Jan 06, 2026

Anaximander: Interactive Orchestration and Evaluation of Geospatial Foundation Models

This talk introduces Anaximander, a system designed to bridge the gap between traditional, GUI-driven Geographic Information System (GIS) workflows and modern, code-heavy machine learning practices. Anaximander integrates geospatial foundation models directly into QGIS, allowing experts to interactively orchestrate, run, and evaluate models for tasks like semantic segmentation and object detection on satellite imagery.

Dec 16, 2025

Why Physical AI Needs a new Data Set | Rerun CEO

Nikolaus West, CEO of Rerun, explains how their data logging and visualization platform, built on an Entity Component System (ECS) inspired by gaming, is unlocking new capabilities in physical AI. He discusses the rapid progress in robot manipulation through imitation learning, the gap between impressive demos and real-world products, and the critical need for better data tooling to handle complex, multi-rate sensor data in robotics and AR/VR.

Nov 06, 2025

Waymo: The future of autonomous driving with Vincent Vanhoucke

Waymo Distinguished Engineer Vincent Vanhoucke discusses the core challenges of autonomous driving, explaining how Waymo fuses data from cameras, LiDAR, and radar to build a robust perception system. He delves into the "closed-loop" problem, the critical role of generative AI and simulation in training and validation, and how modern multimodal models are used in a teacher-student framework to distill vast world knowledge into the vehicle's onboard system, aiming for a safety standard that surpasses human performance.

Nov 03, 2025

Fine-Tuned Models Are Getting Out of Hand

A deep dive into how fine-tuned Small Language Models (SLMs) and RAG systems can be combined to create personalized AI agents that learn user-specific workflows, emulate decision-making, and collaborate with humans, moving beyond conversational interfaces to direct action within enterprise environments.

Jul 31, 2025

Computational models for brain science

Dr. Laschowski discusses his lab's research in computational neuroscience, focusing on three core areas: reverse-engineering human motor control using reinforcement and optimal control models, developing high-accuracy neural decoding algorithms for brain-machine interfaces (BMIs), and creating brain-inspired deep learning models for computer vision. The talk highlights a long-term vision of discovering the fundamental principles of intelligence to build more efficient and robust AI.