The counterpoint goes something like this (not that I necessarily buy it, but this is what I infer to be Tesla's reasoning):
1) We're only going to fully "solve" self-driving with ML techniques to train deployed NNs; it can't be done purely in human-written deterministic code because the task is too complex.
2) Those NNs are only going to come up to the necessary levels of quality with a ton of very-real-world test miles. Various forms of "artificial" in-house testing and simulation can help in some ways, but without the real-world data you won't get anywhere.
3) Deploying cars into the real world (to gather the above) without some kind of safety driver doesn't seem like a great path either. There's no backup driver to take over and intervene / unstick the car, and so far driverless taxi fleet efforts have been fairly narrowly geofenced for safety, which decreases the scope of scenarios they even get data on vs the whole real-world driving experience.
4) Therefore, the best plan to acquire the data to train the networks is to use a ton of customers as safety drivers and let them test it widely on the real routes they drive. This is tricky and dangerous, but if it's not too dangerous and the outcome saves many lives over the coming years, it was worth it.
1) We're only going to fully "solve" self-driving with ML techniques to train deployed NNs; it can't be done purely in human-written deterministic code because the task is too complex.
2) Those NNs are only going to come up to the necessary levels of quality with a ton of very-real-world test miles. Various forms of "artificial" in-house testing and simulation can help in some ways, but without the real-world data you won't get anywhere.
3) Deploying cars into the real world (to gather the above) without some kind of safety driver doesn't seem like a great path either. There's no backup driver to take over and intervene / unstick the car, and so far driverless taxi fleet efforts have been fairly narrowly geofenced for safety, which decreases the scope of scenarios they even get data on vs the whole real-world driving experience.
4) Therefore, the best plan to acquire the data to train the networks is to use a ton of customers as safety drivers and let them test it widely on the real routes they drive. This is tricky and dangerous, but if it's not too dangerous and the outcome saves many lives over the coming years, it was worth it.