Using data I described in last week’s blog post, I created a Google Fusion network visualization showing the connections between various subfields of machine intelligence research. I think looking at this visualization probably does far more for me (with the background research I’ve done on specific projects, scholars, and important dates connected with these research fields) than for anyone without any specific knowledge of this field. Because users of the visualization can’t click through to see other metadata I’ve associated with the nodes in the network, the information conveyed is rather superficial. Still, the network visualization helps to give a general sense of the important fields and their relationships to subfields in machine intelligence.
More on the art history side of the art-and-technology project I described last week, I’ve also generated an ImageQuilt using the ImageQuilt Chrome plugin. These particular images are all associated with Experiments in Art and Technology, a initiative of the 1960s and 1970s founded by Billy Klüver, an engineer at Bell Telephone Laboratories, his colleague Fred Waldhauer, and the artists Robert Rauschenberg and Robert Whitman. The group aimed to collaboratively produce art using new technology. A loose association of artists and technologists, artists including Jean Tinguely, Andy Warhol, Jasper Johns, and Yvonne Rainer were also involved with Klüver’s projects. The idea behind the E.A.T. collaborations was to allow for the creation of works that may not have been possible without the involvement of engineers, who would in turn be inspired by the artists to help shape the future of technology.1E.A.T. Experiments in Art and Technology. Edited by Sabine Breitwieser. For the Museum der Moderne Salzburg, 2015 Many of the images are from a 2015 exhibition, E.A.T. Experiments in Art and Technology at Salzburg’s Museum der Moderne. It’s an interesting way of bringing together some of the more canonical images of works from this movement, but again, I’m not sure how informative it is other than on a superficial level. I found it useful as part of my broader research efforts, but I wouldn’t incorporate it into a final version of a project.
Though neither of these visualizations were what I had in mind as I was plotting out my data last week, and though I’m not sure that they’re useful in conveying or clarifying much information to an outside observer, they’ve helped me to organize some segments of my research in these areas. I’ve also learned how to organize my data in Google Fusion tables as a result of inputting some elements of my research into a spreadsheet.
In the course of looking more into network visualizations and how they have been used in digital art history projects in order to determine what form (in an ideal world, with plenty of time) I’d like my visualization to take, I came across the Performance Artist Database, created by Matthew Miller in conjunction with his MFA thesis at Pratt. Focusing mainly on Fluxus artists, Miller organizes performance artists, events, and interactions through the use of quantitative analysis in order to explore the development of artistic movements and mediums in what is traditionally a difficult art form to preserve. Click ‘View Entire Network’ to see Miller’s network visualization of his research, searchable, and with nodes linked to his own metadata (dates, locations, and performances) as well as corresponding dbpedia items. It’s impressive, useful, accomplishes what he set out to do, and could serve as a starting point for further research in this area.
|↑1||E.A.T. Experiments in Art and Technology. Edited by Sabine Breitwieser. For the Museum der Moderne Salzburg, 2015|