The architecture you describe is similar to that of Tesla's FSD. All the data/compute remains resident on the car, and is not transferred to Tesla (in this case it's not because of Tesla's concern for privacy, it's simply a matter of latency.) The driver does sign up to allow Tesla to interrogate the car's data for FSD debugging and trai…
The architecture you describe is similar to that of Tesla's FSD. All the data/compute remains resident on the car, and is not transferred to Tesla (in this case it's not because of Tesla's concern for privacy, it's simply a matter of latency.) The driver does sign up to allow Tesla to interrogate the car's data for FSD debugging and training purposes, in which case - and in connection with the specific event/accident - Tesla can identify the car (which functionally means the driver as well). Nonetheless it proves that all the compute necessary to run incredibly complex multi-domain ML processes can be localized and miniaturized (in comparison to the giant compute facilities Waymo et all install in their cars.)
Interesting! I knew vaguely that Tesla built its own silicon to handle the FSD locally, but didn't realize it was such a total on-car displacement of the compute. Waymo does the same....but just not as efficiently?
Right. Tesla have done an incredible job of providing a powerful, integrated, fault tolerant stack with extremely low power consumption in-car. Waymo's stack is huge, legacy and limited (geo-fenced, so localized data, with enormous numbers of data points mapped). Takes a formidable amount of power and space!
The architecture you describe is similar to that of Tesla's FSD. All the data/compute remains resident on the car, and is not transferred to Tesla (in this case it's not because of Tesla's concern for privacy, it's simply a matter of latency.) The driver does sign up to allow Tesla to interrogate the car's data for FSD debugging and training purposes, in which case - and in connection with the specific event/accident - Tesla can identify the car (which functionally means the driver as well). Nonetheless it proves that all the compute necessary to run incredibly complex multi-domain ML processes can be localized and miniaturized (in comparison to the giant compute facilities Waymo et all install in their cars.)
Interesting! I knew vaguely that Tesla built its own silicon to handle the FSD locally, but didn't realize it was such a total on-car displacement of the compute. Waymo does the same....but just not as efficiently?
Right. Tesla have done an incredible job of providing a powerful, integrated, fault tolerant stack with extremely low power consumption in-car. Waymo's stack is huge, legacy and limited (geo-fenced, so localized data, with enormous numbers of data points mapped). Takes a formidable amount of power and space!