The Algorithm and The Field

In his 2011 TEDTalk, director Amit Sood introduces Google Art & Culture (GAC) as an online project born of the earnest “desire to make art more accessible…and to supplement the museum experience” for anyone with an internet connection. With a mind already on art publications as a kind of democratising supplement to art viewing, my curiosity about the potential of GAC was instantly piqued. By 2016 the project was updated to include the virtual exploration of spaces, both of institutions and cultural sites around the world. Using the technology that has become simply pedestrian in Google Street View, an interior recording of New York’s Guggenheim Museum means that Sood’s idealistic vision can “imagine…accessibility for a kid in Bombay who’s studying architecture, who hasn’t had a chance to go to The Guggenheim” (“Every Piece”). I am imagining the opening and democratisation of objects in space. I am excited.


I choose to view the GAC Project as a hybrid between a virtual art museum and a vast web-based publication: one which amplifies digitized pieces of material culture through a public interactive portal. Excited as I am, however, GAC raises some important and perplexing questions about the future of sharing, displaying, and looking at art – or indeed, the meaning- and value-making of art entirely. “The Field of Cultural Production,” as coined by sociologist Pierre Bourdieu, consists not only of the artists or artefacts in relation to each other, but also of the players in the field: “the producers of the meaning and value of the work – critics, publishers, gallery directors and the whole set of agents whose combined efforts produce consumers capable of knowing and recognizing the work of art” (qtd. in Squires 55). These agents work to build either market (economic) or cultural (symbolic) capital, depending on their “position” and motivation in the field (56). In traditional publishing, there is a clear and a unified gatekeeper in production: the publishing house, with its acquisitions body of editors and marketers. In the case of GAC, what is the impact of the agents who play a part in the framing and filtering of online content: the GAC editorial team, the partner institutions… the algorithm itself? What happens to the field of cultural production and the symbolic capital of art if a “producer of meaning” is an algorithm? Exploring the complexity of framing and filtering cultural artifacts in GAC may stand as a kind of case study, or early map, of the vast and uncertain field of what digital and online cultural production might become.



In the first place, let’s consider the partnering institution – the museums, galleries, foundations and archives – who must choose to partake in the project and share some or all of their collection for scanning and display. By becoming a part of the “Google Cultural Institute,” a partner is provided access to the patented gigapixel Art Camera to digitize paintings, the Museum View for producing 360º tours, and the Collection Management System to be plugged into an institution’s homepage for creating and showcasing art on a customizable interactive platform. It’s a formidable trade: access and display of hundreds of pieces from a collection in exchange for a world-class digitization, presentation and archiving package. Currently, Google has digitized and collected approximately 6 million art objects in partnership with about a thousand institutions (“Every Piece”). And yet, out of 6 million images, a simple search does not produce Da Vinci’s original Mona Lisa, a substantive body of Cindy Sherman’s oeuvre, or a shred of Bill Reid, to name a random few.


At the time of GAC’s launch in 2011, Tate Media creative director Jane Burton points out that “Google Art risks giving a very skewed image of creative output through time and around the world. At minimum…a large tranche of twentieth-century modernism will be absent because of high reproduction fees or other obstacles” (Proctor 216). As I may have noticed gaps in a North American perspective on what “essential art” should be included but is missing from a virtual world museum, I can only imagine what canonical artists or works might be missing from the contingent representing art coming from China, for example. Furthermore, Sood makes explicit the fact that all of the art “belongs to the amazing museums, archives and foundations that [Google] partners with. None of [it] belongs to Google” (“Every Piece”). This means that those who choose not to participate have chosen to filter their collection out of the publication.


We’re then faced with the issue of completeness: only those artworks which belong to the partner institution’s collection may be documented and shared; if it does not consist of a wide body of an artist’s work (which is often the case for contemporary art, for instance), the rest of an artist or movement database ends up sparse. Additionally, small institutions like artist-run centres don’t own the rights to the artworks they gather for exhibitions, so can’t bring the same weight to Google’s bargaining table. The filtering that happens on the part of member institutions isn’t so much curatorial as it is a question of that institution’s means and model – the breadth or control of an institution’s own collection becomes a measure of its value as a cultural producer.



