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1:58:43EN
Cognitive Revolution "How AI Changes Everything"· Feb 7, 2024PodcastA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.Host Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.Models & trainingAI HistoryNeuroscience & AI+3
2 slides0:42EN
Matthew Berman· Jan 31, 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 AIModels & trainingLocal inference+1
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· Jan 25, 2024Podcast · 2 speakers99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstHost Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.Models & trainingRoboticsFoundation Models+3
Slides35:28EN
Matthew Berman· Jan 23, 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.OpenAIModels & trainingSafety & governance+2
2:30:06EN
Cognitive Revolution "How AI Changes Everything"· Dec 22, 2023PodcastTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfHost 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.Models & trainingTransformers & attentionAI research+3
5 slides14:04EN
Matthew Berman· Dec 15, 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.Models & trainingBenchmarksAI cost & performance+2
8 slides20:49EN
Matthew Berman· Dec 12, 2023Mixtral 8x7B: Mixture-of-Experts Architecture, Setup and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews Mistral AI’s December 2023 open-weight model, demonstrates a cloud setup and tests its coding and reasoning responses.Models & trainingOpen-weight modelsInference+2
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· Nov 17, 2023PodcastA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.Host Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.Models & trainingAI researchML fundamentals+3
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· Nov 14, 2023PodcastA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Host 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.Models & trainingSynthetic dataData quality & curation+3
5 slides11:05EN
Matthew Berman· Nov 7, 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 modelsBenchmarksModels & training+1
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· Nov 2, 2023Podcast · 2 speakersReliability is the bottleneck. Autonomy is what would make AI feel alive.Host 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.Models & trainingAGIScaling Laws+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· Oct 18, 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveHost 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.Models & trainingAI researchModel Interpretability+3
5 slides13:11EN
Matthew Berman· Oct 5, 2023Testing Mistral 7B OpenOrca: A 2023 Cloud Setup and Prompt TrialMatthew Berman · Forward FutureMatthew Berman runs a quantized OpenOrca variant on a rented GPU and reviews coding, writing and reasoning examples.Open-weight modelsLocal inferenceBenchmarks+2
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· Sep 28, 2023Podcast · 2 speakersDNA Is Code, but You Compile It with a PipetteHost 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.Models & trainingAI for scienceBiosecurity+3
6 slides12:49EN
Matthew Berman· Sep 14, 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 modelsBenchmarksModels & training+1
4 slides4:35EN
Matthew Berman· Sep 5, 2023Running WizardCoder on a Cloud GPU: A 2023 RunPod WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates downloading a quantized 13B coding model into a hosted text-generation interface, then explains how to stop the pod and avoid ongoing charges.Models & trainingInfrastructure & deploymentCoding agents
36:33EN
20VC with Harry Stebbings· Aug 31, 2023Podcast · 2 speakersNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AIGuest Noam Shazeer · Host 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 ModelsModels & trainingAI startups & small teams+2
55:08EN
Cognitive Revolution "How AI Changes Everything"· Aug 29, 2023PodcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Host 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.Models & trainingMultimodal AIEvals+3
2 slides6:28EN
Matthew Berman· Aug 29, 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 dataModels & training
52:48EN
Cognitive Revolution "How AI Changes Everything"· Aug 25, 2023PodcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListHost 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.Models & trainingFine-tuningAI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· Aug 24, 2023Podcast · 2 speakersThe Author of Attention Is All You Need Now Compiles mRNA Like BytecodeHost 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.Models & trainingAI for scienceTransformers & attention+2
5 slides7:09EN
Matthew Berman· Jul 28, 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 mediaModels & trainingBrain-Computer Interfaces+2
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· Jul 27, 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Host 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.Models & trainingModel InterpretabilityAI research+3
2 slides4:59EN
Matthew Berman· Jul 25, 2023Testing Airoboros L2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman runs a small set of prompts against a Llama 2 fine-tune, finding useful structured output alongside failures on code and logic.Open-weight modelsEvalsModels & training+1
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