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108개정렬
52:06ENYann LeCun on World Models, Open AI and the Path to Advanced IntelligenceMatthew Berman, Yann Lecun, Lex Fridman · Forward FutureMatthew Berman reacts to excerpts from LeCun’s 2024 conversation with Lex Fridman, covering language models, JEPA, robotics, AI safety and economic incentives.
2:47:17ENIf You Want Human-Level AI, Abandon Generative Models게스트 Yann Lecun · 진행자 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.
35:22ENMatthew Berman’s Ten AI Predictions for 2024Matthew Berman, Mark Zuckerberg, Lex Fridman, Jaron Lanier, Joon Park, Elon Musk · Forward FutureAn early-2024 forecast covers model releases, robotics, agents, synthetic data and the risks of increasingly convincing bots.
2:16:27ENSuperhuman Go Has a Blind Spot. Balaji Bets on a War of the Gods진행자 Nathan LabenzBalaji Srinivasan tells Nathan Labenz why adversarial inputs, missing actuators, and cryptography make a single runaway AI god less likely than a contest among many that still need priests.
33:23ENAI Leaders Debate Open Models, Pricing and the Future of Search게스트 Emad Mostaque, Des Traynor, Jeff Seibert, Yann Lecun, Richard Socher, Cris Valenzuela, Douwe Kiela, Tomasz Tunguz, Miles Grimshaw, Christian Lanng · 진행자 Harry StebbingsA compilation of separate interviews brings together competing 2023 predictions about model economics, assistants and the largest technology companies.
32:57ENA 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AI진행자 Sarah Guo, Elad GilArthur Mensch tells No Priors why Chinchilla made small models valuable, why open-sourcing Mistral 7B is not a bioweapon risk, and why guardrails belong on the application.
1:14:36ENZulu Bypasses GPT-4, and a Hundred Examples Undo the Guardrails진행자 Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.
1:04:38ENHundreds of Miles Apart, Zuckerberg and Fridman Sat Close Enough to See the Wrinkles게스트 Mark Zuckerberg · 진행자 Lex FridmanLex Fridman sat in a dark room in Austin wearing a Quest headset; Mark Zuckerberg sat in another conference room, also wearing one, and it did not feel like a video call.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5진행자 Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
52:48ENOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the List진행자 Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.
59:36ENPascal Pfeiffer on Retrieval, Open Models, Kaggle and Fine-Tuning with H2O LLM StudioMatthew Berman, Pascal Pfeiffer · Forward FutureIn a 2023 interview, H2O.ai’s Pascal Pfeiffer explains document retrieval and parameter-efficient fine-tuning, reflects on Kaggle competitions and demonstrates LLM Studio. He and Matthew Berman also consider coding assistants and unverified rumors about model architecture.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Product진행자 Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:41:58ENZuckerberg showed up as Mark Elliott — then argued for many AIs, not one게스트 Mark Zuckerberg · 진행자 Lex FridmanOn his second Lex Fridman podcast, the Meta CEO treats a disguised first jiu-jitsu tournament, an open research model called LLaMA, and a future of many agents as the same bet: ship before it is perfect, and do not build a single god model.
1:20:49ENA Wafer-Scale Chip So AI Stops Waiting on Amdahl진행자 Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.
1:11:11ENEmad Mostaque on Open AI Models, National Data and the Race to Deploy게스트 Emad Mostaque · 진행자 Harry StebbingsThe Stability AI co-founder describes his 2023 vision for open models, healthcare applications and AI businesses, alongside concerns about a speculative boom.
1:06:02ENYann LeCun on Open AI Models, Safety and the Future of Research게스트 Yann Lecun · 진행자 Harry StebbingsMeta’s chief AI scientist discusses language models, research priorities and his contested views on AI risk and employment.
1:13:52ENAndriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.
40:25ENA 6-Billion-Parameter Clone of ChatGPT’s Core Trick진행자 Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.진행자 Nathan LabenzJunnan Li and Dongxu Li tell Nathan Labenz how BLIP cleaned noisy web captions, and how BLIP-2 connected a frozen vision model to a frozen language model in under ten days on one A100.
53:37EN30 models, 42 scenarios, 7 metrics: Percy Liang's public checkup for language models진행자 Sarah Guo, Elad GilA PubMed-only model once led the U.S. medical licensing exam — until a system roughly 200 times larger overtook it.
37:26ENWithout Open Source, Machine Learning Would Be Decades Behind진행자 Sarah Guo, Elad GilHugging Face CEO Clem Delangue on the Tamagotchi pivot, why the company never hired a community manager, and why he does not believe one model will rule them all.
45:03ENEmad Mostaque’s bet: open small models, not one giant brain진행자 Sarah Guo, Elad GilStability AI’s founder tells No Priors why foundation models should be public infrastructure, why small models can beat giant ones, and what that means for media, medicine, and AGI.
40:00ENThe Winds of Change: How Mistral Plans to Win the Open-Model Era진행자 Jacob EffronArthur Mensch explains Mistral's open-source strategy, enterprise distribution, model research, and views on AI regulation and sovereignty.
1:12:15ENThe Open-Model Vibe Check: China, Containment, and the Data Moat진행자 Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
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