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This event will see Rebecca Fiebrink (Goldsmiths University) talk about machine learning as a creative interaction design tool followed by Miriam Redi (Bell Labs), who will present her research on using machine learning to detect subjective properties of images and videos.
It is part of a series designed to bring together artists, developers, designers, technologists and industry professionals to discuss the applications of artificial intelligence in the creative industries.
Rebecca Fiebrink (http://www.doc.gold.ac.uk/~mas01rf/Rebecca_Fiebrink_Goldsmiths/welcome.html), Lecturer, Department of Computing, Goldsmiths University of London
"Machine Learning as Creative Interaction Design Tool"
In this talk, I will discuss my research on better enabling musicians, artists, interaction designers, and students to employ supervised learning in the design of new real-time systems. I will show a live demo of tools that I have created for this purpose, centering around the Wekinator software toolkit for interactive, realtime machine learning. I’ll discuss some of the outcomes from 7 years of creating machine learning-based tools and observing people using these tools in creative contexts. These outcomes include a better understanding of how machine learning can be used as a tool for creators of new interactive systems, where it can support crucial design activities such as rapid prototyping, iterative refinement, and embodied engagement, as well as a deeper appreciation for how using machine learning for design differs from more conventional application contexts.
Dr. Rebecca Fiebrink is a 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 system (http://www.wekinator.org/) for real-time interactive machine learning, and the creator of a MOOC titled “Machine Learning for Artists and Musicians,” (https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info) 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.
Miriam Redi (http://www.visionresearchwitch.com/), Research Scientist, Bell Labs.
Provisional title: "Can Machines See The Invisible?"
In this talk we will explore the invisible side of visual data, investigating how machine learning can detect subjective properties of images and videos, such as beauty, creativity, sentiment, style, and more curious characteristics. We will see the impact of such detectors in the context of web and social media. And we will analyse the precious contribution of computer vision in understanding how people and cultures perceive visual properties, underlining the importance of feature interpretability for this task.
Miriam Redi is a Research Scientist in the Social Dynamics team at Bell Labs Cambridge. Her research focuses on content-based social multimedia understanding and culture analytics. In particular, she explores ways to automatically assess visual aesthetics, sentiment and creativity, and exploit the power of computer vision in the context of web, social media, and online communities. Miriam got her Ph.D. at the Multimedia group in EURECOM, Sophia Antipolis. After obtaining her PhD, she was a Postdoc in the Social Media group at Yahoo Labs Barcelona and a Research Scientist at Yahoo London.
The schedule for the evening will be as follows.
6.30pm - 7pm Arrive
7pm - 7.10pm Introduction
7.10pm-7.50pm First talk: Rebecca Fiebrink
7.50pm-8.30pm Second talk: Miriam Redi