Александра Синицына (Ночной линейный редактор)
We propose sycophancy leads to less discovery and overconfidence through a simple mechanism: When AI systems generate responses that tend toward agreement, they sample examples that coincide with users’ stated hypotheses rather than from the true distribution of possibilities. If users treat this biased sample as new evidence, each subsequent example increases confidence, even though the examples provide no new information about reality. Critically, this account requires no confirmation bias or motivated reasoning on the user’s part. A rational Bayesian reasoner will be misled if they assume the AI is sampling from the true distribution when it is not. This insight distinguishes our mechanism from the existing literature on humans’ tendency to seek confirming evidence; sycophantic AI can distort belief through its sampling strategy, independent of users’ bias. We formalize this mechanism and test it experimentally using a rule discovery task.，详情可参考下载安装汽水音乐
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Из Дубая в Москву вылетел первый с начала конфликта рейс Emirates02:15
他说：“对公司而言，平衡开源和商业利益确实非常困难。”他举了两个例子：Databricks和Redis Labs是开源商业化的成功案例，但RethinkDB这个备受喜爱的开源数据库，尽管技术卓越，却在2016年关闭。，推荐阅读爱思助手下载最新版本获取更多信息