I would like to understand better how the self driving features work. I know there is a Deep Neural Network whose structure is generally described in available literature, comprised of something like 37,000,000 neurons. And I know that there is a high resolution map that is being continuously improved by all the Testas running AP-2 hardware. But it's hard to tell how these pieces fit together. Here are some questions for discussion: 1) How are the training weights in the neural net updated? Is that all done back at Tesla, or is there any training done in each car as it is driven? Assuming the training is being done centrally, is the neural net updated only when a new software version is installed, or is it done more often? Does every version update deliver a later version of the neural net? 2) What is the high resolution map like? Presumably it has precise location information for permanent features like road edges, signs, maybe telephone poles, etc. But are there other kinds of information as well? I've read stories from people who say that after driving a particular road a few times, the autopilot does a lot better on that road. Why? Presumably more information about the road itself, but is there also information about driver response? Will another Tesla that drives the same road a week later do better because of the mapping done by the first one? 3) Is the mapping and training information being captured by the fleet using all of the cameras? If so, does that mean that Tesla may have different nets they are training that are using data from different subsets of the sensors? 4) Does anyone know anything about plans to start using more of the sensors.