Large language models can reason, generate code, and hold conversations. But they remain trapped behind screens. If AI is to become truly useful in our daily environments, it needs a body, a presence, and natural ways to interact with us.
In this talk, I’ll present Reachy Mini, an open and developer-friendly robot designed to explore what embodied AI can look like today. I’ll walk through how we are building its core software stack, from multimodal perception to real-time voice interaction, and why voice is emerging as the most natural interface for physical AI. We will look at how speech-to-speech pipelines, local inference, and modular backends allow Reachy Mini to move beyond scripted demos and into responsive, real-world interaction.
You will leave with a clearer understanding of what it takes to give AI a body, why voice-first interfaces matter, and how open tools can accelerate the next generation of interactive robotics.

Problem:

Kaufmann argues that humanity stands at a turning point: machines and Generative AI will soon outperform us in many routine and even complex tasks, while our social, economic, and cultural systems are still built around compulsory work and industrial‑age roles of “Homo faber.” Without a new vision, fears of job loss, loss of control, and foreign dominance (China/USA) over AI systems will shape the future instead of our own European values.​

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Approach:

Kaufmann reframes robots and Generative AI as tools, like excavators or calculators, without intrinsic power fantasies, and insists we design them to augment humans rather than replace them. He sketches a near future where every person works with several humanoid or software agents that handle routine tasks, enabling humans to focus on uniquely human, hard‑to‑automate activities. He also calls for sovereign Swiss and European AI (e.g., SwissGPT, AlpineAI) to embed local values, privacy, and trust into foundational Generative AI infrastructure.​

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Key takeaways:

• Generative AI can trigger a shift from “Homo faber” to “Homo gaudens,” freeing people to pursue meaningful work and curiosity instead of mere survival.

• A “Eutopia” – a realistic, golden age – is possible if productivity gains from AI are used to reduce compulsory work and secure public finances.

• Trust, culture, and data protection will be the decisive “currency” in the global race for AI; Switzerland can lead by building reliable, privacy‑preserving Generative AI systems for governments, hospitals, and universities.

"The model isn't sure", "The AI assistant wants to be helpful"…. We use these phrases daily as shorthand, but are they undermining your risk strategy? This talk argues that anthropomorphic language is a big sleeper risk in GenAI governance today. When we assign human verbs—thinking, deciding, lying—to probabilistic systems, we create an "Illusion of Intent." This linguistic drift isn't just a semantic annoyance; it is a governance hazard. It hacks human empathy, creates false trust, and obscures liability by treating system failures as character flaws. In this session, we will dismantle the habit of humanizing the machine. We will explore how precise, de-anthropomorphized language acts as a firewall for ethical safety and improves collaboration between Legal, Tech, and Product teams. Join us to learn why the most critical update to your governance framework isn't new code—it's a new vocabulary. Let’s stop governing the ghost and start governing the tool.

Generative AI has led to an explosion of content creation. Yet discovering relevant and inspiring content and products online is becoming increasingly difficult. Most digital experiences remain backward-looking, optimizing for historical interactions rather than understanding user interests in the moment. As a result, users are often trapped in narrow recommendation loops, while new and diverse content struggles to surface. This talk introduces a shift from AI generation to real-time perception: AI systems that continuously interpret user behavior and context as they evolve within a session. Real-time perception enables adaptive discovery, contextual search, and more effective agentic experiences; moving beyond static personalization toward moment-by-moment intelligence. Drawing from real-world deployments in large e-commerce and marketplace environments, the talk covers perception model architectures, product and system design, and concrete case studies with measurable business impact. Real-time discovery is emerging as a must-have capability for online platforms, one that directly translates into measurable gains in engagement and revenue.

For many financial institutions, Quality Assurance (QA) has ballooned into a massive liability, costing millions and stalling deployment. This session exposes the playbook leading banks use to turn this cost center into a competitive advantage. We explore the shift from manual grunt work to human-supervised AI agents. This approach delivers the speed of automation with the safety of expert oversight. We will analyze real-world cases showing how this specific AI implementation slashes overhead and dramatically.

