Metaphor generation and ML for child welfare

We open season two of Underrated ML with Anna Huang on the show. Anna Huang is a Research Scientist at Google Brain, working on the Magenta project. Her research focuses on designing generative models to make creating music more approachable. She is the creator of Music Transformer and also the ML model Coconet that powered Google’s first AI Doodle the Bach Doodle.

She holds a PhD in computer science from Harvard University and was a recipient of the NSF Graduate Research Fellowship. She spent the later parts of her PhD as a visiting research student at the Montreal Institute of Learning Algorithms (MILA). She publishes in machine learning, human-computer interaction, and music, at conferences such as ICLR, IUI, CHI, and ISMIR.

She has been a judge on the Eurovision AI Song Contest and her compositions have won awards including first place in the San Francisco Choral Artists’ a cappella composition contest. She holds a masters in media arts and sciences from the MIT Media Lab, and a B.S. in computer science and B.M. in music composition both from the University of Southern California. She grew up in Hong Kong, where she learned to play the guzheng.

On the episode we discuss Metaphoria by Kate Gero and Lydia Chilton, which is a fascinating tool allowing users to generate metaphors from only a select number of words. We also discuss the current trends regarding the dangers of AI with a case study on child welfare.

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Anna Huang Twitter: https://twitter.com/huangcza

Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8

Links to the papers:

Gero, Katy Ilonka, and Lydia B. Chilton. "Metaphoria: An Algorithmic Companion for Metaphor Creation." CHI 2019. [paper][online paper] [talk] [demo]

"A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions" - [paper]

Additional Links:

• Compton, Kate, and Michael Mateas. "Casual Creators." ICCC 2015. [paper]
• Fiebrink, Rebecca, Dan Trueman, and Perry R. Cook. "A Meta-Instrument for Interactive, On-the-Fly Machine Learning." NIME 2009. [paper][talk][tool]
• Huang, Cheng-Zhi Anna, et al. "The Bach Doodle: Approachable music composition with machine learning at scale." ISMIR 2019. [paper][blog][doodle]