Great news for the AI for Social Good community: yesterday we (Claudia, Emti, Jacopo, and Ruben) received word from Dagstuhl that our proposal for a third AI for Social Good seminar has been accepted. Mark your calendars folks: AI experts and NGO representatives will convene from 18-23 February 2024 in the Dagstuhl castle to discuss how AI can benefit social good and to do some prototyping. Invitations will be sent out in the coming months.
Today we saw a dozen participants of the last AI for Social Good seminar get together to update each other on the projects that were started at the seminar last year. Some highlights! The Red Cross people are continuing the collaboration with UMass, while we heard from the Open Street Map folks that they have been working on an AI assisted mapping software - see Omran’s blog post: https://www.hotosm.org/tech-blog/hot-tech-talks-fair/. They have / are trying a number of different models, including rampml https://rampml.global/. Mark your calendars: Omran will be giving a talk at the geospatial world forum in Rotterdam in May https://geospatialworldforum.org. An Imperial Master’s student has been working on the Airqo project and getting to grips with the data. Laterite has been working on Geospatial interpolation models with Ruby.
Laterite brought a case study on geospatial interpolation to the the AI4SG seminar at Dagstuhl in February 2022. Rik Linssen (Lead Economist at Laterite) and Jose Rubio Valverde (Associate Economist at Laterite) teamed up with Ruby Sedgwick and Jose Pablo Foch from Imperial College London and Raghu Rajan from the University of Freiburg, to work on a proof-of-concept to predict school dropout rates in Rwanda.
- In March 2022, Subhransu Maji, co-director of the Computer Vision Lab at the University of Massachusetts, Amherst, attends the AI4SG Dagstuhl seminar. Subhransu: “At the seminar, I started collaborating with folks from the Red Cross on an ML tool to assess damage of buildings and infrastructure in the wake of natural disasters.”
- Fast-forward to Summer 2022: Yunfei Luo, Siddhi Brahmbhatt and Shruti Chanumolu, UMass students in the Data Science for Common Good program that’s led by Thomas Bernardin, continue the work on the Red Cross project (with help from Dagstuhl attendee Sara Beery (Caltech)).
- November 2022: the UMass Center for Data Science announces that data scientist Virginia Partridge and MS student Tanmay Agrawal will dedicate time to this project. Jacopo Margutti from the Red Cross is excited: “Having such a serious, longer-term commitment from an academic partner is an enormous boost for our work in the AI/ML space. We’ve already started using the damage assessment tool in real life, but it can certainly benefit from further expansion and refinement.”
Check out this checklist on the prerequisites for implementing digital innovation using machine learning. It’s handy, and it’s co-developed by Airqo, D-tree, and TechnoServe, which are three organizations that participated in the AI4SG Dagstuhl Seminar in 2022 and receive funding from Wehubit.
Yeey! Claudia Clopath, Emtiyaz Khan, Jacopo Marghutti, and Ruben De Winne (co-organizers of and participants in the AI4SG Dagstuhl seminars in 2019 and 2022 have submitted a proposal to Dagstuhl for what will hopefully be a third AI for Social Good seminar.
Artificial Intelligence (AI) and Machine Learning (ML) researchers from various universities with representatives from NGOs based in Benin, Tanzania, Uganda, The Netherlands and globally came together over a 5-day seminar in Dagstuhl, Germany to pursue various social good goals, such as improving air quality, increasing agricultural productivity with the help of technology, transforming health care, providing humanitarian support, and defeating poverty. The seminar facilitated the exploration of possible collaborations between AI and ML researchers and NGOs through a two-pronged approach. This approach combined high-level talks and discussions on the one hand with a hands-on hackathon on the other hand. High-level talks and discussions focused first on the central concepts and theories in AI and ML and in the NGOs’ development work, before diving into specific issues such as generalisability, data pipelines, and explainability. These talks and discussions allowed all participants – in a very short time-frame – to reach a sufficient level of understanding of each other’s work. This understanding was the basis to then start investigating jointly through a hackathon how AI and ML could help addressing the real-world challenges presented by the NGOs.