Über uns
We want to bring people together who are interested in AI and Machine Learning. At our meetups, we have:
- Networking
- Talks
- Fireside chats
- Knowledge exchange
- Applications of AI and Machine Learning
We organize our meetups every other month and due to current restrictions, we can only host virtual events.
We are always looking for innovative and inspiring speakers. If you know somebody who would be an excellent fit for our meetup, we would highly appreciate if you help us and recommend this speaker to us. To recommend a speaker for CAIML, please fill out this form.
Learn more about the organizers:
Bevorstehende Events (4)
Alles ansehen- CAIML #36taod Consulting GmbH, Cologne
CAIML #36 is going to happen on May 20, 2025, at taod Consulting.
We will have two talks with additional time for networking.
Talk 1: Leonard Kunz (Data Scientist at taod Consulting): Beyond the Hype Cycle: Constraint Optimization with OR-Tools
Using a concrete room planning problem in the context of a private university, Leonard Kunz demonstrates how complex business requirements can be formulated and solved as a mathematical optimization problem. We share our experiences with meta heuristics (including Ant Colony) in pygmo, why we ultimately chose a constraint optimization framework (OR-Tools), and which strategies eventually led us to a solution. The focus is primarily on the fundamental principles of modeling and the key lessons learned that will help us in future optimization projects.
Talk 2: Katharina Morik (Prof. Dr. at TU Dortmund, Speaker of SFB 876, co-founder of the Lamarr Institute): How to introduce AI – it is all about agents
Introducing AI does not mean to be the evangelist of the newest AI technique. You better do not talk about the learning method, in the beginning. Instead, find out which action needs to be enhanced and what is the real criterium of success. Then ML becomes an agent performing the application action and you can measure the performance in terms of the real criterium, not in terms of ML loss measures. After a long time of acquiring, understanding, and massaging data, the tool and method selection is up to the ML introducer who knows their properties (correctness proofs, robustness, real-time, energy demands, privacy preserving…). Hardware selection might also be relevant. Finally, the agent is deployed, and you realize whether you have the right allies in the application field. For illustration, I’ll show how to compose an intensive care assisting agent from many classifications. As a second use case, I am prepared to show one of the use cases from steel manufacturing:
• quality assurance in interlinked manufacturing or
• managing several models for steel making (BOF)
Or maybe easy listening to an insurance use case: customer churn prediction? It is up to the audience!We will share an agenda soon. See you in May 🤖
