필터 결과
198개정렬
1:08:02EN
Sequoia Capital· 2026년 10월 13일팟캐스트AlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.
56:31EN
Unsupervised Learning: With Jacob Effron· 2026년 10월 6일팟캐스트Post-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab진행자 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
38:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026년 9월 18일팟캐스트 · 2명Diffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Silicon진행자 Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
1:56:40EN
Cognitive Revolution "How AI Changes Everything"· 2026년 8월 8일팟캐스트The Periodic Table Was Already in the Weights진행자 Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.
47:22ENSequoia Capital· 2026년 8월 4일팟캐스트From One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.
49:12ENSequoia Capital· 2026년 7월 29일팟캐스트The People Writing the Transformer’s Eulogy Want a Lab That Can Take Vacation Without Themo1’s reasoning lead and a Gemini pre-training lead argue the architecture, not more scale, is now the bottleneck — and that replacing it starts with automating kernel generation.
2:06:41ENCognitive Revolution "How AI Changes Everything"· 2026년 7월 9일팟캐스트Zero the J-Space and a Model's Hidden Goal Lights Up on Token One진행자 Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.
50:57ENUnsupervised 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.
1:47:38ENCognitive Revolution "How AI Changes Everything"· 2026년 7월 4일팟캐스트19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip진행자 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
2:59:44ENCognitive Revolution "How AI Changes Everything"· 2026년 6월 3일팟캐스트Two Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.진행자 Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
1:13:33ENUnsupervised 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.
59:41ENUnsupervised Learning: With Jacob Effron· 2026년 5월 22일팟캐스트The GPT moment for video hasn’t arrived, but AGI may already be here진행자 Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.
1:21:56ENUnsupervised 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.
1:48:43ENCognitive Revolution "How AI Changes Everything"· 2026년 5월 1일팟캐스트The reward jumped from 3 to 180. Every Hacker News title was a Google layoff.진행자 Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.
58:46ENUnsupervised 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.
1:49:53ENCognitive Revolution "How AI Changes Everything"· 2026년 3월 5일팟캐스트Don't Fight Backprop: Goodfire's Bet on Designing What Models Learn진행자 Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.
1:34:29ENCognitive Revolution "How AI Changes Everything"· 2026년 2월 18일팟캐스트Three IMO Golds Missed the Same Problem. Harmonic Wants Math to Compile진행자 Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
1:04:51ENCognitive Revolution "How AI Changes Everything"· 2026년 2월 1일팟캐스트125 million cells took a decade. The next billion takes months진행자 Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.
4:25:13EN
Lex Fridman· 2026년 1월 31일팟캐스트The Architecture Barely Changed. Data, Cost, and Open Weights Did.진행자 Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.
1:03:00ENUnsupervised 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.
2:25:10ENCognitive Revolution "How AI Changes Everything"· 2026년 1월 22일팟캐스트Fine-Tuning Isn't Dead — But It Turned a Helpful Model Evil진행자 Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.
1:01:38ENSequoia Capital· 2026년 1월 6일팟캐스트13-Hour Coffee and 2x Throughput: Physical Intelligence Crosses the Deployment LineKarol Hausman and Tobi Springenberg tell Sequoia’s Training Data how a general robot brain, plus real-world RL, turned coffee, laundry, and chocolate boxes into jobs that actually run.
41:36ENSequoia Capital· 2025년 11월 18일팟캐스트Four Months to Drive Tokyo: How Wayve Bet the Company on One End-to-End NetworkWayve CEO Alex Kendall tells Sequoia’s Training Data why AV 2.0 ditches HD maps and hand-written stacks for a foundation model that can adapt to a new car and a new country in weeks.
1:55:12ENCognitive Revolution "How AI Changes Everything"· 2025년 11월 8일팟캐스트28,000 Documents, a 3B Model, 22 People: The Technical Ledger of Catholic AI진행자 Nathan LabenzLongbeard CEO Matthew Harvey Sanders tells The Cognitive Revolution how a mission-driven team is aligning models to the Catholic magisterium — and why fine-tuning was not enough.더 보기198개 중 24개 표시