- Become familiar with RunwayML and understand how to run new models in the browser.
- Machine Learning En Plein Air: Building accessible tools for artists by Cristóbal Valenzuela
- Runway: Adding artificial intelligence capabilities to design and creative platforms by Cristóbal Valenzuela, Alejandro Matamala, and Anastasis Germanidis
- Projects Made with RunwayML
- No Machine is an Island by Yuxi Liu and Larissa Pschetz
- Introspectons by Philipp Schmitt
- Machine Learning En Plein Air: Building accessible tools for artists by Cristóbal Valenzuela
- Runway: Adding artificial intelligence capabilities to design and creative platforms by Cristóbal Valenzuela, Alejandro Matamala, and Anastasis Germanidis
- Pick a model available in RunwayML. Run the model both as a preview and experiment with the "export" option. You can choose a model you worked with in class or a different one. Feel free to try more than one if you like. Consider the following questions:
- What is this model developed to do?
- Can you write a Model and Data "biography" that covers where it came from and what data was used to train it?
- Describe the results of working with the model, do they match your expectations?
- Can you "break" the model? In other words, use it in a way that it was intended for and what kinds of results do you get?
- Document your thoughts on the above questions and your experience working with RunwayML in a blog post. Include screenshots and screen captures of your workflow. Compare and constrast working with RunwayML as a tool for machine learning as related to ml5.js, python, and any other tools explored this semester. Link from the homework wiki.