Most companies are still experimenting with GenAI, but very few manage to scale beyond pilots—especially in highly regulated industries like insurance. In my talk, I will share how we moved from isolated GenAI use cases to designing and delivering the first Agentic AI–native claims-handling platform across three companies and five countries within a global insurance group. I will explain how Agentic AI changes process design, product architecture, and governance, and what it takes in reality to orchestrate multiple specialized AI agents while ensuring safety, reliability, and compliance with the AI Act, GDPR, and DORA. The talk focuses on practical learning: how to design agent roles and orchestration patterns, how to build trust and non-functional safeguards into autonomous flows, how to prepare the workforce for AI-centric operations, and how to balance speed with regulatory requirements. Attendees will leave with a concrete playbook for evolving from GenAI experiments to an AI-native operating model and with insights into the measurable impact on productivity, quality, and customer experience in claims handling.

Relying on black-box AI APIs often means trading data sovereignty and cost control for convenience. This session provides a practical blueprint for architecting a private, sovereign Model-as-a-Service platform using powerful open-source models. Attendees will leave with a concrete playbook to master observability, enforce security policies, and transition from an API consumer to a platform provider.

Building a generative voice assistant demo is easy. Getting it to production is hard. Scaling it to thousands of calls per day? That's where the real learning begins. This talk shares battle-tested lessons from taking a voice AI from prototype to handling thousands of calls daily - covering the unexpected challenges that no tutorial prepared us for.

Problem:‍

In sensitive application domains such as legal AI, impressive demos are easy to produce — reliable systems are not. What matters is the ability to systematically measure, compare, and control model performance. Without transparent statements about precision, error rates, and limitations, GenAI in real-world workflows remains opaque and risk-prone.

Approach:‍

The talk shows why high-stakes AI requires a clearly defined gold standard: structured data, annotated samples, and systematic benchmarking of model performance against human experts. With the emergence of agentic AI, many control steps can be significantly accelerated and partially automated as operational human intervention is reduced. The core principle remains unchanged: transparency about output quality. Model precision and error rates must be measurable and clearly communicated.

Key takeaways:

‍• Why demos fail as a proxy for real AI performance?
• Why human benchmarking remains essential even with agentic systems?
• How agentic AI increases speed without removing responsibility for output quality?

Imagine you're into playing instruments, singing, and producing music, but you’re lacking the decades of practice. Still, you write great lyrics and just "want to make music". As a personal project/side quest, I bridged that gap leveraging GenAI, my production skills, and professional post-production tools.

The result is Windlereye, a fictional alternative rock band with over 100 songs. Some of them are even good!

In this talk, I’ll demystify the "one-click" misconception by sharing details on my hybrid workflow and the workarounds I used to jump the biggest GenAI hurdles (vocal consistency, artifacts, instability). I'll explain how GenAI made me a better lyricist, and how I made my first whooping $0,000,001.12 in royalties.

Finally, I'll touch on my non-expert opinion on ethics and legal matters of this new frontier.

The future of decision-making will be won by organizations that deliberately pair human judgment with AI at scale: decision flows where machines grind through the data and people bring expertise, nuance, and accountability.

This session is for executives and senior leaders who don’t need to code models but do need to own the impact of AI‑infused decisions. We will unpack how data becomes wisdom through four layers, giving participants a clear, practical mental model for designing robust Human‑AI decision systems that are reliable, auditable, and safe to use in the boardroom.

