AI applications are increasingly finding their way into practice, which raises many legal questions. We will discuss them for two paradigmatic applications involving both legal scholars and public administration representatives: the problem of discrimination, which manifests itself in various ways as soon as AI is involved in decisions on the distribution of goods. And automated driving - an area in which the automotive industry successfully lobbied early on for the EU AI Act not to apply.

This track explores the challenges and opportunities in designing software system infrastructure for training and serving large-scale AI models. It covers key aspects such as scalability, resource efficiency, system optimization, and deployment strategies, aiming to foster discussions on practical solutions, emerging techniques, and best practices essential for efficient and efficient AI infrastructure.

The session is divided into two parts, each addressing a distinct aspect of AI’s impact on the financial services sector, with a focus on the application of AI in Switzerland.

Part I explores recent trends in the sector, particularly the transformative potential of Large Language Models (LLMs) and Agentic AI Systems. These advancements present both opportunities and challenges, prompting a rethinking of operations and job profiles within banks. This part will shed light on critical questions related to the AI-driven transformation: What technologies are necessary? What new skills and roles will be needed? Which existing roles may decline in relevance? And what are the most impactful use cases shaping the future of banking operations?

Part II highlights selected, forward-looking AI use cases across specific sub-domains, ranging from real estate energy efficiency to AI-enabled climate and nature impact assessments. These examples demonstrate how AI can contribute to solving domain-specific challenges in the financial service sector. One example is the geospatial AI foundation model “TerraMind” developed by the FAST-EO consortium.

Overall, Part I aims to clarify where and how AI is set to reshape the financial services industry, while Part II offers a glimpse into cutting-edge applications that are actively driving this transformation.

AI is driving transformative progress across the natural sciences and engineering. By enabling the discovery of underlying physical laws directly from data, accelerating simulations through surrogate modeling, and integrating data-driven with physics-based approaches, AI is reshaping how scientific knowledge is generated and applied. These advances are unlocking new opportunities, but they also raise critical questions about the design of appropriate AI architectures, the availability and quality of training data—particularly in domains where data collection is expensive or experimentally constrained—highlighting the need for robust, interpretable, and generalizable models.

This session will explore recent advances at the intersection of AI and science, with applications spanning biology, fluid dynamics, engineering, neuroscience, and beyond. We will discuss methodological innovations, including physics-informed machine learning, generative models, and uncertainty quantification. A panel discussion will address the challenges and limitations of current AI techniques and conclude with perspectives on how AI can be harnessed to reduce engineering costs, accelerate innovation, and contribute to more sustainable design and decision-making.

Step into the future of healthcare & medicine with cutting-edge AI in action:

• ICU-Cockpit: Real-time decision support for intensive care
• OR-X: Surgical robotics that see, hear, feel & act
• Smarter decisions with AI-powered clinical support tools
• Personalized cancer treatment with digital assistants

Get inspired by real-world breakthroughs shaping tomorrow's healthcare.

The rise of large language models (LLMs), pretrained on vast and diverse datasets, has revolutionized the field of artificial intelligence (AI). Finetuning has emerged as a critical next step for adapting these models to a wide range of downstream applications, serving as the “last mile” for various use cases. Compared to pretraining LLMs from scratch, finetuning open-source models offers a more accessible and practical alternative for small to medium-sized businesses and academic researchers, who might not have access to extensive computational resources. Despite its promise, the broad applicability of finetuning also introduces several challenges.

This workshop focuses on efficiency in fine-tuning LLMs, aiming to lower costs and barriers so that even users with consumer-grade GPUs can harness reasonably large models (e.g., 7B parameters). Our goals are twofold: (i) to enable scalable development of LLMs, and (ii) to empower individuals and organizations with limited resources to benefit from modern AI. We will convene recent advances in methods and tools, and foster the exchange of best practices across research and industry.

AI + Education is a multi-layered topic. In this session, we will explore the various ways AI is influencing and transforming both how we educate and what we teach.

We will begin with an overview of the different dimensions of this complex field and look at it from different perspectives:

- The research perspective – focusing on the foundational educational research surrounding the use of AI in schools and universities.

- The development perspective– focusing on the development of AI tools for education.

- The ETH Zurich perspective– presenting how the university is building Ethel, an open-source platform designed to support both lecturers and students.

- The AI Challenge – a project that encourages teenagers in exploring AI through a creative, hands-on project.

The session will conclude with a panel discussion focusing on the role of the ETH AI Center within this dynamic and evolving landscape and how it can contribute to an AI ready education in Switzerland.

Computer Vision aims to endow AI agents with human-level understanding of human performance. To build agents with such advanced machine perception, we must build computational methods and representations to model how humans move within the physical world and interact with it. This track aims to give an overview of efforts in this domain, focusing on different aspects of the machine perception problem.

Artificial intelligence is revolutionizing Industry 4.0 by transforming static automation into dynamic, self-optimizing systems that learn and adapt in real-time. This track introduces AI-based methods for automation in manufacturing, demonstrating how intelligent optimization and adaptive decision-making enhance traditional systems. Key directions include AI-enhanced process simulations, optimization, and control algorithms that enable manufacturing systems to learn from data, respond to changing conditions, and continually improve performance. Presentations will feature novel approaches, real-world case studies, and strategic insights for integrating AI into existing industrial environments, helping attendees understand how to select suitable AI methods and transition from conventional automation to intelligent manufacturing.