Our initial idea was to have three parts to our installation:
Part 1: A great garbage waterfall
Part 2: Performative gestures of discarding garbage, and
Part 3: Textual gameplay with the AI we trained to talk to the audience regarding garbage and its disposal.
All of this eventually combined into a motion-censored interaction with an audience that could turn a potentially overwhelming massive garbage waterfall into a clear and clean flowing water body. During our mentor sessions, it was brought to our attention that our project had too many ideas and that it could board well for us to find the core of our idea, stick with it, and grow it out.
Through the references given to us during our mentor session by Jake Elwes and Madhu Nataraj we discovered that the process of documentation itself could come from the ethics and sentiment of a performer’s core of paying attention to the material, time and body they deal with – in our case, garbage and AI. Here are some of the key artists and references suggested by our mentors that left a strong impact on us:
- ImageNet Roulette: This project gave us an insight into wondering about the kinds of “schooling” that an AI model needs, and what kinds of datasets it can comprehend. The machine is a scientific invention, thus its classifications and categorizations are ipso facto scientific — can we stop there, or must one acknowledge that we project our ingrained human training onto the machine, which has little control over what it is fed?
- From ‘Apple’ to ‘Anomaly’: About 30,000 individually printed photographs made up the work, which was meant to be a form of extended homage to Magritte’s ‘The Treachery of Images’ for the era of machine learning. Taking a close look at a widely used dataset for training AI — ImageNet — it displayed the hazardous links between images and labels, provoking us to rethink how we create meaning through our datasets.
Kate Crawford and Vladan Joler
- Anatomy of an AI: Artificial intelligence (AI) may seem far away and abstract, but it already permeates every aspect of our daily life. ‘Anatomy of an AI’ carefully compiles and condenses this enormous volume of information into a detailed high-resolution graphic by analysing the massive networks that support the “birth, life, and death” of a single Amazon Echo smart speaker. This data visualisation helped us understand the enormous amount of resources that go into the creation, distribution, and disposal of the speaker, breaking down the otherwise strange concept of AI into something we are more familiar with.
- One Year Performance: The works of this American-Vietnamese performer were brought to our attention in order to introduce us to the depth of performance analysis and the importance of performance-related documentation. His works are not particularly AI-related. On the other hand, it has a lot to do with the decision to dedicate oneself to timed documentation of the experiments that the artist decided to undergo just as an artistic exercise. Our mentor, Jake Elwes, introduced us to the world of Hsieh by using the specific example of ‘One Year Performance’ (1980-1981), in which he set up shop in one location, punched a clock every hour, and took a photo of himself on each punch.
Gaining insights from these artworks, we discussed and decided that it was the question of the garbage that concerned us most, so we unanimously decided to set aside the performative gestures and gameplay aspects of our proposal aside. Thus began our deep dive into documenting our garbage.
It has been an interesting journey for us so far on quite a few levels. First and foremost our biggest challenge has been to keep in touch and coordinate our meetings between the four of us across the globe. Initially, given each coordinator’s life and the great fear of dealing with a project that we playfully pitched for that we needed to execute now, we spent a better part of our initial learnings showing up on calls when we said we would. Even though it seems simplistic to just show up, it was harder than we expected it to be. We are relieved to say we got better at organising ourselves come the second month of the project. At this time we have hired a photographer, Ankit Banerjee, to train the three of us to photograph the kind of images we need for the AI to train so that all our images look cohesive under one dataset.
This too might seem simple to an AI expert but to Malavika and Papia it came as quite a learning curve. Sometimes we imagine Asli Dinc nodding her head wondering what could do with our novice skills; however, with patience and some humour, we pull through.
Learning to Document
At the start, we shot the images in our own style. It took us some time to narrow down the type of image we wanted, and more importantly, to understand and acknowledge why an image needs to be a certain way and what that means for training the AI.
For example, initially, when Malavika set up a sample DIY photo booth and went through meticulously recording about 3 weeks’ worth of her garbage, Asli Dinc sweetly but surely shot it all down. Malavika being a visual artist got excited with the shadows that the garbage was casting on the floor and the backdrop.