AI可可AI生活
Episodes
[人人能懂] 不止于“怎么用”：AI运行的五大核心原理
12 Jul 2025
Contributed by Lukas
00:01:30 人工智能的安全带，真的能系牢吗？ 00:06:39 给人工智能算算账：它能力的边界在哪？ 00:11:34 你一“...
[人生智慧] 你最宝贵的资产正在无声流失，而你甚至没有察觉
11 Jul 2025
Contributed by Lukas
我们拼命管理着自己的金钱、时间、人脉，却对自己最重要的核心资产——“情绪资产”——视而不见。
[人人能懂] AI的首席架构师：解构与重组“智能”
11 Jul 2025
Contributed by Lukas
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？&nb...
[人生智慧] 你付出的真正代价，不是时间，而是“重启成本”
10 Jul 2025
Contributed by Lukas
23分15秒，是一个普通人，在注意力被一次微小的干扰打断后，重新回到之前深度专注状态所需要付出的平均时间。...
[人人能懂] AI的“手艺人”精神：从模仿、练习到顿悟
10 Jul 2025
Contributed by Lukas
00:01:33 “差生”配对，如何“炼”出优等生？ 00:06:09 AI的“刻意练习”：怎样探索才最高效？ 00:10:19 让AI学...
[人生智慧] 为什么人生最可怕的不是差评，而是“未读”
09 Jul 2025
Contributed by Lukas
我们真正要警惕的，不是犯错，而是那种‘万一错了怎么办’的恐惧。
[人人能懂] AI的系统之美：当“设计”比“天才”更重要
09 Jul 2025
Contributed by Lukas
00:01:37 你的 AI，是“记性好”还是“真会学”？00:06:19 管好AI的“注意力”：一个“分而治之”的智慧 00:10:22 造...
[感悟]你的快乐，正在“复利”还是“透支”？
08 Jul 2025
Contributed by Lukas
“我此刻的行为，是在点燃一支烟火，还是在种下一棵树？是在透支未来的我，还是在投资未来的我？”
拉开AI的魔法幕布：后台的巧思与匠心
08 Jul 2025
Contributed by Lukas
00:01:26 AI的“心里话”，为什么也可能是装出来的？ 00:06:38 AI的“记忆力”，如何才能赶上一本小说？ 00:12...
[感悟]迷路定律：为何走错路，才是回家的捷径？
07 Jul 2025
Contributed by Lukas
迷路，不是一种状态，它是一种必要的暂停。它强迫我们从“自动驾驶”切换到“手动模式”，用自己的意图，去...
从“战略指纹”到“解耦赋分”：AI的非技术性进化
07 Jul 2025
Contributed by Lukas
00:01:29 AI的“人设”：谷歌“腹黑”，OpenAI“傻白甜”？ 00:06:09 给AI“减肥餐”：为什么数据越多，模型可能越...
[感悟]时间的复利：你亏欠自己的那10小时
06 Jul 2025
Contributed by Lukas
时间本身就是最高形式的财富，而你，需要成为第一个收款人。
当AI撕掉“成长公式”：拥抱一个更真实的智能世界
06 Jul 2025
Contributed by Lukas
00:01:33 AI育儿经：聪明的大脑，是“喂”出来的还是“练”出来的？00:07:02 换个方向看世界：当AI学会“倒着想...
[感悟]为什么九次失败，胜过一次平庸？
05 Jul 2025
Contributed by Lukas
你应该容忍、甚至鼓励那些可能带来巨大回报的、高失败率的实验。因为在商业世界里，一次本垒打的得分，远超...
AI的效率革命：当“更巧”比“更大”更重要
05 Jul 2025
Contributed by Lukas
00:01:45 AI 炼丹术：发现模型训练的“万能公式”？ 00:07:01 AI军备竞赛，弹药快打光了怎么办？ 00:12:16 让 AI ...
[感悟]你是在“发脾气”还是在“解决问题”？
04 Jul 2025
Contributed by Lukas
我们每个人的人生，都充满了各种各样的“刺激”——可能是老板的一句批评，伴侣的一句抱怨，甚至只是社交媒...
AI的“书房”与“镜子”：探索智能的基石与反思
04 Jul 2025
Contributed by Lukas
00:01:19 高手过招，拼的不是“脑子”，而是“书房”？ 00:05:35 AI的“自我反思”：如何让机器像高手一样思考？...
