To achieve full self-driving, we need to recreate how humans drive, which involves using optical sensors and neural networks to interpret the road environment.

We're transitioning all training in Tesla's self-driving systems to use surround video, which is crucial for improving efficiency and accuracy in recognizing real-world environments.

The future of spatial intelligence includes generating immersive 3D worlds for various applications, such as gaming and education, rather than just static images or videos.

We partner with the largest satellite companies to source high-quality imagery and collect lidar data, which is crucial for improving our algorithms and expanding our model globally.

The challenge of self-driving includes solving perception problems, such as recognizing objects and predicting their movements, even when they are occluded.

We're using drones to analyze the built environment, focusing on processing lines rather than points, which is traditionally more challenging in computer vision.

To prevent drivers from clustering in one area based on demand predictions, Uber provides them with a heat map that shows underserved demand, allowing for better distribution of drivers.

By integrating a 3D representation into models, we can better align the model's capabilities with the tasks we want it to perform, enhancing user affordances. Spatial intelligence is crucial for creating technology that understands and interacts with the 3D world, enabling a wide range of applications.

The goal is to create a world where driving a car or building a city can actually benefit nature rather than harm it, fostering a synergy between humans and the environment.