Category Archives: Precedents

[thesis 1] Interview #1 – Jer Thorp

Bio

Jer Thorp defines his works as ‘software-based’ and ‘data-focused.’ He is the co-founder of The Office for Creative Research, along with Ben Rubin and Mark Hansen, and teaches at NYU’s ITP Program.

Some of his works pre-OCR include the algorithm for the 9/11 Memorial and Project Cascade. The latter was developed by Jer as a Data Artist in Residence at the NY Times R&D Group.

Interview

I talked to Jer for about 40 minutes. I didn’t conduct a formal interview. Instead, we talked about some of my ideas for this project and things he was working on. I tried to expand this to a conversation about broader themes in the field as well — future directions for data visualization, the role of data art, cultural changes related to data, etc. That is why this transcription doesn’t follow a Q&A format.

On the idea of ‘direct visualization’ as a form of representation closer to the artifact (the thing itself)

That’s a philosophically very deep question. You can get closer to the measurement, but it’s very hard to get close to the thing. That requires you analysing not only the act of representation, but you have to also consider the act of measurement, you have to consider the intent of the measurement, it’s all built into it. In that case [Manovich’s film visualizations], I would consider the dataset this kind of analytics that’s running on the images. There’s a lot of decisions being made there.

On the limitations of algorithmic-based cultural analysis

There’s this myth that the computer and algorithms allow you some type of purity. That is not true at all. This analytics allow us to do things we’ve never been able to do before. But they also don’t mean that we can dispose of ethnographical and the old-fashioned ‘talk to people,’ and do some journalistic research and so on. [Cultural analytics] It’s a tool, and a very powerful one. But it needs to be paired with other pieces.

About 3d (sculptural) visualisations versus 2d, in terms of perception

Scale and perspective make a huge difference for everything. I’m skeptical about those studies though, because if you dig up the papers half of them were done with a group of about 20, almost all white, grad students. And then we build all of our decisions based on them.

For us [OCR], it’s all about communication, which is a different thing. David Carson has this famous quote: “don’t confuse legibility with communication.” Because most of the times in our projects our fundamental goal is not to allow people to see “this is 6.8 or 6.2,“ but instead to give them a way to interpret that, or to have a feeling about it, or to construct a narrative from there. Of course rigour is important, so we’re not gonna show things in a way that is misleading. But I don’t believe in best practices in that sense. What’s best practices for your magazine might not be best practices for another magazine. And what if I’m projecting on a wall on a building, or what if it’s a sculptural form? These are things that we have no rules for, which I think is why I like those things more.

About future directions for the data visualization field

We’ve been working a lot with performance, and trying to think what that means perform data. We’re doing a long residency with the Museum of Modern Art. It’s an algorithmic performance that we write the scripts for actors that perform them in a gallery. It’s very much like traditional theatre in a way, but the content is generated using this data techniques we developed. That’s pretty interesting to me.

I think sculpture, data in a physical form it’s still in its infancy. Most of the works we’ve seen in the past 6 years look like something you just pulled out of the screen and plotted on a table. A lot of them look like a 3d renders, because of the 3d printers. There’s a ton of possibilities that haven’t been explored. We think about shape a lot in sculpture, but we don’t think a lot about material, its relation to the body or to a room, architecture, design, temperature… There’s a ton of room to do interesting things in that department.

On the reasons for a large number of recent data-related projects

Something about this movement has to do with something that’s happening in culture. It’s about and around this kind of data-based transformation that’s happening in the world. So it’s less about how it’s being done and more about why. It’s about the NSA, radical changes in transparency, wearable sensors, all those things coming together in this really big cultural change.

I’m definitely skeptical about the advertising slogans that have been used to promote this stuff, but I’m optimistic about its potential. Last year was really interesting, because for the first time we started to have real conversations about the exclusionary nature of big data. What does data mean for underprivileged communities? What decisions have been made based on a completely white North-American frame of thought? And how can we do better? That to me is really exciting.

The work we do here and the work that are a lot of people are doing is fundamentally about trying to push a cultural change on how we think about data. And that is gonna take a long time, it’s probably gonna require a lot more than a 9-person studio pushing against it, but what we need is a generation of people who understand data and collectively can make decisions.

Most people don’t even have a good understanding of what is data. And it’s fundamentally easy to talk about data as measurements of something. If you want to be more accurate, you can say it is records of measurement of something. And it’s important to include ‘measurement’ in it because it is a human act. So it is a human artefact. We can program machines to do it, but they’re still doing it based on our decisions — until A.I., strong A.I. development there’s no data that is not fundamentally human data.

Take-aways

This interview was made in a previous phase of my project, when my main questions were about representation — how to get close to the thing, or the artifact. It had some impact on my later decisions, leading me to turn my focus to the process instead of the result. In other words, if every data visualization process is based on decisions and implies lossy, is it possible to make it transparent? Can data visualization lead to a better understanding of data itself?

[Thesis 1] Been there, done that.

Some precedents from the previous MFA DT thesis shows that I find related to my domain:

Data Rush

1. Data Rush
Apon Palanuwech, 2014

1.1. Description
Critical piece about the use of data by tech companies. Users are asked to navigate through Facebook and/or Google pages using their phones. This interaction triggers the gears of the interactive installation. A digital display shows the amount of user data being collected by the companies. After a certain number of bytes, a candy is released from the piece to the user.

1.2. Connection
I like how Apon’s project utilizes means other than charts to tell a data-related story. The analogy with the Gold Rush is historically consistent and effective in terms of delivering his message.
Also, user interaction and system feedback work together to craft an interesting narrative.

Qualified Life

2. Qualified Life
Fei Liu, 2014

2.1. Description
Another data-related critical piece. Fei Liu satirizes the ‘quantified self’ trend with a corporate machine aimed to increase employees productivity. Users have to perform a series of physical activities dictated by the machine in an ironically authoritative manner.

2.1. Connection
‘Qualified Life’ succeeds in provoking reflection on the self-tracking trend. The machine’s lines are subtle in revealing its critical tone. In addition to the demanding physical interaction, they make the installation also entertaining.
Like in ‘Data Rush,’ metaphor and interaction create a well-crafted narrative.

Skylines 03

3. Skylines
Patricio González Vivo, 2014

3.1. Description
Series of projects exploring how the tools we use shape our vision of the world. Skylines 03 is a set of postcards that show skeleton-like image of NYC. The images were constructed using the depth information from the Google Maps Street View.

3.2. Connection
‘Hacking’ the Google data is not only an ingenious trick, but also a very consistent statement on Patricio’s point on how our current tools impose a single view on the world. It also creates an interesting connection between representational art pre-20th century and new technologies.

[Thesis 1] Background and Domain

  • Your favorite project (it doesn’t have to be your best, but that would be great too).
  • A few slides depicting things you know how to do really, really, well.
  • Technologies you love.
  • People you admire.
  • A piece of content (media, sound, writing, etc.) that relates to the field or domain you want to situate your thesis in.
  • Precedents for the kind of work you want to make.

All slides here.