[感悟]你的“情绪豁免权”已过期，请向内续费
03 Jul 2025
Contributed by Lukas
真正困住我们的，是我们为自己亲手建造的那座“心牢”。只要你还被关在这座牢里，那么无论你逃到天涯海角，...
不止是强大，更是聪明：AI如何学会思考、复盘与好奇？
03 Jul 2025
Contributed by Lukas
00:01:19 AI的“偏科”难题：学好数理化，走遍天下真的不怕吗？00:05:08 AI 也会“复盘”？聊聊如何让机器像高手一样...
[感悟]打不赢的仗，换个打法
02 Jul 2025
Contributed by Lukas
人生，没有真正的绝境。
AI的“顿悟”时刻：从玩游戏到省大钱，揭秘智能进化的新路径
02 Jul 2025
Contributed by Lukas
本期「人人能懂的AI前沿」，我们重点介绍五篇最新的AI论文：00:00:27 高手过招：AI是如何在游戏中“悟道”的？00:...
[感悟]警惕！那个正在“喂养”你大脑的隐形厨师
01 Jul 2025
Contributed by Lukas
有一个我们不易察觉的变化，正在重塑我们和知识、和世界相处的方式。
你以为AI在理解语言？其实它在开一场“关系”派对
01 Jul 2025
Contributed by Lukas
[LG] Transformers are Graph Neural Networks[University of Cambridge]arxiv.org
AI的“顿悟”：机器如何学会了举一反三？
01 Jul 2025
Contributed by Lukas
[LG] Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoni...
想看懂未来？先学会给机器讲个好故事
01 Jul 2025
Contributed by Lukas
[LG] Performance Prediction for Large Systems via Text-to-Text Regression[Google Research]arxiv.org
AI当医生，怎样才能不把天“聊死”？
01 Jul 2025
Contributed by Lukas
[CL] Sequential Diagnosis with Language Models[Microsoft AI] arxiv.org
你的大脑里，是不是也住着一个“CEO”和一个“项目经理”？
01 Jul 2025
Contributed by Lukas
[LG] Hierarchical Reasoning Model[Sapient Intelligence, Singapore]arxiv.org
你以为AI是学霸，其实它只是个“刷题匠”
01 Jul 2025
Contributed by Lukas
[CL] OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization[dmodel.ai & UC Berke...
AI写作的秘密：高手不是“教”出来的，而是“练”出来的
01 Jul 2025
Contributed by Lukas
[CL] LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning[Singapore University of Technology and Design & Tsinghua Uni...
思想的“锚点”：AI是如何抓住重点的？
01 Jul 2025
Contributed by Lukas
[LG] Thought Anchors: Which LLM Reasoning Steps Matter?[Duke University & Aiphabet]arxiv.org
AI前沿：从大脑启发到硬件彩票
01 Jul 2025
Contributed by Lukas
[LG] Hierarchical Reasoning Model[Sapient Intelligence, Singapore]https://arxiv.org/abs/2506.21734---[CL] Sequential Diagnosis with Language Models[Mi...
[感悟]如何干掉你99%的烦恼？
30 Jun 2025
Contributed by Lukas
成年人最高级的活法：在自己能掌控的领域里，拼尽全力；在自己无法掌控的领域里，保持内心的安宁。
寻找“标准答案”：在混乱中导航的终极技巧
30 Jun 2025
Contributed by Lukas
[LG] Gaussian Invariant Markov Chain Monte Carlo[Google DeepMind & UCL]arxiv.org
AI瘦身指南：如何让“大胖子”模型变得又快又好？
30 Jun 2025
Contributed by Lukas
[LG] Distilling Normalizing Flows[University of Oregon & HSE University & Picsart AI Research]arxiv.org
为什么“精益求精”反而会把事情搞砸？
30 Jun 2025
Contributed by Lukas
[LG] Overtuning in Hyperparameter Optimization[LMU Munich]arxiv.org
AI界的“学霸”和“学神”：差的不是智商，是训练方法
30 Jun 2025
Contributed by Lukas
[CL] OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling[Shanghai Jiao Tong University]arxiv.org
AI前沿：从图像生成到数学推理
30 Jun 2025
Contributed by Lukas
[LG] Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales[ETH Zürich & DisneyResearch|Studios]https://arxiv.org/abs/...
