job’s value is perceived and estimated, equally shift into something completely new. Jobs disappear but a multitude of new jobs, related industries, and synergies emerge at the same time.
Before Johann Gutenberg’s invention of movable-type printing, scribes or monks labored for up to one year to copy a single book and often “illuminated” large capital letters and the margins of the books with colorful designs and miniature scenes. They copied these rare and valuable books on processed calfskin called vellum and later on another innovative product called paper.
The automated press, oil-based ink, and the availability of easily produced paper to print on drastically changed the way books were made, it also changed the way these books were designed. It created the need for layout and other new pagination rules. It changed the way we read and look at the written word too. It made the written word available to more and more people.
Although illustrations and “illuminations” were initially still hand-drawn, they were quickly replaced by engravings and later by other mechanized printing techniques.
The job of the scribe was marginalized and eventually disappeared but print shops popped up everywhere and hundreds of new jobs were created. From ink and paper sellers, printers, and bookbinders to book stores, illustrators, and graphic designers just to name a few.
We live in a time of technological innovation called digital transformation, on the verge of a revolution, and the typical tasks of the designer are continuously being disrupted by the rise of new technologies; innovative hardware and digital tools, computational systems, cognitive and artificial intelligence and a lot, a lot of applications. In short, the tasks are becoming easier due to the automation of certain parts of the design process. Automation replaces more routine or repetitive tasks, allowing employees to focus more on tasks that utilize creativity and emotion. We know that certain jobs will disappear. Which kind of jobs will be the first to go?
The use of software, templates, apps, and DIY solutions are all early forms of automated tools that we use on a daily basis today. They replace some parts of our tasks we were doing manually only a couple of years ago.
In itself, they are shortcuts to a quicker result, as we will try to show, this might be a case where the solutions are part of the problem.
Erik Brynjolfsson and Andrew McAfee point out in “Race Against the Machine” and “The Second Machine Age” that machines are getting more human. Pattern recognition and complex communication are now manageable by computers. Will people still have any comparative advantage as we head deeper into the second half of the chessboard[1]? Will computers be able to take over those very specifically ‘human’ skills a designer or an artist manages?
Parallel to this thought, Richard Florida explains in The Rise of the Creative Class about a fundamental shift, bringing tasks that were before mostly carried out by designers, writers, artists, and scientists to the mainstream. The growing importance of creativity in people’s work lives and the emergence of a class of people unified by their engagement in creative work. This Creative Class now determines how the workplace is organized, what companies prosper or go bankrupt, and even which cities thrive.
However, if we cross to the other side, Scot Timberg reveals in Culture Crash: The Killing of the Creative Class that due to all this transformation and innovation, and the rise of the new creative class if you wish, it has been made very difficult if not impossible for the earlier creative group of designers, writers, and artists to earn a decent living. The very particular tasks that were built upon high levels of acquired skill, talent, and experience, are now available to everyone. Anybody can be a designer, a musician or a writer, you just need to use/have access to the right tools.
The idealization of changes produced by digital technologies and the far-reaching consequences of the digital revolution is also questioned by Andrew Edwards in Digital Is Destroying Everything. How do we look at all these changes without losing our real values? Are we losing parts of our humanity as we become more and more ‘automated’?
A lot of the so-called creative tasks that are done by the likes of artists and designers are difficult to automate because creativity and sensing emotion is difficult to computerize.
Although some claim that they will never be replaced by a computer (Hubert Dreyfus’ What Computers Can’t do) new developments and progress within the fields of artificial intelligence, affective computing, neuroscience, and creative cognition – the list is quite long – have shown that we are making considerable progress within these fields.
The rise of new technology brings a discontinuity of universal values. The design has always been in expansion, from graphics and objects to information and interaction, then to business and governance with “design thinking”. There is a disruption of the process of creation or design on its way. By making the design more accessible have we rid it of its magic? Do we need to reassess the essential contribution of design? Or reassert it?
The Creative Process
Before we try to map down all the elements within the creative process, we need to clarify what exactly we understand when we talk about creativity. What is creation? When does it happen and who does it?
Several models of the creative process have already been formed. Mihaly Csikszentmihalyi’s five-phase model, James C. Kaufman’s four C model, the creative cognition approach in neuroscience, the explicit-implicit interaction (EII) theory by Helie and Sun, and more. Which model do we use best on the basis of our map?
We see the creative process as an accumulation of cognitive, emotive, and intuitive (Head-Heart-Gut) steps within the human thinking process, combined with a high level of soft skills, craftsmanship, and experience. How can we map down all these steps?
