June 25, 2022


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Appreciating the Performative Quality of Computer Generated Art

Should we look at digital, computer-generated artwork in the same way we evaluate performative happenings? Can electronic generative art be interpreted as performance with machines instead of bodies? What if artists, critics and the public are too focused on results, rather than the process?

Computer-generated art has been around for over 60 years, since the early adopters of computers experimented with the creative potential of machines that were originally designed for crunching numbers and computing calculations too difficult or time consuming to be solved by humans. These pioneers of computer art, engineers and mathematicians such as A. Michael Noll (born 1939), Frieder Nake (born 1938) and Georg Nees (1926–2016), wrote instructions that were formally essentially the same of math problems they usually posed to machines.

The way these artists worked, a machine is programmed to create as many possible answers to the artist’s instructions as are allowed by the arbitrary parameters imposed by the latter. The two key aspects of early digital generative art art were the instructions given the machine and the calculation of possible results.

odo (the persona of an anonymous generative artist), “hfold 1.1” (2021) (image courtesy the artist)

This focus can be seen in the works of another pioneer of computer art, Vera Molnár (born 1924). An exception among the many computer scientists who explored the creative potential of digital technology at the time, Molnár had a background in aesthetics and art history when she produced her first computer-generated artworks in the early 1960’s. Instead of creating visual patterns that were heavily inspired by the works of the most popular Op artists of the time as other engineers did — most notably Noll’s interest in the works by Bridget Riley — Molnár developed an original style which seems unprecedented, as if it could not have been developed with traditional art tools. Works such as “Untitled (5)” (1972) and “Au commencement était le carré” (1973) show simple geometric figures arranged by a computer following Molnár’s instructions. These rules allowed the machine to calculate many different results which were then selected by the artist.

The principles of contemporary digital generative art are the same that governed the production of these first works 50 years ago. The works of contemporary artists as different as Rafaël Rozendaal (born 1980) and Zach Lieberman (born 1977) seem not to be the fruit of a particular interest in how the work visually manifest themselves, but in the potential given by the code they wrote and the arbitrary results calculated by the machine.

The imposition of instructions with the purpose of making things happen relatively freely is an art practice that has been particularly explored in the last 100 years, since the industrial production of goods and images has led artists to study process more than results. An example that predates both computer-generated art and performance art is the “Telephone Pictures” (1923) by László Moholy-Nagy (1895–1946). In 1922, the artist contacted an enamel factory by telephone and ordered porcelain enamel paintings. As told by the artist in his book Abstract of an Artist, he had the factory’s color chart before him and sketched his paintings on graph paper. “At the other end of the telephone the factory supervisor had the same kind of paper, divided into squares. He took down the dictated shapes in the correct position.” The results of this operation are three painted enamels of different sizes which, beyond their aesthetic value, represent a fundamental moment in the short history of remote production of works of art.

Among the artists and movements that have explored the creative potential of giving instructions and relating their unfathomable results, Allan Kaprow (1927–2006) and the happenings played a fundamental role. Happenings were usually introduced by the distribution of instructions to the public to make the event truly participatory. Instructions played a fundamental role in many of the actions carried out by Fluxus artists. An exemplary exhibition was “Art by Telephone”, (November –December 1969) the title of which recalls the aforementioned Moholy-Nagy experiment carried out almost 50 years earlier. Held at the Museum of Contemporary Art in Chicago in late 1969, the exhibition consisted of thirty-six artists invited to instruct the museum staff about the contributions they were to implement on the artists’ behalf. Many of them provided directions for creating objects and installations, while others tried to make the process itself the actual work, such as Wolf Vostell (1932–1998) who shared a list of phone numbers that visitors dialed to receive instructions for one-minute happenings.

A key aspect of performance artwork is that they are implicitly constructed and presented in such a way to be appreciated for their potential, for the surprise factor they generate within the rules they impose on reality. What makes performance art such a stimulating practice is not just what observably happens during this or that enactment, but the very embodied act of participating, albeit as a spectator, in the action.

On the contrary, digital generative art seems to be appreciated by experts and collectors only for the results it produces, mostly ignoring the craft that went into planning the script or the calculation process of the machine. Seen from this point of view, works like “Piece “P-777_D” (2002-2004) by Manfred Mohr (born 1938) or “Untitled Computer Drawing” (1982) by Harold Cohen (1928-2016) should be appreciated as selected documentation of their original scripts’ potentials in the same way we look at pictures and video recordings of performance art events.

 Morteza Shahbake, “O K V E” (2021) (image courtesy the artist)

The artists using computers to create algorithmic art show only some of many possible results, arbitrarily taken out of the flux of the action. If the artist doesn’t stop the machine and leaves it calculating without extrapolating any partial outcomes, what would the actual work constitute? Maybe it would be the original script?

The way artists use algorithms to develop art is reminiscent of how pachinko games work. The balls fall vertically through an array of pins and obstacles until they enter a payoff target or reach the bottom of the playing field. Part of the thrill of playing this traditional Japanese game is witnessing the trajectory of the ball behind a transparent plate, hoping it reaches the cups at the bottom. Looking at computer-generated art is like looking at a still pachinko ball that already reached the cup, putting aside the initial gesture of inserting the ball and watching the course it took leading to its final position.

From a market perspective, it is much easier to trade self-contained objects, whether these are computer-generated pictures, sounds or interactive apps; it would be very difficult to do the same with something as volatile and unrepeatable as the calculation of a machine or, one of Kaprow’s happenings. As Kaprow himself wrote in the 1961 essay “Happenings in the New York Scene,” their activity embodied “the myth of nonsuccess, for they cannot be sold and taken home; they can only be supported.” In a similar way, computer-generated art calls for a deeper and insightful understanding of what it means to create art in collaboration with a machine, a perspective that cares more about the performative quality of its making than its crystallization in tangible forms.