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32:57EN
No Priors: AI, Machine Learning, Tech, & Startups· Nov 9, 2023Podcast · 2 speakersA 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AIHost Sarah Guo, Elad GilArthur Mensch tells No Priors why Chinchilla made small models valuable, why open-sourcing Mistral 7B is not a bioweapon risk, and why guardrails belong on the application.Open source & communityOpen-source ecosystemsOpen-weight models+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
30:03EN
No Priors: AI, Machine Learning, Tech, & Startups· Sep 7, 2023Podcast · 2 speakersA Dinner-Plate Chip Cut Training From 60 Days to 3.5Host Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.Infrastructure & deploymentInferenceAI cost & performance+3
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
1:01:26EN
Cognitive Revolution "How AI Changes Everything"· Jul 25, 2023PodcastAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.Host Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.Infrastructure & deploymentAI cost & performanceDistributed training & inference+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
4 slides7:21EN
Matthew Berman· Jun 26, 2023Testing Orca Mini 13B, an Unofficial Model Inspired by OrcaMatthew Berman · Forward FutureMatthew Berman loads a quantized Orca Mini 13B and tries coding, reasoning, arithmetic, and summarization prompts. His 2023 informal test produces several failures and a few successes.Open-weight modelsEvalsDistillation+2
3 slides8:17EN
Matthew Berman· Jun 22, 2023Using OpenLLM to Serve Open Models Through an APIMatthew Berman · Forward FutureMatthew Berman demonstrates a local model server, its interactive API and a Python client in a 2023 walkthrough.Open-weight modelsInferenceInfrastructure & deployment+1
1:20:49EN
Cognitive Revolution "How AI Changes Everything"· May 25, 2023PodcastA Wafer-Scale Chip So AI Stops Waiting on AmdahlHost Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.Infrastructure & deploymentInferenceAI cost & performance+3
1:00:36EN
No Priors: AI, Machine Learning, Tech, & Startups· Feb 2, 2023Podcast · 2 speakersForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonHost Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.AgentsAI researchReasoning & test-time compute+3
26:02EN
Lex Fridman· Feb 9, 2020PodcastMoore’s Law Is Not Dead—It Is a Cascade of S-CurvesHost Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.Infrastructure & deploymentSemiconductorsScaling Laws+3
23:57EN
No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 speakersChips, Agents and a Reckoning: Sarah Guo and Elad Gil on the 2023 AI BoomHost Sarah Guo, Elad GilThe hosts answer listener questions on the GPU crunch, how soon enterprises will ship AI products, how to build agents, and what 2024 holds for tech valuations.Enterprise & strategyInfrastructure & deploymentAI cost & performance+3
1:18:48EN
Lex FridmanPodcastData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesHost Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.Infrastructure & deploymentInferenceAI cost & performance+3
57:21EN
Cognitive Revolution "How AI Changes Everything"PodcastGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build NextHost Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.Models & trainingBenchmarksAI cost & performance+3
31:46EN
No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 speakersWhy Rivian Threw Out Its First Self-Driving Stack and Built Its Own BrainHost Sarah Guo, Elad GilRivian founder RJ Scaringe explains the neural-net reset, in-house chips, software-defined architecture and the mass-market R2 bet.Enterprise & strategyAutonomous DrivingInference+3