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245 件
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5:06:18EN
Lex Fridman· 2025年2月2日ポッドキャストDeepSeek's $5 million training claim and the five-year bet on computeホスト Lex FridmanDylan Patel and Nathan Lambert walk Lex Fridman through DeepSeek-V3 and R1, export controls, TSMC, megaclusters, and who actually makes money in the AI race.企業と戦略Open-weight modelsPretraining & midtraining+3
スライド 3 枚6:44EN
Matthew Berman· 2025年2月1日Testing DeepSeek Janus-Pro for Image Understanding and GenerationMatthew Berman · Forward FutureMatthew Berman tries the 7B multimodal model on diagrams, memes and image prompts using a sponsored cloud GPU demonstration.画像と動画Generative mediaMultimodal AI+2
スライド 5 枚7:41EN
Matthew Berman· 2025年1月30日CES 2025 and Running DeepSeek R1 Distills on an RTX 5090Matthew Berman · Forward FutureMatthew Berman tours AI demonstrations at CES and runs two quantized DeepSeek R1 distilled models locally on an NVIDIA GPU.Local inferenceOpen-weight modelsSemiconductors+2
1:00:02EN
20VC with Harry Stebbings· 2025年1月29日ポッドキャスト · 2 人Jonathan Ross on DeepSeek, Open Models and AI Infrastructureゲスト Jonathan Ross · ホスト Harry StebbingsThe Groq CEO discusses reported training costs, inference demand, competitive strategy and the security concerns surrounding AI.InferenceAI cost & performanceGeopolitics+2
スライド 3 枚5:27EN
Matthew Berman· 2025年1月29日Ways to Try DeepSeek R1: Hosted and Local Distilled ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates DeepSeek chat, Groq’s hosted distill and a local model in LM Studio.Local inferenceOpen-weight modelsQuantization
スライド 4 枚20:34EN
Matthew Berman· 2025年1月28日DeepSeek R1 and the Debate Over AI Costs and ComputeMatthew Berman · Forward FutureMatthew Berman examines 2025 reactions to DeepSeek, from training efficiency and market losses to open weights, privacy and the possibility of a demand rebound.モデルと学習AI cost & performanceOpen-weight models+2
1:48:20EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月25日ポッドキャストFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Tableホスト Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.モデルと学習Reinforcement LearningReasoning & test-time compute+3
スライド0:55EN
Matthew Berman· 2025年1月23日A Quick RTX 5090 Local AI TestMatthew Berman · Forward FutureMatthew Berman runs an 8B DeepSeek R1 distillation locally and reports the speed of one generation.Local inferenceSemiconductorsOpen-weight models
スライド 4 枚15:10EN
Matthew Berman· 2025年1月22日DeepSeek R1 Tested on Coding, Reasoning and Sensitive QuestionsMatthew Berman · Forward FutureMatthew Berman runs the full R1 model on cloud GPUs and tries game programming, reasoning puzzles and politically sensitive prompts.Reasoning & test-time compute評価(Evals)Open-weight models+2
スライド 5 枚8:52EN
Matthew Berman· 2025年1月21日Industry Reactions to DeepSeek R1’s 2025 ReleaseMatthew Berman · Forward FutureMatthew Berman reviews early reactions to DeepSeek R1, from open-weight reasoning models and local hardware to benchmark results and API pricing.Open-weight modelsReasoning & test-time computeBenchmarks+2
1:24:57EN
Unsupervised Learning: With Jacob Effron· 2025年1月21日ポッドキャストA 50,000-GPU cap and a 1,700-chip loopholeホスト Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.インフラとデプロイAI RegulationGeopolitics+3
スライド 3 枚12:15EN
Matthew Berman· 2025年1月20日DeepSeek-R1: Open-Weight Reasoning, Benchmark Results and a First TestMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s January 2025 release, compares reported benchmarks and prices, and tries a handful of reasoning prompts.Reasoning & test-time computeOpen-weight modelsReinforcement Learning+2
1:00:05EN