[感悟]你的下一个机会，藏在“无用”的角落里
29 Jun 2025
Contributed by Lukas
我们真正安身立命的核心竞争力，往往就藏在那些看起来最“没用”、最“务虚”的地方。
喂养AI的新姿势：不加料，只调序
29 Jun 2025
Contributed by Lukas
[CL] Data Efficacy for Language Model Training[Microsoft Research]arxiv.org
AI的“学霸秘籍”：原来功夫在“节奏”上
29 Jun 2025
Contributed by Lukas
[CL] Bridging Offline and Online Reinforcement Learning for LLMs[FAIR at Meta]arxiv.org
“听起来很美”的AI创意，为何一做就“翻车”？
29 Jun 2025
Contributed by Lukas
[LG] The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas[Stanford University]arxiv.org
AI的“懂”，是真的懂吗？
29 Jun 2025
Contributed by Lukas
[CL] Potemkin Understanding in Large Language Models[MIT & University of Chicago & Harvard University]arxiv.org
给 AI 一个“提示”，还是给它“上一课”？
29 Jun 2025
Contributed by Lukas
[CL] Can Gradient Descent Simulate Prompting?[MIT CSAIL]arxiv.org
AI前沿：从数学推理到模型优化
29 Jun 2025
Contributed by Lukas
[CL] OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling[Shanghai Jiao Tong University]https://arxiv.org/abs/2506.20512---[LG] Overt...
[感悟]这个时代最高级的“活法”
28 Jun 2025
Contributed by Lukas
这个时代对普通人最底层的要求，其实就是做一个“正常人”。
AI成长的秘密：如何拿捏“奖”与“罚”的尺度
28 Jun 2025
Contributed by Lukas
[LG] Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards [FAIR at Meta] arxiv.org
聪明的“调度员”：AI如何决定谁来干活？
28 Jun 2025
Contributed by Lukas
[LG] Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer [Courant Institute of Mathematical ...
AI的“情商”密码：它怎么学会既说真话，又不得罪你？
28 Jun 2025
Contributed by Lukas
[CL] Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs [Harvard University] arxiv.org
让AI自己造AI，这事儿靠谱吗？
28 Jun 2025
Contributed by Lukas
[LG] Language Modeling by Language Models [Allen Institute for AI] arxiv.org
你的“思考”方式，是唯一的吗？
28 Jun 2025
Contributed by Lukas
[CL] DiffuCoder：Understanding and Improving Masked Diffusion Models for Code Generation [Apple] arxiv.org
AI前沿：从梯度下降模拟提示到数据效能的革命
28 Jun 2025
Contributed by Lukas
[CL] Can Gradient Descent Simulate Prompting?[MIT CSAIL]https://arxiv.org/abs/2506.20989---[CL] Potemkin Understanding in Large Language Models[MIT &a...
[感悟]两把尺子，量出两种人生
27 Jun 2025
Contributed by Lukas
决定你人生品质的，不是世界怎么看你，而是你如何看待你自己的世界。
给大象绣花，换个针法就简单了？
27 Jun 2025
Contributed by Lukas
[LG] Orthogonal Finetuning Made Scalable[Max Planck Institute for Intelligent Systems & University of Cambridge]arxiv.org
AI公司的“裁员”秘密：谁是真正的骨干？
27 Jun 2025
Contributed by Lukas
[LG] Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units[University Medical Center Eppendorf &...
AI换脑术：如何让模型一键学会新本事？
27 Jun 2025
Contributed by Lukas
[LG] Command-V: Pasting LLM Behaviors via Activation Profiles[CMU]https://arxiv.org/abs/2506.19140
AI的秘密：打碎了还能认出来？
27 Jun 2025
Contributed by Lukas
[CL] Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizations[University of Washington]https://arxiv.org/abs/2506.19004
高手过招：接力赛为何胜过群英会？
27 Jun 2025
Contributed by Lukas
[LG] Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models[Northwestern University]https://arxiv.org/abs/2506.18945
AI前沿：从代码生成到自动化科研
27 Jun 2025
Contributed by Lukas
[CL] DiffuCoder：Understanding and Improving Masked Diffusion Models for Code Generation[Apple]https://arxiv.org/abs/2506.20639---[LG] Language Model...
