[LeifCarrotson]

I write code for industrial equipment and often get the request to fix a problem with software. The question "Can a computer do X" is too easy to answer in the affirmative - "Yes, but less accurately and only most of the time, and with a lot of time and money" gets condensed to "Yes" quickly.

And it is indeed an impressive and heroic piece of work when you can fix sensor problems with clever filtering, or fix mechanical problems with clever control algorithms. But when designing new equipment or deciding a path to fix a bad design, you never want to hamstring yourself from the start with poor quality input data and output actuators. That approach only leads to pain.

Once you have lots of experience with a particular design - dozens of similar machines running successfully in production for years - then you can start looking for ways to be clever and improve performance over the default or save a little money.

I understand Elon's desire to get lots of data. But there will be a much greater chance of success if it starts with Lidar + cameras, and a decade down the road you can work on camera-only controls and compare what they calculated and would have done to what the Lidar measured and the car actually responded. Only when these are sufficiently close should you phase out the Lidar.

Remember, you're comparing bad input data going to the best neural net known in the universe (the human brain) with millenia of evolution and decades of training data to sensor inputs to brand new programming. Help out the computer with better input data.