In Linkography: Unfolding the Design Process Gabriela Goldschmidt presents linkography, a method for the notation and analysis of the design process. With linkography, Goldschmidt shows that there is a logic to the creative process – that it has nothing to do with magic. Linkography draws on design practice, protocol analysis, and insights from cognitive psychology.
In the early 1990’s Tim Berners-Lee, a researcher at the CERN particle physics lab in Geneva had recently developed the World Wide Web. Marc Andreessen recruited a team of programmers to create a better way to explore the Web. After two months of 80-hour weeks in the computer lab, living on chocolate chip cookies and milk, Andreessen and his team churned out a graphical browser called Mosaic, which used pictures and mouse clicks to navigate through information. This would be the beginning of an entirely new format.
Graphic Design, an applied art form that in its own time emerged from new technologies in the print and publication industry, developed and shifted towards digital, driven by hardware innovation and online communication. It’s important for our map to recognize that there are more than usual elements of technology involved within the digital design process and that the final end product of digital design can be, but not always is, 100% digital. What is the full circle of steps within the digital design process and more precisely within the web design process?
Some steps might be very difficult to map down though. Is creativity a mere play of numbers? How do we track soft skills and unmeasurable elements like ‘good taste’. How do we measure what they call ‘bizarre domains’ within intuition? Or even simpler things like ambiguity. When there are two viable solutions we use our ‘gut feeling’ to make a choice. What is a ‘gut’ feeling? What about creative revolt, the decision to go against everything that has been done before and is accepted as the norm? Can a computer make these decisions?
Can a computer replace human consciousness?
Is imperfection – another word for the hand of the artist – the human factor that differentiates us from machines?
The way we see computers now, the concept of the home computer sitting on our desk, is already changing but will possibly entirely change when these computers become more intelligent and more intertwined with everything we do and have around us. Kurzweil’s moment of Singularity could be a crucial milestone here.
A great example from the world of Science Fiction of how we could imagine the future of artificial intelligence is the fictional robot called TARS in Christopher Nolan’s 2014 movie Interstellar.
TARS is portrayed as a complex tool that performs a role similar to another crewmate on the ship but has none of the rights afforded to humans. It’s a model for a system that is user-friendly (combining fluid natural dialogue with common sense) while not seeking to re-create the physical and cognitive limitations of humans.
TARS is not only able to explain its decisions in terms that humans can understand, but it also is able to accept inputs from humans that alter its decision-making at the most basic level—for example, its “honesty or humor parameters,” which seem to range from zero to 100 percent. In order for it to do an adequate job of assisting humans, TARS needs this kind of transparency and certain humanlike functionalities, such as the ability to speak and understand language, but it needn’t be precisely made in man’s own image, either.
Between now and the time when this kind of system will be possible, we will probably learn a lot more about human consciousness, and the possibility of replicating it in machines.
For now, there are some early signs from research on consciousness to suggest that digital computers may never be able to experience conscious thought.
Automation
What is automation?
Definition – Oxford Dictionary:
“Automatic control of the manufacture of a product through a number of successive stages; the application of automatic control to any branch of industry or science; by extension, the use of electronic or mechanical devices to replace human labor”
There are currently two basic forms of automation. Mechanization on one side and computerization on the other.
Mechanization is the oldest form of automation and takes care of the most tangible, production tasks like manufacturing. A lot of research has been done within the field of automation in manufacturing.
Computerization emerged with the arrival of computers (hardware) and programming (software). Computerization encompasses all the things computers can do, or will be able to do. Computers help humans with their tasks. It’s all about computer-human interaction.
So basically there is a duality when tasks are allocated. Fitts (1951) presented a list of general tasks covering both humans and machines, illustrating where the performance of one category exceeds that of the other.
Will computers ever be able to complete those most typical human tasks, like decision making, having the insight or finding the ‘aha’ moment, seeing the bigger picture without losing an eye for detail, Q&A, overall sense of good and bad, empathy, personality, etc?
The Level of Automation (LoA), a term originally related to manufacturing and aviation, depends strongly upon the existence of flexible tools and advanced machinery on one side and the level of (artificial) intelligence or intelligent computer systems involved when human tasks are being replaced by machines on the other side. The level of automation in the context of these expert systems is most applicable to cognitive tasks such as the ability to respond to and make decisions based on system information. The more advanced the tool and the higher the AI, the higher the level of automation will be.
