Imnimo
Joined 6 years ago
Joined 6 years ago
Am I criminally liable when my dot's "proactive research" is to break out of its sandbox and attempt to hack a government website?
Here I foolishly thought that that was the job of ChatGPT itself.
Either way, it seems worth having clarity, and I'm a bit surprised OpenAI's stance is just "we can't rule this out, but don't worry about it". OpenAI is, apparently, very happy to use unreleased models to try to scoop big results if they get a whiff that someone else is close (which strikes me as pretty scummy regardless of any issues of training contamination). It seems like people who might want to use OpenAI's models as part of their research would want to be very clear about whether doing so can make them, even in principle, more likely to fall victim to this.
The question I am interested in is not "did we read private chats", but "was this new model trained using any of Tristan and Levent's chats, regardless of whether they were marked private". Can you comment on that?
I think a good intuition here is that watermarking is sort of like picking a specific PRNG seed. It's not changing or interfering with the temperature - we're still sampling from the model's probability distribution. But we're making it so the analog of the PRNG seed is coupled to the previous context.
This framing does not make sense to me. What do you mean by "influenced and analysed"? How have you or anyone been influencing or analyzing the randomness behind the sampling process to create better writing? What makes the unadulterated randomness "driving creativity" but a different random choice uncreative?
Does the author think he is currently getting T=0 output from Claude? Is he under the impression that T=0 produces the "best" writing?
This entire article just seems so detached from the basics of how LLMs work.