hyunsoo90

4 ms·

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by hyunsoo90 8y ago
As far as I know, some do but most don't.
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by hyunsoo90 8y ago
Thank you for the interest! We have a team of ~50 in-house annotators that use our AI tools and we are looking to expand out to Southeast Asia, namely Vietnam or the Philippines, to set up our annotator workforce there in the near future. A
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by hyunsoo90 8y ago
We have both AI-assisted and manual inspections in the pipeline. A good analogy would be an assembly line where humans and machines collaborate not only for building things but also for the quality control (ie. vision inspection system + ma
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by hyunsoo90 8y ago
It depends on how accurate our AI performs on a particular task, but as a back-of-the-envelope calculation, if we had a 90% accurate AI that means human annotators only have to work on the remaining 10%, giving us 10x boost. Obviously, ther
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by hyunsoo90 8y ago
Thank you so much! The human-in-the-loop piece really starts to kick in for larger volumes of data where we can iterate the fine-tuning process several times. As an example, we were able to achieve +30% speed boost for a client after a few
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by hyunsoo90 8y ago
Thanks for pointing this out. Although using crowdsourced labor could be the most cost-effective, we don't think it can guarantee the level of quality we get from in-house team. We believe our AI will be able to automate a lot of the p
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by hyunsoo90 8y ago
Great question! Our AI is trained specifically for data annotation and that leads to a few differences. For example, our data annotation pipeline is not "end-to-end" and would not be suitable for a real-time deployment -- it works
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by hyunsoo90 8y ago
Thank you! Scale is doing great especially in the autonomous vehicles vertical. We are different in a few ways from Scale or other data annotation companies -- one, we really focus on building AI tools that can automate data annotation all
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Launch HN: Superb AI (YC W19) – AI-Powered Training Data
47 points by hyunsoo90 8y ago|22 comments