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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, amou
by edot 11d ago
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?
- hangrymoon01 11d agoyou 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: https://x.com/CompleteSkeptic/status/2100067328620896408?s=20 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 10d agoThanks. 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.