Berlin · Wednesday 11 Nov

Nov 11 - AI, ML and Computer Vision Meetup

Berlin AI Machine Learning and Computer Vision Meetup Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. **Time, Date and Location** Nov 11, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-november-11-2026)** **Agentic RAG: Beyond Retrieve-and-Generate** Retrieval-Augmented Generation (RAG) has become the default pattern for grounding large language models in external knowledge, but most implementations still follow a rigid retrieve-once, generate-once pipeline — one that struggles with multi-hop questions, ambiguous queries, and knowing when its own retrieved context is insufficient. This talk introduces Agentic RAG, where retrieval is treated as an action within an agent's reasoning loop rather than a fixed upstream step. We'll examine query decomposition and routing for breaking complex questions into targeted sub-queries, self-reflective and corrective retrieval loops that let an agent judge and re-query its own results, and tool-orchestration patterns (via MCP) that let retrieval sit alongside other agent actions like database lookups and API calls. Using a live architecture — evolving a standard RAG chatbot into an agentic, MCP-connected system — we'll walk through what changes in design, and where these systems introduce new failure modes: grounding drift, latency and cost from repeated retrieval loops, and cases where a simpler RAG pipeline still wins. Attendees will leave with a practical framework for deciding when the added complexity of agentic RAG is worth it, and a set of design patterns for building it correctly. *About the Speaker* [Balaji Venkatasubramaniyar](https://www.linkedin.com/in/balaji-venkatasubramaniyar/) is a Technical Lead at Wisdom Infotech, leading a 15+ person engineering team delivering enterprise solutions. With 13+ years of experience in enterprise software and insurance technology, he specializes in agentic AI systems, RAG architectures, and vector databases. **GeoAI for the Physical World: Earth Observation, Foundation Models, and Urban Digital Twins** Earth observation provides a unique form of computer vision for understanding the physical world at city to continental scales. In this talk, I will show how satellite imagery, geospatial data, machine learning, and foundation-model representations can be combined to characterize urban environments and environmental conditions. I will present UrbanScope Open, an open GeoAI digital-twin prototype integrating Earth observation with 3D buildings, vegetation, land-surface temperature, air quality, noise, population, and other urban data. I will also share lessons from my research using geospatial foundation-model embeddings for environmental prediction across Europe. The talk will discuss how these approaches can contribute to increasingly multimodal AI systems capable of reasoning about real-world environments. *About the Speaker* [Cesar Alvarez](https://voxel51.com/events/cesar.alvarez@uni-a.de) is a researcher at the University of Augsburg working at the intersection of GeoAI, Earth observation, remote sensing, and environmental intelligence. His research applies machine learning, computer vision, and geospatial foundation models to problems including urban environments, climate risk, air quality, and agriculture. **Can agents get curious?** Most AI agents are good at answering a question once we tell them exactly what to look for. The harder problem is building agents that can explore a complex dataset autonomously: generating hypotheses, deciding which analyses are worth running, allocating additional compute when evidence is ambiguous, and knowing when they have enough evidence to stop. In this talk, I’ll show an architecture for autonomous research agents that combines structured knowledge, iterative tool use, and explicit evidence tracking to turn open-ended questions into a sequence of testable investigations. I’ll discuss practical lessons from building and evaluating these systems, including why more test-time compute does not automatically produce better research and how provenance and evaluation can make long-running agents more reliable. I’ll close with a live example of an agent exploring a dataset, revising its hypotheses, and choosing what to investigate next. *About the Speaker* [Srivatsa P](https://www.linkedin.com/in/srivatsa-p) is a member of technical staff at Sigma Computing, where he works on AI agents that reason over complex enterprise data. Previously, he worked on machine learning at Apple and conducted research at the MIT Media Lab; outside of traditional ML, he has also worked on mapping coral reefs through underwater imaging, which sparked an enduring interest in how intelligent systems make sense of messy real-world data. **Does Cross-Validation Work in Deep Learning?** Cross-validation is a standard in hyperparameter optimization in classica

When

Wednesday 11 Nov · 18:00 – 20:00 (Europe/Berlin)

Where

Berlin

Organiser

Berlin AI, ML and Computer Vision

Source

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