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Zhang Xiaojun Podcast· 15 oct 2026Pódcast · 2 vocesLindon Gao on Building Dyna Robotics and Finding a Path Through the FogInvitado: Lindon Gao · Presenta: 张小珺Dyna Robotics CEO Lindon Gao explains why his company builds intelligence through real workflows, how hardware and research must evolve together, and how a founder can find clarity by letting go of the need to be right.
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Sequoia Capital· 13 oct 2026PódcastAlphaGo 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.
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Unsupervised Learning: With Jacob Effron· 6 oct 2026PódcastPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabPresenta: 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.
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No Priors: AI, Machine Learning, Tech, & Startups· 18 sept 2026Pódcast · 2 vocesDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconPresenta: 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.
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Cognitive Revolution "How AI Changes Everything"· 8 ago 2026PódcastThe Periodic Table Was Already in the WeightsPresenta: 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· 4 ago 2026PódcastFrom 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· 29 jul 2026PódcastThe 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.
2:59:03ENZhang Xiaojun Podcast· 28 jul 2026Pódcast · 2 vocesYou Kaichao on How vLLM Grew from Open-Source Project to AI Infrastructure CompanyInvitado: You Kaichao · Presenta: 张小珺Inferact co-founder You Kaichao traces vLLM’s path from Berkeley research to a company, then explains why model, inference, hardware and agent design increasingly have to move together.
3:46:12ENZhang Xiaojun Podcast· 16 jul 2026Pódcast · 2 vocesKay Ke on Physical Intelligence, Robot Learning, and the Future of RoboticsInvitado: Kay Ke · Presenta: 张小珺Physical Intelligence researcher Kay Ke traces the field's research lineages and explains Pi's progression from π₀ to π*₀.₆. She also reflects on education, fiction, ambition, and what it means to build robots that work beyond a demo.
2:06:41ENCognitive Revolution "How AI Changes Everything"· 9 jul 2026PódcastZero the J-Space and a Model's Hidden Goal Lights Up on Token OnePresenta: 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· 9 jul 2026PódcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberPresenta: 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"· 4 jul 2026Pódcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipPresenta: 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"· 3 jun 2026PódcastTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Presenta: 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· 3 jun 2026PódcastA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissPresenta: 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· 22 may 2026PódcastThe GPT moment for video hasn’t arrived, but AGI may already be herePresenta: 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· 15 may 2026PódcastYann LeCun Left Meta Because LLMs Will Not Get You to IntelligencePresenta: 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"· 1 may 2026PódcastThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.Presenta: 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· 9 abr 2026PódcastTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028Presenta: 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"· 5 mar 2026PódcastDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnPresenta: 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"· 18 feb 2026PódcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompilePresenta: 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"· 1 feb 2026Pódcast125 million cells took a decade. The next billion takes monthsPresenta: 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.
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Lex Fridman· 31 ene 2026PódcastThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Presenta: 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· 29 ene 2026PódcastA Model That Solves New Math Problems Every Week Still Isn't AGIPresenta: 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"· 22 ene 2026PódcastFine-Tuning Isn't Dead — But It Turned a Helpful Model EvilPresenta: 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.Cargar másMostrando 24 de 182