#emerging processes

I posted the following in May 2013, before starting to engage with my PhD thesis full time, at the now discontinued on-painting.tumblr.com. All new content obviously goes to beyondmimesis.tumblr.com for quite a while already. The post has been moved here for archival purposes.

You know what you can name. Understanding the reference systems you move in holds one of the tougher learning curves you need to continuously handle when starting to learn any craft. Sometimes that's easy - in painting for example, when interested in depicting objects, faces or basic gradients, the defining words of their basic topoi ("still life", "portrait", "abstraction" might be good guesses) are readily available to most of us.

We know these quantifications almost archetypically, having heard some of them maybe even before attending kindergarten. Each of the three mentioned example areas have tremendous histories to dig into, and are thus more like vectors for further research (to find the proper sub-topoi you care about) than clear cut mathematical classifications that would rule some things in, while keeping others out. But already, these vectors can be used for getting more information on the topics they point (or appear to point) towards.

Although we have this kind of predetermined knowledge about what certain topics are supposedly all about (meaning we also know what they are not about - a classic portrait in general is not about depicting a snake, for example), we will find that any topic's scope and depth becomes seemingly bottomless once we start focusing it. Additionally, this kind of knowledge sometimes turns out to be harmfully simplistic: When, for example, was a portrait ever the pure, pristine, virginal depiction of a face.

Nevertheless: Only if we know a thing's name, title or context, only if we know something verbal about the topic we want more information on, are we able to research into it in any decent way - by using a search engine, thesaurus, dictionary, your university library's referencing system, or a talk over coffee with a friend or mentor.

Limitation by quantization. An important aspect of defining anything is the potential downside of its delimiting nature: Defining ("Naming") something limits it - that's the whole idea of doing it, oviously: to delimit something's scope and/or depth - something's universality.

So, although it's the whole reason why one would probably ever define something, doing so unaware on certain meta-levels can result in problems - especially if (a) your quantification is off (and you thus look for the wrong thing, not knowing that yet), or if it (b) becomes off: if the research subject changes away from a previously fitting quantification - which, if not noticed, obviously won't automatically result in you looking for a new, "updated" definition.

Romantically speaking: Quantifications tame the beast, but beasts do not tend to stay tame. You need to stay aware of their wild nature, so you don't miss your pretty defining cage becoming rusty: You need to be aware of the inherently volatile nature of any quantifications you use - the implicit wrong sense of security they might offer you.

Once your point of view changes, you need to understand its shift properly: What changed, and whether previous knowledge is in any way still usable, or has to be thrown away altogether. "Kill your babies," as a programmer once said about code he wrote that was good in itself, but became obsolete by the knowledge he gained by writing it.

Extending your vocabulary. What if your work doesn't focus on something that fits into well-established systems? What if it fits into categories/systems you don't know yet?

What if your work incorporates fragments of differing contexts, some of which you know, others you aren't sure about, but where (and that's where the fun starts) the permutations of dynamics between these fields (both known/named, and unknown/unnamed) are frighteningly (or excitingly) endless?

  The humanistic approach when encountering The Foreign: To investigate. We try to understand it, make meanings; usually by talking to others, or reading - by focusing our energies on the unknown topic.

So the first, and probably most obvious thing is to talk to peers. Two probably rather obvious ways to put yourself in environments of latent verbal exchange within your creative everyday:

Consider joining a group studio: An assistent at an art academy once told me she never met anyone that got succesful* who didn't at one previous point in time move their studio from their flat to the academy. You will have less "working time" after this (especially if you exclusively consider "swinging the brush" as your work), and less hours of actual work: Consider the commute, which usually takes time, consider coffee/social breaks etc. But, needless to say, you gain something far more important: contacts, opinions, social skills. Your work (and thus: you!) will be questioned at uncomfortable times and in uncomfortable ways, leading to growth. Over the years, you will know your very personal needs regarding a working space - but until then: simply try one situation after the other.

Apart from getting feedback on your work, get yourself in talks about art, and about your work. You don't need to consider it critique - but get yourself in atmospheres where your artistic troubles are heard, and can be discussed.

  Apart from that: Start a reading routine. Through this, the old wisdom will come alive once more: The more you know, the better you understand how little you know.

Read art theory - this will help you get to know both main and side concepts that art is produced on. Doing so will help to establish both basic and extended vocabulary.

