- Getting into JS in the first place;
- Move toward a routine based on small incremental projects;
- A nice way of practicing my NLP tools (alongside NLTK in Python).
I should add that Shifman is at this very moment developing a series of tutorials introducing TensorFlow.js, anticipating the irruption of ever easier machine learning libraries for creative purposes:
Methodology / Tetralogy
An idea that came to me would be to follow Lior Ben-Gai’s methodology for his course ‘Programming for Artist II’ (which unfortunately clashed with another course I took last term), in which he distributed possible projects/assignments into four categories:
- Random (using the built-in random function(s) in the programming language of your choice, which in this case was Processing);
- Functional (adapt a mathematical concept, e.g. the Calabi–Yau manifold, but it could be any other idea taken from the sciences, for instance Markov models for text?, to a creative project of your choice);
- Data-driven (use a dataset as a basis for your project);
- Emergent (devise simple rules appyling to agents or objects and study the result of having many of these ‘evolving’ or ‘acting’ together, as can be seen in flocking or other systems, focussing on the consequences of a bottom-up form of artistic organisation).