Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). Where does Jev add value here?
you will need to collect data for every decision/usecase and then train a model. But this can be used for different use cases with just a prompt.
Founders response to a similar question on X: <a href="https://x.com/CompleteSkeptic/status/2100067328620896408?s=20" rel="nofollow">https://x.com/CompleteSkeptic/status/2100067328620896408?s=2...
pasting it here:
zero-shot + general == programmable
I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits
Thanks. I do concede it’s very general but that is a double-edged sword. I don’t need a general fraud identification algorithm. I need an accurate one. If I have another classification task I’ll train another model for that task.
edot · · focus · HN ↗
hangrymoon01 · · focus · HN ↗
Founders response to a similar question on X: <a href="https://x.com/CompleteSkeptic/status/2100067328620896408?s=20" rel="nofollow">https://x.com/CompleteSkeptic/status/2100067328620896408?s=2...
pasting it here: zero-shot + general == programmable
I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits
edot · · focus · HN ↗