Neptune-1

In Maria Teresa Cruz’s Art Curation and Critique in the Age of Digital Humanities, Cruz frameworks the ways in which we are caught in the intersections of art as a continuously growing form. She discusses the dichotomy of art as something that is simultaneously growing apart from its historical links, while it also integrates these historical details into new creations on a digital platform. Among these ideas, Cruz asks one critical question: what remains in and as the “value” of art if it is becoming increasingly shapeless and formless, to the point that it can indeed exist or surface as everything that surrounds us? One of the ways in which she explores this “value” of art is through the curation of media and cultural information in digital applications, which are often distributed to us in a highly personalized manner. Cruz references Morris’s concept of “curation by code”, which is built upon the process of “‘monitoring our tastes and suggesting future content’ [and] is daily performed by companies like Netflix, Apple or Amazon and it tends to replace the task of critique by ‘automated recommendation systems’ that now ‘occupy a central role in the circulation of cultural products’” (Cruz 190). That is to say, algorithms are using the method of direct visualization (as Cruz had earlier quoted from Manovich in the article) to single out individual data objects from the mass sea of data sets, so these individual objects can be recommended to us as suggested content that are indicative of our particular interests and preferences. This idea of crafting user-focused content reminded me of an Instagram post that I saw from a while ago.

The Instagram account @neuralnet.ai explained how Netflix uses ranking algorithms to suggest content that are considered most relevant for the user; this ranking classification can be seen in the structural layout of Netflix’s homepage, where the movies and TV shows are placed in a visual hierarchy with the leftmost option being the one that is considered most relevant by the algorithm. It also extracts and analyzes metadata from each frame of the specific movie/TV show, including brightness, skin tone, and the number of appearing faces in each frame, so that it can effectively optimize the movie/TV show’s appeal in accordance to each individual user.

Netflix's ranking algorithm

Netflix’s algorithms is a vivid example showing how technology automates our judging and selection processes; we no longer need to visit the cinema or the DVD store, walk through isles and galleries of options, and repeat the process of picking up a DVD to look at its front cover, the description on the back before putting it back on the shelf again, because as soon as you open the homepage of the app, the tailored options are already collated and organized in front of us in a readily available manner. While the personalized recommendation of media content can arguably enhance user satisfaction, it closely ties back to Cruz’s attempt to implore us in thinking about what art really means for us if it’s consistently curated in ways that are supposedly most fitted to us. In fact, she succinctly summarizes this phenomena in her argument of how data aggregation is a “pervasive, automatised activity, continuously producing transient collections for data processing” (Cruz 188). The ethical dilemma of art curation through technological means thus lies in the emerging reality that everyone has the freedom to make art and be creative, yet the value of art becomes ambiguous in itself when it is reproduced through the intentions of technology, and not our own. It is to say, we are largely interacting with “filtered” data - technology can sometimes be the monster parent that wants to constantly hold our hands, provide us with pre-made information architectures, and dissuade us from the many experimentation and failed lessons when we have the ability to work with the raw data ourselves.

Netscape Homepage, an example of Web 1.0 (left),
Wix Homepage selection, an example of Web 2.0 (right)

For many people growing up navigating both versions of the web, Wagner succinctly encapsulates this experience in a “web evolution” analogy. One aspect that I would argue is that while pre-configured interfaces and templates can diminish the ability of customization, it can also encourage us to explore another kind of creativity challenge as we think of ways to change these interfaces into something that is our own. Reading about Cruz and Wagner’s ideas made me realize the importance for us to take responsibility: to seek out information that falls beyond our regular interests, to deliberately run into problems in the data sets, and to carefully consider the historical significance of the forms of art we encounter (in terms of how they reflect the changes that have occurred in the disciplines of visual and cultural information).

Thank you for stopping by! :-]

References:

Cruz, Maria Teresa. “Art Curation and Critique in the Age of Digital Humanities.” International Journal of Performance Arts and Digital Media, vol. 15, no. 2, pp. 183–196., doi:https://www.tandfonline.com/doi/full/10.1080/14794713.2019.1638647.

Wagner, Kate. 404 Page Not Found: Kate Wagner. The Baffler.com, 8 Jan. 2019, thebaffler.com/salvos/404-page-not-found-wagner.

Pictures:

Jung, Chloe. How to Make a Fly 90’s Website: A GeoCities Tribute. Business 2 Community, 30 Apr. 2016, www.business2community.com/social-buzz/make-fly-90s-website-geocities-tribute-01531323.

Purchasing, Downloading, or Exporting a Wix Template. WIX, support.wix.com/en/article/purchasing-downloading-or-exporting-a-wix-template.

Neuralnet.ai. "How Netflix Uses Machine Learning". Instagram, 16 December 2020, https://www.instagram.com/p/CI25iUiAXyM/?utm_source=ig_web_copy_link