Ah I understand you now. Yes I could have had a service do the digitizing then only done delivery myself. And given the time investment that probably would have been more sound. I don't think I'd do it all myself if I did it again.
I didn't know Gemini models were that capable. I admit I'm still skeptical about this approach though - even if it were capable of accurately labeling people and locations across decades, there's no way it could know when a scene is of personal interest. I kept a running log for each sibling as I was manually doing the labeling, knowing what they'd want to see, which presumably is only possible for me and my siblings to do with any accuracy.
If AI could ever do that then we've definitely hit ASI!
> I kept a running log for each sibling as I was manually doing the labeling, knowing what they'd want to see, which presumably is only possible for me and my siblings to do with any accuracy.
But you could feed that back in! Just write it down. It's all tokens. As you read over descriptions and note down key pieces of family history or per-sibling details, that provides information about better annotating the next video for possible points of interest. And you can chat with the LLM and write down more general principles. It's not like a LLM like Gemini doesn't know an enormous amount about family life and things of sentimental value, and can't make good initial guesses. And when you do this, you still haven't used up more than a small fraction of the context window with these image references and text profiles and principles...
I didn't know Gemini models were that capable. I admit I'm still skeptical about this approach though - even if it were capable of accurately labeling people and locations across decades, there's no way it could know when a scene is of personal interest. I kept a running log for each sibling as I was manually doing the labeling, knowing what they'd want to see, which presumably is only possible for me and my siblings to do with any accuracy.
If AI could ever do that then we've definitely hit ASI!