AI, ML and Computer Vision Meetup - December 10, 2026

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

A single mAP can look acceptable while one slice is unusable, small objects, occlusion, or a confused class. I will run a pretrained detector on a few hundred public images (COCO-style, not a production crawl), report the headline metric, then break the same run by slice and walk through the worst samples.

The claim is modest: you do not need a million images or a trained-from-scratch model to see what the mean is hiding, and you cannot relabel everything, so the next labeling budget should follow those slices. No employer data and no confidential dashboards.

Robots are moving beyond perception systems that only detect and classify objects toward systems that must understand a scene, learn behaviors, and act in the physical world. In this talk, I’ll show a practical Physical AI workflow using OpenUSD, BowerBot, NVIDIA Isaac Sim and Isaac Lab to build a robot’s virtual world, including its body, cameras, sensors, and environment.

We’ll explore how demonstrations and reinforcement learning can teach behaviors in simulation, how vision and sensor data become observations for a robot policy, and what needs to match when transferring that behavior to real hardware. The demo follows a small robot from a simulated environment toward a real-world task, exposing both the power and the limitations of sim-to-real.

The goal is to make the path from pixels to actions concrete for computer vision and machine learning practitioners.

Building an AI agent is easy. Knowing whether it is behaving reliably in production is much harder. This session explores how to evaluate agents that use tools and external systems, including tool-call validation, tracing, observability, failure analysis, guardrails, permissions, and human review.

Attendees will learn practical patterns for understanding not just what an agent answered, but why it acted and whether the action was safe.

Video-language models can now describe, tag and answer questions about long-form video, but production results depend heavily on prompt design and evaluation. This talk shares field lessons from deploying such models for media, news, streaming and sports organizations, including how to structure prompts for consistent, schema-bound outputs across hours of footage.

On one sports video-tagging project, consolidating the prompt definitions cut estimated processing cost about 3x, and about 90% of the originally reported issues were later resolved through prompting alone. I'll also walk through building evaluation frameworks for video outputs, including for hard domains like egocentric video, and what changes when migrating between model versions.

Attendees will leave with reusable prompt patterns and an evaluation checklist they can apply to their own video pipelines.