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We are living in an era where computers are increasingly capable of performing tasks that, until recently, were thought to require human intelligence. From natural language understanding to decision-making, these advancements have reshaped our perceptions of what machines can achieve. However, framing AI technology as merely ‘human-like’ oversimplifies the reality of intelligence. Intelligence is not only a cognitive phenomenon but is also profoundly social. It is embedded in our science, societies, and cultures, arising through the intricate interactions among individuals, communities, and institutions.
AI holds the potential to enhance these forms of social intelligence, strengthening our collective capacity to tackle complex and emerging challenges while preserving and enriching the fabric of our shared humanity. This vision of AI as a tool for fostering collective intelligence is pivotal, particularly as global challenges—such as climate change, public health crises, and economic inequality—demand collaborative and interdisciplinary solutions. The aim is not to replace human intelligence but to augment it, enabling communities to thrive and societies to innovate in inclusive, equitable, and sustainable ways.
The concept of collective intelligence is deeply rooted in many areas of modern science. In disciplines such as physics, biology, and mathematics, studying phenomena emerging from the interactions of diverse entities has long been a cornerstone of research. Similarly, economics and the social sciences have focused on designing mechanisms, institutions, and systems that are both effective and resilient in complex and dynamic environments. These fields offer invaluable insights into how AI can be developed, deployed, and governed to serve the broader good.
The interdisciplinary conference “AI, Science, and Society: Connections, Collectives, and Collaboration” will provide a platform for exploring how AI can be informed by and contribute to understanding collective intelligence in science, economics, and beyond. By fostering dialogue between experts in AI, natural sciences, and social sciences, the conference seeks to highlight the synergies between these perspectives—not only to deepen our understanding of the current state of AI technology but also to shape its future development in a direction that aligns with societal values and priorities.
Pr. Michael Jordan
Inria/University of California, Berkeley.
President of Institut Polytechnique de Paris
French Minister in charge of Artificial Intelligence and Digital Affairs
French President's special envoy for the AI Action Summit
Mohamed Bin Zayed University of AI and Carnegie Mellon University
Stanford University
MIT
Moderated by Sébastien Meyer, AI Project Manager at the French Ministry of Ecology
From balancing energy efficiency with computational demands to ensuring transparency, reproducibility, and ethical alignment, this panel will explore the essential innovations and frameworks for developing robust and responsible foundation models. Special attention will be given to real-world applications in enterprise, healthcare, and biology, as well as the emerging role of agentic systems capable of interacting with tools and environments. In this roundtable, leading researchers and industry pioneers will highlight pathways to ensure AI’s transformative power remains sustainable, inclusive, and aligned with human values.
Mohamed Bin Zayed University of AI and Carnegie Mellon University
Stanford University
MIT
IBM Research
Modern generative AI relies on groundbreaking methods like transformers and diffusion processes, rooted in advanced mathematics such as probability theory, functional analysis, and optimization. Transformers, with self-attention mechanisms, revolutionized sequence modeling by capturing complex global data dependencies, driving breakthroughs in natural language processing, computer vision, and multimodal applications. Diffusion processes use stochastic frameworks to generate high-dimensional data, refined through Itô calculus, offering advantages in uncertainty and complexity management. Stable diffusion enhances this by focusing on numerical stability and robust optimization for high-quality content generation. This workshop explores the mathematical foundations, scalability solutions, and future directions of these technologies, emphasizing their role in AI-driven scientific discovery, creative applications, and computational science’s broader landscape.
Mines Paris / Bioptimus
Collège de France
Technical University of Munich
CNRS, École Normale Supérieure
Foundation models have revolutionized AI by enabling versatile representations across diverse disciplines, from natural language processing to images, videos, and structured data in physics, biology, and engineering. They excel in learning from vast datasets and fine-tuning for various applications with exceptional accuracy and efficiency. Key advancements such as self-supervised learning, attention mechanisms, and scaling laws drive their success. Despite their progress, challenges in interpretability, efficiency, and domain-specific adaptations persist. Addressing these requires deeper exploration of their mathematical foundations and optimization techniques. This workshop focuses on bridging theory and practice, fostering collaboration between fundamental research and applied innovation.
