Comment by orbital-decay
7 hours ago
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.
Correct, but zero-shot classifiers are also not new.
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.
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Fixed guard today is not very fixed. For ex, the safeguard qwen released is a full 4b llm model. It has no different to normal llm model arch except tuned for this specific purpose,
So it is tuned specifically to classify content for safeguarding? I'm not familiar with this particular model, but it most likely has a specific classifier head that is tuned for the safeguard task. This is completely different to zero-shot classification.
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.
Agreed. I tested Jev on OpenRouter this past weekend and it’s “okay” but a specific classifier is significantly better. It used to require skill to import sklearn (ok, not really), but now it’s literally one prompt and upload your Excel file or whatever and you can get your classifier out. It’ll run free, instant, more accurate.
This is predicated on you having training data already. I approach Jev more like Langchain -- you can prototype something new extremely fast and cheap, and if the use case works well enough, rip it out and build something bespoke. If it doesn't, you didn't spend a bunch of time curating a training dataset anyway.
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I think the main argument would just be that because the model is general, you don't need to retrain it from scratch for a new problem - just tweak the input prompt. For a typical classifier there's a lot more hassle - collecting the data, training it yourself, retraining under distribution shift... In that sense Jev seems great for prototyping or small-scale use cases.
Counterargument: this works for quick prototyping, but for any serious business, you will eventually develop a benchmark/eval to track how well the general model is working, and once you have that dataset, you might as well train a specific model
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Training a classification model is trivial these days, even for a number far bigger than what Jev can do.
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.
Using Jev as a plain classifier is the least interesting case. See robotic control, navigation, computer use examples, none of it possible with a classifier.
That's the point, they're classification in disguise. Agentic game engines/mods started doing this long ago due to the latency requirements (although they're typically using small BERT-like models that need to be finetuned, or low TTFT generative models and structured outputs). New or newly discovered use cases are great, sure.
Prompt ingestion is going to be the biggest differentiator.
Being able to route prompt to features that then route to special models would be a really solid implementation.
It starts to break down once you go over 20 classifications. Which is very basic routing that can easily be done with typical ML models for cheaper and faster.
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This. It’s machine learning vs. “AI” for the uninitiated. Soon there will be a new ground breaking model that does k-means clustering and will get a billon dollar funding (but only if you're young and live in SF)
The good news is it’s fun to see people discover and get excited about things that I like as well.
Entertainingly, OpenAI had a general purpose zero-shot classifier API built on GPT-3! Just no one ever cared that much about it, so I guess it got dropped somewhere along the way since 2020/2021.
"I don't see much substance to this buzz..."
Agreed. This isn't new. I led a research team at a Fortune 500 that used a transformer based classifier approach in a commercial product as far back as 2022 and we didn't come up with it. It was already common enough that we found the inspiration for our implementation on some web forum. Models like RouteLLM have been around for a long time. The news here isn't that a new model type came about, its that a large percentage of people messing around with this stuff that are new to AI just learned that not all transformer based implementations need to be autoregressive.
> Agreed. This isn't new. ...
It doesn't have to be new, it just has to be consumable by devs.
You could send text before Twilio. You could process credit cards before Stripe.
Jev, at the end of the day is an easy to use API.
Everyone seems to forget that usability is a thing.
It took us a few hours to implement that one we used in 2022. This isn't about usability its about a huge population working on this stuff not really knowing what is available until it becomes a meme.
For me, I think the big deal is that it promises to be general and broadly applicable and high quality. That's new and special. But we'll wait to see if the claims actually hold.
> Every major AI shop has a ton of in-house classifiers already, big, small, generalist
I think building generalist classifier is some open ended research task, where frontier labs can contribute: different internal reasoning, instruction tuning, building datasets and benchmarks, building and distilling super large models.
If your "master AI" is good enough, it should be able to find and learn about and use specialized tech AI like Jev if it suits your goals
and then whatever tech it is will be absorbed/assimilated/Sherlocked into the leading products anyway