How about the publishers of GAC itself? Perusing the main page, it is obvious that the wizards behind the Google curtain exercise editorial direction over the content. The front page is decorated with disparate magazine-style digest and stories such as “Surprising Facts: The Last Works of Vincent Van Gogh” or “Fashion in Focus: How to Make a Kimono” (Google Arts & Culture). There are also a variety of “Theme” channels, which group individual story posts by curatorial contributors. Topics within the Themes range from popular Western artistic mediums, trends or icons to lesser-known or international movements. The impression is that the display of well-produced “articles” are desperately trying not to present content from only one particular era or aspect of art: “Tribal Chinese embroidery” sits next to “Inspiring British Women Musicians.” It isn’t difficult to see that GAC is trying to appeal to the broadest possible audience: the entire world. One might even think the project as trying to reframe Bourdieu’s field. The mainstream and the niche are presented on the same platform with the same level of disinterestedness (that is, perceived as being unconcerned with consumption or economic capital) as a way of enticing the viewer with pure cultural capital or taste (Squires 55; Curation 294). Taste can be exercised by the viewer as well. Like so many online pinboards, GAC allows viewers to save works they like and organize them as they please (fig. 1). Suddenly, the field includes the public-as-curator, not public-as-consumer; the dream of accessibility is taken to a new level.


Fig. 1: Selected works from the Google Arts & Culture search results “the field”. Curated by Emma Walter. Screenshot Nov. 25 2017  Source:


As I do not personally possess machine learning (just human learning), my grasp of how the main GAC algorithm works is still forming and rudimentary, but this is what I’ve gathered.  For simplicity’s sake, I will refer to the coding substructure of the GAC as “the algorithm,” but I acknowledge that this is a minimizing view of GAC’s engineering. Regardless, when each artwork is initially added to GAC, it is tagged with “creator”, “medium” and “topic” tags, whereupon it is grouped with other images with matching tags (“Tag Items”). This can be considered a “semi-supervised” learning environment for a training algorithm, as “association rules” (code based on the patterns of relationships between things) will develop on their own and the algorithm will take over organising the body of GAC (Flach 14). The various filters which can aggregate channel content by popularity or by colour, for example, are patterns found through user interaction (click-rankings of images tagged with “Leonardo DaVinci”) or visual recognition (the images tagged in “oil painting” with the highest proportion of red hues) (18).  In action, this grouping seems like an automated and reductive way of filtering and framing a collection of art.  It’s easy to disregard an algorithm’s potential as an “agent in the production of meaning” – algorithms don’t have feelings or taste.


This dismissal is a human, self-aggrandizing idea. Taste is merely the way certain choices define an entity in relation to others: “it’s about separation by making statements that distinguish us from those ‘above’ or ‘below’ us in the perceived hierarchy…Having internalized various elements of taste, we then ‘self-select’ into a certain class” (Curation 294).  With association rules, algorithms study hierarchies taught by user clicks or visual information and produce classifications, just as artefacts in Bourdieu’s field are defined by their relationship to other cultural artefacts via the agent (Squires 55). While the algorithm that controls the GAC database is relatively simple, the technological possibilities linked to it, the Experiments, signal ways in which algorithms can be compelling agents in meaning-making.


Fig.2: The Algorithm and the Field. t-SNE Map Screenshot, Nov. 25 2017. Source:


By 2016 GAC had hosted a series of “Experiments”, collaborations between programmers and artists using machine learning and the GAC database. For instance, digital interactive artists Cyril Diagne, Nicolas Barradeau and Simon Doury used the t-SNE algorithm (originally developed to recognize, pick out, and debug static images) to create t-SNE Map, which clustered the images in the GAC collection by visual recognition only. Clustering is a way in which an algorithm groups data or digital objects without any attached metadata; by assessing similarities between different data sets, it creates association rules on its own, unsupervised (Flach 14). The result is a literal – albeit virtual – cultural field (fig. 2). Zooming in, we can find an Alaskan Mask clustered with a kerosene lamp and an Egyptian Djed-Pillar, and as viewers we can’t help but imagine the dialectic between these things (fig.3). Is this choosing and arranging of art objects any different than what a publisher or curator does? Facetiousness aside, virtual space and algorithmic processing are now inextricable parts of our contemporary culture. As Nancy Proctor writes in support of the GAC, “We need to move beyond false binaries and futile contests between the ‘real thing’ and its online representation” (221).  This holds true in the ways we envision art publishing moving forward – that the digital framing, filtering and amplification of artworks will not only enrich the field of cultural production, but grow it into a vast and beautiful plain, open to all.  


Fig.3: A carved Alaskan mask, kerosene street post lamp, and Egyptian pillar: the t-SNE Dialectic. Screenshot, Nov. 25 2017. Source:





Bhaskar, Michael. The Content Machine. Anthem Press, 2013.


Bhaskar, Michael. Curation: The Power of Selection in a World of Excess. Piatkus, 2016.


Diagne, Cyril, Nicolas Barradeau & Simon Doury. t-SNE Map. Arts & Culture Experiments. Accessed 27 Nov. 2017.


Flach, Peter. Machine Learning: The Art and Science of Algorithims that Make Sense of Data. Cambride Unviersity Press Textbooks, 2012.


