For me, so much about building a traditional classifier goes into measuring and improving its performance on the data you’re making decisions about. With Jev, we seem to have just ignored all that. There seems to be…
> We didn't train a model with Reinforcement Learning for Calibrated Decisions (RLCD) to calibrate the decisions and probabilities But aren’t calibrated predictions one of the defining features?
This is a good explainer: https://magazine.sebastianraschka.com/p/claude-watermarking
Isn’t this like arguing we shouldn’t have food standards because people should be free to decide what they put in their bodies? Sure, in principle that’s true. But in practice, is it really fair to expect everyone to…
In my experience, what you're describing would more specifically be called Fraud Prevention rather than Fraud Detection. Both tend to coexist and are complementary in a mature setup. For Prevention, you're always going…
For me, so much about building a traditional classifier goes into measuring and improving its performance on the data you’re making decisions about. With Jev, we seem to have just ignored all that. There seems to be…
> We didn't train a model with Reinforcement Learning for Calibrated Decisions (RLCD) to calibrate the decisions and probabilities But aren’t calibrated predictions one of the defining features?
This is a good explainer: https://magazine.sebastianraschka.com/p/claude-watermarking
Isn’t this like arguing we shouldn’t have food standards because people should be free to decide what they put in their bodies? Sure, in principle that’s true. But in practice, is it really fair to expect everyone to…
In my experience, what you're describing would more specifically be called Fraud Prevention rather than Fraud Detection. Both tend to coexist and are complementary in a mature setup. For Prevention, you're always going…