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Using Deployment to Make the Models More Reliable
The front end of the api is in part to an understand what the possible risk slows look like. How do you take that information and iterate to a beyond human, you know, kind of safety model? So far, for dipitistry, for example. A, initially we opened up access to use cases that we felt we had the right mitigations in place. But we were not quite comfortable with open handed generation. And so we worked with industry experts from different domains, as well as other researchers to red tim the model a bit further.