Google Cultural Institute. Google. 2015, Accessed 23 Nov. 2017.


Proctor, Nancy. “The Google Art Project: A New Generation of Museums on the Web?” Curator: The Museum Journal, vol. 54, no. 2, Apr. 2011, pp. 215–21. Wiley Online Library, doi:10.1111/j.2151-6952.2011.00083.x.


Sood, Amit. Building a Museum of Museums on the Web. TED Conferences 2011, Accessed 20 Nov. 2017.


Sood, Amit. Every piece of art you’ve ever wanted to see — up close and searchable. TED Conferences 2016, Accessed 20 Nov. 2017.


Squires, Clare. Marketing Literature. Palgrave MacMillan, 2007.


“Tag Items”. Cultural Institute Platform Help. Google. 2017, Accessed 23 Nov. 2017.  


Thompson, Hilary H. “International Law and Its Vision of the Ideal Museum.” Curator: The Museum Journal, vol. 51, no. 1, Jan. 2008, pp. 5–10. Wiley Online Library, doi:10.1111/j.2151-6952.2008.tb00289.x.

Cooking with video

Now that personalized entertainment is more readily accessible than ever, people are experiencing bursts of entertainment anywhere an internet connection is available. As video consumption has shifted from prime-time to all-the-time–and to address this shift in behavior, there is a need for new marketing models when it comes to video strategy. No longer do people have to share the television, when they can access the web.  Are publishers waiting for them when they log-in?

Kleiner Perkins Caufield & Byers, a venture capitalist firm that has helped build and accelerate growth at pioneering companies like Amazon, Google, Lending Club, Nest, Twitter, projects that by 2017, 74% of all internet traffic will be video (Meeker, 2015), and with mobile watch time on YouTube already surpassing desktop in 2015, the time for brands to make sense of their online video content marketing strategy is now — like yesterday.

Three hundred hours of video are uploaded to YouTube every minute, so when a consumer turns to their mobile device, tablet, laptop, or desktop computer, they can choose from a nearly limitless library of on-demand content. This makes what they choose to watch more personal than ever.

What is a video micro-moment?

When consumers looks for answers, discover new things, or make decisions, (this sounds like a good opportunity for any number of books/magazines), these instances are called “micro-moments,” a term coined by Google. They can happen in search, on a brand’s website, in an app, and, increasingly, they are happening on YouTube.

These moments of intent are redefining consumer behaviour. In order for a company to win at video micro-moments, they have to know how to identify them and how to respond.  

Video micro-moments generally fall into four broad categories:


In a micro-moments world, intent trumps identity.

Lucas Watson, VP of Global Brand Solutions and Innovations at YouTube, suggests that brands can remain relevant and useful by understanding the intention of their prospective consumers. Though it remains significant to know “who” the consumer is (age, gender, interests, etc.), for the video micro-moment play to work, one must understand “why” and “what/how”. Why is this person searching and what do they hope to do/how do they intend to use the information, once they have it. Is there other related information that could be presented? In a micro-moment world, intent trumps identity.

Publishers have an opportunity, books and magazines, as media for the transfer of information, have built up consumer trust. People are willing to turn to books and magazines to 1. be entertained, 2. be informed, 3. learn “how-to,” 3. purchase (primarily magazines).

Creating video content can be expensive, and there may not be enough time, money, or other resources. The recommendation is to create content gradually and build an engaging library over time. With a traditional production mind-set, this may sound daunting, but to produce at scale requires rethinking that production process, and getting a little help while you’re at it.






That’s where “CCC” comes into play—Create, Collaborate, Curate. The idea is to use this framework to “feed the content monster,” so that content creation—video production, specifically—no longer feels like a barrier to entry into the video marketplace.

Some publishers have already begun using this model, most notably, Harper Collins. They started an online video content division in 2010, focused on Young Adult books, called Epic Reads.

They have gained over 10.5 million views to date and use the CCC model to some degree. They are currently continuing their efforts to aggressively  target collaboration opportunities, and to branch out beyond the obvious “new book release” tagline.

Start up costs may seem prohibitive, but at this juncture the book crowd must flex the brain muscle to figure out how to get his done or face loosing more ground to other media formats. There exists opportunities to create high quality videos on a small budget, e.g. working with up coming videography groups or  film students, even if infrequently, and using  other collaboration opportunities to generate the additional content, perhaps with YouTubers looking for content, as the CCC model suggests.

Here’s some on the CCC model:


The first type of content in the CCC framework is created by the brand. It feels like the brand, captures the brand’s tone, and offers a more traditional creative polish. It tells a story about the brand that’s entertaining, educational, or inspiring. “Create” content might simply be entertaining video that gets people’s attention, or it might deliver on the specific micro-moments we talked about earlier, such as how-to content in an I-want-to-do moment.


