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115 件
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42:08EN
Sequoia Capital· 2024年7月30日ポッドキャストMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.オープンソースとコミュニティOpen-weight modelsDistillation+3
スライド 3 枚13:40EN
MBMatthew Berman· 2024年7月29日Testing Llama 3.1 8B: Benchmark Gains and Mixed Practical ResultsMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and other prompts on the small model and finds several failures despite stronger reported benchmarks.Open-weight models評価(Evals)モデルと学習+1
1:54:01EN
Cognitive Revolution "How AI Changes Everything"· 2024年6月26日ポッドキャストA Tiny Network Grew a Salt Crystal the Training Data Never Showedホスト Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.モデルと学習AI for science機械学習の基礎+3
スライド 3 枚16:06EN
MBMatthew Berman· 2024年6月18日AI News: Video Generation, Apple Intelligence, Robotics and DeepSeek Coder V2Matthew Berman · Forward FutureMatthew Berman reviews a week of launches and research, from Luma and Runway to NVIDIA, robotics and coding models.Generative mediaRoboticsOpen-weight models+1
スライド 4 枚9:53EN
MBMatthew Berman· 2024年5月28日Testing Microsoft Phi-3 Medium Locally: Mixed Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Phi-3 Medium on a MacBook and explores coding, arithmetic and reasoning prompts.Open-weight modelsLocal inferenceBenchmarks+1
スライド 2 枚12:50EN
MBMatthew Berman· 2024年5月22日Testing Smaug: Hosted 70B Versus a Smaller Local ModelMatthew Berman · Forward FutureMatthew Berman compares two Smaug configurations on Snake, math and reasoning puzzles.Open-weight models評価(Evals)モデルと学習
1:42:17EN
Cognitive Revolution "How AI Changes Everything"· 2024年4月20日ポッドキャストThe Cheapest Inference Provider Is the Most Dangerousホスト Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.インフラとデプロイInferenceAI cost & performance+3
スライド 3 枚15:01EN
MBMatthew Berman· 2024年4月19日Testing Llama 3 at Launch: Code, Reasoning and Image GenerationMatthew Berman · Forward FutureMatthew Berman tries Meta AI’s new interface on a snake game, logic puzzles and rapid image generation.Open-weight models評価(Evals)モデルと学習+1
スライド 3 枚14:23EN
MBMatthew Berman· 2024年3月31日Testing 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 modelsモデルと学習評価(Evals)+1
スライド 4 枚14:11EN
MBMatthew Berman· 2024年3月23日LM Studio in 2024: Local Models, Parallel Inference and a Local APIMatthew Berman · Forward FutureMatthew Berman demonstrates model downloads, hardware settings, JSON output and serving multiple local models.Open-weight modelsInference
2:47:17EN
Lex Fridman· 2024年3月7日ポッドキャスト · 2 人If You Want Human-Level AI, Abandon Generative Modelsゲスト Yann Lecun · ホスト 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.モデルと学習World modelsAGI(汎用人工知能)+3
スライド 3 枚23:00EN
MBMatthew Berman· 2024年2月22日Testing 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 models評価(Evals)モデルと学習
3:37EN
MBMatthew Berman· 2024年1月18日Mark Zuckerberg Announces Llama 3 Training and Meta’s AI PlansMatthew Berman, Mark Zuckerberg · Forward FutureIn January 2024, Matthew Berman reacts to Zuckerberg’s remarks on general intelligence, compute and AI devices.Open-weight modelsAI Historyインフラとデプロイ+1
19:10EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年1月11日ポッドキャスト · 2 人The World's Only Capped-Profit Companyホスト Sarah Guo, Elad GilNo Priors looks back at 2023 through eight conversations on money, intelligence, jobs, and who should put the guardrails on a model.企業と戦略AI StrategyCompany Building+3
2:16:27EN
Cognitive Revolution "How AI Changes Everything"· 2023年12月12日ポッドキャストSuperhuman Go Has a Blind Spot. Balaji Bets on a War of the Godsホスト Nathan LabenzBalaji Srinivasan tells Nathan Labenz why adversarial inputs, missing actuators, and cryptography make a single runaway AI god less likely than a contest among many that still need priests.企業と戦略安全性とガバナンスOpen-weight models+3
32:57EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年11月9日ポッドキャスト · 2 人A 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AIホスト 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.オープンソースとコミュニティOpen-source ecosystemsOpen-weight models+3
スライド 3 枚4:42EN
MBMatthew Berman· 2023年10月17日Connecting AutoGen to a Local Model With LM StudioMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup and explains where agent behavior still needs work.エージェントOpen-weight modelsインフラとデプロイ
1:14:36EN
Cognitive Revolution "How AI Changes Everything"· 2023年10月13日ポッドキャストZulu Bypasses GPT-4, and a Hundred Examples Undo the Guardrailsホスト Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.セキュリティと安全性AI security安全性とガバナンス+3
1:04:38EN
Lex Fridman· 2023年9月28日ポッドキャスト · 2 人Hundreds of Miles Apart, Zuckerberg and Fridman Sat Close Enough to See the Wrinklesゲスト Mark Zuckerberg · ホスト Lex FridmanLex Fridman sat in a dark room in Austin wearing a Quest headset; Mark Zuckerberg sat in another conference room, also wearing one, and it did not feel like a video call.マルチモーダルとメディアProduct StrategyHuman-AI Collaboration+3
29:38EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月21日ポッドキャスト · 2 人A Beginner Just Shipped a $250K Business. Professionals Got 30 Percent.ホスト Sarah Guo, Elad GilReplit CEO Amjad Masad on Ghostwriter, why a 3-billion-parameter model was enough, and the agents he wants earning bounties while you sleep.コーディングエージェントDeveloper productivityOpen-weight models+3
30:03EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月7日ポッドキャスト · 2 人A Dinner-Plate Chip Cut Training From 60 Days to 3.5ホスト 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.インフラとデプロイInferenceAI cost & performance+3
52:48EN
Cognitive Revolution "How AI Changes Everything"· 2023年8月25日ポッドキャストOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the Listホスト 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.モデルと学習ファインチューニングAI cost & performance+3
スライド 3 枚12:20EN
MBMatthew Berman· 2023年7月10日Testing 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 models評価(Evals)モデルと学習+1
59:28EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月16日ポッドキャストFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Productホスト Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.モデルと学習ファインチューニングPretraining & midtraining+3
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