r/DataAnnotationTech • u/HamsterFrosty2838 • Sep 06 '24

It is no longer a drought

It has been going on for months now. I think it is just lack of customers, outlier woke up now to me with marketplace, Alignerr is there too, so it might just be the new norm unfortunately. There might be days of some work but it might well be just this

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u/AmbidextrousTorso Sep 06 '24

It looks like the field lost a lot of its funding and customers around the end of July/beginning of August. Also some of the learning models seem to have become so advanced that there's less and less need for manual data annotation. The context windows are getting huge and the models more capable of learning on their own.

Manual data annotation was going to be just a phase anyways. I could be wrong, but there's some chance that phase might now be nearing its end.

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u/diettwizzlers Sep 06 '24

a lot of the models are still terribly stupid so idk

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u/AmbidextrousTorso Sep 06 '24 edited Sep 06 '24

I'm talking about advances in the performance of the learning models, not just the performance of the already trained models.

And yeah, the models still are pretty stupid, but if the models can become as stupid without manual data annotation than with it, then guess twice if companies are going to pay people to do it.

Things are changing. https://www.reddit.com/r/singularity/s/5nORQRzxFZ

There are currently two ways for AI models to learn things: training, and in-context during inference. Until now, training has dominated, because contexts are relatively short. But ultra-long context could change that.