Dec 10 - AI, ML and Computer Vision Meetup

Welcome to the Berliner Bitcoin-Stammtisch, where we gather to talk about the digital currency that's worth more than gold and sometimes causes more drama than a Netflix series. Join our tribe of business beings, developers, and activists who believe that Bitcoin is the future of money, and El Salvador seems to agree! Here you can learn the ropes of Bitcoin, from buying your first fraction to safely storing your private keys. Our current hangout spot is Friedel Richter on Torstraße, because ROOM77 is closed until the last block is mined. You can pay in Bitcoin ₿ or Lightning ⚡ and enjoy awesome regional food with an international twist. So come join the fun, the food, and the revolution! We're waiting for you. 🍔🍔🍔

Join our APAC time-zone friendly virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Time, Date and Location Oct 1, 2026 6:00 PM - 8:00 PM PDT Online. Register for the Zoom! Beyond Exact Matches: Detecting Modified 3D Assets at Marketplace Scale How can a marketplace identify copied 3D assets when their orientation, geometry, or composition has changed? Drawing on my work in 3D content understanding at Roblox, this talk will explore multi-view and rotation-invariant representations for similarity and duplicate detection, including the challenges posed by deformed and fragmented copies. It will examine how geometric and semantic signals can complement one another, and discuss practical trade-offs in evaluating detection quality and deploying these methods at scale. The presentation will draw on published patent applications and publicly shareable examples to offer practical lessons for engineers building visual search, content-understanding, and marketplace-safety systems. About the Speaker Phani Harish Wajjala is a Principal Machine Learning Engineer at Roblox specializing in 3D computer vision, multimodal AI, and large-scale content understanding. Sign Language: Towards Sign Understanding for Robot Autonomy Navigational signs are common aids for human wayfinding and scene understanding, but are underutilized by robots. We argue that they benefit robot navigation and scene understanding, by directly encoding privileged information on actions, spatial regions, and relations. Interpreting signs in open-world settings remains a challenge owing to the complexity of scenes and signs, but recent advances in vision-language models (VLMs) make this feasible. To advance progress in this area, we introduce the task of visual sign grounding, which parses locations and associated directions from signs, and maps them to region in the sign’s local environment. Additionally, we present a baseline approach using VLMs, and d