Grey Areas

Grey Areas

I hit ‘P’ to activate the Pen tool. Then I click my touchpad while holding the Shift key. I repeat this action in front of my screen, carefully nudging the connecting nodes between curves, searching for the ‘perfect’ shapes. Of course, fully automated hardware and software can work without human intervention of this sort, but most programmes still require human assistance. Streamlined screens and friendly interfaces enable us to perform any number of functions quickly and seamlessly. Feeling overwhelmed by the tedium of these tasks, I wonder who is really making these shapes. Is it me, pushing the buttons, or is it the software, which provides a range of options that I simply choose from?

Boot, hover, enter a value and watch it happen! But in turn, the operator becomes controlled by the machine’s invisible interior, hidden beneath sleek frames and sharp pixels.¹ And what if you run out of shortcuts to hit or plug-ins to install, and there is nothing left to optimise in your digital workflow? Can these habits adapt towards a more deliberate approach to working with software? Can we create more collaboratively with design programmes? In this era of accessible (or cracked) software, presets and algorithmic solutions, it is critical to find an answer for how we define our relationships with programmes and to consider meaningful ways to interact with them.

This essentially means thinking more deeply about the creator and their tools. In every creative field, an author’s work is influenced by the tools available to them, and it isn’t any different with computers and software. From type design to photography, from music to visual arts, we are funnelled towards using particular technologies which in turn define our contemporary landscape. As newer and more powerful creative tools are introduced, there is opportunity to explore alternative visual forms and new working methodologies. Such developments need an explanation for us to understand more clearly the possibilities and pitfalls of these new functions, as well as how they disclose previously unexplored difficulties and biases. My own urge to probe software’s influence on visual practice is rooted in a duality – on the one hand, an optimism towards technology and what it can allow us to do, and on the other, an awareness of the constraints it fosters and how it can often inhibit certain ways of producing.

Computers were invented for processes that are expressed in numbers. Vilém Flusser concluded that ‘thinking in numbers overrides linear, historical thinking.’² So to practise a re-encoding of thought into numbers can be challenging because it requires looking beyond pure computational thinking to more human thought patterns, similar to how Ada Lovelace envisioned certain possibilities upon encountering Charles Babbage’s Analytical Engine, such as creating music or weaving elaborate patterns into fabric.³ But how do we translate our ideas into actions that a computer will understand? Is it pointless for most of us to try to comprehend how computers assimilate our instructions?

It might be argued that ‘apparatuses that are not fully automated play and function better than the human beings that operate them’⁴ but cultivating a common ground between user and tool can prove more fertile. The operator can be free to play while the computer does the thinking. Yet it is also true that computers mechanise thinking in such a way that the operator may become less skilled over time, relying more and more on their tools for control.⁵ ‘Technology promises to increase our access to knowledge and make the world more transparent, undermining ignorance and superstition. At least, that’s what we are told. But it sometimes feels like we have entered a new dark age, in which our lives play out amid a profusion of black boxes…’⁶ This is increasingly true for software, where our dependency means that we have little to no understanding of how applications actually work.

This becomes even more interesting when examining A. Michael Noll’s assertion that neither computers or software are inanimate. He regards them as active, creative partners, which, if fully exploited, can produce entirely new aesthetic experiences. The distinction between tool and computer software lies within its apparent participation.⁷ Even a relatively passive tool like a paintbrush can inspire the artist as they physically engage with it. But even if we regard brushes to be different from software, they leave a trace and it is natural to speculate how software might also do the same.

When writing about computer cybernetics in 1969, Robert Mallary suggested six different stages for the computer’s role in creative acts. In the first stage, the computer presents the artist with a set of proposals for consideration. These lack any qualitative judgement, yet show the symbiosis between man and machine. In the second stage, the relationship gets closer as the computer becomes an indispensable component in a creative act. In stage three, autonomous decisions are made by the computer with alternative possibilities that will ultimately govern the results of the work. However, these decisions still rely on the programme’s predefined parameters. The computer makes its decision only by selecting or eliminating between these options. As the computer reaches the fourth stage, the artist receives results that were not anticipated as they were not included in the predefined parameters. In the fifth stage, the artist can be completely eliminated as they are no longer required for a process which is now governed solely by the machine; the artist can only be a hindrance. However, the ability to ‘pull the plug’ is still in the hands of the human, a capability he will not possess if and when the computer reaches the sixth stage of ‘pure disembodied energy’.⁸