Read monographs about those artists influental to you - This will help you understand the art world's definitions and thoughts about your paragon's work. Good texts will always help demystify!

Read texts written by artists influental to you. Not all artists write/wrote about their work, but if they did, they might offer more intimate, personal insights into their minds than anything else.

Read/watch interviews of the artists influental to you. Witnessing an artist will offer the most direct glimpses into their being (except meeting them in person); to get you started, consider Borremans or the famous one with Francis Bacon, by David Sylvester. But do not disregard written interviews either - experiencing words at your own pace sometimes results in deeper bonding than watching a video, with its own rhythm and timing etc.

Read magazines. Consider them an entrance to the art world's tendencies, much like the contemporary galleries your city might offer. They usually are the best place for quickly glancing into what's going on internationally, for spotting trends (and their potential pitfalls) and getting to know your contemporaries. But don't get wasted on magazines: They usually don't overcome their superficiality.

  Using your vocabulary. Take time to write about your work. Especially in those phases where doubt and self-criticism take the better of you, establish a daily routine of writing: What is your work right now. What problems do you see with it. Where did other artists encounter similar problems, and where did they differ, and how could they transcend the problem? For this verbal route, caring about words will be beneficial.

Use basic, non-art vocabulary to describe your work. As an example, imagine you never heard of the art historic topos of portraiture. You could still find words like face, head, background etc., and use them to continue finding better ones as your journey progresses. 

Use technical attributes to describe your work. When thinking about lines, think of their attributes: line strength, line color, line continuation. It doesn't matter so much whether these words are commonly used, as long as they clearly denote your focus - optimally not only to you, but to anyone who wants to understand them. Line beginning, line ending: Who wouldn't understand that? 

Use physics vocabulary to describe your work. When having an array of lines, consider words like frequency, line, moiré, oscillation, etc. Look them up in a dictionary to understand their definitions, and whether they help you understand what you do. In addition, think in abstract terms about the formal aspects of your work, potentially disregarding the pathos that higher-level forms (face, nudity, gestures etc) might implicitly bring into your descriptions.

Building Dreams, 2015

In June 2015, Alexander Mordvintsev, Christopher Olah and Mike Tyka published a post on the Google Research blog about a visualization tool written to gain understanding in how artificial neural networks develop their data. Their specific focus was to improve knowledge on how interconnected layers of artificial neural networks communicate and enhance their individually developed information – what gets created when, and how this is then adapted while being passed from one layer to the next. They explain a self-feeding system whose feedback loop's initial layers tend to highlight and produce visual artefacts (e.g. by strengthening contours), and whose later, “higher level” layers use these previous computation’s results to further manifest visual ciphers. They note that “[b]y itself, [this system] doesn’t work very well”, but that by manually inserting a visual bias (a specific animal, a building, etc.), the algorithm is able to manifest these objects – even from the neutrality of random visual noise. To this end, all layers work serially to gravitate towards specific visual preferences – they curate. Based on crowdsourced Big Data imagery, the algorithm thus visualizes how the virtual neural network imagines things to look like. The blog post uses the image of a dumbbell as example for a virtual neural network's semantic error: for the AI, it mistakenly seems to always include a lifting person's hands. Understanding the AI's projected semantics is crucial to its development, with misinterpreted data serving as proof of the algorithm's potency. Ultimately though in its blog post, the team wonders “whether neural networks could become a tool for artists—a new way to remix visual concepts—or perhaps even shed a little light on the roots of the creative process in general”. Might the currently described algorithm have more potential than your average Photoshop filter? Could it be used for artistic processes in a way that mattered aesthetically – or even societally, as is expected from contemporary arts? Could the underlying algorithm constitute a self-learning, self-adaptive system similar to contemporary art practices, or even be related to the cultural, non-teleological drive of our species?

Image: Noise to Banana, 2015

Images: Left: Original photo by Zachi Evenor. Right: processed by Günther Noack, Software Engineer

Image: A virtual neural network’s dumbbells visualization, 2015

I would like to elaborate on art as a contingent process closer related to developers researching their topic (here: neural networks), than to mechanically applying results (here: an algorithmic visualization method) of any previously contingent process. Although this case initially produces precanonized visual data, the highly volatile nature of canonization results in anything new quickly becoming established – and hence losing its freshness. The attribution of "newness" is therefore less relevant when found within an object, than when constituting a processes' target. Mechanically applying an algorithm stands far apart from a definition of art that cares about finding individual parameterizations for quality, since there no longer is the need to gain any personal understanding of the process's quality attributes. Calling an algorithm's output "art" turns out to be similar to calling a Fordist assembly line's output "art": it is the consequence of treating art as a mechanistic, teleological dynamic.

