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244개정렬
32:22ENQwen Image Edit Plus Compared With Nano Banana, Seedream and GPT Image 1Matthew Berman · Forward FutureMatthew Berman compares four image-editing models across compositing, precise edits, style transfer, object removal and weather effects.
1:29:30EN
Cognitive Revolution "How AI Changes Everything"· 2025년 10월 14일팟캐스트2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics진행자 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.
36:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 10월 9일팟캐스트 · 2명STaR's Author Wants Models With EQ, Not Just More IQ진행자 Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.
1:01:49ENCognitive Revolution "How AI Changes Everything"· 2025년 10월 2일팟캐스트ChatGPT's Co-Creator Left to Put Nature in the Reward Loop진행자 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.
1:22:07ENCognitive Revolution "How AI Changes Everything"· 2025년 9월 25일팟캐스트Every payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%진행자 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.
2:02:17ENCognitive Revolution "How AI Changes Everything"· 2025년 8월 27일팟캐스트SAEs Find the Nouns. Parameter Decomposition Looks for the Verbs진행자 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.
13:30EN
3Blue1Brown· 2025년 8월 17일How AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.
57:52EN
Unsupervised Learning: With Jacob Effron· 2025년 8월 13일팟캐스트They Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.진행자 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.
10:28ENOpenAI’s gpt-oss Models: Open Weights, Benchmarks and SafetyMatthew Berman · Forward FutureMatthew Berman reviews the 120b and 20b models, local deployment claims, selected evaluations and the scope of OpenAI’s safety testing.
3:13ENClaude Opus 4.1: Anthropic’s August 2025 Benchmark UpdateMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported coding, reasoning and tool-use results, including one benchmark that declined.
30:11EN
Sequoia Capital· 2025년 7월 30일팟캐스트From 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.
23:39ZH
跟李沐学AI· 2025년 7월 23일李沐：给文本大模型加入一千万小时语音，开源 Higgs Audio李沐 · Boson AI李沐介绍团队如何在文本大语言模型的训练里加入大量语音数据，做成能听能说的 AudioLLM，并把 Higgs Audio 开源。他讲了架构取舍、token 压缩、数据从哪来，以及怎么在 3090 上跑起来。
1:07:41EN
20VC with Harry Stebbings· 2025년 7월 21일팟캐스트 · 2명Edwin Chen on Building Surge and the Data Behind AI Models게스트 Edwin Chen · 진행자 Harry StebbingsThe Surge founder discusses a bootstrapped business, training-data quality and the limits he sees in AI benchmarks and synthetic data.
47:05ENLogan Kilpatrick on AI Developers, Interfaces, and the Future of ModelsMatthew Berman, Logan Kilpatrick · Forward FutureIn a July 2025 conversation, Google’s Logan Kilpatrick discusses Windsurf, agent interfaces, coding skills, Veo, and the shifting boundary between models and application scaffolding.
38:10ENCognitive Revolution "How AI Changes Everything"· 2025년 7월 16일팟캐스트Plus Five for Format, Plus Ten if It Compiles진행자 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.
12:14ENAI News: Windsurf’s Acquisition, Meta Compute Plans, Grok and VoxtralMatthew Berman · Forward FutureA July 2025 roundup examines developer-tool deals, AI infrastructure, model behavior, reward methods and speech recognition.
1:42:06ENCognitive Revolution "How AI Changes Everything"· 2025년 7월 8일팟캐스트Every Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It Goes진행자 Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.
1:09ENA Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.
1:37:05ENCognitive Revolution "How AI Changes Everything"· 2025년 6월 19일팟캐스트An Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lie진행자 Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.
48:51ENSequoia Capital· 2025년 6월 17일팟캐스트The Breakthroughs for AGI Are Already Done, Says OpenAI’s Former Research HeadBob McGrew tells Sequoia’s Training Data that 2025 is the year of reasoning, agents will be priced at the cost of compute, and nothing fundamental will be added to the stack by 2035.
1:54:08ENCognitive Revolution "How AI Changes Everything"· 2025년 5월 29일팟캐스트Why Anthropic's first check went to a company building microscopes for neural nets진행자 Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.
2:12:00ENCognitive Revolution "How AI Changes Everything"· 2025년 5월 15일팟캐스트Gradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens진행자 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
47:00ENUnsupervised Learning: With Jacob Effron· 2025년 5월 8일팟캐스트An Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade진행자 Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
38:10ENNo Priors: AI, Machine Learning, Tech, & Startups· 2025년 5월 1일팟캐스트 · 2명o3 should wait exactly as long as the answer takes진행자 Sarah Guo, Elad GilOpenAI researchers Eric Mitchell and Brandon McKinzie tell No Priors why o3's leap is a chain of tools during reasoning — and why you should send the same prompt a hundred times.더 보기244개 중 24개 표시