[感悟]你的“监狱”有多大，世界就有多大
26 Jun 2025
Contributed by Lukas
这个世界上最大的“监狱”，不是用钢筋水泥建造的，而是我们自己思维的产物。
AI界的“好学生”：不仅会答题，还会写参考文献
26 Jun 2025
Contributed by Lukas
[CL] Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models[Duke University & Meta]https://arxiv.org/abs/2506.17585
聪明人的“笨功夫”，和“笨人”的聪明劲
26 Jun 2025
Contributed by Lukas
[LG] In-Context Learning Strategies Emerge Rationally[Stanford University & Harvard University]https://arxiv.org/abs/2506.17859
AI变聪明的秘密：请个好管家，而不是多招人
26 Jun 2025
Contributed by Lukas
[LG] Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection[Microsoft]https://arxiv.org/abs/2506.18145
AI变聪明的秘密：刷新“脑回路”，而不是扩容“硬盘”
26 Jun 2025
Contributed by Lukas
[LG] The 4th Dimension for Scaling Model Size[University of Illinois at Urbana-Champaign & University of Toronto]https://arxiv.org/abs/2506.18233
AI大模型：大力真能出奇迹吗？
26 Jun 2025
Contributed by Lukas
[LG] These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised Pretraining[Facebook AI Research (FAIR) at Meta & ...
[感悟]你的“倒霉”，是世界给你的“私教课”
25 Jun 2025
Contributed by Lukas
当你给出“新答案”的瞬间，你会发现，那道反复折磨你的题，就这么烟消云散了。
如何让AI“明辨是非”，而不是“投机取巧”？
25 Jun 2025
Contributed by Lukas
[LG] Robust Reward Modeling via Causal Rubrics[Google DeepMind]https://arxiv.org/abs/2506.16507
我们怎么知道，AI是真的“懂了”？
25 Jun 2025
Contributed by Lukas
[LG] Latent Concept Disentanglement in Transformer-based Language Models[Purdue University & University of Southern California]https://arxiv.org/a...
人多，真的力量大吗？
25 Jun 2025
Contributed by Lukas
[CL] When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework[University of Chicago & Together AI]https://arxiv.org...
给AI做“CT扫描”：一份来自科学家的操作手册
25 Jun 2025
Contributed by Lukas
[LG] On the Theoretical Understanding of Identifiable Sparse Autoencoders and Beyond[Peking University & MIT]https://arxiv.org/abs/2506.15963
养一个“AI神童”，原来是个精细活儿
25 Jun 2025
Contributed by Lukas
[CL] EvoLM: In Search of Lost Language Model Training Dynamics[Harvard & Stanford & EPFL]https://arxiv.org/abs/2506.16029
[感悟]放下“自证”：成年人最高级的自由
24 Jun 2025
Contributed by Lukas
真正的自由，是从不再解释开始。当你不再需要向世界证明什么时，你才真正开始拥有了自己的人生。
AI画画，怎样才能“又快又好”？
24 Jun 2025
Contributed by Lukas
[CV] Align Your Flow: Scaling Continuous-Time Flow Map Distillation [NVIDIA] https://arxiv.org/abs/2506.14603
AI 的“读心术”：我们如何才能信任一个“聪明的大脑”？
24 Jun 2025
Contributed by Lukas
[LG] Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders [Yale University & Shanghai Jiao Tong University] &nb...
你的AI在偷偷“修炼”：通往无穷的平坦大道
24 Jun 2025
Contributed by Lukas
[LG] Flat Channels to Infinity in Neural Loss Landscapes [EPFL & Flatiron Institute] https://arxiv.org/abs/2506.14951
从死记硬背到融会贯通，AI的“开窍”秘籍
24 Jun 2025
Contributed by Lukas
[LG] GrokAlign: Geometric Characterisation and Acceleration of Grokking [Rice University & Brown University] https://arxiv.org/abs/250...
[感悟]高手过招：如何砍掉内心的“全都要”？
23 Jun 2025
Contributed by Lukas
真正的自由，从来不是拥有无限的选择权。
AI育儿经：学霸是“刷题”刷出来的，还是“试错”试出来的？
23 Jun 2025
Contributed by Lukas
[CL] AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy [NVIDIA] https://arxiv.org/abs/2506.13284
AI黑箱里的一张新地图
23 Jun 2025
Contributed by Lukas
[LG] Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models [Huazhong University of Science and Technolog & UC Berkeley...
AI程序员“封神”？别急，先看看“奥赛冠军”的体检报告
23 Jun 2025
Contributed by Lukas
[LG] LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? [New York University & Princeton University] ...
顶尖高手过招，为何有时“笨办法”反而更有效？
23 Jun 2025
Contributed by Lukas
[LG] Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling [Max Planck Institute for Intelligent Systems] https...
[感悟]如何干掉那个叫“明天再说”的自己？
22 Jun 2025
Contributed by Lukas
你会发现，决定你五年、十年后成为一个什么样的人，根本不是你那些写在纸上、存在手机里的宏伟计划，而是你...
