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Cognitive Revolution "How AI Changes Everything"· 25 août 2023PodcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListAnime : 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.Modèles et entraînementFine-tuningAI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· 24 août 2023Podcast · 2 voixThe Author of Attention Is All You Need Now Compiles mRNA Like BytecodeAnime : Sarah Guo, Elad GilJakob Uszkoreit on why Transformers won, why models spend compute on the wrong things, and why drug discovery has to become generation rather than screening.Modèles et entraînementAI for scienceTransformers & attention+2
5 diapositives7:09EN
Matthew Berman· 28 juil. 2023AI News in July 2023: SDXL, Worldcoin, and Biological ComputingMatthew Berman · Forward FutureMatthew Berman reviews a week of AI developments, from Google’s Gemini project and new generative models to biometric identity and experiments with cultured neurons.Generative mediaModèles et entraînementBrain-Computer Interfaces+2
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Cognitive Revolution "How AI Changes Everything"· 27 juil. 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Anime : Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.Modèles et entraînementModel InterpretabilityAI research+3
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Matthew Berman· 18 juil. 2023Petals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.Distributed training & inferenceLocal inferenceInference+2
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Matthew Berman· 10 juil. 2023Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight modelsÉvaluationsModèles et entraînement+1
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Cognitive Revolution "How AI Changes Everything"· 6 juil. 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandAnime : Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.Modèles et entraînementFondamentaux du MLScaling Laws+3
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Matthew Berman· 29 juin 2023Six Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.BenchmarksOpen-weight modelsModèles et entraînement+1
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Cognitive Revolution "How AI Changes Everything"· 29 juin 2023PodcastA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayAnime : Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.Modèles et entraînementMultimodal AITransformers & attention+3
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Cognitive Revolution "How AI Changes Everything"· 27 juin 2023PodcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeAnime : Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.Modèles et entraînementModel InterpretabilityFondamentaux du ML+3
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Matthew Berman· 20 juin 2023Testing WizardCoder 15B as a Local Coding AssistantMatthew Berman · Forward FutureMatthew Berman runs the 2023 WizardCoder model locally, testing small Python exercises and a longer Snake game.Modèles et entraînementLocal inferenceSoftware engineering+2
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Cognitive Revolution "How AI Changes Everything"· 16 juin 2023PodcastFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductAnime : Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.Modèles et entraînementFine-tuningPretraining & midtraining+3
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Cognitive Revolution "How AI Changes Everything"· 6 juin 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Anime : Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…Modèles et entraînementSynthetic dataData quality & curation+3
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Matthew Berman· 1 juin 2023Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.Modèles et entraînementÉvaluationsAI Safety+1
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Cognitive Revolution "How AI Changes Everything"· 21 mai 2023PodcastTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical SchoolAnime : Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.Modèles et entraînementAI in healthcareFine-tuning+3
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No Priors: AI, Machine Learning, Tech, & Startups· 18 mai 2023Podcast · 2 voixTen Percent of Search Is Health — and the Doctor Is Googling TooAnime : Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.Modèles et entraînementAI in healthcareSécurité et gouvernance+3
Diapositives5:24EN
Matthew Berman· 12 mai 2023Google I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.AI StrategyProduit et UXModèles et entraînement+1
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Lex Fridman· 9 mai 2023Podcast · 2 voixThe Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational TruthInvité : Stephen Wolfram · Anime : Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.Modèles et entraînementPhilosophyAI research+3
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No Priors: AI, Machine Learning, Tech, & Startups· 6 avr. 2023Podcast · 2 voixA 6-Billion-Parameter Clone of ChatGPT’s Core TrickAnime : Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.Modèles et entraînementOpen-weight modelsFine-tuning+3
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Matthew Berman· 29 mars 2023A 2023 Guide to Running the Original GPT4All Model LocallyMatthew Berman · Forward FutureMatthew Berman demonstrates an early CPU-based GPT4All release, its terminal installation and the resources available for fine-tuning.Open-weight modelsModèles et entraînementInfrastructure et déploiement
8 diapositives1:03:29ZH
跟李沐学AI· 12 janv. 2023多模态论文串讲:BLIP、CoCa 与 BEiT-3从图文数据清理到统一预训练,梳理三种视觉语言模型的结构、目标函数与相互联系。Multimodal AIModèles et entraînementData quality & curation
6 diapositives1:07:10ZH
跟李沐学AI· 29 déc. 2022InstructGPT 论文精读:从人工反馈到语言模型对齐从监督微调、奖励模型到 PPO,梳理 InstructGPT 的训练流程、实验结果与局限。Reinforcement LearningModèles et entraînement
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Lex Fridman· 29 oct. 2022Podcast · 2 voixKarpathy Calls Trained Neural Nets Complicated Alien ArtifactsInvité : Andrej Karpathy · Anime : Lex FridmanOn Lex Fridman's podcast, Andrej Karpathy refuses the brain analogy, walks the Fermi paradox through Nick Lane's chemistry, and treats the Transformer as a general-purpose differentiable computer.Modèles et entraînementAGIAutonomous Driving+3
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Lex Fridman· 26 juil. 2022Podcast · 2 voixGato Is Only the BeginningInvité : Oriol Vinyals · Anime : Lex FridmanDeepMind's Oriol Vinyals talks with Lex Fridman about generalist agents, thrown-away weights, and whether human-level AGI arrives in his lifetime.Modèles et entraînementAGIAI research+3
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