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
春节假期刚过,宇树科技股份有限公司的机器人实验室已经恢复了忙碌。一组技术人员正在测试集群协同程序,大屏幕上实时跳动着定位数据与运动轨迹。不久前,这些机器人动作登上总台春晚,成为科技与传统文化融合的亮点之一。,更多细节参见safew官方版本下载
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