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2 diapositivas0:42EN
Matthew Berman· 31 ene 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 AIModelos y entrenamientoLocal inference+1
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 25 ene 2024Pódcast · 2 voces99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstPresenta: Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.Modelos y entrenamientoRoboticsFoundation Models+3
2:30:06EN
Cognitive Revolution "How AI Changes Everything"· 22 dic 2023PódcastTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfPresenta: 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.Modelos y entrenamientoTransformers & attentionAI research+3
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 17 nov 2023PódcastA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.Presenta: Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.Modelos y entrenamientoAI researchFundamentos de ML+3
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· 14 nov 2023PódcastA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Presenta: 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.Modelos y entrenamientoSynthetic dataData quality & curation+3
41:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2 nov 2023Pódcast · 2 vocesReliability is the bottleneck. Autonomy is what would make AI feel alive.Presenta: 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.Modelos y entrenamientoAGIScaling Laws+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 18 oct 2023Pódcast1,000 H100s Take 115 Days; 22,000 Take FivePresenta: 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.Modelos y entrenamientoAI researchModel Interpretability+3
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 28 sept 2023Pódcast · 2 vocesDNA Is Code, but You Compile It with a PipettePresenta: 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.Modelos y entrenamientoAI for scienceBiosecurity+3
6 diapositivas12:49EN
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 modelsBenchmarksModelos y entrenamiento+1
36:33EN
20VC with Harry Stebbings· 31 ago 2023Pódcast · 2 vocesNoam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AIInvitado: Noam Shazeer · Presenta: 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 ModelsModelos y entrenamientoAI startups & small teams+2
55:08EN
Cognitive Revolution "How AI Changes Everything"· 29 ago 2023PódcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Presenta: 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.Modelos y entrenamientoMultimodal AIEvaluaciones+3
2 diapositivas6:28EN
Matthew Berman· 29 ago 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.Ajuste finoSynthetic dataModelos y entrenamiento
52:48EN
Cognitive Revolution "How AI Changes Everything"· 25 ago 2023PódcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListPresenta: 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.Modelos y entrenamientoAjuste finoAI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· 24 ago 2023Pódcast · 2 vocesThe Author of Attention Is All You Need Now Compiles mRNA Like BytecodePresenta: 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.Modelos y entrenamientoAI for scienceTransformers & attention+2
5 diapositivas7:09EN
Matthew Berman· 28 jul 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 mediaModelos y entrenamientoBrain-Computer Interfaces+2
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· 27 jul 2023PódcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Presenta: 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.Modelos y entrenamientoModel InterpretabilityAI research+3
2 diapositivas5:53EN
Matthew Berman· 18 jul 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 diapositivas12:20EN
Matthew Berman· 10 jul 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 modelsEvaluacionesModelos y entrenamiento+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 6 jul 2023PódcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandPresenta: 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.Modelos y entrenamientoFundamentos de MLScaling Laws+3
6 diapositivas18:08EN
Matthew Berman· 29 jun 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 modelsModelos y entrenamiento+1
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 29 jun 2023PódcastA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayPresenta: 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.Modelos y entrenamientoMultimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 27 jun 2023PódcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeePresenta: 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.Modelos y entrenamientoModel InterpretabilityFundamentos de ML+3
7 diapositivas4:36EN
Matthew Berman· 20 jun 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.Modelos y entrenamientoLocal inferenceSoftware engineering+2
59:28EN
Cognitive Revolution "How AI Changes Everything"· 16 jun 2023PódcastFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductPresenta: Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.Modelos y entrenamientoAjuste finoPretraining & midtraining+3
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