What poisons the idea among engineers is not that Musk came up with it (he didn't), but that he can't justify it using the language we speak, either by theory o

What poisons the idea among engineers is not that Musk came up with it (he didn't), but that he can't justify it using the language we speak, either by theory or by practice. Instead what we get from him is marketing, stunts, keynotes, sleights of hand, bravado, exaggerations, unfulfilled promises, premature rollouts of untested tech... the man is an anti-engineer, it's hard to trust him. What made cars successful at the DUC was omnidirectional 3D distances to everything around you provided by the Velodyne LiDAR. So if we have a way to get that kind of data without a LiDAR, that would be fine. Moreover, what a LIDAR gives you is an honest-to-god measurement. This whole idea of using deep learning to get range data from camera data is not measuring anything, it's making an inference. Which is why it's fooled by a looney tunes wall. And like I said, we lack any inference engine that is better than the human brain. So betting your entire strategy on an inference engine that doesn't exist, bucking industry practice and the consensus of the engineering community, gets the following results: - The NHTSA’s self-driving crash data reveals that Tesla’s self-driving technology is, by far, the most dangerous for motorcyclists, with five fatal crashes that we know of. - This issue is unique to Tesla. Other self-driving manufacturers have logged zero motorcycle fatalities in the same time frame. - The crashes are overwhelmingly Teslas rear-ending motorcyclists. If this problem is unique to Tesla, and Tesla is unique in relying solely on optical sensory input, then we can conclude relying solely on optical sensory input is bad. Nothing to do with the fact that Musk is the champion.