Anthropic says the change was motivated by a "collective action problem" stemming from the competitive AI landscape and the US's anti-regulatory approach. "If one AI developer paused development to implement safety measures while others moved forward training and deploying AI systems without strong mitigations, that could result in a world that is less safe," the new RSP reads. "The developers with the weakest protections would set the pace, and responsible developers would lose their ability to do safety research and advance the public benefit."
On the other hand, generative models should be useful when directly creating the artifact is hard for the user, but verifying the artifact is trivial. This could be the case for artifacts that require cross-referencing extremely specific information that is time consuming for a user to do, but once done, is trivial to check. It could also be the case for generative models integrated into formal verification systems with extremely reliable and highly automated verification, where no knowledge of the artifact being generated is necessary. But in general, it is unlikely to be the case for a novice in some domain trying to generate a complex artifact, since the user will not have the expertise to ensure the output meets requirements. This predicts there will still be a need for users of generative models to have domain expertise.。wps对此有专业解读
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