AI黑箱？不如跟它聊聊天
22 Jun 2025
Contributed by Lukas
[LG] Because we have LLMs, we Can and Should Pursue Agentic Interpretability[Google DeepMind]https://arxiv.org/abs/2506.12152
用嘴造工具：AI定制进入“白话时代”
22 Jun 2025
Contributed by Lukas
[LG] Text-to-LoRA: Instant Transformer Adaption[Sakana AI]https://arxiv.org/abs/2506.06105
AI绘画的秘密：创造力竟然源于“学不会”？
22 Jun 2025
Contributed by Lukas
[LG] On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity[CNRS]https://arxiv.org/abs/2506.03719
AI的记忆革命：如何只记重点，忘掉其它？
22 Jun 2025
Contributed by Lukas
[CL] Don't Pay Attention[Avey AI]https://arxiv.org/abs/2506.11305
[感悟]别再“收藏”了！把笔记变成一台思想印钞机！
21 Jun 2025
Contributed by Lukas
真正的知识管理，不是收藏，而是创造。
让AI自己给自己“立规矩”，结果会怎样？
21 Jun 2025
Contributed by Lukas
[LG] AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning[CMU]https://arxiv.org/abs/2506.15651
你的“土办法”过时了，AI正在打造“策略工具箱”
21 Jun 2025
Contributed by Lukas
[LG] HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges[Microsoft Research Asia]https://arxiv.org/abs/2506.15196
AI大脑里的“万金油”神经元，是臭虫还是宝贝？
21 Jun 2025
Contributed by Lukas
[LG] Dense SAE Latents Are Features, Not Bugs[MIT & ETH Zürich]https://arxiv.org/abs/2506.156
AI界的“调参玄学”：一个被遗忘的旋钮
21 Jun 2025
Contributed by Lukas
[LG] Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size[UC Berkeley & Microsoft Research]https://arxiv.org/abs/2506.15025
AI的“读心术”：模型的大脑会出卖它的秘密吗？
21 Jun 2025
Contributed by Lukas
[CL] Approximating Language Model Training Data from Weights[Cornell University]https://arxiv.org/abs/2506.155
[感悟]如何成为一个高明的“刻意选择者”？
20 Jun 2025
Contributed by Lukas
不被外界的噪音裹挟，不被内心的惯性驱动，清清楚楚地知道自己要什么，不要什么，然后全力以赴，成为一个清...
AI的“强迫症”：为什么你一句话没说完，它就“疯了”？
19 Jun 2025
Contributed by Lukas
[CL] Sampling from Your Language Model One Byte at a Time[University of Washington]https://arxiv.org/abs/2506.14123
为什么“抓重点”让AI学得更快？
19 Jun 2025
Contributed by Lukas
[LG] Transformers Learn Faster with Semantic Focus[IBM Research]https://arxiv.org/abs/2506.14095
AI的“识字”革命：我们离“读懂”世界又近了一步？
19 Jun 2025
Contributed by Lukas
[CL] From Bytes to Ideas: Language Modeling with Autoregressive U-Nets[FAIR at Meta]https://arxiv.org/abs/2506.14761
AI的“选择困难症”：通往更高智慧的秘密通道
19 Jun 2025
Contributed by Lukas
[CL] Reasoning with Exploration: An Entropy Perspective[RUC & MSRA & SJTU]https://arxiv.org/abs/2506.14758
AI训练的“七分饱”智慧
19 Jun 2025
Contributed by Lukas
[LG] Less is More: Undertraining Experts Improves Model Upcycling[Université de Montréal & Concordia University]https://arxiv.org/abs/2506.14126
[感悟]你的效率，藏在截止日期里
19 Jun 2025
Contributed by Lukas
工作量会像气体一样膨胀填满给定的时间容器，因此主动设定紧迫的截止日期能激发最高效率，让你成为时间的主...
为什么你总觉得，没人能真正懂你？
18 Jun 2025
Contributed by Lukas
[LG] Wanting to Be Understood Explains the Meta-Problem of Consciousness[Google DeepMind]https://arxiv.org/abs/2506.12086
AI的“分身术”：为什么你的万能助手不会再轻易“忘事”了？
18 Jun 2025
Contributed by Lukas
[CL] Multipole Attention for Efficient Long Context Reasoning[UC Berkeley]https://arxiv.org/abs/2506.13059