# Smarter Sampling And Active Learning

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When dealing with thousands or even millions of images, it's crucial to identify which ones are the most valuable for annotation.

Right now, many AI teams have to do it manually, relying on guesswork.

This not only takes up a lot of time, but it's also not very accurate.

I heard from someone in the fishery management field that they have to go through tons of video footage manually to select frames, and sometimes they miss important things because it's so tedious.

Plus, staring at similar frames for too long can be exhausting.

That's why it's important to have a way to automatically curate data, filtering out the most important frames or sections of video.

This would save time and improve the quality of the data fed into the model.

Decision: pursue automated curation to replace manual, guess‑based frame selection with a systematic, repeatable pipeline that reduces effort and captures critical events.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal