Join us for an exclusive Spotify Research event. We will welcome two influential guest speakers, Rebecca Fiebrink and Andrew Trask, along with our director of Tech Research, Mounia Lalmas. The event will also feature a poster session where we will showcase Spotify's research across a wide variety of disciplines, from sociological studies, to algorithmic bias, to deep learning for audio processing.
Dinner and refreshments will be served.
Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in the Personalization mission of Spotify. Mounia also holds an honorary professorship at University College London. Before that, she was a Director of Research at Yahoo, where she led a team of researchers working on advertising quality for Gemini, Yahoo native advertising platform. She also worked with various teams at Yahoo on topics related to user engagement in the context of news, search, and user generated content. Prior to this, she held a Microsoft Research/RAEng Research Chair at the School of Computing Science, University of Glasgow. Before that, she was Professor of Information Retrieval at the Department of Computer Science at Queen Mary, University of London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now audio.
Andrew Trask leads OpenMined - a community focused on building open-source technology for the decentralized ownership of data and intelligence. Andrew is also a PhD candidate at the University of Oxford researching Privacy and Deep Learning. He is also the author of Grokking Deep Learning, a Manning Publications introductory book which has sold over 10,000 copies, an instructor in Udacity's Deep Learning Nanodegree (14,000+ students), Secure & Private AI Course, and the author of a popular machine learning blog http://iamtrask.github.io. Andrew also started the OpenMined community, a group of other 3500 machine learning researchers, practitioners, and enthusiasts building open source tools for safe, privacy preserving Deep Learning.
In this talk, Andrew will present a high level introduction to techniques for privacy preserving Artificial Intelligence including Differential Privacy, Federated Learning, and Secure Multi-Party Computation (i.e. Encrypted Deep Learning). He will then walk through several tutorials using a recently created toolkit called PySyft, which gives an intuitive API for these techniques from within the PyTorch Deep Learning framework.
Dr. Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, University of London. 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. Fiebrink is the developer of the Wekinator, open-source software for real-time interactive machine learning whose current version has been downloaded over 25,000 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.
Machine learning algorithms are now capable of creating new images, sound, and other media content, which we can reasonably call novel, sophisticated, and even compelling. What do these algorithms mean for the future of human creative practice? Will all of us soon sit at home watching algorithmically-generated music videos after robots take our jobs? In this talk, I will show how machine learning is instead opening doors to new forms of human creative expression. For instance, by modelling data that captures how we move, play, and make art and music, machine learning algorithms can help computers better understand our most expressive human activities. Furthermore, these algorithms can augment human capabilities by allowing people to more easily discover and explore new ideas. By enabling more natural ways for humans to express their ideas to machines, machine learning can even make it easier for more people to engage in creative work and technical innovation. This talk will include live demonstrations of machine learning used to make new musical instruments and interactive art.