This content is the product of the brand’s collaboration with digital influencers. It’s often content that features a YouTube creator and is produced and promoted in partnership with the creator’s channel. Ultimately, the goal of “Collaborate” content is to help brands broaden their relevance and connect with a uniquely engaged fan base while leveraging the expertise of experienced creators.


Make a story, arrange the videos into distinct groups to be enjoyed by the consumer in a block, at their leisure. An example is a series of videos with interviews or DIY tutorials.

Book publishers are in the game, many medium to large outfits have some online presence, but are they branching out to meet their consumers, or are they predominantly waiting on consumers to be interested in a particular title and then go searching. How can they bring people to the books without screaming “hey, new book!” Here are some recommendations on that front (some already being used by the other side of the business, the magazine gang).

1. Identify the micro-moments where your audience’s goals and your brand’s goals intersect



People go to YouTube millions of times each day, looking for videos that meet their needs, wants, and interests. Once a publisher has mapped out their consumer’s micro-moments, they can then move to understand their own place on the map: Where does the brand have the right to play?

Beauty brand Sephora, for example, knew that beauty content on YouTube grew by 50% from 2014 to 2015 and that YouTube searches related to “how-to” were up 70% year over year. For Sephora, how-to videos and tutorials were the magical intersection of the brand’s beauty-centric message and its audience’s beauty needs. That how-to and tutorial content now makes up more than 60% of Sephora’s library of video content. (ThinkWithGoogle, 2015)

Closer to home, in magazine world, TeenVogue started a YouTube channel back in 2006, to meet their customers where they live. They were ready and waiting. Their articles, and advertisers offer information and products on health, celebrity gossip, social issues, and much more.

teen vogue

2. Be there when your audience is looking with useful content that answers their needs

With an understanding of the pathways your consumer might take, plan a strategy to intercept them at the most opportune times. The first step is creating relevant, useful YouTube content that adds value in those key micro-moments. The second is making sure your brand shows up when they need you, with organic and paid search, for example, or with shopping ads on YouTube.

3. Help your audience find you, even when they’re not looking, with relevant video ads
Even when people aren’t actively looking for answers, brands can “delight” them by showing up with messaging that’s relevant to their interests. That means going beyond demographic targeting and connecting with viewers based on signals of intent or context.

Here are some scenarios:

  • Create — A person is online searching for fantasy related information e.g. are ghosts real, what are some super human abilities? Perhaps run the video below as an ad, before they watch the content they searched for:

  • Collaborate — A Beauty YouTuber wants to create another beauty vlog, but wants to set it a part in some way from all the others she has done, and all the others that the other Beauty Vloggers have done. A publishing house wants to promote a new book it thinks is hot and the lead character at some point in the story gets all “glammed” up. Here’s an opportunity to cross-pollinate — this YouTuber has 16, 000+ subscribers.

  • Curate — Put all those lovable videos you’ve created or collaborated on into a playlist, you’d be surprised that people will sit and let one video run into the next, after they’ve clicked through from their search for superhuman strengths or their quest to find out the truth about ghosts.

Finally, context is key, beyond sharing video ads before or during video content, you can share your ads when people are in the mood for that messaging. For example, when consumers are already watching a commentary video on feminism, then perhaps an in-video ad on a book about successful women in the workplace or how to be successful as a woman in the workplace would be a good fit.

It is important to be where the customers are, not just in terms of where they are when making purchases i.e. on e-commerce sites, but also where they lounge around, and hang out with friends (real or of the online variety). There are even opportunities to meet consumers in real time via some sites, but that can be discussed at another time. Companies that prove themselves useful and relevant in the most micro-moments—will establish the greatest brand equity in an era of infinite consumer choice. If your brand isn’t there in your audience’s moments of need, another brand will be.


  • Google Consumer Survey. U.S. online population ages 18-34; n=385. April 2015
  • Google Data, Q1 2014–Q1 2015, U.S.
  • Google Consumer Surveys. U.S. 10 platforms surveyed: YouTube, Hulu,, Facebook,, Tumblr, Instagram, Vimeo, AOL,  March 2014
  • Larson, Kim. Building a YouTube Content Strategy: Lessons From Google BrandLab. Google. July 2015
  • Meeker, Mary. “2015 Internet Trends Report.” Kleiner Perkins Caufield & Byers. May 2015
  • Watson, Lucas. “Video micro-moments: What do they mean for your video strategy?.” Google. October 2015
  • The Consumer Barometer Survey, Question asked: “Why did you watch online video(s)” n=2,119, Base: internet users (accessing via computer, tablet or smartphone) who have watched online video in the past week, answering based on a recent online video session, 2014/2015.