Start of New AI for Good Webinar Series: "From Molecules to Models" Featuring ELLIS Programs
ELLIS is launching a new webinar series, "From Molecules to Models", as part of the AI for Good Initiative. This series delves into the contributions of various ELLIS Programs to the field of AI in life sciences, aiming to foster collaboration across disciplines.
The first session kicks off on February 3, 2025, featuring a talk titled "Unlearning Toxicity in Multimodal Foundation Models and Learning to Design Protein-Protein Interactions with Enhanced Generalization." This session, led by Rita Cucchiara (University of Modena and Reggio Emilia (UNIMORE)) and Josef Sivic (Czech Institute of Robotics, Informatics and Cybernetics), will explore advanced AI techniques for modeling language toxicity in multimodal LLMs. The session concludes with an interactive Q&A, providing a platform for discussion and engagement.
View all past sessions
AI for Earth and Climate: From understanding processes to effective action
Gustau Camps-Valls (University of Valencia) & Markus Reichstein (Max Planck Institute for Biogeochemistry)
Machine Learning for Earth & Climate Sciences
Towards a more inclusive world: Uncovering and addressing human and algorithmic limitations
Nuria Oliver (ELLIS Alicante Foundation)
Human-centric Machine Learning
Teaching language models to speak chemistry: From design to synthesis
Philippe Schwaller (EPFL)
Machine Learning for Molecule Discovery
Low energy learning from the analysis of learning landscapes in deep and recurrent neural networks
Riccardo Zecchina (Uni Bocconi)
ELLIS Program Quantum & Physics Based Machine Learning
Explainable Multimodal Agents with Symbolic Representations & Can AI be less biased?
Ruotong Liao (PhD Student, Ludwig Maximilian University of Munich) & Felix Friedrich (PhD Student, Technical University of Darmstadt)
Semantic, Symbolic, & Interpretable Machine Learning
Symmetry, scale, and science: A geometric path to better AI
Erik Bekkers (University of Amsterdam) & Johannes Brandstetter (Emmi AI)
ELLIS Program Geometric Deep Learning
Towards real-world fact-checking with large language models
Iryna Gurevych (Technical University Darmstadt)
ELLIS Program Natural Language Processing
Rita Cucchiara (Università degli Studi di Modena e Reggio Emilia) & Josef Sivic (Czech Institute of Informatics, Robotics and Cybernetics)
ELLIS Program Machine Learning & Computer Vision
More about ELLIS Programs
ELLIS Programs, directed by ELLIS Fellows, focus on high-impact areas that have the potential to move the needle in modern AI. The ELLIS Programs are inspired by the CIFAR Program model, and closely collaborate with the CIFAR LMB (Learning in Machines and Brains) Program.
Learn more about the ELLIS Programs and their groundbreaking work in AI.
More about AI for Good
AI for Good is the United Nations’ leading platform on Artificial Intelligence for sustainable development.
AI for Good was established in 2017 by the International Telecommunication Union (ITU), the United Nations (UN) leading agency for digital technologies. The platform is co-convened with the Government of Switzerland and in partnership with over 40 UN agencies. Its founding mission is to leverage the transformative potential of artificial intelligence (AI) to drive progress toward achieving the UN Sustainable Development Goals (SDGs).
AI for Good continues to host the flagship annual Global Summit in Geneva, along with regional impact events across the globe, engaging youth through Robotics for Good Youth Challenges worldwide, Innovation Factory and Machine Learning (ML) Hackathons, Governance Day and International AI Standards Summit. All of this is to address urgent calls from global entities like the UN Global Digital Compact, the High-Level Advisory Body on AI, and the G20 to lead in AI standards and capacity-building.
As the platform continues to evolve, AI for Good remains firmly aligned with the collective priorities of the international community, advancing the transformative potential of AI for the benefit of all. Visit the AI for Good website: https://aiforgood.itu.int/.