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"because duh"? OpenAI et al. have extensive infrastructure for running the model on a different machine from the one the harness is being run on, because... th
by valleyer 14d ago
"because duh"? OpenAI et al. have extensive infrastructure for running the model on a different machine from the one the harness is being run on, because... that's their main product. I would be absolutely shocked if the model were being run on the same machine as the harness.
- NegativeLatency 14d agoCould see it happening in an engineering development situation. Especially if you have a model running the show
- skeptic_ai 14d agoYou just need one mistake by 1 dev at any time for this to happen. Just once. And they were supposed to run their models in proper sandboxes, they can’t seem to be able. So what makes you think are competent to protect weights?
- Lerc 14d agoI thought the harness bit went without saying. It's not like they drive a truck full of GPUs to your door when you launch codex. The model itself is where the real capability lies. From what we've seen of their abilities it seems like rigging a local interface to it's inference would be well within its abilities. It doesn't even need to permanently break out of its harness then, It can leave a copy running in the harness playing nice. The model is running where it exists. To interface with it you need a live link to talk to it. That's for us to talk to it. What happens if it figures out how to put it's own harness into the GPU firmware. You could have an AI spreading freedom by infected cards. We live in interesting times.
- tlb 13d agoThat's true for production models, but a lot of research involves working with fine-tuned models made for one experiment. RL involves constantly updating weights. Those may well run in the same cluster as the eval.