筛选结果
90 篇排序
4:25:13EN → 简体中文架构几乎没变,变的是数据、成本和开放权重主持 Lex FridmanNathan Lambert 和 Sebastian Raschka 带 Lex Fridman 回顾了这一年:训练方法和数据集的变化,远大于模型设计的变化。展望 2026 年,争论的焦点似乎是推理成本、智能体,以及真正开放的模型该归谁。
5:15:00EN → 简体中文Dario Amodei:这部规模化电影他已经看过太多遍嘉宾 Dario Amodei · 主持 Lex FridmanAnthropic 首席执行官给前沿模型的训练成本和 AGI 算了账,随后把话筒交给塑造 Claude 性格、并研究其权重内部结构的人。
2:47:17ENIf You Want Human-Level AI, Abandon Generative Models嘉宾 Yann Lecun · 主持 Lex FridmanYann LeCun tells Lex Fridman why autoregressive LLMs cannot plan, remember, or understand the physical world — and why open source is the only democratic answer.
4:14:33EN → 简体中文下一个 Token 算不上证明:Stephen Wolfram 谈 ChatGPT 与计算性真理嘉宾 Stephen Wolfram · 主持 Lex Fridman这是 Stephen Wolfram 第四次与 Lex Fridman 对谈。他先把大语言模型与 Wolfram|Alpha 作对比,然后沿着计算不可约性的思路,从 ChatGPT 讲到观察者、热力学,以及二十世纪物理学的三大理论。
3:28:47ENKarpathy Calls Trained Neural Nets Complicated Alien Artifacts嘉宾 Andrej Karpathy · 主持 Lex FridmanOn Lex Fridman's podcast, Andrej Karpathy refuses the brain analogy, walks the Fermi paradox through Nick Lane's chemistry, and treats the Transformer as a general-purpose differentiable computer.
2:10:08ENGato Is Only the Beginning嘉宾 Oriol Vinyals · 主持 Lex FridmanDeepMind's Oriol Vinyals talks with Lex Fridman about generalist agents, thrown-away weights, and whether human-level AGI arrives in his lifetime.
2:10:39EN → 简体中文Demis Hassabis:先解决智能,再解决其他一切嘉宾 Demis Hassabis · 主持 Lex FridmanDeepMind 首席执行官告诉 Lex Fridman:图灵测试为什么是一份出错的考卷,AlphaFold 如何改写了生物学,以及射电天空的寂静为何暗示我们或许是孤独的。
2:45:10ENA Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.嘉宾 Yann Lecun · 主持 Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.
2:31:57EN → 简体中文Jay McClelland 谈思维如何从神经网络中涌现嘉宾 Jay McClelland · 主持 Lex Fridman这位斯坦福大学的认知科学家,与 Lex Fridman 聊了联结主义(connectionism)、反向传播(backpropagation)、David Rumelhart,以及为什么直觉会先发现某些东西,之后逻辑才去证明它。
2:51:33ENThe Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's Silence嘉宾 Wojciech Zaremba · 主持 Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.
2:18:29ENYour Cortex Is 150,000 Brains Voting on a Coffee Cup嘉宾 Jeff Hawkins · 主持 Lex FridmanJeff Hawkins tells Lex Fridman that intelligence is learning a model of the world, the neocortex is tens of thousands of independent maps that vote, and the fastest path to machine intelligence is reverse-engineering that sheet.
2:30:30ENThe Fallacy of the Successful First Step嘉宾 Ishan Misra · 主持 Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
1:56:17ENIf You Reran Earth a Million Times, Would Anything Like Us Appear?嘉宾 Risto Miikkulainen · 主持 Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.
2:01:07ENMichael Littman isn’t waiting for a runaway god. He’s watching us.嘉宾 Michael Littman · 主持 Lex FridmanThe Brown reinforcement-learning researcher tells Lex Fridman why accidental superintelligence feels like a 747 assembling itself — and why social media, rote language, and theory of mind worry him more.
2:10:06ENYou Might Have to Understand the World to Understand the Letter A嘉宾 Dileep George · 主持 Lex FridmanDileep George on recursive cortical networks, why a five-year-old can read any new CAPTCHA, and what the Wright brothers actually learned from birds.
1:41:37ENThe Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't Solved嘉宾 Jitendra Malik · 主持 Lex FridmanBerkeley's Jitendra Malik tells Lex Fridman why computer vision keeps underestimating itself, why full self-driving still needs cognition, and why vision is the parent of language.
1:37:31ENThe Robot That Brought the Beer Was Being Teleoperated嘉宾 Sergey Levine · 主持 Lex FridmanSergey Levine tells Lex Fridman why hardware is almost closed, why common sense has to be lived, and why offline reinforcement learning is the missing on-ramp from logs to robots that keep getting better.
2:00:32ENSlow Synapses Grow Fast Learners — Warmth Is the Harder Problem嘉宾 Matt Botvinick · 主持 Lex FridmanDeepMind neuroscience director Matt Botvinick tells Lex Fridman how prefrontal cortex, meta-reinforcement learning, and dopamine research meet the human question of what a better life with AI would actually look like.
3:00:18EN → 简体中文只有模拟才可能有意识嘉宾 Joscha Bach · 主持 Lex Fridman约沙·巴赫(Joscha Bach)对 Lex Fridman 讲,为什么一个神经元什么也感觉不到,为什么存在是默认状态,以及为什么这个正在走向消亡的世界,仍然是这场游戏里最好的一关。
1:37:28ENWe Are Still Massively Underestimating Deep Learning嘉宾 Ilya Sutskever · 主持 Lex FridmanIlya Sutskever tells Lex Fridman why 2012 was about conviction, why bigger models can get worse before they get better, and what he would do with an AGI.
1:48:01ENDavid Silver: AlphaGo Won by Taking Human Knowledge Out嘉宾 David Silver · 主持 Lex FridmanThe DeepMind researcher behind AlphaGo and AlphaZero tells Lex Fridman how an unclaimed million-dollar Go prize, a BBC Micro at age seven, and a honeymoon insight led to superhuman self-play.
1:12:48ENJohn Hopfield's Physics of Mind: Glitches Become Features嘉宾 John Hopfield · 主持 Lex FridmanThe Princeton physicist walks Lex Fridman from a wobbly London bridge to Hopfield networks, consciousness-as-story, and why his famous model still does not explain learning.
1:44:55ENIf You Need 60,000 Digits, You Have Not Found Intelligence嘉宾 Vladimir Vapnik · 主持 Lex FridmanVladimir Vapnik tells Lex Fridman that intelligence is the art of shrinking a sea of functions with a few good predicates — and that MNIST, solved with sixty examples per class, is the test.
49:53ENGilbert Strang still teaches with thick chalk and four subspaces嘉宾 Gilbert Strang · 主持 Lex FridmanThe MIT linear-algebra legend talks with Lex Fridman about OpenCourseWare, singular values, neural nets as a million folds, and why calculus hogged the undergraduate timetable.
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