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Computing with (Im)possibility

Can the numerically unstable field of activity (the program is showing no error but it is not performing according to code) perhaps be the place to look for machine art?​

produced by: Ioannis Gkigkelos, Abi Price, Katarina Popovic

The terror of ‘no error’

We are ever more fascinated by our machines. And by numbers.  And by the possibility for the numbers to simulate reality. Ultimately, by our ability to predict and put everything in order, a process in which computers are essential.
“Our information age utopia is an error-free world of efficiency, accuracy, and predictability.” (Nunes, 2011, 4)In Error, Glitch Noise and Jam in New Media Cultures, editor Mark Nunes states in the intro, we are basically borrowing this expectation of perfect order or the absence of the mistake. Unfortunately, what hides behind this obsession of order is actually the objective of total control, loss of privacy, basic freedoms to decide along with human rights in which ‘Big data meets Big brother’ systems are possible at the social level as high as the state you happen to be a citizen of.
 “Singularitarians believe that the world is “knowable” and computationally simulatable and that computers will be able to process the messiness of the real world just like they have every other problem that everyone said couldn’t be solved by computers.” (Ito, 2018)
The problem with this seemingly hopeful idea is that it is profit and progress driven, or as Ito formulates it, it is “the natural evolution of the worship of exponential growth applied to modern computation and science.”(Ito, 2018) In such an environment an unpredictable change, an ‘accident’ or an (unaccounted for) error becomes an enemy. 

As Newton’s III law declare - For every action, there is an equal and opposite reaction.  One part of the world is largely fascinated by the order, but the other part is not. For the artistic community, the disturbance is one of the golden opportunities to open the other doors. It is almost as if the more control one tries to have, the more that particular system is interesting to be mined by the artistic community. The great example of the ‘unacceptable’ in the established state of things was the urinal Duchamp displayed in 1917, kind of marking the death of ‘art as it should be’ once again, a few years after 1913 when his Nude Descending a Staircase stirred the Art scene in New York. 
Not only the ready-made was born and the concept of the non-retinal art, but it opened a whole new field of artistic practice and research. Similar happened to the perfect Cartesian coordinate system. Fed by small deviations, in the works of Georg Nees, we watch it produce interesting graphics outcomes with many variations (depending on the type of ‘anomaly’ introduced). Disturbing the expected (or – purposeful) order is the birth of a possible new order. And the error is detrimental to opening new possibilities for machine creativity. This is the field that caught our curious eye- what happens when the system is dancing on that thin line between the possible and impossible, between stability and instability in the program that finds a way to execute code it previously accepted as possible.

The Beauty in Numerical Instability 

The artifact we have produced during our research is based on the variation of the code for the spiral example from Mastering OpenFrameworks: Creative Coding Demystified (2013, 50-55) where a line is drawn between the previous and the current position of a point. Current position rotates at an angle “a” and “a” is incremented by a variable “b” which is also incremented by a variable change, and those values are initially doubles (the numbers with many decimals). However, the instability is introduced by changing the type of variables from doubles to floats. In the images below, you will see the stable variant on the left side, and on the right side, the unstable one. In the image no1, notice the regular spiral form on the left side and many diverse shapes on the right side (circles, octagons, rectangles…) and connections.  
Experimenting with the rate of change of b (bchange1, bchange2) we realized that decimal numbers except 0.5 produced another kind of instability (image no2). Now the stable spiral shapes would rotate around the center of the canvas producing a new pattern. So we set out to play with doubles and floats only on the bchange1, bchange2 variables.(image no3) As time progressed this rotation of the two spirals would get out of control and produce another chaotic pattern as in the left picture below while on the right picture the pattern would stay the same. The end result is somehow surprising since our stable example this time produces more instability than our unstable one. (image no4)

According to the deterministic chaos theory, the minimal interventions in the initial variables in a system produce a very complex result. And this is exactly what we have observed in this process.

Is this error or possibility?

Playing with instability in the code allowed us to see more than we could have expected, giving way to possibilities that we perhaps wouldn’t have thought of executing before. Also, it drew us away from intention and towards discovery instead.

‘We have also seen several times that creative behavior from a program was reported when something went wrong - a misconception by the programmer, a syntax error that fortuitously produced a viable program, etc. …The crux of this view is that their behavior surprises their creator, and there does seem to be some link between surprising behavior and creativity.’ (Partridge and Rowe, 1994, 151)

Rather than seeing it as a ‘mistake’, an error in the system comes with the field of possibility that following Deleuze’s philosophy of the virtual as conditions from which the experience emerges, Tim Barker describes as a potential:

“The error is potential in a sense that it is not pre-formed or pre-programmed by the artist. It can only be described as potential, which is inherent in the machine… It is only by allowing the capacity for potential errors, by moving away from the territory of the preconceived aesthetics of errorless machines, that we may provide the opportunity to think the unthought, to allow digital technologies to become-other.” (Nunes, 2011, 52)
Just as during the Cage’s 4’33’’ (1952) composition and in Rauschenberg’s White Painting (1951), the empty, void or the ‘unexpected’ is to be filled by the audience’s intervention, the system is expected to fill the instability potential with an outcome it executes out of the human eye or intervention.

Control – Surrender Axis

We can also look at this process from the perspective of the role of the artist, where Brian Eno (Edge, 2011) talks about creation process moving away from that of an architect knowing exactly what the final product will look like to approaching it as a gardener. After initially setting up the system, we let go of part of the control, relying on the dynamics of the system to finish the work. The actual computer is given part of the creation of the artwork.
Surrender, in Eno’s words, is what we go to galleries and church for - to be taken away. This, as an artistic practice, means incorporating this field of ‘impossibility’ that opens new dimensions for research and creation.
In our example, after planting our ‘seeds’ we had no access to the process. Once the ‘build ’was done, the outcomes were surprising and much different from the ‘stable’ example. What is truly impressive is there was nothing a program ‘looked at’ or was being previously fed with. It got the simple code and all of a sudden we were in the area of computer agreeing to something it is ‘almost impossible’ to do. So we basically surrendered our intentions to the machine and waited for the outcome. And the system delivered an array of aesthetical outputs.


Given the meaning of the word ‘to err’: ‘to wander’ off the beaten path, the machine actually has shown a significant amount of flexibility, basically adjusting the impossible to become possible and displayed. Going back to the modern social objective of the perfect predictable order (without the threshold of flexibility the perfect order simply crashes) we can conclude that the research into the (im)possibility has a larger field of implications then purely aesthetic and software ones. 
Due to the negative nature in which the ‘error’ is assumed to be in the computer system as well as in our cultural and political framework, highlighting the instability, ‘mistake’, process of surrender and the computational system’s sensitivity seems to be a bright torch to lit as a contribution to keeping things real in the current AI and Singularity one-truth overhype.


Nunes, M. editor, (2011)  Error: Glitch, Noise, and Jam in New Media Cultures. New York London: Continuum Books.

Ito, J. (2018), Resisting Reduction: A Manifesto (Designing our Complex Future with Machines). [online] Available from [Accessed December 2018].

Partridge, D. and Rowe, J. (1994) Computers and Creativity. Oxford, England: Intellect.

Perevalov, D. (2013) Mastering OpenFrameworks: Creative Coding Demystified. Packt Publishing.

Edge (2011) Composers as Gardeners. [online] Available from [Accessed November 14th, 2018].