[人人能懂] AI的首席架构师：解构与重组“智能”

00:01:50 人工智能界的“分工”智慧：如何让天才更天才？ 

00:06:40 人工智能预测那么准，它真的“懂”了吗？ 

00:11:52 人工智能的“最强大脑”？不，是“最省大脑” 

00:16:14 训练人工智能，少吃多餐还是狼吞虎咽？ 

00:20:39 从笨拙到精通：机器人如何“看”会我们的本事？ 

本期介绍的五篇论文：

[LG] Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving

[Tencent AI Lab]

arxiv.org

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[LG] What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models

[Harvard University & MIT]

arxiv.org

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[CL] Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation

[Microsoft]

arxiv.org

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[LG] Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful

[New York University & Columbia University]

arxiv.org

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[LG] Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement

[Google Deepmind]

arxiv.org