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.

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.

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.

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.

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.

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