Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

As generative AI moves from experimentation to enterprise deployment, reliability becomes critical. This session outlines a strategic approach to building robust AI apps using Monocle for observability and the VS Code Extension for diagnostics, and bug fixing. Discover how to create AI systems that are not only innovative but also predictable and trustworthy.

World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.

We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.

In the complex environments, the anomalies are sometimes highly contextual and also ambiguous, and thereby, uncertainty quantification (UQ) is a crucial capacity for a multi-modal LLM (MLLM)-based video anomaly detection (VAD) system to succeed. In this talk, I will introduce our UQ-supported MLLM-based VAD framework called ALARM. ALARM integrates UQ with quality-assurance techniques like reasoning chain, self-reflection, and MLLM ensemble for robust and accurate performance and is designed based on a rigorous probabilistic inference pipeline and computational process.

In this talk we’ll discuss how the orchestration space is evolving with the current AI landscape, and provide a peak at Flyte 2.0, which makes truly dynamic, compute aware, and durable AI orchestration easy for any type of AI application, from computer vision, agents, and more!

Flyte, the open source orchestration platform, is already being used by thousands of teams to build their AI pipelines. In-fact it’s extremely likely you’ve interacted with AI models trained on Flyte, while on social media, listening to music on using self driving technologies.

Video streams combine vision, audio, time-series and semantics at a scale and complexity unlike text alone. At TwelveLabs, we’ve found that tackling this challenge doesn’t start with ever-bigger models — it starts with engineering the right context. In this session, we’ll walk engineers and infrastructure leads through how to build production-grade video AI by systematically designing what information the model receives, how it's selected, compressed, and isolated. You’ll learn our four pillars of video context engineering (Write, Select, Compress, Isolate), see how our foundation models (Marengo & Pegasus) and agent product (Jockey) use them, and review real-world outcomes in media, public-safety and advertising pipelines. We’ll also dive into how you measure context effectiveness — tokens per minute, retrieval hit rates, versioned context pipelines — and how this insight drives cost, latency and trust improvements. If you’re deploying AI video solutions in the wild, you’ll leave with a blueprint for turning raw video into deployable insight — not by model size alone, but by targeted context engineering.