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      "text": "Finding and solving edge cases is critical to the deployment of safe self-driving. Our Active Learning pipeline is fed by uploading fleet data to our cloud-based selection engine. The selection engine identifies edge cases by uniformly sampling and analyzing detection uncertainty for object existence, class and position across multiple permutations of the deployed model, culminating with a rank order of relative data value. With the most valuable data automatically identified, we can focus our labeling and training efforts to provide the quickest, most effective feedback loop for the Embark Driver – resulting in constantly improving performance",
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