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25 件
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スライド 2 枚17:42EN
AI Engineer· 2026年10月21日Is a Faster Model Actually Faster? Measure the Finished Job, Not the TokensMorgan Suo · MiniMaxMiniMax's Morgan Suo takes the engineering changes inside M3.1 Flash one by one and asks the question that matters to users: does the job an analyst needs actually get done sooner, cheaper and correctly?AI cost & performanceInferenceQuantization+2
1:26:46EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月8日ポッドキャストThere Likely Is a Level of Intelligence We Shouldn't Go Pastホスト Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.企業と戦略安全性とガバナンスAGI Timelines+3
56:31EN
Unsupervised Learning: With Jacob Effron· 2026年10月6日ポッドキャストPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Labホスト 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.モデルと学習Post-trainingReinforcement Learning+3
2:19:30EN
Cognitive Revolution "How AI Changes Everything"· 2026年9月5日ポッドキャストThe investigators were allowed six days. The word protein appeared once.ホスト Nathan LabenzA week of Cognitive Revolution morning shows on OpenAI’s agent-swarm incident, GPT-6 Astra’s loop transformer, and whether the pause debate is already over.セキュリティと安全性安全性とガバナンスAgent red teaming+3
2:06:41EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月9日ポッドキャストZero the J-Space and a Model's Hidden Goal Lights Up on Token Oneホスト 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.モデルと学習Model Interpretability安全性とガバナンス+3
1:26:16EN
Cognitive Revolution "How AI Changes Everything"· 2026年5月9日ポッドキャストMilliseconds to Match: How Criteo Bids on a Billion Anonymous Profilesホスト Nathan LabenzCriteo’s CTO and AI lab head walk through the millisecond auction, the DeepKNN-to-foundation-model stack, and why a live product catalog still beats a frozen LLM.企業と戦略Recommendation systemsEmbeddings+3
1:57:29EN
Cognitive Revolution "How AI Changes Everything"· 2026年4月4日ポッドキャストFrontier vision models score 12.5%: Joseph Nelson on the real waterline of computer visionホスト Nathan LabenzThe Roboflow CEO tells Nathan Labenz why vision still lags language, how to distill SAM 3 into an edge model for Wimbledon, and why the United States has almost never led open-source vision.マルチモーダルとメディアMultimodal AI画像と動画+3
4:25:13EN
Lex Fridman· 2026年1月31日ポッドキャストThe Architecture Barely Changed. Data, Cost, and Open Weights Did.ホスト 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.モデルと学習Scaling LawsOpen-weight models+3
1:22:07EN
Cognitive Revolution "How AI Changes Everything"· 2025年9月25日ポッドキャストEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%ホスト 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.モデルと学習金融 AIEmbeddings+3
1:32:47EN
Cognitive Revolution "How AI Changes Everything"· 2025年9月13日ポッドキャストA transformer co-author wants a $160 million model no company ownsホスト Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.インフラとデプロイInferenceAI security+3
59:06EN
Unsupervised Learning: With Jacob Effron· 2025年9月10日ポッドキャストInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't Holdホスト Jacob EffronThe FlashAttention author and Together AI chief scientist on why hardware portability is a myth, how Claude Code made him 1.5x faster, and why workloads will split the chip market.インフラとデプロイInferenceAI cost & performance+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· 2025年7月16日ポッドキャストPlus Five for Format, Plus Ten if It Compilesホスト 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.モデルと学習ファインチューニングPost-training+3
2:12:00EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月15日ポッドキャストGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokensホスト Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.モデルと学習Agent memoryコンテキストエンジニアリング+3
45:06EN
Unsupervised Learning: With Jacob Effron· 2025年3月5日ポッドキャストMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a Yearホスト Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.インフラとデプロイAI StrategyOpen-weight models+3
1:24:57EN
Unsupervised Learning: With Jacob Effron· 2025年1月21日ポッドキャストA 50,000-GPU cap and a 1,700-chip loopholeホスト Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.インフラとデプロイAI RegulationGeopolitics+3
55:50EN
Unsupervised Learning: With Jacob Effron· 2024年12月16日ポッドキャストThere will not be one model. Fireworks is betting on hundreds of small expertsホスト Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.インフラとデプロイInferenceModel routing & gateways+3
1:42:16EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月4日ポッドキャスト267 Papers in Seven Months: Albert Gu Explains the State Space Model Revolutionホスト Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.モデルと学習Transformers & attentionInference+3
1:42:17EN
Cognitive Revolution "How AI Changes Everything"· 2024年4月20日ポッドキャストThe Cheapest Inference Provider Is the Most Dangerousホスト Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.インフラとデプロイInferenceAI cost & performance+3
1:22:43EN
Cognitive Revolution "How AI Changes Everything"· 2024年1月9日ポッドキャストFive billion minutes of calls: why Dialpad trained its own business LLMホスト Nathan LabenzDan O’Connell on real-time speech, verticalizing the stack, and why he and Nathan Labenz disagree about when AI employees show up.企業と戦略音声 AIエンタープライズ AI+3
52:48EN
Cognitive Revolution "How AI Changes Everything"· 2023年8月25日ポッドキャストOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the Listホスト Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.モデルと学習ファインチューニングAI cost & performance+3
1:01:26EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月25日ポッドキャストAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.ホスト Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.インフラとデプロイAI cost & performanceDistributed training & inference+3
1:20:49EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月25日ポッドキャストA Wafer-Scale Chip So AI Stops Waiting on Amdahlホスト Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.インフラとデプロイInferenceAI cost & performance+3
26:02EN
Lex Fridman· 2020年2月9日ポッドキャストMoore’s Law Is Not Dead—It Is a Cascade of S-Curvesホスト Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.インフラとデプロイSemiconductorsScaling Laws+3
1:18:48EN
Lex FridmanポッドキャストData Movement, Not Math: How to Make Deep Learning Fit on Robots and Phonesホスト Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.インフラとデプロイInferenceAI cost & performance+3
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