EDIT: We're also looking for people to become trained Honest debaters, which requires a greater time commitment (ideally >=5 hours per week for >= 2 months) but for which we're offering $30/hr. If you're interested in doing that, please fill out this form: https://forms.gle/2bv1Z8eCYPfyqxRF9
We’re looking for people to help us adversarially test our Debate protocol.
We have a set of questions, a (somewhat complicated) set of rules and mechanisms for how the debate should work, a (slightly janky) web interface for conducting the debates, and a protocol for judging: we have a pipeline for selecting good judges through MTurk, and they get 5 minutes to judge the final round from the debate.
We think that the person who gives the better answer to the question at the start (the “honest debater”) should win the debate, if they understand why that answer is good, and they have practiced the “honest debater strategy” a bit.
We’re looking for people to play the ‘dishonest debater’ role, and win against our trained honest debaters.
We’re ideally looking for people who:
Have good physics ability and can understand the questions in the problem set (mostly a few of the harder questions from the first few sections of Thinking Physics, plus a few probability/stats puzzles/paradoxes)
Are very good at argumentation and deception
Believe that there’s a dishonest strategy that should win in these debates
Will be adversarial in the debates, but constructive and cooperative in figuring out the rules for the adversarial testing and overall experimental setup
Are available during daytime PST
More details, rules, experiment plan and tips for debaters are here.
First we want to pilot our overall protocol with only a small number of adversarial collaborators, and we’ll probably find some holes in our experiment rules and general setup that are unrelated to the properties of the debate mechanism itself.
If we manage to fix the holes in the experimental protocol, but don’t believe we’ve found problems in the actual debate mechanism yet, we’ll probably try to escalate the adversarialness, for example by scaling it up to a larger number of people, or offering prizes for dishonest wins.
If you're interested, comment below or email me: barnes [at] openai.com.
If you would be interested in participating conditional on us offering pay or prizes, that's also useful to know.
OpenAI is looking for people to help test their <@debate@>(@Writeup: Progress on AI Safety via Debate@) protocol, to find weaknesses that allow a dishonest strategy to win such debates.
EDIT: We're also looking for people to become trained Honest debaters, which requires a greater time commitment (ideally >=5 hours per week for >= 2 months) but for which we're offering $30/hr. If you're interested in doing that, please fill out this form: https://forms.gle/2bv1Z8eCYPfyqxRF9
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We’re looking for people to help us adversarially test our Debate protocol.
We have a set of questions, a (somewhat complicated) set of rules and mechanisms for how the debate should work, a (slightly janky) web interface for conducting the debates, and a protocol for judging: we have a pipeline for selecting good judges through MTurk, and they get 5 minutes to judge the final round from the debate.
We think that the person who gives the better answer to the question at the start (the “honest debater”) should win the debate, if they understand why that answer is good, and they have practiced the “honest debater strategy” a bit.
We’re looking for people to play the ‘dishonest debater’ role, and win against our trained honest debaters.
We’re ideally looking for people who:
More details, rules, experiment plan and tips for debaters are here.
First we want to pilot our overall protocol with only a small number of adversarial collaborators, and we’ll probably find some holes in our experiment rules and general setup that are unrelated to the properties of the debate mechanism itself.
If we manage to fix the holes in the experimental protocol, but don’t believe we’ve found problems in the actual debate mechanism yet, we’ll probably try to escalate the adversarialness, for example by scaling it up to a larger number of people, or offering prizes for dishonest wins.
If you're interested, comment below or email me: barnes [at] openai.com.
If you would be interested in participating conditional on us offering pay or prizes, that's also useful to know.