Created by Dr. Rebecca Fiebrink in 2009

 

About

AI. Culture. Creativity. (MTEC-498E-01)

Instructor: Christine Meinders

Community: Andrew Piepenbrink, Sara Sithi-Amnuai, Karan Vohra, Odie Desmith, Jeong Woo Seo, Kam Ying Lee, Charles Danner

Primary Design Tool: Wekinator

Design Methodology: Cultural AI Design

Additional Research & Design Tools: ML5.JS, ML4A, Tensorflow Playground, Magenta Colab, Coral Dev Board.

Class show: AI.Culture.Creativity. student art show at Supplyframe DesignLab

General Class Info

In this class we explored the current positioning of Artificial Intelligence (AI), utilized a methodology to design AI systems, and explored AI through several prototyping tools. The primary tool we used was Wekinator, created by Dr. Rebecca Fiebrink in 2009. One of the primary benefits of Wekinator is that because it uses OSC, it allows for communication between multiple creative coding programs like Processing, Max, Chuck, Arduino, Ableton, SuperCollider, and Pure Data (Pd). Another benefit is that it can be used in general ML project creation although it is very specific to the music community — and ideal for the creation of new musical instruments and interfaces. With the recent emergence of so many AI products it was great to work with one that has a significant history in design and learning.

We used the Cultural AI Design Methodology to design our machine learning projects (for both Wekinator and non-wekinator ML projects) and the majority of our group projects. The poeito (formerly Cultural AI Design Tool) is used as a problem framing which incorporates socio-cultural considerations into the design of AI systems. 

 
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In weeks 1-12 the readings and lectures and guest presenters focused on:

  • Systems: AI, A-life and Ecosystems 

  • AI: Definitions

  • Data/Input: Bias 

  • Rules: Supervised Learning: Classification & Regression (Wekinator and Tensorflow Playground)

  • Form/Material:  Explored the relationship between material (physical or sound) and the output. Can  material or form change based on the presence of specific data or models?

  • ML + Connectivity: Federated, Distributed, Decentralized 

  • Design / Frameworks: AI Design, Posthuman, Feminist, Anthropocene

  • Culture: Defining Cultural Perspectives and potential social implications across cultures

  • Ethics: Goals for AI systems, The Design of AI Systems (participatory, critical, transparency)

During our final weeks we continued to prototype and prepare for our show at Supplyframe DesignLab.

 

RESOURCES:

AI PROTOTYPING TOOLS (SOFTWARE):

AI PROTOTYPING TOOLS (HARDWARE):

EXAMPLE EXPERIMENTS:

CULTURAL ETHICS IN AI DESIGN (METHODOLOGY INSPIRED BY COMMUNITY):

  • CULTURAL AI DESIGN TOOL (DESIGN METHODOLOGY)

AI PLATFORM:

CORPORATIONS / ACADEMIC ETHICS: