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
Что думаешь? Оцени!
。关于这个话题，heLLoword翻译官方下载提供了深入分析
MiniMax作为国内主流通用人工智能企业，已经构建起多元化产品矩阵。2026年1月27日，MiniMax官方披露，已为OpenClaw提供模型能力维度的技术赋能。结合海外开发者实际落地案例，搭载MiniMax M2.1作为核心引擎的OpenClaw，在工具调用准确率、任务执行效率及成本管控方面均表现优异。，详情可参考爱思助手下载最新版本
Creates unique content。关于这个话题，PDF资料提供了深入分析
on docs.rs showed that there’s only