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The main problem in my mind with purely vision based FSD is that it just isn’t as smart as a real human. A real human can reason about what they see, detect inconsistencies that are too abstract for current ML algorithms to see, and act appropriately in never before seen circumstances. A real human wouldn’t drive full speed through very low visibility areas. They can use context to reason about a situation. Current ML algorithms can’t do any of that, they can’t reason. As such they are inherently incapable of using the same sensors (cameras/eyes) to the same effect. Lidar is extremely useful because it helps get a bit better of a picture that cameras can’t reliably provide. I’m still not sure that even with lidar you can make a fully safe FSD car, but it definitely will help.
The assumption that ML lacks reasoning is outdated. While it doesn’t “think” like a human, it learns from more scenarios than any human ever could. A vision-based system can, in principle, surpass human performance, as it has in other domains (e.g., AlphaGo, GPT, computer vision in medical imaging).
The real question isn’t whether vision-based ML can replace humans—it’s when it will reach the level where it’s unequivocally safer.
The main problem in my mind with purely vision based FSD is that it just isn’t as smart as a real human. A real human can reason about what they see, detect inconsistencies that are too abstract for current ML algorithms to see, and act appropriately in never before seen circumstances. A real human wouldn’t drive full speed through very low visibility areas. They can use context to reason about a situation. Current ML algorithms can’t do any of that, they can’t reason. As such they are inherently incapable of using the same sensors (cameras/eyes) to the same effect. Lidar is extremely useful because it helps get a bit better of a picture that cameras can’t reliably provide. I’m still not sure that even with lidar you can make a fully safe FSD car, but it definitely will help.
The assumption that ML lacks reasoning is outdated. While it doesn’t “think” like a human, it learns from more scenarios than any human ever could. A vision-based system can, in principle, surpass human performance, as it has in other domains (e.g., AlphaGo, GPT, computer vision in medical imaging).
The real question isn’t whether vision-based ML can replace humans—it’s when it will reach the level where it’s unequivocally safer.