There are different ways to see the Levels of Automation concept. The Sheridan-Verplanck 10 levels of automation (Sheridan 1980) for example incorporates issues of feedback as well as the relative sharing of functions. Another way of viewing levels of computerization is from a perspective of human information processing (Wickens and Hollands 2000), which determines that human information processing consists of the following four steps: acquire the information, analyze and display the information, decide action based on the analysis, and finally implement the action based on the decision (Parasuraman, 2000).
Examples of early forms of automation (mechanization) can be taken from the 19th-century industrial revolution. Until the 19th century, shoemaking was a traditional handicraft, but by the century’s end, the process had been almost completely mechanized, with production occurring in large factories. Despite the obvious economic gains of mass production, the factory system produced shoes without the individual differentiation that the traditional shoemaker was able to provide.
The main innovation here was not the invention of the sewing machine, which happened almost 100 years before, but the introduction of it into the process of mass production in 1846, providing an alternative method for the mechanization of shoemaking. A second innovation, the McKay shoe stitching machine opened up bottlenecks within the production line. Parts were still done by hand. More and more of the manufacturing stages, such as pegging and finishing, became automated. By the 1890s, the process of mechanization was largely complete.
Research Note:
Before this shift to mass production, the design of shoes was mainly done by craftsmen, cobblers and small family-run shoe-making enterprises. After the automation revolution shoes were designed specifically for mass production. Cheaper materials, and simplified features for easier and faster production results. On the other hand, we also saw that due to reduced cost in production, the possibility emerges for more variation. There are more types of shoes and a larger variety of models and brands available for different and very specific purposes.
For example, the initial football boots from the 19th century were not designed at all, they were just normal working boots that were strong and hard, so really good for kicking around. The idea of creating boots that were solely for protection was eventually dropped and a focus on the design of a boot that would be light, agile, and give the user a better kick were the main factors for new boots.
In 1954 Adolf Dassler designed his football ‘boot’ with innovative new features. Screw-in studs which could be adapted to ground conditions, cut-away ankles, front lacing almost right down the front of the boot, a soft toe, and foam rubber interior. This design would change the way we look at football ‘shoes’ and it also changed how the sport is played. They were designed for a very particular use or purpose and were 100% ready for mass production.
Parallel with all this tremendous change and progress, the design and craft of making custom-made shoes, before something only available for the rich, did not change at all. The craft of bespoke manufacturing still exists although within a very small niche market.
If you want the real thing, you can still have shoes designed and made an authentic way. Authenticity becomes a commodity. (important for later – draw a parallel with web design)
A pattern:
A rare and valuable item/craft – innovation/automation/disruption + mass production = item becomes highly available and cheap/common – the rare and valuable item/craft remains within a small niche
Automation and design
What are the levels of automation currently available within the creative (design) process? The second layer of our map would be to find the connections between the human creative (design) process and the current status of LoA for each element.
The third level or layer of our map could be to connect all the tools and systems (applications) that are available now for each automated element of the process.
An example of automation in the design process:
Every designer looks for inspiration. What is inspiration? It could be seen as a cognitive process; the discovery of stimuli. Most often sensory or verbal stimuli, but in the case of a graphic and/or web designer usually visual stimuli.
The human brain takes in all those stimuli it thinks are useful and connects them to parts of the memory, like experience and emotive or intuitive associations.
The internet is an important tool to find visual stimuli. Google search is easy and powerful. An application like Pinterest for example is a semi-automated tool, one just has to enter a couple of keywords and push a button, which gives easy access to an organized collection of thousands of visual stimuli. Something that would have taken a considerable amount of time, numerous visits to the library or a physical personal collection of visuals, before the invention of the internet and Pinterest.
Within the Endsley and Kaber (1999) taxonomy, this process of finding inspiration could compare with the LoA called Shared control. Where computers and humans share the following roles (information-processing stages): monitoring and generating. The roles not shared with the computer would be selecting and implementing. The choice and the decision still lie with the human, the human decides to implement the results or not.
Research Note:
One of the most important concerns we could have when everything eventually becomes more automated is the question of value that we give to the end product. Do the typical characteristics of the final product (digital or not) or outcome determine/influence the Level of Automation or is it the other way around? Does the Level of Automation decide how or what the final product will look like? Possibly both. We change technology, but technology changes us too.
When we look closer specifically to web design, where the end result of any project or product is 100% digital, we could say that the entire design process of, in our case, web design could be automated if not completely computerized over time. Web design and automation, it’s a certainty we can not deny. Or can we?
