The computational artist, Memo Akten, explores the interactions and tensions between ecology, technology, science, and spirituality. During his 2019 residency in the Studio, he contributed to ML5.js, an open-source software toolkit that provides artists with resources and knowledge for implementing machine learning in their art and designs. It makes machine learning accessible to a wide range of audiences, more than visual artists but also to creative coders and students.
Memo Akten developed the Stateful LSTM and CharRNNs toolkit in ML5. js. As a type of Neural Network architecture, RNN and LSTMs (Long Short Term Memory networks) are useful for working with sequential data (like characters in text or the musical notes of a song) where the order of the sequence matters.
The ML5.js website has a class that allows users to run a pre-trained model on a body of text for generating new text. Other handy tutorials and machine-learning models of images, sounds, and texts can be found on their website.