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3 Folien14:23EN
Matthew Berman· 31. März 2024Testing DBRX: Coding Strengths and Reasoning GapsMatthew Berman · Forward FutureMatthew Berman examines Databricks’ open-weight model and puts it through coding, arithmetic and reasoning prompts.Open-weight modelsModelle & TrainingEvals+1
1:20:44EN
Cognitive Revolution "How AI Changes Everything"· 30. März 2024PodcastSeeing Like a Mamba: Vision, Rotting States, and a Cell Model in 90 DaysModeration: Nathan LabenzNathan Labenz and Jason Meaux survey the vision papers, long-context experiments, and biology work that followed the original Mamba release.Modelle & TrainingAI researchTransformers & attention+3
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Matthew Berman· 21. März 2024Q* Rumors, Energy-Based Models, and Research on ReasoningMatthew Berman, Yann Lecun · Forward FutureMatthew Berman examines an anonymous, unverified account of Q* and compares its ideas with published research on teaching language models to reason.Reasoning & test-time computeModelle & TrainingAI research+2
2:47:17EN
Lex Fridman· 7. März 2024Podcast · 2 StimmenIf You Want Human-Level AI, Abandon Generative ModelsGast: Yann Lecun · Moderation: Lex FridmanYann LeCun tells Lex Fridman why autoregressive LLMs cannot plan, remember, or understand the physical world — and why open source is the only democratic answer.Modelle & TrainingWorld modelsAGI+3
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Matthew Berman· 22. Feb. 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 modelsEvalsModelle & Training
1:58:43EN
Cognitive Revolution "How AI Changes Everything"· 7. Feb. 2024PodcastA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.Moderation: Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.Modelle & TrainingAI HistoryNeuroscience & AI+3
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Matthew Berman· 31. Jan. 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 AIModelle & TrainingLocal inference+1
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No Priors: AI, Machine Learning, Tech, & Startups· 25. Jan. 2024Podcast · 2 Stimmen99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstModeration: Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.Modelle & TrainingRoboticsFoundation Models+3
2:30:06EN
Cognitive Revolution "How AI Changes Everything"· 22. Dez. 2023PodcastTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfModeration: 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.Modelle & TrainingTransformers & attentionAI research+3
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Cognitive Revolution "How AI Changes Everything"· 17. Nov. 2023PodcastA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.Moderation: Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.Modelle & TrainingAI researchML-Grundlagen+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.Moderation: 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.Modelle & TrainingSynthetic dataData quality & curation+3
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2. Nov. 2023Podcast · 2 StimmenReliability is the bottleneck. Autonomy is what would make AI feel alive.Moderation: 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.Modelle & TrainingAGIScaling Laws+3
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Cognitive Revolution "How AI Changes Everything"· 18. Okt. 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveModeration: 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.Modelle & TrainingAI researchModel Interpretability+3
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 28. Sept. 2023Podcast · 2 StimmenDNA Is Code, but You Compile It with a PipetteModeration: 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.Modelle & TrainingAI for scienceBiosecurity+3
6 Folien12:49EN
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 modelsBenchmarksModelle & Training+1
36:33EN
20VC with Harry Stebbings· 31. Aug. 2023Podcast · 2 StimmenNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AIGast: Noam Shazeer · Moderation: 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 ModelsModelle & TrainingAI startups & small teams+2
55:08EN
Cognitive Revolution "How AI Changes Everything"· 29. Aug. 2023PodcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Moderation: 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.Modelle & TrainingMultimodal AIEvals+3
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Matthew Berman· 29. Aug. 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 dataModelle & Training
52:48EN
Cognitive Revolution "How AI Changes Everything"· 25. Aug. 2023PodcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListModeration: 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.Modelle & TrainingFine-TuningAI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· 24. Aug. 2023Podcast · 2 StimmenThe Author of Attention Is All You Need Now Compiles mRNA Like BytecodeModeration: 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.Modelle & TrainingAI for scienceTransformers & attention+2
5 Folien7:09EN
Matthew Berman· 28. Juli 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 mediaModelle & TrainingBrain-Computer Interfaces+2
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· 27. Juli 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Moderation: 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.Modelle & TrainingModel InterpretabilityAI research+3
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Matthew Berman· 18. Juli 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. Juli 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 modelsEvalsModelle & Training+1
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