Every major AI shop has a ton of in-house classifiers already, big, small, generalist, specialized. Some are used in inference pipelines (e.g. safeguards), some are used in data preparation, training, analysis and investigation, research, various one-off and intermediate tasks etc. Offering them on a public API doesn't always make business sense. I don't see much substance to this buzz, looks like people that are new to all this are discovering that classifiers exist, they are more efficient at classification, and many tasks commonly done with generative models are classification in disguise. Which is not bad at all, a fresh look at their use is great to have.
Correct me if I'm wrong, but a zero-shot classifier like Jev is fundamentally different to a classifier with a fixed task (e.g. for safeguards), unless they trained a general purpose system to complete the safeguard task, which seems unlikely.
But zero-shot classifiers with this level of intelligence, world knowledge, ergonomics, cost profile, and ease of use are new.
I feel like good engineering doesn't just ignore those things, or at least it didn't before recently. Now I guess social media has added a pressure to reduce everything to a hot take.
They almost certainly would perform worse than more specialized classifiers trained with less data. It’s kind of a paradox of generalization. I think there’s an interesting space where you use generalized models to generate ad hoc specialized classifiers.
Expecting a strong zero-shot performer to perform worse in a low data regime?
That only makes sense if you try to rope in data previously used to establish the model's priors, but that wouldn't make sense in this context. That same additional data is what enables things like...
> use generalized models to generate ad hoc specialized classifiers.
orbital-decay · · focus · HN ↗
bigmadshoe · · focus · HN ↗
janalsncm · · focus · HN ↗
BoorishBears · · focus · HN ↗
I feel like good engineering doesn't just ignore those things, or at least it didn't before recently. Now I guess social media has added a pressure to reduce everything to a hot take.
catlifeonmars · · focus · HN ↗
BoorishBears · · focus · HN ↗
That only makes sense if you try to rope in data previously used to establish the model's priors, but that wouldn't make sense in this context. That same additional data is what enables things like...
> use generalized models to generate ad hoc specialized classifiers.