CART 398/A (Fall, 2023): TOPICS IN COMPUTATION ARTS.
Creative Artificial Intelligence: Embodied Interactions Using Interactive Machine Learning.
This course is designed to develop both technical and personal skills for those interested in utilizing machine learning (ML) in creative contexts, with a particular focus on embodied interactions and interactive environments. The course objectives fall under four main categories:
1. Understanding of Machine Learning and Human Agency: By the end of this course, students should have a fundamental understanding of the ML paradigm, its components, its application in creative contexts, and the role of human agency in collaborative creativity with machines.
2. Promotion of Open-source Tools: Students will be introduced to freely available open-source tools for human-computer interaction and interactive machine learning like Wekinator, ML5.js, MIMIC platform, learner.js, and OSC. They will learn how to select appropriate tools for their unique creative needs.
3. Community Building: The course aims to stimulate multidisciplinary discussion on creative AI and co-creation with machines by fostering connections among participants through collaborative reflection on AI's potentialities and limitations in creativity.
4. Sharing Research-Creation Methodologies: Students will be guided through a complete research-creation project using ML—from conception to design, implementation, and delivery—and learn about addressing biases introduced by datasets or data structures as well as choosing ML techniques. This will raise awareness about potential issues when applying ML tools in artistic practice.
1. Understanding of Machine Learning and Human Agency: By the end of this course, students should have a fundamental understanding of the ML paradigm, its components, its application in creative contexts, and the role of human agency in collaborative creativity with machines.
2. Promotion of Open-source Tools: Students will be introduced to freely available open-source tools for human-computer interaction and interactive machine learning like Wekinator, ML5.js, MIMIC platform, learner.js, and OSC. They will learn how to select appropriate tools for their unique creative needs.
3. Community Building: The course aims to stimulate multidisciplinary discussion on creative AI and co-creation with machines by fostering connections among participants through collaborative reflection on AI's potentialities and limitations in creativity.
4. Sharing Research-Creation Methodologies: Students will be guided through a complete research-creation project using ML—from conception to design, implementation, and delivery—and learn about addressing biases introduced by datasets or data structures as well as choosing ML techniques. This will raise awareness about potential issues when applying ML tools in artistic practice.
- Teacher: GABRIEL VIGLIENSONI