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5 diapositives13:57EN
Matthew Berman· 27 févr. 2024Mistral Large: A 2024 Review of Coding, Reasoning and Safety ResponsesMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s launch claims and tests the model with a small set of prompts.Modèles et entraînementBenchmarksAI Safety+1
3 diapositives23:00EN
Matthew Berman· 22 févr. 2024Testing Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.Open-weight modelsÉvaluationsModèles et entraînement
1:58:43EN
Cognitive Revolution "How AI Changes Everything"· 7 févr. 2024PodcastA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.Anime : Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.Modèles et entraînementAI HistoryNeuroscience & AI+3
2 diapositives0:42EN
Matthew Berman· 31 janv. 2024LLaVA 1.6 Adds Higher-Resolution Image UnderstandingMatthew Berman · Forward FutureMatthew Berman reviews the January 2024 release of LLaVA 1.6 and its local availability through Ollama.Multimodal AIModèles et entraînementLocal inference+1
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 25 janv. 2024Podcast · 2 voix99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstAnime : Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.Modèles et entraînementRoboticsFoundation Models+3
Diapositives35:28EN
Matthew Berman· 23 janv. 2024Sam Altman on Future AI Models, Copyright and the Limits of DeploymentMatthew Berman, Ina Fried, Sam Altman · Forward FutureIn a January 2024 Davos interview, OpenAI’s CEO discusses model progress, election risks, military use and customization. Matthew Berman responds to the claims and their implications.OpenAIModèles et entraînementSécurité et gouvernance+2
2:30:06EN
Cognitive Revolution "How AI Changes Everything"· 22 déc. 2023PodcastTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfAnime : Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.Modèles et entraînementTransformers & attentionAI research+3
5 diapositives14:04EN
Matthew Berman· 15 déc. 2023Mistral Medium in 2023: API Pricing and Informal Model TestsMatthew Berman · Forward FutureMatthew Berman compares the original Mistral Medium prototype with Mixtral and GPT-4, then tests coding, writing and reasoning through the API.Modèles et entraînementBenchmarksAI cost & performance+2
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 17 nov. 2023PodcastA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.Anime : Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.Modèles et entraînementAI researchFondamentaux du ML+3
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· 14 nov. 2023PodcastA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Anime : Nathan LabenzGretel AI founder Alex Watson tells Nathan Labenz why most company data is still a spreadsheet, why GPT-4 is the wrong tool for generating it, and how differential privacy lets a model learn a disease without learning a patient.Modèles et entraînementSynthetic dataData quality & curation+3
5 diapositives11:05EN
Matthew Berman· 7 nov. 2023Testing Dolphin 2.2, a Mistral 7B Fine-TuneMatthew Berman · Forward FutureMatthew Berman tries a small open-weight model on coding, formatting and reasoning prompts, with mixed results.Open-weight modelsBenchmarksModèles et entraînement+1
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2 nov. 2023Podcast · 2 voixReliability is the bottleneck. Autonomy is what would make AI feel alive.Anime : Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.Modèles et entraînementAGIScaling Laws+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 18 oct. 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveAnime : Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.Modèles et entraînementAI researchModel Interpretability+3
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 28 sept. 2023Podcast · 2 voixDNA Is Code, but You Compile It with a PipetteAnime : Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.Modèles et entraînementAI for scienceBiosecurity+3
6 diapositives12:49EN
Matthew Berman· 14 sept. 2023Falcon 180B: A 2023 Review of TII’s Open-Weight ModelMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and writing prompts while examining the model’s training scale and license.Open-weight modelsBenchmarksModèles et entraînement+1
36:33EN
20VC with Harry Stebbings· 31 août 2023Podcast · 2 voixNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AIInvité : Noam Shazeer · Anime : Harry StebbingsThe Character.AI co-founder explains his consumer strategy, language-model training and why he expects users to invent applications the company cannot predict.Foundation ModelsModèles et entraînementAI startups & small teams+2
55:08EN
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
Matthew 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
52:48EN
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
2:13:07EN
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
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
3 diapositives12:20EN
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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