本期节目，我们将一起挑战几个关于AI的“想当然”：它真的无所不能，又或者只是个模式复读机？我们会发现，AI能反过来给人类科学论文“挑错”，但它自己预测的数据也可能布满陷阱。更进一步，我们将从逻辑的根源探讨机器创新的“天花板”，并揭示让AI实现“协调”与“自我进化”的巧妙新思路。00:00:28 AI当监工：我们读的顶会论文，到底有多少bug？00:05:55 你的AI为什么总“犯傻”？缺的不是智商，是“协调”00:12:48 给AI的狂热泼一盆冷水：为什么机器无法真正创新？00:19:44 AI预测的数据，是馅饼还是陷阱？00:30:00 AI的自我修养：没有人类老师，它如何变得更聪明？本期介绍的几篇论文：[AI] To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis[Together AI & NEC Labs America]https://arxiv.org/abs/2512.05925---[AI] The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics[Stanford University]https://arxiv.org/abs/2512.05765---[AI] On the Computability of Artificial General Intelligence[N/A]https://arxiv.org/abs/2512.05212---[LG] Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data[Fred Hutchinson Cancer Center & University of Washington]https://arxiv.org/abs/2512.05456---[CV] Self-Improving VLM Judges Without Human Annotations[FAIR at Meta]https://arxiv.org/abs/2512.05145

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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