Ashok Elluswamy on transportation policy

Ashok Elluswamy, head of the Tesla AI team, presents at Scaled ML 2026 Chapters: 0:00 - Introduction and Tesla AI Team Mission 0:20 - Tesla’s Mission: Producing Abundance 0:32 - Robotaxi Service in Austin, Driven by Cameras and AI 1:20 - Humanoid Robots and Automating Physical Work 2:05 - Full Self-Driving (FSD) Deployment and Safety 2:38 - FSD vs. Manual Driving Safety Metrics 3:30 - How the Software Works and Challenges 3:42 - End-to-End Driving System vs. Modular Approaches 4:34 - Why End-to-End Systems are Better for Robotics 5:17 - Example: Solving the Trolley Problem with a Puddle 6:06 - Example: Waiting for Chickens and Geese to Cross 7:20 - Advantages of Neural Networks: Latency and Scaling 8:01 - The Curse of Dimensionality 9:57 - The Role of Fleet Data and Collecting Interesting Scenarios 11:40 - Proactive Safety and Generalization to Rare Events 13:30 - Debugging and Interpretability in End-to-End Systems 14:26 - 3D Geometric Reasoning with Generative Gaussian Splatting 16:25 - Text-Based Reasoning for Situational Understanding 17:08 - Evaluation: The Long-Tail Problem and World Simulators 18:32 - Neural Network-Generated Simulation Video 19:34 - Using the Simulator for Policy Evaluation and Regression Testing 20:27 - Injecting Novel Issues and Adversarial Scenes 20:56 - Real-Time Simulation with Reduced Compute 21:48 - The Foundational Neural Network for All Robotics 22:06 - Generalization to Optimus Indoor Scenes and Manipulation 23:02 - Future Roadmap: Cybercabs and Optimus 24:05 - Join the Team 24:30 - Q&A: Cameras vs. Other Sensors 26:05 - Q&A: Rewards and Penalties in the World Simulator 26:24 - Q&A: Adding External Infrastructure 27:06 - Q&A: Control Command Frequency (36 Hz) 27:32 - Q&A: Human Voice Interaction 28:42 - Q&A: Improving Visual Perception and 3D Understanding 29:22 - Conclusion