Raw LLM Responses

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Having worked in computer vision for a while, I can say these neural nets, which Tesla uses, can already handle 97-99% of cases. They are incredibly advanced, even with foggy images. If the network finds the right path, it can infer the structure using temporal information, motion cues, and contrasting pixel information, among other methods. You might have heard how firefighters used to struggle in heavy smoke, they couldn’t see anything and few years ago, company built an HMD device powered by neural networks that allowed firefighters to see scene edges through dense smoke. Even from a single camera frame, these networks can extract depth, object detection, semantic segmentation, lane geometry and more. But again here’s the thing- you can’t defy physics. It cannot detect a child hidden behind another car. You can’t extract more information than what exists in the signal. No matter how advanced the AI is, cameras are limited by photons. And since full autonomy means handling worst-case scenarios, Tesla will eventually need additional sensors to cover those edge cases. That said, it’s still mind-blowing what Tesla has achieved so far just with cameras. I bet they will eventually retrofit a sensor to cover all the edge cases.
youtube 2025-06-24T12:2… ♥ 51
Coding Result
DimensionValue
Responsibilityunclear
Reasoningunclear
Policyunclear
Emotionunclear
Coded at2026-04-27T06:24:59.937377
Raw LLM Response
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