The insufficiency of ground truth data is usually a barrier when researchers are trying to train machine learning models for real-world applications. In this talk I will be discussing some of the computer vision applications I have worked on in the past, discussing some interesting techniques as well as lessons learned while creating ground truth labels in order to answer research questions around ways to study the effects of Spatial Apartheid in South Africa.

On-device Machine Learning applications provide a wealth of opportunities for sensing and analytics, particularly when cloud connectivity is not always readily available. Making these applications more energy-efficient and private can reduce their reliance on batteries and/or excessive data collection. Solutions in this space would have significant implications for a new generation of sensing and monitoring applications for environmental monitoring, population-wide analytics, scientific exploration, and climate/weather prediction. In this talk I will provide an overview of recent attempts in this space, and challenges ahead for providing reliable, secure, and private client-side applications.

Xiaorong Ding, University of Electronic Science and Technology of China

Issa Karambal, Quantum Leap Africa

Amara Kenza, ETH Zürich

Reinhard Scholl, International Telecommunication Union (ITU)

Link to the papers.

I organized this talk around some challenges that my colleagues and I face as Argentinean researchers and some of the coping mechanisms that we use. I also relate these coping mechanisms to general ethical principles for the international community. My talk is based on the assumption that machine learning research needs researchers from all over the world if the goal is to develop products that are beneficial for the whole world (and not only for some part of it). I will show that the research community in natural language processing is far from including georgraphically diverse researchers. I close this talk with my proposal as the first Latin American chair in the chapter of the Association for Computational Linguistics that serves the American continent, which was created in 1999.

Link to the papers.

Encoding Upper Nasal Airway Structure with U-Net for respiratory healthcare applications. Bruno Pazos, Pablo Navarro, Soledad De Azevedo, Claudio Delrieux, Rolando González-José

Detecting landfills using multi-spectral satellite images and deep learning methods.Anupama Rajkumar

Surrogate Ensemble Forecasting for Dynamic Climate Impact Models. Julian Kuehnert, Deborah McGlynn, Sekou L. Remy, Anne Jones, Aisha Walcott-Bryant

Why So Inflammatory? Explainability in Automatic Detection of Inflammatory UsersCuong Nguyen, Daniel Nkemelu, Michael Best, Ankit Mehta

Barriers and opportunities to improve renal outcomes in South Africa using AI technology for pediatric ultrasound interpretation. Lauren Erdman, Karen Milford, Zubrina Solomon, Mandy Rickard, Armando Lorenzo, Andrew Grieve, Anna Goldenberg

Nursing is centered on the theory and practice of caring. Caring goes well beyond caring to bring patients to health. It starts with respect and equanimity that is achieved by respecting the values of all patients, even if they are different from yours, and treating patients as they are. Taking inspiration from nursing, in this talk, I will discuss how a carative approach to AI should start with the real-world problem as experienced by the most vulnerable, listening to and understanding their values, meeting them where they are, working toward a solution to their problem all the way to the end (even if it involves a lot of grunt work and doesn't lead to an ICLR paper), and conducting a qualitative assessment of the entire solution by interviewing the affected communities.