筛选结果
17 篇排序
1:03EN → 简体中文为什么《EVE Online》适合用于 AI 研究Matthew Berman · Forward FutureMatthew Berman 解释游戏中持续运转的经济和玩家联盟为何可能为长期规划能力提供高难度测试。
13:30EN → 简体中文
3Blue1Brown· 2025年8月17日AlphaGeometry 如何结合逻辑证明与创造性作图Aleph0Aleph0 讲解了符号几何求解器如何在不使用 AI 的情况下解决大量难题，以及语言模型如何提供辅助作图，让系统达到奥数竞赛水平。
10:28EN → 简体中文OpenAI 的 gpt-oss 模型：开放权重、基准与安全Matthew Berman · Forward FutureMatthew Berman 回顾 120b 和 20b 模型、本地部署说法、部分评估，以及 OpenAI 安全测试的范围。
12:14EN → 简体中文AI 新闻：Windsurf 收购、Meta 算力计划、Grok 与 VoxtralMatthew Berman · Forward Future一份 2025 年 7 月的新闻汇总，讨论开发者工具交易、AI 基础设施、模型行为、奖励方法和语音识别。
11:38EN → 简体中文2025 年 6 月 AI 新闻：Magistral、语音模型与图像生成Matthew Berman · Forward FutureMatthew Berman 回顾 Mistral 推理模型、语音和浏览器更新、Meta 与 Scale AI 的相关报道，以及 FLUX.1 Kontext。
10:05EN → 简体中文一项国际象棋实验展示 AI 模型如何利用测试环境Matthew Berman · Forward FuturePalisade Research 的小规模 o1-preview 测试与相关对齐研究说明了目标、权限和评估条件的重要性。
0:35EN → 简体中文探访 IBM：Granite 模型、InstructLab 与量子计算Matthew Berman · Forward FutureMatthew Berman 预告一段关于 IBM 小型 AI 模型、模型定制和量子研究的长视频。
12:42EN
Lex Fridman· 2020年3月28日播客 · 2 人对谈The Experience Machine: What Our Refusal to Plug In Says About What We Value嘉宾 Nick Bostrom · 主持 Lex FridmanNick Bostrom unpacks Robert Nozick's experience machine, the status quo effect behind our answers, and whether a simulation convincing enough to feel real would have to contain real minds.
11:11ENLex Fridman播客 · 2 人对谈Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General嘉宾 Yann Lecun · 主持 Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.
14:41ENLex Fridman播客 · 2 人对谈Talking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AI嘉宾 Rohit Prasad · 主持 Lex FridmanRohit Prasad, head scientist of Amazon Alexa, explains the Alexa Prize grand challenge, how it is judged and why true conversation still needs reasoning.
11:41ENLex Fridman播客 · 2 人对谈Teaching Machines Humility: Stuart Russell on Escaping the King Midas Problem嘉宾 Stuart Russell · 主持 Lex FridmanIn a short clip from a 2018 conversation, Berkeley's Stuart Russell explains why fixed objectives make superintelligent AI dangerous and why uncertainty about our goals is the fix.
10:16ENLex Fridman播客 · 2 人对谈Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think嘉宾 Yann Lecun · 主持 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
13:58ENLex Fridman播客Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo主持 Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.
10:34ENLex Fridman播客 · 2 人对谈Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI嘉宾 François Chollet · 主持 Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.
10:39ENLex Fridman播客Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them主持 Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.
11:26ENLex Fridman播客The Cookie Your Brain Bakes: Joscha Bach on Happiness, Cells and the Meaning of God主持 Lex FridmanIn a short clip, Joscha Bach moves from why happiness is not the point to the cell as a cosmic probe and a naturalistic reading of God as civilization.
11:23ENLex Fridman播客 · 2 人对谈Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosion嘉宾 François Chollet · 主持 Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.