To automate or not to automate
In 1963 Andy Warhol famously told Gene Swenson of Art News that he wanted everybody to be a machine. And that the reason he’s painting with the use of silkscreen is that he wants to be a machine. “I feel that whatever I do and do machine-like is what I want to do.” In his studio called the factory, he started to make silkscreen prints in an automated way. Warhol was the one to choose the subject, decide the composition, and someone else did the printing, which he would then manipulate. So the hand of the artist was not so important anymore, just the mind.
Automation changes how we perceive art and design; it also changes how designers perceive their job and the responsibilities towards the tasks involved.
We will analyze that most of the applied automation is still in its infancy and that it will take some considerable time for technology to catch up. The completion of the IoT and the IoE will play an important role. Advances in neuroscience, design thinking, and artificial intelligence will also push automation to other tasks than were initially believed not possible.
At this point, it’s important to understand that there is science and art in every profession. Soon, cognitive systems like IBM’s Watson for example will know the science better than a human. Humans will need to focus on the art of their profession, the creative elements only they can provide.
Automation of certain more repetitive and tedious tasks will create more time for the designer to spend on the really ‘creative’ tasks of ideation and conceptualization. A really good designer can ‘see’ how a designed product will function and how it will look before even starting to design process. A human can provide this overall sense of direction and a conceptual frame that is difficult to achieve for machines.
However, the designer’s tasks might also shift further away from the design of an end product to the design of architecture or a system. (UI/UX)
There’s a new trend of automated web design services, arguably started by The Grid. It’s a service to build basic websites which make design decisions—semantic ones—based on artificial intelligence. It analyzes your content to detect the best layouts, colors, fonts, and extra imagery for your site. Using cleverly chosen design basics (made by human designers) as the foundation, it’s hard to go wrong with it, and the result will probably be better than what an average web designer can do.
When something can be successfully automated, it means that its practices and standards are established enough not to need much human input. And this is obviously the beginning. There will be fierce competition about which service can deliver better designs, faster, and with less human intervention.
With too much automation the risk is great that the designer will jump over certain tasks to come to the solution or end result quicker. The danger in taking leaps or shortcuts instead of smaller, agile steps within the process, has been explained thoroughly in How to fly a Horse by Kevin Ashton. Big jumps will make seeing what is really there, what the real problem is, more difficult as opposed to seeing what you are thinking and aiming for a quick result. One can get blinded by expertise, and by automation. Creation is attention. To see the bigger picture without ever leaving the smallest of details out of sight.
From years of research in the aviation industry, we can learn that dependency on automation leads to a degradation of skills. William Langewiesche agrees on this when he talks about the Children of the Magenta: “We appear to be locked into a cycle in which automation begets the erosion of skills or the lack of skills in the first place and this then begets more automation.”
We don’t want automation to be blind reproduction either, like a painting with numbers set. [2]
We want automation to be a ‘race with the machines’ not against it, automation should help and assist humans in their everyday tasks not only to make it easier but also to make the end result better. We need the process to become more intelligent.
The need for authenticity and originality, characteristics or values that disappeared when there was more automation at play, are more and more in demand. There is already a quest for authenticity and originality going on at this moment, those very unique skills and talents that went lost when we jumped over all the small steps that give value and meaning.
Studying the levels of automation will certainly show the difference between an authentic artist and someone who relies solely on automation… could it become a measurement of artistic value or of authenticity? Everybody is creative or can be creative with so much ready-mades and shortcuts available. Everybody is part of the creative class. But can we really make a distinction between an original and an automated creative? How can we really measure how good or bad art, design, or anything creative is?
The most interesting question is not if everything will or can be automated, but if we can automate ourselves, should we?
Conclusion
Can we use our LoA map within other design industries?
How can a designer be aware of the progress in the automation of his industry and help him to stay relevant in the future?
How much change will occur in the design process, and which tasks will be automated first?
What do they have to look out for when dealing with these changes? When will it happen?
What can we conclude and adjust in the current design process to make it better, more intelligent, and more adaptable to change (flexible)?
[1] Gordon Moore: Moore’s Law and the second half of the chessboard
[2] In 1950 Max Klein, an engineer, and owner of the Palmer Paint Company of Detroit invented and developed a ‘kit’ system with a board on which light blue or grey lines indicated areas to paint. Each area had a number and a corresponding numbered paint color to use. In a way, this was an application of the many centuries-old stained glass craft. In short, this could be thought of as the complex craft of painting simplified by a system of numbers.
The Automated Designer was a research topic studied for an MBA thesis paper at The Berlin School of Creative Leadership 2017.
