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It's wild to me that even by their own internal measures (which are no doubt inflated) the effectiveness of the various mitigations they have put in place to pr
by paxys 2y ago
It's wild to me that even by their own internal measures (which are no doubt inflated) the effectiveness of the various mitigations they have put in place to prevent undesired behavior are mostly in the 95-98% range, and some even dip into the 80s. I can see why they can't make it more robust, since ultimately it is all a matter of who can best fine-tune and prompt hijack the underlying model, but in what other industry or problem space is this kind of failure rate acceptable? And at the end of it how do they look at the results and pat themselves on the back for a job well done? Like "yeah this tech we have released may be able to identify anyone in the world by just a sample of their voice and dox them, but it will only be abused 2% of the time!"
- potatoman22 2y agoI think this is a common scenario when deploying machine learning models or just operating in a complex world. Netflix won't always recommend you the best video, automated inspections in a factory line can fail, COVID tests have imperfect precision and recall, etc. All models are wrong, but some are useful.