S01: Introduction to Cultural Appropriation with Machine Learning
Channel: pkmital
Date: 2020-12-19

LECTURE SLIDES

S01: https://drive.google.com/file/d/170yBr9UfLWOFxh2SNhaVu4_9aoigdb9k/view?usp=sharing

COURSE DESCRIPTION

This course guides students through state-of-the-art methods for generative content generation in machine learning (ML) with a special focus on developing a critical understanding surrounding its usage in creative practices. We begin by framing our understanding through the critical lens of cultural appropriation. We then extend our understanding into topics such as deep-fakes and bias. Next, we look at how machine learning methods have enabled artists to create digital media of increasingly uncanny realism aided by larger and larger magnitudes of cultural data, leading to new aesthetic practices but also new concerns and difficult questions of authorship, ownership, and ethical usage. Finally we speculate on the future of computational practices, as machine learning becomes an increasingly predominant tool for creatives, and ask of its trajectory, "Where is it all going?".

Shared here are lecture sessions spanning 7 sessions and a guest lecture by the artist and creative technologist Holly Grimm. Students were also engaged through critical assessments of their own work and understanding of the concepts in separate sessions that are not shared here for privacy reasons.

COURSE INSTRUCTOR

This course is taught by Parag K. Mital, CTO and Head of Research at Hypersurfaces, Ltd. Based in Los Angeles, CA, Parag K. MITAL (US) is a computational artist and interdisciplinary researcher obsessed with the nature of information, representation, and attention. Using applied machine and deep learning, film, eye-tracking, EEG, and fMRI recordings, he has worked on computational models of audiovisual perception from the perspective of both robots and humans, often revealing the disjunct between the two. His artistic practice combines generative film experiences, augmented reality hallucinations, and expressive control of large audiovisual corpora. The balance between his scientific and arts practice allows both to reflect on each other: the science driving the theories, and the artwork re-defining the questions asked within the research. His work has been published and exhibited internationally including the Prix Ars Electronica, Walt Disney Concert Hall, ACM Multimedia, Victoria & Albert Museum, London’s Science Museum, Oberhausen Short Film Festival, and the British Film Institute, and featured in press including BBC, NYTimes, FastCompany, and others. He has also taught at University of Edinburgh, Goldsmiths, University of London, Dartmouth College, and California Institute of the Arts in both Undergraduate and Graduate levels in primarily computational arts applied courses focusing on computer vision, signal processing, algorithmic sound, and machine learning. More info at: http://pkmital.com

LECTURE OUTLINE

S01: Introduction to Cultural Appropriation with Machine Learning
S02: Introduction to Python, Colab, and Datasets
S03: Neural Networks, Feature Extractions, and Manifolds
S04: Searching and Matching
S05: Generative Models for Image Generation
S06: Generative Models for Text Generation
S07: Generative Models for Sound Generation
Guest Lecture: Holly Grimm

LEARNING OUTCOMES 

* Understand critical discussions surrounding the usage of machine learning
* Understand what datasets are and how they are created
* Understand how machine learning can be used by artists
* Understand how to generate content within images, text, and audio digital media

CREDITS

This course was presented Fall 2020 at UCLA DMA (Special Topics, DMA171). Special thank you to all the students who continued to challenge and support each other throughout this course. The videos are edited by Katherine Sweetman. RunwayML generously provided Pro accounts/support to students! Zhengyang Huang TA'ed the course. Jonathan Cecil provided additional support as did many wonderful DMA staff/faculty including Lauren McCarthy, Kyle Clausen, and Hope Stutzman.

LICENSE

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Other authors' works may be included during presentations, and additional copyrights may apply.