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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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