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.
I fed into the hype at first. Testing Jev and Laya, they both suffer from the same issues as LLMs that stop them being useful beyond limited classifications.
I can't see any benefits that a typical ML classifier would not be better at.
I guess I'm circling toward this view. The question is, are there things that are 1. worth doing, 2. for which jev (or jev-like systems) works well, and 3. are not worth the effort to train a custom classifier. Probably yes, but it seems like it might be a pretty narrow path. But a lot depends on #2. The trade-off between #1 and #3 is less stark the more successful one shot models are at handling use cases successfully.
Scripts and debugging, one-off log parsing or filtering.
I saw an article about 2+ years ago of a researcher using a small local AI strapped into excel to evaluate the abstract and intro of 10000 papers for "papers that research X in domain of Y", and let it loose.
jev is probably more capable avd faster than that workflow was, but saved one dude a few very grindy weeks for a litteratur review.
It's amusing how long it took, and much hype it gets for someone releasing the least revolutionary ML architecture in a new package. But i can see a fair few uses.
orbital-decay · · focus · HN ↗
EagnaIonat · · focus · HN ↗
I can't see any benefits that a typical ML classifier would not be better at.
sanderjd · · focus · HN ↗
aDyslecticCrow · · focus · HN ↗
I saw an article about 2+ years ago of a researcher using a small local AI strapped into excel to evaluate the abstract and intro of 10000 papers for "papers that research X in domain of Y", and let it loose.
jev is probably more capable avd faster than that workflow was, but saved one dude a few very grindy weeks for a litteratur review.
It's amusing how long it took, and much hype it gets for someone releasing the least revolutionary ML architecture in a new package. But i can see a fair few uses.
sanderjd · · focus · HN ↗