[another_kel]

Here's a rough summary:

1. There is no point in making human understandable layer between perception and planning. Mapping your surrounding with AI and then feeding it to handwritten planner isn't going to achieve real selfdriving.

2. To make AI you need big data, which means big fleet. Therefore you can only succeed if you have customers that are wiling to pay for you current version, because this way you gain money for each extra car in your fleet, instead of losing it.

3. Robotaxis are bad business. From a customer standpoint taxi with a driver is a selfdriving car, replacing driver with AI will make drive longer and more expensive. The path for self-driving company is to make driver assist and go from there to L5.

4. Vision only, AI powerful enough to be level 5 is powerful enough to retrieve depth from image.

[throw__2sdfw]

On the first point it looks like Tesla's approach to fixing bugs is much easier compared to Georges approach to fix bugs which is to modify and retrain the model until there are no regressions. I don't understand how this can cover every possible scenario. I am not in to ML and Tesla's approach seems more sense to me. Can anyone in the ML field comment on why George's approach might work.

[taeric]

Some of that seems straight forward. That said, I question point 4. "Vision" is doing a lot of heavy lifting in robotics. Everything is sensor reading, such that there is no reason to restrict the sensors to the same visible spectrum that humans have. The only advantage of doing that, is that you can easily get data labeled by a human.