Generative AI is beginning to reshape how news is created, translated, verified, and delivered — but for a global news organization like Reuters, innovation must go hand in hand with trust. In this talk, I will share how Reuters is approaching generative AI as both a powerful technological enabler and a responsibility‑critical capability.
Drawing on practical experience, the session explores how generative AI is being applied across the news lifecycle, including support for journalists and editors, workflow efficiency, multilingual content, and product experiences. The focus is not on experimentation for its own sake, but on real deployment decisions in a high‑stakes environment where accuracy, independence, and transparency are essential.
The talk will highlight key design choices and trade‑offs: where generative AI delivers clear value, where it must be constrained, and how human editorial judgment remains central. I will also discuss governance, risk management, and cultural adoption challenges when introducing generative AI into a trusted media organization.
The session concludes with practical lessons for media leaders and technologists navigating generative AI in environments where credibility is the product.

Enterprises have long relied on systems of record: databases and applications optimized for capturing and reconciling business data as the authoritative source of “what happened”. These human-centric architectures with rigid schemas and batch processes create barriers for agentic AI, which requires perceiving context, reasoning, and taking real-time action. This session explores the shift to systems of action, where an intelligence layer augments (rather than replaces) existing systems of record, enriching business objects with agent-generated insights to enable faster, smarter decisions.

GenAI promises to transform how organizations work — but turning that promise into secure, scalable, real‑world solutions is often harder than it seems. In this talk, discover how Migros built a flexible, enterprise‑grade chatbot platform that enables teams to create powerful assistants in just minutes, without compromising on security, governance, or quality.
Two years ago, we created our first chatbot — and quickly learned that technical innovation was only one part of the challenge. Alongside building early prototypes, we had to navigate aligning stakeholders and define governance models that satisfied both business needs and strict security requirements. This journey laid the foundation for what would eventually become our Chatbot Creator Platform.
In this session, we’ll show you that platform in action, share the lessons we learned while creating it, and explain how we built a system that now empowers teams across Migros to ship AI assistants in record time. You’ll walk away with practical insights for scaling GenAI in a complex enterprise environment — beyond the hype and toward real, sustainable impact.

Over 10,000 SAP BW systems face end-of-support by 2030 — a migration challenge so complex that traditional automation cannot scale to meet it. Manual migrations take two or more years, are error-prone, and require scarce expertise. We're using agentic AI to change that.

We'll show how multi-agent systems reverse-engineer legacy platforms, construct knowledge graphs of thousands of interdependent objects, and autonomously generate complete modernized systems — including the migration tooling itself.

Our three-phase framework — Reverse, Rethink, Rebuild — is a generalizable pattern for any complex system transformation, compressing years of work into months with automated validation at every step.

Attendees will leave with practical insights on multi-agent orchestration, knowledge graphs for spec-driven-development, synthetic data strategies for safe testing at scale, and hard-won lessons about where AI excels versus where human judgment remains essential.

Grounded in production experience, not theory.

Not a single day goes by without a new record investment in the field of artificial intelligence. The unit of measurement is no longer billions, but trillions and beyond. The arms race between tech giants seems limitless – and in any case defies the physical limits of our planet.

AI is infiltrating all areas of human activity at an unimaginable speed. Mastering artificial intelligence is the most significant quantum leap since the advent of the atomic bomb.

Does the future belong to the American and Chinese tech giants? Are Switzerland and Europe doomed to follow and submit?

There is an alternative. Sovereign, collaborative, efficient and ethical.

Giotto.ai seeks to exceed the current capabilities of AI to push the boundaries towards artificial intelligence capable of going beyond memorisation – towards reflection. Giotto.ai is developing technology that stands out for its ability to generalise tasks, solve problems and offer transformative potential for industries and society in general.

Way more efficient than large LLMs and infinitely less energy-and data-intensive, the solution advocated by Giotto.ai is based on two fundamental pillars: sovereignty and efficiency.

- Yes, it is possible to escape the frantic race for resources and infrastructure.
- Yes, it is possible to assert Swiss and European leadership in artificial intelligence that serves society, democracy and humanity.

Switzerland and Europe have all the talent, universities and centres of expertise needed to succeed. Together, let's create networks of investors, developers and conditions to control our own destiny.

Submitting is not an option. We can decide.