Hypothesis:
* Unknown unknowns: Dataset is always imperfect, all scenarios are not represented well yet and can always be more diverse
* Capable base model/architecture: Improving dataset improves AI/product guarantees
**Inspirations**:
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**1. Become one with the data**\
**2. Set up the end-to-end training/evaluation skeleton + get dumb baselines**\
**3. Overfit**\
**4. Regularize**\
**5. Tune**\
**6. Squeeze out the juice**
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from 6th to 15th minute
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"The only sure certain way I have seen of making progress on any task is, you curate the dataset that is clean and varied and you grow it and you pay the labeling cost and I know that works.”
"Potentially nitpicky but competitive advantage in AI goes not so much to those with data but those with a data engine. And whoever can spin it fastest. Slide from Tesla to \~illustrate but concept is general”
[QualEval: Qualitative Evaluation for Model Improvement](https://qualeval.org/#:~:text=QualEval%3A%20Qualitative%20Evaluation%20for%20Model%20Improvement)
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