most "tech founders": train ai using human-labeled data to distinguish between 'good' and 'bad' actions. simple enough, right? but, as we dive deeper, we encounter complexities that challenge our initial perceptions: 🚧 reality check: mislabeling: bad labeled as good leads ai astray. overfitting: ai performs well in training but fails elsewhere. how do you safeguard #ai training from these critical pitfalls that always existed?
Saurav Agarwal’s Post
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