
11 月 10 日 · 26 分钟
![[人人能懂] 不止于“怎么用”:AI运行的五大核心原理](https://media-one.wavpub.com/covers/1b225f87-4eea-40e2-9522-fb4563ec9b2c/d48cb32e6dd1d52c.png?x-oss-process=image/resize,m_lfit,w_520,h_520/format,webp)
人工智能的安全带,真的能系牢吗?
给人工智能算算账:它能力的边界在哪?
你一“喜欢”,人工智能就变乖?这事儿没那么简单
高手与普通人的差距,不在于答案,而在于“清单”
想成事?别总想细节,试试“打包”你的行动
本期介绍的五篇论文:
[LG] On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
[Ludwig-Maximilians-Universität in Munich & UC Berkeley]
https://arxiv.org/abs/2507.07341
---
[CL] Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
[Stanford University & VianAI Systems]
https://arxiv.org/abs/2507.07505
---
[LG] Principled Foundations for Preference Optimization
[Google DeepMind]
https://arxiv.com/abs/2507.07855
---
[LG] PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
[Google]
https://arxiv.com/abs/2507.07495
---
[LG] Reinforcement Learning with Action Chunking
[UC Berkeley]
https://arxiv.com/abs/2507.07969
说明里标出的时间点,点一下跳过去

11 月 10 日 · 26 分钟

[人人能懂AI前沿] 3D持续打磨、多智能体博弈与微米级控制
11 月 9 日 · 24 分钟

[人人能懂AI前沿] 从元技能提炼、对抗性推理到物理世界的“柔性破界”
11 月 8 日 · 23 分钟

[人人能懂AI前沿] 从分配脑容量、无反馈破局到预测未来的代价
11 月 7 日 · 24 分钟
![[人人能懂] 不止于“怎么用”:AI运行的五大核心原理](https://media-one.wavpub.com/covers/1b225f87-4eea-40e2-9522-fb4563ec9b2c/d48cb32e6dd1d52c.png?x-oss-process=image/resize,m_lfit,w_400,h_400/format,webp)
AI可可AI生活 · 2025 年 7 月 12 日 · 26 分钟
人工智能的安全带,真的能系牢吗?
给人工智能算算账:它能力的边界在哪?
你一“喜欢”,人工智能就变乖?这事儿没那么简单
高手与普通人的差距,不在于答案,而在于“清单”
想成事?别总想细节,试试“打包”你的行动
本期介绍的五篇论文:
[LG] On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
[Ludwig-Maximilians-Universität in Munich & UC Berkeley]
https://arxiv.org/abs/2507.07341
---
[CL] Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
[Stanford University & VianAI Systems]
https://arxiv.org/abs/2507.07505
---
[LG] Principled Foundations for Preference Optimization
[Google DeepMind]
https://arxiv.com/abs/2507.07855
---
[LG] PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
[Google]
https://arxiv.com/abs/2507.07495
---
[LG] Reinforcement Learning with Action Chunking
[UC Berkeley]
https://arxiv.com/abs/2507.07969
说明里标出的时间点,点一下跳过去

https://feed.xyzfm.space/wl9t7httkfd3复制内容版权归创作者,声湃按创作者开放的 RSS 收录。了解收录与 RSS →

11 月 10 日 · 26 分钟

[人人能懂AI前沿] 3D持续打磨、多智能体博弈与微米级控制
11 月 9 日 · 24 分钟

[人人能懂AI前沿] 从元技能提炼、对抗性推理到物理世界的“柔性破界”
11 月 8 日 · 23 分钟