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Cognitive Revolution "How AI Changes Everything"· 29 août 2023PodcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Anime : Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.Modèles et entraînementMultimodal AIÉvaluations+3
2 diapositives6:28EN
MBMatthew Berman· 29 août 2023A Historical GPT-3.5 Fine-Tuning WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates generating synthetic examples in Colab and training a model to use a distinctive tone.Fine-tuningSynthetic dataModèles et entraînement
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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
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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
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MBMatthew 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
2 diapositives5:53EN
MBMatthew 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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MBMatthew 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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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
7 diapositives4:36EN
MBMatthew 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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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
MBMatthew 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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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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Lex Fridman· 1 juil. 2022Podcast · 2 voixDemis Hassabis: Solve Intelligence, Then Solve Everything ElseInvité : Demis Hassabis · Anime : Lex FridmanThe DeepMind CEO tells Lex Fridman why the Turing Test is the wrong exam, how AlphaFold rewrote biology, and why the quiet radio sky suggests we are probably alone.Modèles et entraînementAI researchAGI+3
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Lex Fridman· 22 janv. 2022Podcast · 2 voixA Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.Invité : Yann Lecun · Anime : Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.Modèles et entraînementWorld modelsComputer Vision+3
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Lex Fridman· 20 sept. 2021Podcast · 2 voixJay McClelland on How Thought Emerges From Neural NetworksInvité : Jay McClelland · Anime : Lex FridmanThe Stanford cognitive scientist talks with Lex Fridman about connectionism, backpropagation, David Rumelhart, and why intuition discovers what logic later proves.Modèles et entraînementFondamentaux du MLDeep Learning+3
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