Ardavan Bidgoli will present the proposal for the thesis, “Situated Toolmaking for Creative Computing: A Framework for Context-aware Machine Learning in Design, Art, and Making,” as a candidate of the PhD in Computational Design (PhD-CD) on Wednesday 18 December at 2:00pm.
Title: “Situated Toolmaking for Creative Computing: A Framework for Context-aware Machine Learning in Design, Art, and Making”
By Ardavan Bidgoli, PhD-CD Candidate
Date: Wednesday, 18 December 2019
Time: 2:00-4:00pm
Location: MMCH 121
PhD Advisory Committee
Dr. Daniel Cardoso Llach (Chair)
Associate Professor, School of Architecture, Carnegie Mellon UniversityDr. Eunsu Kang
Visiting Professor of Art and Machine Learning, Carnegie Mellon UniversityProf. Golan Levin
Associate Professor of Art, School of Art, Carnegie Mellon UniversityDr. Barnabás Póczos
Associate Professor in Machine Learning, Machine Learning Department, Carnegie Mellon University
Abstract
Recent advancements in artificial intelligence and its sub-branch machine learning (ML) have led a growing number of artists, designers, and architects to explore these techniques’ affordances in developing tools that support their creative practices. However, the distinct separation between expert users and ML experts in the process of toolmaking results in abstract and decontextualized tools that are incapable of serving in real environments. This research proposes a paradigm shift in the process of ML-based creative computing toolmaking that accommodates expert users and contextual data as integral elements of the toolmaking process. It grants expert users more involvement and control over the process, mitigating the adverse effects of the decontextualization. The framework utilizes human-centered ML techniques such as interactive ML, Learning from Demonstration, and generative models to improve expert users’ ability to build their own creative computing tools and introduce contextual data into it without engaging with the complexities of the backend ML systems.