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슬라이드 3장13:34EN
Matthew Berman· 2024년 9월 23일Sam Altman’s “Intelligence Age”: Superintelligence, Scaling and WorkMatthew Berman · Forward FutureMatthew Berman reads Altman’s 2024 essay and discusses its predictions for AI assistants, education, infrastructure and jobs.AGI(범용 인공지능)Scaling Laws에이전트+1
1:18:32EN
Cognitive Revolution "How AI Changes Everything"· 2024년 9월 20일팟캐스트A DNA Model That Can Tell Which Genes You Cannot Touch진행자 Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.모델과 학습AI for scienceComputational Biology+3
49:14EN
Sequoia Capital· 2024년 9월 17일팟캐스트Humanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.에이전트RoboticsScaling Laws+3
44:16EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 9월 5일팟캐스트 · 2명A perfect demo is ten years from a product you can pay for진행자 Sarah Guo, Elad GilAndrej Karpathy uses Waymo's decade-long gap, Tesla's software bet, and a new AI course to argue that autonomous intelligence will stall at product and globalization, not at the first impressive clip.기업과 전략Transformers & attentionScaling Laws+3
1:27:02ZH
Zhang Xiaojun Podcast· 2024년 9월 5일팟캐스트 · 3명广密:Self-play RL 可能开启 AGI 的新 Scaling Law게스트 广密 · 진행자 张小珺广密认为,纯靠语言预训练扩展模型的边际收益可能正在减弱,self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域,将决定下一阶段的 AGI 竞赛。Reinforcement LearningAGI(범용 인공지능)Scaling Laws+2
1:23:57EN
Cognitive Revolution "How AI Changes Everything"· 2024년 9월 4일팟캐스트A Training Run Is a Particle Collider—and the China Contest Is a Fifty-Year Race진행자 Nathan LabenzSamo Burja tells Nathan Labenz why ChatGPT's virality may have slowed AI science, and why Washington should treat Beijing as an economic rival rather than a military one.기업과 전략GeopoliticsAI Strategy+3
50:20EN
20VC with Harry Stebbings· 2024년 8월 28일팟캐스트 · 2명Arvind Narayanan on AI Scaling, Real-World Risks and the Limits of Model Benchmarks게스트 Arvind Narayanan · 진행자 Harry StebbingsThe Princeton professor challenges simple claims about scaling, AGI timelines and AI adoption while discussing deepfakes, healthcare, education and security.모델과 학습Scaling LawsAI Safety+2
42:42EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 8월 22일팟캐스트 · 2명Ethereum Once Held Two Thousand GPT-3 Clusters of GPUs진행자 Sarah Guo, Elad GilFoundry CEO Jared Quincy Davis on idle H100s, why today's AI cloud is still colocation, and how compound systems move work off giant interconnected clusters.인프라와 배포AI cost & performanceDistributed training & inference+3
1:33:07ZH
Zhang Xiaojun Podcast· 2024년 8월 17일팟캐스트 · 2명广密复盘全球大模型这一年:Scaling Law、AGI 豪赌与中美格局게스트 李广密 · 진행자 张小珺拾象创始人李广密从硅谷一线观察出发,回顾 OpenAI、Anthropic、Google 与中国模型公司的竞争。他认为,模型能力与成本共同决定新一轮应用爆发,而巨额投入押注的是智能能否持续扩展以及 AI 对经济和产业的长期重构。모델과 학습Scaling LawsAI research+2
46:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 8월 1일팟캐스트 · 2명Oriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numbered진행자 Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.모델과 학습AGI TimelinesPretraining & midtraining+3
42:08EN
Sequoia Capital· 2024년 7월 30일팟캐스트Meta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.오픈소스와 커뮤니티Open-weight modelsDistillation+3
1:00:28EN
Sequoia Capital· 2024년 7월 9일팟캐스트Scaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.기업과 전략Scaling LawsInference+3
55:13EN
Sequoia Capital· 2024년 7월 2일팟캐스트The Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.평가와 관측 가능성BenchmarksAGI Timelines+3
1:01:55EN
Cognitive Revolution "How AI Changes Everything"· 2024년 6월 1일팟캐스트A $9 AI nurse, an 86 on the medical boards, and a 10% extinction line진행자 Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.기업과 전략AI StrategyScaling Laws+3
29:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 5월 9일팟캐스트 · 2명The Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plant진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.인프라와 배포AI cost & performance컨텍스트 엔지니어링+3
31:24EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 4월 25일팟캐스트 · 2명Sora's Leads: A Minute of HD Video Is Just the GPT-1 of Video Models진행자 Sarah Guo, Elad GilAditya Ramesh, Tim Brooks, and Bill Peebles tell No Priors why text-to-video sits on the path to AGI, why they started from scratch instead of extending an image generator, and why they still have no product date.멀티모달과 미디어비전과 영상World models+3
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 2024년 4월 13일팟캐스트GPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?진행자 Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.모델과 학습Scaling LawsSynthetic data+3
슬라이드 5장14:12EN
Matthew Berman· 2024년 3월 26일Matthew Berman Reviews Sora’s Early Filmmaker ExperimentsMatthew Berman · Forward FutureA look at early Sora-generated films, surreal creatures and the questions they raise about creative work, compute and video simulation.Generative media비전과 영상Creativity+2
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 1월 25일팟캐스트 · 2명99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses First진행자 Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.모델과 학습RoboticsFoundation Models+3
32:57EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 11월 9일팟캐스트 · 2명A 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.오픈소스와 커뮤니티Open-source ecosystemsOpen-weight models+3
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 11월 2일팟캐스트 · 2명Reliability is the bottleneck. Autonomy is what would make AI feel alive.진행자 Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.모델과 학습AGI(범용 인공지능)Scaling Laws+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 2023년 10월 18일팟캐스트1,000 H100s Take 115 Days; 22,000 Take Five진행자 Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.모델과 학습AI researchModel Interpretability+3
30:03EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 9월 7일팟캐스트 · 2명A 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.인프라와 배포InferenceAI cost & performance+3
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023년 7월 6일팟캐스트The AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.모델과 학습머신러닝 기초Scaling Laws+3
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