James Douma and I discuss latest Tesla FSD developments, FSD beta v11 release notes, Elon Musk, Twitter, TSLA and more. Bonus section after 1 hour mark on language learning and mental skills development.

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James Douma on AI playlist, https://www.youtube.com/playlist?list=PLfibpgBinf9R7KIedEU3y-YjrA63LSKHX

Tesla FSD Beta v11 release notes:

- Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta's multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.

- Improved Occupancy Network's recall for close by obstacles and precision in severe weather conditions with 4x increase in transformer spatial resolution, 20% increase in image featurizer capacity, improved side camera calibration, and 260k more video training clips (real-world and simulation).

- Improved merging behavior by leveraging lane geometry and lane bounds, association with coarse map information and better gap selection algorithms, allowing for a smoother and safer experience.

- Added highway behavior to offset away from blocked lanes and generic obstacles like road debris while also adding a smooth handoff between in-lane offsetting and lane changing.

- Improved speed-based lane change decisions to better avoid slowing down traffic in fast lanes, and interfere less with navigation.

- Reduced sensitivity for speed-based lane changes in CHILL mode.

- Improved lane changes to allow higher jerk maneuvers if required to stay on-route or to move away from lane blockages.

- Improved smoothness at highway lane splits by being less strict about centering between lane lines and allowing lower jerk maneuvers, where safe to do so.

- Reduced latency of trajectory optimization by 20% on average, without sacrificing behavior, by leveraging numerical tricks for more efficient computations.

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