Sept 25 - Berlin Physical AI, ML, and Computer Vision Meetup

Tauche ein in die Zukunft der intelligenten Maschinen! Erlebe spannende Einblicke in modernste Technologien und erfahre, wie du deine Datenpipelines effizienter gestaltest und echte Mehrwerte in der Automatisierung schaffst. Lass dich von Experten inspirieren, die zeigen, wie man komplexe Herausforderungen in der Praxis meistert und Projekte zum Erfolg führt. Vernetze dich mit Gleichgesinnten in Berlin, tausche dich über innovative Ansätze aus und nimm wertvolle Impulse für deine eigenen Projekte mit. Sei dabei, wenn wir gemeinsam die Grenzen des technisch Machbaren neu definieren!

Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date, Time and Location Sep 25, 2026 5:30 PM - 8:30 PM CEST w3.hub, Möckernstraße 120, 10963 Berlin, Germany One Pipeline, Any Device: Swap the Reader, Not the Pipeline Every new robot, camera, or sensor vendor in physical AI usually means rebuilding the data pipeline from scratch, even though the features you actually need, a calibrated frame, a fused object distance, stay the same. This talk demos mloda, an open-source Python framework where a feature pipeline is written once, and only the reader plugin, the small piece that knows how to read one specific device's raw format, ever changes. Live, using two small synthetic datasets shaped like two different devices' raw output (no hardware involved), I run the same pipeline against both, swapping only the reader plugin, and show the calibration, fusion, and feature extraction steps running unmodified on either. I also show OpenTelemetry lineage tracing, so when a feature looks wrong, you can trace it straight back to the raw reading that produced it, regardless of which device it came from. The point: stop rebuilding your data pipeline for every new device, reuse it. About the Speaker Tom Kaltofen is a Berlin-based data and AI engineer and the creator of mloda, an open-source (Apache-2.0) Python framework for declarative, plugin-based data access in AI workflows. Toward the best ROI: choosing algorithms for AI-powered workstations Robots are becoming smarter and more affordable—but which automation projects actually deliver a return on investment? Drawing on RemBrain’s real-world experience across delivery, retail, construction, and manufacturing, this presentation reveals why many promising robotics concepts fail to become viable products. It introduces a practical, skill-based approach to flexible automation and shows how compact AI-pow...