Art and Craft

While working on my PhD thesis on the effects of digital culture on the expectations of contemporary painting, it became clear that a purely image-analytical approach would not facilitate a clear understanding of any visual medium's attribution. Instead, although by some understood as “fool’s errand”, I realized for the thesis to require a clear cut definition of contemporary art as its foundation – without it, discussing media specifics seemed inappropriate: how could a specific medium be discussed without first knowing the system it operates within? I ultimately defined art as a process towards understanding individual quality attributions – resulting in its native media independence, and highly volatile nature: artistic practices are, most of all, dynamics with only temporary validity. Media specific thinking in terms of e.g. painting, dancing, cooking is not required to discuss art ontologically, and doesn't necessarily help in understanding artistic processes. Instead, it can even block the view on what art can be. Craft on the other hand denotes the mechanical, latently repetitive application of a known process towards an preknown, teleological quality (a "goal"). Just like art, craft is a volatile dynamic – changing not only its object, but also the craftsman's individual mental and physical knowledge of it (something not necessarily the case with algorithms). Repeatedly doing something might feel to clone actions, but actually yields different results because of the operation's impact on object and subject.

I understand art and craft as a Möbius strip's two sides: Every action within the artistic process (whether mental, physical, digital, algorithmical etc.) is in constant individual and societal flux between the poles of art and craft, with neither being able to exist without the other. Making a painting can fulfill art's criterion, as long as the painting process's qualities are unclear to its author. Cooking a soup can qualify as well, as long as its "quality" hasn't individually been clearly understood. If instead it is known (which is usually the case once a “recipe” has been formulated), the process qualifies as craft.

Image: Art and Craft as one Dynamic, by Christian Bazant-Hegemark, 2015

Both terms' modernist narratives therefore need to be transcended: there is no generally attestable beauty in art's objects or processes, and there is no hierarchy between art and craft. Calling something "art" or "craft" does not elevate or diminish it.

Assembly Lines

Canonization dynamics are highly relevant in understanding the connection between art and craft: what is initially new and fresh (precanonized), quickly becomes canonized on individual and/or societal levels (not necessarily influencing each other). Once individually canonized, mechanical repetition can be established to have an artistic process become (individual) craft; this establishing process itself usually can be understood as art: mechanizing/streamlining an open process requires an understanding that usually doesn’t match the initial artistic process.

Image: General Canonization Dynamic, by Christian Bazant-Hegemark, 2015

The Google Research blog post includes algorithmically produced imagery which at first seems visually fresh and pre-canonized. Skimming through more than just a few of these "deep dream" images (e.g. at http://psychic-vr-lab.com/deepdream/) helps to understand their algorithmic base: strong brilliance and contrasts, strengthened contours, and a preference for curves. Like a Photoshop filter, the neural network’s visualization operates on adapted output from some initial image input. What makes the neural network’s algorithm stand out is its capacity for a curatorial preference, used to implement an allegedly individual goal – to have it operate autonomously. This must not be mistaken with agency though – it’s still the human user who choses the visual bias, not the algorithm itself. Let's compare this consistent, algorithmically produced and guaranteed likeness, to the serial consistency offered by Fordist production lines. Do they constitute art? Isn't it rather the process (potentially resulting in the definition of said assembly line) that can qualify as such – because it is there that newness (precanonization) is aimed for? Shouldn't these autonomous agencies be treated as art's defining factors? How do algorithms satisfy these parameters? Removing humans from production processes will always result in minimized contingency, therefore purifying any algorithmic process and output. But isn’t art (and culture) exactly about contingency? Can’t craft be translated to algorithmic form especially because of its lack of contingency - and isn’t that the reason why art doesn’t yet exist in algorithmic form?

Image: Craft and Contingency, by Christian Bazant-Hegemark, 2015

Art and craft form a circular topology in which they continuously mutate into each other – neither can exist without the other, and both happen only because of the other. The question of “which came first” constitutes another chicken/egg causality dilemma.

