The ribbon.Py programme—developed in collaboration with Isaac Clarke—reveals the logic employed by computer vision and computational prediction as the gymnast performs. Here, performance becomes a way of generating data and diagramming the logic behind models used to forecast so much about our lives, from the COVID-19 pandemic to climate change.
In the performances during London Design Festival, rhythmic gymnast Mimi-Isabella Cesar executes a choreographed routine with their ribbon apparatus. This is processed by various computer vision algorithms including MoveNet and multi-dimensional data visualisation tools such as UMAP. The shape, colour and motion of the dancer and their ribbon is registered as data points, training a programme made for this performance, ribbon.Py, to identify patterns with confidence and thereby make assumptions about the choreography and the ribbon’s curve. An Encoder-Decoder LSTM (long short term memory) model uses these patterns to extrapolate possible choreographic sequences.
Referencing the Three Fates––mythological figures who determine human destiny by spinning, measuring and then finally cutting the thread of each individual life––the programme follows similar logic live during the performance, collecting data (spinning), analysing it (measuring) and forming predictions (cutting), each leading to a possible future.
ribbon.Py programme is developed with Isaac Clarke/Black Shuck Coop
Music developed with Tommie Introna
ribbon.Py is supported by the Creative Industries Fund NL