Sequoia Capital· 2025年1月14日ポッドキャストIf Someone Builds a Better Spark Product, We Deserve to LoseIon Stoica on how Databricks turned academic open source into a data-and-AI company — and which problems he thinks will matter more tomorrow than today.企業と戦略Company BuildingOpen-source ecosystems+3
3:53:49EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月3日ポッドキャストWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4oホスト Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.マルチモーダルとメディアMultimodal AI画像と動画+3
1:57:00EN
Cognitive Revolution "How AI Changes Everything"· 2024年12月28日ポッドキャストLess Than 1%: Why OpenAI's roon Thinks the Good Guys Get AGI Firstホスト Nathan LabenzDoom Debates host Liron Shapira presses OpenAI technical staffer roon on extinction odds, alignment-by-default, and a plan that even he admits is balancing on razor wire.セキュリティと安全性安全性とガバナンスAGI Timelines+3
27:07EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年12月26日ポッドキャスト · 2 人After adding $100 billion a month, Jensen Huang says NVIDIA is selling the data centerホスト Sarah Guo, Elad GilSarah Guo and Elad Gil clip their favorite 2024 No Priors conversations into a year-end reel of NVIDIA, Karpathy, Sierra, Sora, Waymo, Figma, and Scale.企業と戦略AI Strategyインフラとデプロイ+3
スライド6:16EN
Matthew Berman· 2024年12月8日Running Stable Diffusion 3.5 with ComfyUI on a Cloud GPUMatthew Berman · Forward FutureIn a Vultr-sponsored 2024 tutorial, Matthew Berman sets up a GPU instance and generates images with ComfyUI. Licensing, security, and prices require attention beyond the demonstration.Generative mediaインフラとデプロイOpen-weight models+1
スライド 7 枚27:54EN
Matthew Berman· 2024年12月5日AI News in December 2024: Genie 2, World Models, MCP and New AI ReleasesMatthew Berman · Forward FutureMatthew Berman surveys early interactive world models, voice and video tools, QwQ, distributed training and Amazon’s Nova announcements.World modelsOpen-weight modelsAI agents+1
1:49:41EN
Cognitive Revolution "How AI Changes Everything"· 2024年11月21日ポッドキャストA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Baseホスト Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.モデルと学習Post-trainingData quality & curation+3
0:28EN
Matthew Berman· 2024年11月6日Comparing Three Local Coding ModelsMatthew Berman · Forward FutureMatthew Berman introduces a sponsored comparison of DeepSeek Coder V2, Yi-Coder and Qwen Coder running on a workstation.Local inferenceコーディングエージェントOpen-weight models
スライド 3 枚11:56EN
Matthew Berman· 2024年11月3日IBM Granite 3.0 and InstructLab: Small Models for Enterprise AIMatthew Berman · Forward FutureMatthew Berman reviews IBM's 2024 model family and its approach to adding domain knowledge. IBM partnered on the video and invited him to its event.モデルと学習エンタープライズ AIファインチューニング+2
スライド 4 枚12:50EN
Matthew Berman· 2024年11月2日Comparing Three Local Coding Models on Games and Programming ChallengesMatthew Berman · Forward FutureMatthew Berman runs DeepSeek-Coder-V2 Lite, Yi-Coder 9B, and Qwen2.5-Coder 7B on a sponsored workstation. Their results vary by task and include manual corrections.Open-weight models評価(Evals)Software engineering+2
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月30日ポッドキャストMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.ホスト Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.モデルと学習Local inference評価(Evals)+3
スライド 6 枚10:39EN
Matthew Berman· 2024年10月29日Zamba2-7B: A Hybrid Mamba–Transformer Model Tested on Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman compares Zyphra’s efficiency claims with his own informal tests, including Python games, spatial puzzles and simple word tasks.モデルと学習Open-weight modelsBenchmarks+2
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