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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 slides5:53EN
Matthew Berman· Jul 18, 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 slides12:20EN
Matthew Berman· Jul 10, 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 modelsEvalsModels & training+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· Jul 6, 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandHost 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.Models & trainingML fundamentalsScaling Laws+3
6 slides18:08EN
Matthew Berman· Jun 29, 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 modelsModels & training+1
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· Jun 29, 2023PodcastA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayHost 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.Models & trainingMultimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· Jun 27, 2023PodcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeHost 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.Models & trainingModel InterpretabilityML fundamentals+3
7 slides4:36EN
Matthew Berman· Jun 20, 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.Models & trainingLocal inferenceSoftware engineering+2
59:28EN
Cognitive Revolution "How AI Changes Everything"· Jun 16, 2023PodcastFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductHost Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.Models & trainingFine-tuningPretraining & midtraining+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· Jun 6, 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host 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…Models & trainingSynthetic dataData quality & curation+3
6 slides11:49EN
Matthew Berman· Jun 1, 2023Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.Models & trainingEvalsAI Safety+1
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· May 21, 2023PodcastTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical SchoolHost 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.Models & trainingAI in healthcareFine-tuning+3
42:18EN
No Priors: AI, Machine Learning, Tech, & Startups· May 18, 2023Podcast · 2 speakersTen Percent of Search Is Health — and the Doctor Is Googling TooHost 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.Models & trainingAI in healthcareSafety & governance+3
Slides5:24EN
Matthew Berman· May 12, 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 StrategyProduct & UXModels & training+1
4:14:33EN
Lex Fridman· May 9, 2023Podcast · 2 speakersThe Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational TruthGuest Stephen Wolfram · Host 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.Models & trainingPhilosophyAI research+3
40:25EN
No Priors: AI, Machine Learning, Tech, & Startups· Apr 6, 2023Podcast · 2 speakersA 6-Billion-Parameter Clone of ChatGPT’s Core TrickHost 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.Models & trainingOpen-weight modelsFine-tuning+3
5 slides8:05EN
Matthew Berman· Mar 29, 2023A 2023 Guide to Running the Original GPT4All Model LocallyMatthew Berman · Forward FutureMatthew Berman demonstrates an early CPU-based GPT4All release, its terminal installation and the resources available for fine-tuning.Open-weight modelsModels & trainingInfrastructure & deployment
5 slides7:55EN
Matthew Berman· Feb 20, 2023How to Train a Custom Image Model in Leonardo.aiMatthew Berman · Forward FutureMatthew Berman walks through Leonardo.ai’s 2023 model galleries, training-set guidance, and custom model workflow, then shares results from several experiments.Generative mediaModels & training
8 slides1:03:29ZH → English
跟李沐学AI· Jan 12, 2023A Multimodal Paper Review: BLIP, CoCa, and BEiT-3From cleaning image–text data to unified pretraining, a guide to the architectures, objectives, and connections behind three vision–language models.Multimodal AIModels & trainingData quality & curation
6 slides1:07:10ZH → English
跟李沐学AI· Dec 29, 2022InstructGPT: From Human Feedback to Language Model AlignmentA guide to InstructGPT’s training process, results, and limitations, from supervised fine-tuning and reward modeling to PPO.Reinforcement LearningModels & training
3:28:47EN
Lex Fridman· Oct 29, 2022Podcast · 2 speakersKarpathy Calls Trained Neural Nets Complicated Alien ArtifactsGuest Andrej Karpathy · Host 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.Models & trainingAGIAutonomous Driving+3
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