Attempting to define these different levels of participation seems more difficult nowadays. As users, how can we be sure about a programme’s predefined boundaries when the inside of the machine is concealed? This is also why unexpected results, glitches or bugs can be compelling. They inadvertently expose something about the programme that we usually don’t – and are not meant to – encounter, a fracture in the machine’s routine operation. It can seem that we are one step closer to discovering the machine’s secrets when we witness its wrongdoings. The scope of this territory is usually hidden from us and we can not really map out or come close to understanding its borders. Even during active exploration, there is an acceptance of the inner workings of the machine, but this new way of creating can nurture entirely original, open-ended processes and results, facilitated by programmes and their interfaces, and which are not possible to repeat without the computer’s participation.

Understanding this while staring too long at my screen, I decided to abandon the search for perfect curves and shapes. Rather, when designing my typeface Neureal, I tried to find a process that was faithful to the nature of the computer: looking for a way to work with the installed software that was not only deliberate but attentive to the trace of this particular tool, the kind of tool which ultimately accepts binary commands. I didn’t specifically attempt to decipher the level of its participation, but I deliberately blurred the lines between the two creators – me and the software. I thought I must understand more about not only the results that were created with programmes but how they impose their structure on the work created within their boundaries. It is not only about aesthetic results, but also how the process is influenced – click by click.

For the initial steps, I needed to search for a method that connected to the notion of the ‘essence’ of the computer, meaning their mathematical foundations and tools as proposed by Friedrich Kittler.⁹ Moreover, I wanted to relate this to the ‘aesthetics of interference’, informally termed by Kittler as visual results formed from a set of stylistic techniques that intentionally deploy machine noise, distortion, and clashing elements under the broad categorisation of postmodern aesthetics.¹⁰ With these in mind, my first digital drafts in Glyphs were based on visual distortions prompted mostly by screen errors or printing limitations. The deformity of the letters resulted in a reversed contrast, which was then perpetuated by experimenting with the extrapolation technique in Glyphs.

In mathematics, extrapolation is a type of estimation which is beyond the original observation range.¹¹ Translated to type design, it essentially means a calculation predicting how the relationship between two vector points of a letter will change and what they will look like. It is possible to generate an estimated visual result based on the software’s computations, but as it falls outside the primary points included in the drawing, the outcome becomes more uncertain. Unlike shapes created with interpolation, my set of extrapolated letters looked rough, unrefined, faulty, imperfect. This provided insight into how these ideas materialised inside the programme, and explains why the ‘defects’ are retained in the final design. The generated curves looked almost broken. Strange bumps and gaps were introduced by the machine as it positioned its output towards one axis.

As a next step, selecting from the new variations of letterforms, I redrew them in a new file, temporarily gaining control over the design. However, sometimes I went back to the machine for further guidance and commanded it to produce alternative results for some characters. In other cases, I asked it to improve certain predictions, based on newer drawings that I proposed after evaluating its previous suggestions. It became interesting to observe this two-way relationship: the output the programme made from my input and vice versa. In the end, I transformed the function within the software into a collaborator of some sort. Of course, the process of designing and finishing the full typeface took much longer after this phase of experimentation. However, aesthetically, the typeface’s DNA bears vestiges of this experimentation.

The final typeface has two cuts, defined by this process. The proportional cut is optimised for display sizes, and the monospaced version is for use with smaller text sizes. Taking advantage of spatial constraints, the monospace version further disrupts the broken shapes of the original design. The constructions of some letters become increasingly extreme, but at the same time allow the style to have more white space between letters and lines, making it possible to have a surprisingly readable result even at 5 or 6 point, despite the abnormal shapes. However, it disintegrates text set in display sizes. Together, the two styles support each other across a wide range of use cases. In the final character set, there is also an alternative set of letters, chosen from one of the directions in the original pool of shapes. This set fills some of the gaps with dark pixels, distorting and fragmenting the characters even further.