从“短平快”转向“中长线”。 当短途东亚目的地卷无可卷时，10天左右的“中航线”，乃至探索东南亚、南太平洋的“长航线”，将成为新的增长点和利润来源。
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.
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The benefits are palpable, and clicking into a label to see a handful of emails all from the same senders or regarding the same general topics was immediately less stressful than sorting through a completely disorganized inbox.
一旦组织强行将这一有机体切割为水平分工的流水线，技术领袖便失去了对模型整体生命力的把控，沦为庞大机器中的一颗螺丝钉。