[人人能懂] 从编织因果、解释对错到脑补真实

当AI看似无所不知时，它真的理解自己说的因果关系吗？我们如何训练AI学会“解释自己”而不是“强词夺理”？本期节目，我们将从几篇最新论文出发，揭示AI如何学会编织知识地图、为何会“一本正经地胡说八道”，并一窥它那“不断复读”的内在工作模式，以及为电影特效“脑补”真实细节的惊人能力。00:00:29 AI时代的“寻龙诀”，我们如何挖掘知识的因果龙脉00:07:37 AI正在给你一种“知道”的幻觉00:14:50 AI看病，如何才能“说人话”还“负责任”？00:19:48 AI的“偷懒”智慧，为什么顶尖模型都在悄悄“复读”？00:25:42 AI正在“脑补”你看不到的真实本期介绍的几篇论文：[LG] Large Causal Models from Large Language Models[Adobe Research]https://arxiv.org/abs/2512.07796---[AI] Epistemological Fault Lines Between Human and Artificial Intelligence[Sapienza University of Rome & University of Milan Bicocca & University of Maribor]https://arxiv.org/abs/2512.19466---[LG] Reason2Decide: Rationale-Driven Multi-Task Learning[University of Alberta]https://arxiv.org/abs/2512.20074---[CV] Block-Recurrent Dynamics in Vision Transformers[Harvard University]https://arxiv.org/abs/2512.19941---[CV] Over++: Generative Video Compositing for Layer Interaction Effects[University of North Carolina at Chapel Hill & University of Maryland]https://arxiv.org/abs/2512.19661在小宇宙查看该单集文稿