Where Fordist mass production's core is the production line, post-Fordism extends it to embrace a later generation's individualization and fragmentation – in a way, Fordism establishes and parallelizes craft processes, into which post-Fordism tries to insert “art” as an illusion of contingency (industrially produced deckle edges on books are an easy example for this). Today's Big Data algorithms then natively blend Fordist and post-Fordist strategies to implement even more streamlined production environments; by using masses of anonymized user data, Big Data appears to be smart without requiring a human workforce: artificial intelligence. By offering an unexpected update on nonnegotioable bartering agreements (free services instead of monetary payment, in return for unlimited usage of user data), Big Data’s AI is based on unlimited harvesting of individual data. This leads to the production of objects without physicality, creating an environment that's virtual except for its economic worth. All of this seems to only be possible because of the societal change in economic expectations: we don't need physicals as much as even just ten years ago – lives have become far more virtual than expected (in 2015, Amazon.com's biggest profit gain didn’t happen because of their sale of physical objects, but through AWS, their virtual cloud computing service). So many physical domains have been replaced by virtual ones, that Star Trek's holodeck can easily be understood as the (virtual) invention of a pre-virtual mind: communication, education, leisure activities; movies, books, music, letters, money; transportation, physical storage spaces, etc.

Image: Fordist assembly line, 1913

Image: Google’s data center in Council Bluffs, Iowa, 2012

In this global economy, humans are no longer the limiting factor: plattform efficiency can be improved through parallelization and virtualization of processes (hence AWS’ success), transcending health or social security payments – and many other constraints of physical existences. Algorithms are a new workforce that only requires humans for being established – but not for being operated. The algorithmic creation of algorithms would diminish this requirement for human’s agency even further – and it would be only then that we’d have to discuss art as algorithmic process. In a way, the autonomous agency that makes humans such excellent problem solvers is what still keeps us far apart from machines. It's also what drives culture, and why art matters: for as a species, we survive because we expand our cultural normatives.

Algorithms

Since they are not teleological, artistic processes are always pre- or post-Fordist – they can't easily be assembly lined, whether their medium is painting, or the team effort of creating a video game. Only craft can be Fordist (e.g. the repetitious operation of creating video game assets), and therefore be represented in algorithmic form: algorithms constitute craft – it's only their creation that can qualify as art. Algorithms can be understood as artistic processes once they are able to exist according to their own agency. Postmodernist individualism severly disrupts the Fordist production line: in addition to individualized (physical) mass products (individually engraved iPods, custom covers for smartphones, etc.), contemporary digital mass products tend to contain virtual elements to implement their individualization natively – something an earlier era of desktop computing didn't have to account for. Search engines, cloud based services (archives or document editing, mail services, etc.) are all natively virtual platforms, replacing a previous generation's need for their physical predecessors.

Because it works along a predefined process, it's possible to clearly locate Google's deep dream algorithm as craft – any deterministic algorithm with predefined goals can only count as such: It's only self-developing, self-modifying algorithms that could implement artistic processes – and with their individualized agenda, their own agency, they most probably would. Their media will be as varied as their results: for that is the single attribute uniting art through the millennia: the strive for the unknown, for the precanonized. For what is culturally essential for later generations, but doesn’t yet exist: fire, the wheel, telegraph and phone, the touch paradigm etc. Once machines are able to adapt themselves individually in a currently unknown mode of agency, we will be able to discuss the difference between humans and machines for real. For now, agency in algorithms is mostly faked by processing global data sets. Ultimately, algorithms will gradually replace all mechanical, repetitive labor (sorting, assessing, driving etc.):

"Interconnectedness and increasing computational power will continue to automate work and outsource any job that can be standardized. New businesses are employing fewer employees, while manufacturing is moving to an increased use of robots." (http://jarche.com/2015/06/turmoil-and-transition/)

Algorithms continuously dig deeper into traditionally analog domains, as these slowly get digitalized (Google Books Library Project for digitalizing preexisting objects; the Internet of Things for digitalizing nonexisting attributes traditionally analog). For now though, art can well be defined as whatever can't be produced algorithmically.

An algorithm designing and implementing a process similar to an artist entering an empty studio – creating meaning from nothing, rephrasing and permuting semantics, highlighting individual preferences. Autonomous algorithms with an individual agenda. Art not only as culture, but as humanity's defining driving force. Once code is be able create art, we will have to rethink what it means to be human.