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.
> Ergonomics, cost profile, and ease of use are new.
Following AI from the academic papers side; jev really feels silly. They one-pass the LLM tranformer stack and tune the output network for a probability value.
(some clever pararellization optimisations to make it viable to offer as an api, since the normal kv cashing no longer works if you oneshot the tranformer)
The largest change is the packaging; An api with a tolken based pricing, and a schema to define the output structure for quick setup.
Previous projects would probably involve installing pytorch, running a converter script on Qwen, and write a fair bit of matrix math to change the output shape.
I'm kinda amused that it took this long though.
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.
Depends what you man by "more specialised". You wont train very good language understanding without alot of data. It probably uses the core tranformer stack from an LLM.
Classic classifiers are regularly just tuned general models; Training a CCN on ImageNet and tune it for cats and dogs gives better results than just training it on cats and dogs.
There is likley a small network used to tranform model output vector to probabilities, but that wouldn't be massive. Retraining that small network for specific task may beat jev; but that's bairly considered training by modern standards.