10:00 - 10:30 Welcome from the organizers, Political greetings
Opening Ceremony Hosts: Digital Hub Applied AI (Germany) and Hub France IA (France)
By Thomas Jarzombek Commissioner for the Digital Industry and Start-ups and Federal Government Coordinator of German Aerospace Policy , Christel Fiorina Coordinator of the AI national strategy for the economy, French Ministry of the Economy and Finance
10:30 - 10:45
Exclusive Release of the first European AI & Climate Landscape by European Applied AI Alliance
By Dr. Andreas Liebl Managing Director at UnternehmerTUM Projekt GmbH and Head of appliedAI Initiative
10:45 - 12:15
Part 1: Sustainability: Use Cases of AI in Energy, Water & Resources
Michael Klingel: Network state estimation for the renewable energy transition
The energy transition is taking place - through the expansion of renewable energies and the market ramp-up of electromobility - in the distribution grid. As a result, power generation is increasingly migrating to medium and low voltage. This dynamic is accompanied by new challenges for distribution network operators: they have to deal with bidirectional power flows and enormous peak loads - and all this with huge network areas. This is only possible with innovative approaches and the inclusion of artificial intelligence!
Dr. Cyrille Waguet: AI for resilient infrastructures
Timely maintenance of railway tracks is crucial for resilience, comfort and safety in rail traffic. It increases the acceptance of rail transport and its sustainability.
incontext.technology developed a smart monitor for rails which identifies and categorises anomalies on railway tracks by analysing sensor data based on daily train operations. Connected to the operator’s business processes, the resulting digital twin of the tracks keeps the maintenance teams up to date about the rail conditions and their expected evolution.
12:15 - 13:15
13:15 - 13:25
Political Greetings from Baden-Württemberg & Grand Est
By Dr. Nicole Hoffmeister-Kraut Minister of Economics, Labor and Housing of the State of Baden-Württemberg, François Werner Vice-President in charge of the coordination of European policies, higher education and research of the Région Grand Est
Grand Est Region: an ambition to lead in terms of innovation, competitiveness, job creation and attractivity.
The 2019 adopted regional plan for Artificial Intelligence is built around 5 strategic axes:
Boosting business competitiveness through AI
Supporting scientific excellence, ensuring its outreach and valorization
Energizing AI start-ups.
Training AI competences and skills
Securing an ethical, transparent and inclusive IA
It focuses on 3 sectors : health, industrie and bioeconomy.
13:25 - 13:40
Exclusive Release of the French version of the DENA Study “Artificial Intelligence – from Hype to Reality for the Energy Industry”
By Stéphane Canu Artificial Intelligence Officer, Lisa Kratochwill Expert Digital Transformation of the Energy System, German Energy Agency at DENA
Stéphane Canu: French Strategy on artificial intelligence
French-German cooperation and its implementation modalities, in particular via calls for projects already implemented or to be implemented in the future will be presented.
Lisa Kratochwill: Artificial Intelligence – from Hype to Reality for the Energy Industry
Looking at artificial intelligence as hyped technology no longer seems to be appropriate - it has already penetrated most sectors as a key technology. Especially in the energy sector the high efficiency potential of AI has been very well received - much faster than expected. In dena’s new AI-analysis the most promising fields of application for AI in the energy industry are classified and an initial assessment is carried out to examine the opportunities and challenges.
13:40 - 15:10
Part 2: Industries: Use Cases of AI in Manufacturing, Food Industry & Agriculture
Prof. Dr.-Ing. Gisela Lanza: Artificial Intelligence - The new production manager
The possible industrial applications of artificial intelligence (AI) are manifold. There are AI systems that perform quality inspection more reliably than humans as well as systems that support engineers in the design phase. Thus, will entire factories soon be managed by AI? Practical examples demonstrate what AI systems can already do, where they are already applied and what limits prevail. Altogether, the new role of production managers will be projected.
Korbinian Weiß: Sorting Guide – AI development at TRUMPF
To tackle the complexity of sheet metal production AI solutions can help make this manageable. The overall efficiency of laser cutting of sheet metal can be greatly improved by new tools to minimize waste an increase speed in production. Learn how a AI solution utilizes computer vision to support the worker on the shop floor.
Dr. Hannes Sieling: AI-Enabled Price Markdowns for Groceries in Real-Time
AI allows retailers to translate business decisions at scale with a high degree of automation. As grocery retailers experience intensifying competition, they need to manage pricing processes for expiring products to balance profitability and waste. Learn how AI enables the full utilization of intraday POS data and real-time decision making to manage markdowns. Markdown pricing contributes to sustainability goals by minimising and controlling waste based on the retailer’s business strategy.
Guillaume Cazenave: Computer Vision for gains in efficiency and cost saving
There are a growing number of Computer Vision (CV) use cases for increasing efficiency and cost savings of organisations. Prior the Edge AI CV revolution, cameras were used by agents for limited visual monitoring actions. Most of the time, agents could at best see an incident at it was occurring or investigate after it happened. With the implementation of Edge AI CV, a single agent can monitor multiple sites remotely and leverage camera infrastructure and AI to prevent incidents and take proactive decisions. This result in significant improvement in business process and reduction in product loss for an increase in ROI of organisations.
Sébastien Picardat: The challenges of AI and data in agriculture
Decision tools used in precision agriculture and integrating AI are designed using useful digital agricultural data. These data come from the many connected objects in place in farms: weather sensors, drones, electronic loops, etc. By relying on reliable data, exchanged with the consent of farmers, AI can make a major contribution to the sustainability of the agricultural sector.
AI will open up remarkable opportunities by learning from our data. However, we need to ensure that this exact data is kept safe during the process.
AI is the driver of progress of this century - the automation of processes, enabling opportunities for more sustainable prosperity.
The industry desperately lacks historical failure data to train Machine Learning algorithms. Blind failure prediction means we predict equipment failures without historical failure data. Magical? No, we invented scientific methods to generate highly discriminant health indicators from industrial time series.
15:45 - 16:15
Panel Discussion: “Future Outlook of AI: secure and sovereign AI Infrastructures for Europe”