As AI projects gain traction in the humanitarian sector, securing their funding and long-term sustainability remains a critical challenge. This session explores how AI initiatives can align with the SDGs and address pressing climate concerns, while also examining innovative funding models and cross-sector partnerships. From philanthropic investments to public-private collaborations, join us to uncover strategies for ensuring AI projects not only launch successfully but also endure to create lasting, scalable impact in humanitarian efforts. Participants will gain insights into best practices for funding AI projects and explore case studies showcasing successful funding models and partnerships.
Key Learning Objectives:
Target Audience: This event is designed for humanitarian workers at all levels, policymakers, academics, data specialists, communication specialists, and technology experts who are involved in crisis response and interested in the ethical use of AI.
Prerequisites: No prerequisite knowledge is required. Basic understanding of AI and humanitarian principles is recommended.
Institutions
Explore the transformative potential of the Population Dynamics Foundation Model (PDFM), a cutting-edge AI model designed to capture complex, multidimensional interactions among human behaviors, environmental factors, and local contexts. This workshop provides an in-depth introduction to PDFM Embeddings and their applications in geospatial analysis, public health, and socioeconomic modeling.
Participants will gain hands-on experience with PDFM Embeddings to perform advanced geospatial predictions and analyses while ensuring privacy through the use of aggregated data. Key components of the workshop include:
By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges.
Institution
As the planet faces accelerating environmental degradation and biodiversity loss, emerging technologies have an increasingly vital role to play in enhancing conservation efforts. Artificial intelligence (AI), in particular, is emerging as a powerful tool to address complex environmental challenges, from monitoring ecosystems and predicting habitat changes to combatting illegal wildlife trafficking.
In this webinar, Professor Tshilidzi Marwala, United Nations Under-Secretary-General and Rector of the United Nations University (UNU), will outline the transformative potential of AI for biodiversity and conservation. He will examine how AI can support the ambitious targets of the Kunming-Montreal Global Biodiversity Framework by enabling scalable, data-driven solutions that enhance our ability to understand and protect our ecosystems.
Drawing on UNU’s global work at the intersection of AI and sustainability, Prof. Marwala will also reflect on the ethical, environmental, and equity-related challenges associated with AI, such as algorithmic bias, the digital divide, and its carbon footprint. Realising the promise of AI for biodiversity conservation will depend on our ability to govern it wisely, ensuring it serves as a force for equity, sustainability, and environmental stewardship.
Learning Objectives:
By the end of this webinar, participants will have developed their ability to:
Institutions
Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.
Learning Objectives:
Institutions
Universität Hamburg
Adeline Scharfenberg
Universität Hamburg
Adeline Scharfenberg
Universität Hamburg
Adeline Scharfenberg