The recent expanded use of machine learning techniques in real-world applications has been driven by data availability and processing power. Vast electronic data troves have become available to practitioners, making relatively old data analysis tools much more likely to solve difficult challenges, and massive improvements in processing power have significantly reduced the time taken to find solutions to these challenging problems.
This rapid growth in applied machine learning has come so fast that there is widespread potential for disruption and harmful misapplication. This is especially true since there is a troubling lack of diversity among highly skilled practitioners – the “technical elite.”
This Bass Connections project team will examine the disruptive and potentially harmful implications of machine learning, when applied by a not-so-diverse elite of highly skilled practitioners. The project will seek to open the closed world of applied machine learning to students and the public through development of a performance-driven workshop. Team members will be tasked with creating performance art that addresses the disruptive nature of the technology and its potential for harm if misapplied.
The project team will apply open source machine learning tools to develop a workshop in applied machine learning for performance artists. The goal is to distill the tools into a user-friendly set of interfaces that could be applied by artists.
The project will use the cross-listed multidisciplinary course Performance and Technology as a testing ground for tools in development and datasets. Team members will initially define a few core tools and application areas that are practical yet intriguing enough to reveal the potential for disruption and potentially harmful misapplication of machine learning.
Possible focus areas include tracking objects and people for identification and classification and filtering applicant pools for desirable traits through survey data analysis. These themes will set the stage for a great dramatic performance topic and present artists with an opportunity to use realistic machine learning tools to address these issues in believable ways.