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56:31EN
Unsupervised Learning: With Jacob Effron· Oct 6, 2026PodcastPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabHost 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.Models & trainingPost-trainingReinforcement Learning+3
1:56:40EN
Cognitive Revolution "How AI Changes Everything"· Aug 8, 2026PodcastThe Periodic Table Was Already in the WeightsHost 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.Models & trainingModel InterpretabilityAgent engineering+3
2:06:41EN
Cognitive Revolution "How AI Changes Everything"· Jul 9, 2026PodcastZero the J-Space and a Model's Hidden Goal Lights Up on Token OneHost 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.Models & trainingModel InterpretabilitySafety & governance+3
50:57EN
Unsupervised Learning: With Jacob Effron· Jul 9, 2026PodcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberHost 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.Models & trainingAGIAI research+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· Jul 4, 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipHost 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.Models & trainingLocal inferenceScaling Laws+3
2:59:44EN
Cognitive Revolution "How AI Changes Everything"· Jun 3, 2026PodcastTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Host 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.Models & trainingContinual learningTransformers & attention+3
1:13:33EN
Unsupervised Learning: With Jacob Effron· Jun 3, 2026PodcastA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissHost 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.Models & trainingAI researchCoding agents+3
59:41EN
Unsupervised Learning: With Jacob Effron· May 22, 2026PodcastThe GPT moment for video hasn’t arrived, but AGI may already be hereHost 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.Models & trainingWorld modelsReinforcement Learning+3
1:21:56EN
Unsupervised Learning: With Jacob Effron· May 15, 2026PodcastYann LeCun Left Meta Because LLMs Will Not Get You to IntelligenceHost 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.Models & trainingWorld modelsAI research+3
1:48:43EN
Cognitive Revolution "How AI Changes Everything"· May 1, 2026PodcastThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.Host 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.Models & trainingReinforcement LearningPost-training+3
58:46EN
Unsupervised Learning: With Jacob Effron· Apr 9, 2026PodcastTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028Host 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.Models & trainingContinual learningReinforcement Learning+3
1:49:53EN
Cognitive Revolution "How AI Changes Everything"· Mar 5, 2026PodcastDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnHost 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.Models & trainingModel InterpretabilitySafety & governance+3
1:34:29EN
Cognitive Revolution "How AI Changes Everything"· Feb 18, 2026PodcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompileHost 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.Models & trainingReasoning & test-time computeAI research+3
1:04:51EN
Cognitive Revolution "How AI Changes Everything"· Feb 1, 2026Podcast125 million cells took a decade. The next billion takes monthsHost 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.Models & trainingAI for scienceData quality & curation+3
4:25:13EN
Lex Fridman· Jan 31, 2026PodcastThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Host 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.Models & trainingScaling LawsOpen-weight models+3
1:03:00EN
Unsupervised Learning: With Jacob Effron· Jan 29, 2026PodcastA Model That Solves New Math Problems Every Week Still Isn't AGIHost 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.Models & trainingReinforcement LearningAGI Timelines+3
2:25:10EN
Cognitive Revolution "How AI Changes Everything"· Jan 22, 2026PodcastFine-Tuning Isn't Dead — But It Turned a Helpful Model EvilHost 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.Models & trainingFine-tuningSafety & governance+3
1:55:12EN
Cognitive Revolution "How AI Changes Everything"· Nov 8, 2025Podcast28,000 Documents, a 3B Model, 22 People: The Technical Ledger of Catholic AIHost 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.Models & trainingRetrieval-augmented generationFine-tuning+3
1:29:30EN
Cognitive Revolution "How AI Changes Everything"· Oct 14, 2025Podcast2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New AntibioticsHost Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.Models & trainingAI for scienceDrug Discovery+3
1:01:49EN
Cognitive Revolution "How AI Changes Everything"· Oct 2, 2025PodcastChatGPT's Co-Creator Left to Put Nature in the Reward LoopHost Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.Models & trainingAI for scienceReinforcement Learning+3
1:22:07EN
Cognitive Revolution "How AI Changes Everything"· Sep 25, 2025PodcastEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Host Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.Models & trainingAI in financeEmbeddings+3
2:02:17EN
Cognitive Revolution "How AI Changes Everything"· Aug 27, 2025PodcastSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsHost Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.Models & trainingModel InterpretabilityAI research+3
57:52EN
Unsupervised Learning: With Jacob Effron· Aug 13, 2025PodcastThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.Host Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.Models & trainingAI for scienceComputational Biology+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· Jul 16, 2025PodcastPlus Five for Format, Plus Ten if It CompilesHost Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.Models & trainingFine-tuningPost-training+3
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