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
Карина Черных (Редактор отдела «Ценности»)。业内人士推荐快连下载安装作为进阶阅读
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При этом Леви подчеркнул, что не все в Белом доме согласны с курсом Трампа по Ирану. По его словам, такая стратегия вызывает недовольство тех чиновников, которые нацелены на сдерживание Китая, а не ввязывание в ближневосточные конфликты.，推荐阅读电影获取更多信息
John Chapman SSAO Tutorial (2013) A tutorial on implementing SSAO