Google DeepMind
IBM Research
Instadeep
Inria
Large language models (LLMs) are a cornerstone of modern AI, enabling breakthroughs in natural language processing and the achievement of various, complex tasks such as translation, conversational agents, content creation or documents summarisation and analysis. They quickly found their way toward a wealth of practical applications, ranging from web search, customer management or software engineering, to scientific discovery. These advances are driven by innovations in training strategies, scaling laws and fine-tuning that enable LLMs to learn versatile representations from huge datasets.
Despite their potential, LLMs face challenges in terms of efficiency, interpretability, fairness and robustness, as well as concerns about their environmental footprint and ethical use. This workshop will bring together experts to discuss advances in LLM architecture, training, optimization and new applications, and bridge theory and practice in this transformative field.
TU Darmstadt
Tsinghua University
Hong Kong University
Kyutai
This session delves into two core pillars of responsible AI: fairness and privacy. As AI increasingly influences critical areas like healthcare and finance, ensuring unbiased decisions and protecting sensitive data are paramount.
The discussion will highlight cutting-edge research and practical strategies to address bias in AI systems, promote fair decision-making, and build trust through transparency and explainability. It will also explore innovative privacy-preserving techniques, such as federated learning and differential privacy, that balance data protection with analytical effectiveness.
A central focus will be the interaction between fairness and privacy, addressing the challenges and trade-offs in achieving both. Experts from academia, industry, and policymaking will share their perspectives, offering attendees valuable insights into creating ethical AI systems that meet societal needs.
Inria
Max Planck Institute for Intelligent Systems, Tübingen
École Polytechnique - IP Paris
University of Southampton
Max Planck Institute, Tübingen
Courant Institute and Meta FAIR
Inria/Berkeley
Max Planck Institute, Tübingen
Courant Institute and Meta FAIR
Collège de France
While technological developments in the field of AI evolve rapidly, the environmental footprint of AI systems – including energy and water consumption, carbon emissions, and materials needs – calls for a critical analysis.
This workshop explores the complex relationship between artificial intelligence development and sustainability, with a primary focus on environmental considerations. It aims first to present the latest state of-the-art research and ongoing initiatives that leverage AI to achieve sustainable development goals across various sectors. Concurrently, it seeks to address current and prospective strategies to minimize the environmental impact of AI systems themselves, promoting energy-efficient algorithms and sustainable infrastructures.
Télécom Paris - IP Paris
École Normale Supérieure
KTH Royal Institute of Technology
Hertie School
While machine learning systems are becoming ubiquituous and are evolving at a fast pace, their theoretical understanding necessiates a diverse spectrum of sophisticated mathematical tools and even rethinking or inventing some of these tools.
These frontiers not only open the door to a deeper theoretical understanding but also allow to improve efficiency, address limitations of the current systems and address some of their practical challenges. The workshop will feature a series of four talks by world renowned experts in the mathematics of machine learning.
Stanford University
Crest, ENSAE & Criteo AI Lab
MIT
Inria
Artificial intelligence is transforming medicine and healthcare by enabling breakthroughs in diagnosis, treatment, and personalized care. This workshop will explore cutting-edge AI applications in areas like medical imaging, genomics, electronic health records, and wearable devices. Key advancements include disease detection, patient outcome prediction, and drug development. AI also supports public health by analyzing trends, optimizing resources, and devising prevention strategies.
However, challenges such as ensuring fairness, transparency, interpretability, data privacy, and regulatory compliance remain barriers to widespread adoption. Experts from AI, medicine, and computer science will discuss recent innovations and chart a path forward for AI-driven healthcare solutions.
University of Oxford
Collège de France, Inria
Institut Pasteur
Institut Curie
DETAILS COMING SOON
University of California, Berkeley and Inria
University of Montreal
Harvard
Stanford
Collège de France, London School of Economics and INSEAD
Moderated by Alice Albizzati, Founding Partner at Revaia
This roundtable will explore the transformative impact of AI on society, technology, and sustainability. Bringing together leading experts in economics, technology, ethics, and innovation, the discussion will delve into how AI is reshaping industries, redefining social interactions, and challenging traditional educational paradigms.
From the regulatory frameworks needed to balance innovation and ethics to the role of AI in addressing global challenges like climate change and inequality, this panel will provide a comprehensive overview of the imperatives for shaping a future built on AI. The discussion will explore the opportunities and responsibilities that lie ahead for governments, companies, and individuals in the AI-driven world.
Stanford
Harvard
University of Montreal
Collège de France, London School of Economics and INSEAD
University of Southampton
French Minister for Higher Education and Research