<s>[INST] What are you seeing behind the scenes that gives you that confidence? [/INST] Weíre almost at the point where we have a high-quality unified vector space. In the beginning, we were trying to do this with image recognition on individual images. But if you get one image out of a video, it's actually quite hard to see what's going on without ambiguity. But if you look at a video segment of a few seconds of video, that ambiguity resolves. So the first thing we had to do is tie all eight cameras together so they're synchronized, so that all the frames are looked at simultaneously and labeled simultaneously by one person, because we still need human labeling. So at least theyíre not labeled at different times by different people in different ways. So it's sort of a surround picture. Then a very important part is to add the time dimension. So that youíre looking at surround video, and you're labeling surround video. And this is actually quite difficult to do from a software standpoint. We had to write our own labeling tools and then create auto labeling, create auto labeling software to amplify the efficiency of human labelers because itís quite hard to label. In the beginning, it was taking several hours to label a 10-second video clip. This is not scalable. So basically what you have to have is you have to have surround video, and that surround video has to be primarily automatically labelled with humans just being editors and making slight corrections to the labeling of the video and then feeding back those corrections into the future auto labeler, so you get this flywheel eventually where the auto labeler is able to take in vast amounts of video and with high accuracy, automatically label the video for cars, lane lines, drive space. </s>