Doesn't the fact that it's general purpose warrant a new term? It's partly that it doesn't need to be trained, but it's also able to play games based on game state, I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states. The general purpose llm world understanding underneath it allows for this.
I've used it to do web research where it follows the most appropriate links, decides what to record in state, etc. I struggle to see how you could implement something with a classifier. That said, I have no idea how deep the technology is and it might be replaced with open source pretty quickly since its drafting of the frontier models and the open source models seem almost as good.
I like the term decision model and I think it's warranted.
> I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states
Yes, one general classifier would be very hard to train. However, you can create a sort of ensemble of classifiers, each trained in different tasks
I’m currently experimenting with this. So far I’ve combined classifiers for 13 different datasets, my target is 95 (the ones Laya used for training)
outside of ML, for normies i mean, classifier models are supposed to be general purpose. Like I don't think we say segmentation model to do X. In ML, we know of their constraints so we usually say classifier models trained to do X.
Not complaining that decision models are a better term though.
it's a generalistic classifier w/training needed tho. as far as I'm concerned that is somehwat novel and very practical. you get an ok classifier without working out training data and whatnot. sure, you can do classic ML experiments, but the closest that comes to mind is auto ml. not sure how far we got there, it's been a minute for me on this front.
so I don't agree on the premise that it's "just a classifier". it's not something earth shattering, but practical nonetheless
/e: there's another post from sebastian raschka on this: <a href="https://magazine.sebastianraschka.com/p/classifier-history-and-jev" rel="nofollow">https://magazine.sebastianraschka.com/p/classifier-history-a...
RamblingCTO · · focus · HN ↗
But funny that jev is getting its lunch eaten apparently in under two weeks?
danieltanfh95 · · focus · HN ↗
santadays · · focus · HN ↗
I've used it to do web research where it follows the most appropriate links, decides what to record in state, etc. I struggle to see how you could implement something with a classifier. That said, I have no idea how deep the technology is and it might be replaced with open source pretty quickly since its drafting of the frontier models and the open source models seem almost as good.
I like the term decision model and I think it's warranted.
nico · · focus · HN ↗
Yes, one general classifier would be very hard to train. However, you can create a sort of ensemble of classifiers, each trained in different tasks
I’m currently experimenting with this. So far I’ve combined classifiers for 13 different datasets, my target is 95 (the ones Laya used for training)
danieltanfh95 · · focus · HN ↗
Not complaining that decision models are a better term though.
RamblingCTO · · focus · HN ↗
so I don't agree on the premise that it's "just a classifier". it's not something earth shattering, but practical nonetheless
/e: there's another post from sebastian raschka on this: <a href="https://magazine.sebastianraschka.com/p/classifier-history-and-jev" rel="nofollow">https://magazine.sebastianraschka.com/p/classifier-history-a...