Art vs. Tech Timelines

Wayfinding: Spotlights > Fast Prototyping > Art vs. Tech Timelines

Clemence Debaig has prototyped both hardware devices and software programs for many dance performances, and pointed out the importance of rapid prototype development in order to work in a rehearsal context:

… there's almost no point in getting like one week residency, with all your performers in the room, and you arrive fresh and you've made nothing, because it might just take an entire week to create a prototype, and then you have everyone sitting around; or, you might arrive with the prototype, and on the first day you realize that something is not working, and then you're going to need an afternoon to fix it. And then in the meantime everyone is sitting around. So, I think it's really important to understand those constraints where, if you're just working with performers in a rehearsal studio, if you have an idea, it takes 30 seconds to prototype it. You go in and you experiment with your thing.

Debaig also noted the speed at which large corporations develop new technology and release it, which can cause friction during a production and rehearsal process: ‘The tech is moving so fast. … by the time I’m done exploring my teeny, tiny niche of what I wanted to make, there's another thing that came out. And that is very overwhelming, because you can just spend your time just staying up to date, and then you have made nothing; or you can start making things, and you're going to fall behind’.

This can feel especially stressful with ‘advancements’ in artificial intelligence (A.I.) and generative programs like image generators or text generators, which are constantly being fed new training data and being updated. Debaig has herself worked with some of these tools alongside Brendan Bradley during OnBoardXR 6: its pronounced ‘gybe’, when the team made a seven-minute musical titled ‘i can build it’ that integrated audience-generated prompts into DALL-E, which then displayed the images as the show’s background/set. Part of the show’s good humour with technology required the images to be poorly constructed and easy to distinguish from photos or human-created artworks; since that show’s premier in 2023, several ethical problems with AI-generated art have come to light, including the rapid development of the tools leading to greater difficulty distinguishing fake images, and Bradley himself becoming a training data source/victim of AI generated images.

It seems, based on these experiences, it is best to test potential tools and decide early in the production process what you want to use and how it can be implemented. In an interview, Dr. Katie Hawthorne, a researcher in Edinburgh conducting a similar project to this GitBook, said that ‘the biggest investment is time: working with new tools can feel like a learning curve, and it’s important for artists and programmers to be able to take the time to find their voice’. This might seem in contrast to Debaig’s concern about staying up to date with technology, but Hawthorne’s statement might suggest a dramaturgical approach based on practical strategy: pick a tool and explore it, with enough production time to learn the tool in-depth. Even if there is a new version of something like a text generator or virtual world, it is possible to avoid using the absolute newest version of a digital tool. And, when you take digital tools and prototypes into a rehearsal, setting aside time to work with just the technology as a form of testing will ensure both the performers and the digital technology will be ready for performance.