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50:57EN
Unsupervised Learning: With Jacob Effron· 2026년 7월 9일팟캐스트A Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen Schmidhuber진행자 Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.모델과 학습AGI(범용 인공지능)AI research+3
1:13:33EN
Unsupervised Learning: With Jacob Effron· 2026년 6월 3일팟캐스트A Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss진행자 Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.모델과 학습AI research코딩 에이전트+3
1:21:56EN
Unsupervised Learning: With Jacob Effron· 2026년 5월 15일팟캐스트Yann LeCun Left Meta Because LLMs Will Not Get You to Intelligence진행자 Jacob EffronThe Turing Award winner tells Jacob Effron why he spun AMI Labs out of Meta, why JEPA-style world models beat pixel generators, and why he thinks Hinton changed his mind in 2023.모델과 학습World modelsAI research+3
58:46EN
Unsupervised Learning: With Jacob Effron· 2026년 4월 9일팟캐스트Two Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028진행자 Jacob EffronJakub Pachocki tells Jacob Effron why those timelines still hold, why continual learning is not a detour, and why showing chain of thought in the product would train the monitor away.모델과 학습Continual learningReinforcement Learning+3
1:03:00EN
Unsupervised Learning: With Jacob Effron· 2026년 1월 29일팟캐스트A Model That Solves New Math Problems Every Week Still Isn't AGI진행자 Jacob EffronFormer OpenAI research VP Jerry Tworek on why scaling still works, why models go hopeless when they fail, and why he left to chase continual learning.모델과 학습Reinforcement LearningAGI Timelines+3
1:18:05EN
Unsupervised Learning: With Jacob Effron· 2025년 12월 18일팟캐스트The Real Bottleneck in AI Research Is Compute, Not Ideas진행자 Jacob EffronAfter NeurIPS, Ari Morcos and Rob Toews sit down with Jacob Effron to argue about plateauing models, SSI’s secrecy, OpenAI’s cash burn, and a set of spicy 2026 bets.기업과 전략AI StrategyScaling Laws+3
57:31EN
Unsupervised Learning: With Jacob Effron· 2025년 5월 22일팟캐스트The New Ruler for Models Is How Long They Can Work진행자 Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025년 3월 17일팟캐스트A Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped진행자 Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.모델과 학습Reasoning & test-time computeScaling Laws+3
1:06:48EN
Unsupervised Learning: With Jacob Effron· 2024년 10월 3일팟캐스트o1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point진행자 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.모델과 학습Reasoning & test-time computeAI research+3
1:10:19EN
Unsupervised Learning: With Jacob Effron팟캐스트The Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers진행자 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.모델과 학습RoboticsAI research+3
58:19EN
Unsupervised Learning: With Jacob Effron팟캐스트Runway's Chris Valenzuela on Why Taste Beats Tools in Generative Media진행자 Jacob EffronChris Valenzuela, CEO of Runway, explains why generative media is still early, how taste and iteration shape the work, and what has to change before AI media becomes a new art form.멀티모달과 미디어Generative media비전과 영상+3
57:25EN
Unsupervised Learning: With Jacob Effron팟캐스트From RAG to Reasoning: Why the Future of AI Is a System, Not a Model진행자 Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.모델과 학습Reasoning & test-time computePost-training+3