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HCI Seminar 4/20, Rebecca Fiebrink, Goldsmiths College — Machine learning for creativity, interaction, and inclusion

Michael Bernstein mbernst at stanford.edu
Mon Apr 16 09:34:49 PDT 2018


Machine learning for creativity, interaction, and inclusion
Rebecca Fiebrink, Goldsmiths College

April 20, 2018, 12:30-1:30pm, Gates B01 · Open to the public
CS547 Human-Computer Interaction Seminar (Seminar on People, Computers, and
Design)
http://hci.st/seminar
http://cs547.stanford.edu/speaker.php?date=2018-04-20

In computer science, machine learning algorithms are typically framed as a
set of techniques for building accurate models of data, and research has
traditionally focused on building better models more quickly, and for more
types of modeling problems. Popular narratives around machine learning and
AI center around visions of progress and disruption driven by automation,
around a Terminator-style singularity, or increasingly around a dystopian
future of surveillance and manipulation of the public.

In this talk, I’ll discuss how these perspectives obscure many of the
opportunities for human benefit that can be found by applying machine
learning to vastly different types of problems and by making it usable by
more diverse people. I’ll describe my work exploring questions such as: How
can machine learning support human creativity and embodied interaction? How
can machine learning enable new groups of people to create and customise
interactive technologies? How can machine learning fundamentally change
people's relationships with technology? When and how should our
computational framing of machine learning change to reflect different
contexts of use or user goals? How can we build usable machine learning
tools for people such as professional musicians and artists, kids, or
software developers? And how might we change machine learning education to
reflect machine learning's growing importance across society?

Dr. Rebecca Fiebrink is a Senior Lecturer (like an Associate Professor, but
British) in the Computing Department at Goldsmiths, University of London.
Much of her research focuses on designing new ways for humans to interact
with computers in creative practice, including on the use of machine
learning as a creative tool. Much of her work is also driven by an interest
in promoting inclusion, participation, and accessibility. She works
frequently with human-centred and participatory design processes, and she
is currently working on projects creating new accessible technologies with
people with disabilities, designing inclusive machine learning curricula
and tools, and applying participatory design methodologies in the digital
humanities.

Fiebrink is the developer of the Wekinator, open-source software for
real-time interactive machine learning whose current version has been
downloaded over 10,000 times. She is the creator of a MOOC titled “Machine
Learning for Artists and Musicians,” which launched in 2016 on the Kadenze
platform. She was previously an Assistant Professor at Princeton
University, where she co-directed the Princeton Laptop Orchestra. She has
worked with companies including Microsoft Research, Sun Microsystems
Research Labs, Imagine Research, and Smule, where she helped to build the
#1 iTunes app "I am T-Pain." She holds a PhD in Computer Science from
Princeton University.
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