Comment by sanderjd
3 hours ago
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.