Overall, when we look at the future we see a huge opportunity ahead to use software to learn from the 10 trillion miles humans drive every year and build solutions drastically better than what exists today. At Nexar, we have already proven many things along the way that demonstrate this approach is indeed the right way to build a big data deep learning application. We run non-trivial deep learning models on devices at the edge. We move millions of miles of driving knowledge per week to the cloud. We add new perception-to-warning models for driving in a week. We have market segments for which our application already provides tremendous value.
… — in comparison — is a cheap but extremely rich medium (the density of information is very high). And when the camera does not work (namely at night), neither does LiDAR (we can’t interpret point clouds for which there are no semantics and people can’t label what they don’t see) and the only true fallback is radar (or V2V). So, we need to stop solving problems that don’t scale on the tagging side. If we can’t automatically tag all the data — not just a ground truth dataset — we’re likely looking…
… In autonomous driving, it’s challenging but critical to tag all the data — more so than in search. As a side note, this is a key reason why I think LiDAR will continue to lag for a long time. There is too much complexity in making sense of LiDAR data in an automated way. Video — in comparison — is a cheap but extremely rich medium (the density of information is very hi…