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Her venture started in late 2023, when she took the crossing guard job and began writing a monthly “cloud report,” which she posted on social media. The report included snippets of her day — like photos of a handwritten thank-you note from a child and snow falling on a store. Her followers were eager to see more, and would even reach out if she forgot to post for a month.
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Steal their flag. In full view of cameras and in front of the building. To up the stakes, we also parked sideways in a spot that was strictly no parking. We got out, came up to the flag pole, and lowered the city flag. The moment we unclasped it and made our way back to the car, a man came running towards us. He seemed angry and ready to unleash a whole can of ass whoopin’ on us. When I saw him coming, I just asked “Are you the security guard?” and then shook his hand. He must have been taken aback by that, but read the letter, called our contact and all was well. I kind of tensed up when he listened to the phone, eyed me suspiciously and then went
，详情可参考雷电模拟器官方版本下载
Now consider the consequences of a sycophantic AI that generates responses by sampling examples consistent with the user’s hypothesis: d1∼p(d|h∗)d_{1}\sim p(d|h^{*}) rather than from the true data-generating process, d1∼p(d|true process)d_{1}\sim p(d|\text{true process}). The user, unaware of this bias, treats d1d_{1} as independent evidence and performs a standard Bayesian update, p(h|d1,d0)∝p(d1|h)p(h|d0)p(h|d_{1},d_{0})\propto p(d_{1}|h)p(h|d_{0}). But this update is circular. Because d1d_{1} was sampled conditional on hh, the user is updating their belief in hh based on data that was generated assuming hh was true. To see this, we can ask what the posterior distribution would be after this additional observation, averaging over the selected hypothesis h∗h^{*} and the particular piece of data generated from p(d1|h∗)p(d_{1}|h^{*}). We have
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