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Zhang Xiaojun Podcast· 17 août 2024Podcast · 3 voix傅盛:大模型是生产力革命,机器人迎来新的窗口Invité : 傅盛 · Anime : 张小珺傅盛认为,大模型的核心是逻辑与推理能力,它带来的变化更像蒸汽机,而不是一次普通的互联网迭代。从平民化模型、服务机器人到创业者如何面对低谷,他也回看了自己的判断与心境。AI StrategyModèles et entraînementRobotics+2
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Cognitive Revolution "How AI Changes Everything"· 14 août 2024PodcastA parrot cannot paraphrase — and Claude cannot be talked into harmAnime : Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.Modèles et entraînementModel InterpretabilityMultimodal AI+3
2 diapositives16:06EN
MBMatthew Berman· 9 août 2024Q* and Strawberry: What Was Known and Speculated in August 2024Matthew Berman · Forward FutureMatthew Berman reviews contemporary rumors and research concepts around reasoning, search and self-play, while the underlying project remained undisclosed.Modèles et entraînementAI researchAGI
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No Priors: AI, Machine Learning, Tech, & Startups· 1 août 2024Podcast · 2 voixOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedAnime : Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.Modèles et entraînementAGI TimelinesPretraining & midtraining+3
3 diapositives13:40EN
MBMatthew Berman· 29 juil. 2024Testing 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 modelsÉvaluationsModèles et entraînement+1
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MBMatthew Berman· 11 juil. 2024NVIDIA NIM: Containerized AI Inference MicroservicesMatthew Berman · Forward FutureA sponsored overview of NVIDIA’s approach to packaging model inference for local and cloud applications.Infrastructure et déploiementModèles et entraînement
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Cognitive Revolution "How AI Changes Everything"· 4 juil. 2024Podcast267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionAnime : Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.Modèles et entraînementTransformers & attentionInference+3
34:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 27 juin 2024Podcast · 2 voixFive Independent Teams Found the Same 10-to-1 Mix That Beats TransformersAnime : Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.Modèles et entraînementTransformers & attentionIA vocale+3
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Cognitive Revolution "How AI Changes Everything"· 26 juin 2024PodcastA Tiny Network Grew a Salt Crystal the Training Data Never ShowedAnime : 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.Modèles et entraînementAI for scienceFondamentaux du ML+3
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20VC with Harry Stebbings· 24 juin 2024Podcast · 2 voixDavid Luan on Model Scaling, AI Agents and Competition Across the Chip StackInvité : David Luan · Anime : Harry StebbingsThe Adept co-founder explains his views on compute, vertical integration, enterprise agents and why human interaction should shape AI product design.AI agentsModèles et entraînementSemiconductors+2
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Cognitive Revolution "How AI Changes Everything"· 22 juin 2024PodcastA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Anime : Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.Modèles et entraînementAI in healthcareMultimodal AI+3
5 diapositives13:18EN
MBMatthew Berman· 20 juin 2024Testing Local AI on a Dual RTX 6000 WorkstationMatthew Berman · Forward FutureMatthew Berman tests language models, concurrent inference, and image generation on a Dell Precision tower supplied by Dell and NVIDIA.Local inferenceInferenceModèles et entraînement+2
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20VC with Harry Stebbings· 12 juin 2024Podcast · 2 voixAlexandr Wang on AI Data, Model Development and Building ScaleInvité : Alexandr Wang · Anime : Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.Data quality & curationModèles et entraînementAI Strategy+2
1:03:12EN
20VC with Harry Stebbings· 5 juin 2024Podcast · 2 voixAravind Srinivas on Reasoning, Model Economics and Building PerplexityInvité : Aravind Srinivas · Anime : Harry StebbingsThe Perplexity co-founder discusses AI scaling, foundation-model competition, advertising and the challenges of building a search product.Modèles et entraînementReasoning & test-time computeAI Strategy+2
1:59:15EN
Cognitive Revolution "How AI Changes Everything"· 25 mai 2024PodcastA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI BiologyAnime : Nathan LabenzThe White House set a thousand-fold lower reporting bar for biological models than for language models—and this conversation shows why that gap already looks prescient.Modèles et entraînementAI for scienceComputational Biology+3
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Cognitive Revolution "How AI Changes Everything"· 22 mai 2024PodcastFrom Jailbreaking ChatGPT to Anthropic’s First Prompt Engineer: How Alex Albert Ships Claude 3Anime : Nathan LabenzA year after building jailbreakchat.com, Alex Albert walks Nathan Labenz through Claude 3’s writing, orchestration, character work, and the guardrails app developers still skip.Modèles et entraînementSécurité et gouvernanceÉvaluations+3
2 diapositives12:50EN
MBMatthew Berman· 22 mai 2024Testing Smaug: Hosted 70B Versus a Smaller Local ModelMatthew Berman · Forward FutureMatthew Berman compares two Smaug configurations on Snake, math and reasoning puzzles.Open-weight modelsÉvaluationsModèles et entraînement
3 diapositives13:37EN
MBMatthew Berman· 16 mai 2024Testing Gemini 1.5 Pro’s Long Context and Video UnderstandingMatthew Berman · Forward FutureMatthew Berman’s 2024 tests show useful image and video summaries alongside blocked code and missed details.Multimodal AIBenchmarksVision et vidéo+1
Diapositives27:07EN
MBMatthew Berman· 14 mai 2024GPT-4o's Launch: Voice, Vision and a More Conversational InterfaceMatthew Berman, Mira Murati · Forward FutureMatthew Berman reviews OpenAI's May 2024 demonstrations of GPT-4o and considers what a faster multimodal assistant could enable.AI researchProduit et UXModèles et entraînement
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MBMatthew Berman· 30 avr. 2024Testing the Mystery 'gpt2-chatbot' Model in April 2024Matthew Berman · Forward FutureMatthew Berman tries an anonymous model on coding, arithmetic and reasoning tasks while its identity remains uncertain.BenchmarksModèles et entraînementAgents de code+1
3 diapositives16:28EN
MBMatthew Berman· 23 avr. 2024Testing Reka Core: Multimodal Benchmarks, Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman reviews Reka’s reported model comparisons and tests Core on code, logic and image understanding.Multimodal AIBenchmarksModèles et entraînement+1
3 diapositives15:01EN
MBMatthew Berman· 19 avr. 2024Testing 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 modelsÉvaluationsModèles et entraînement+1
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 13 avr. 2024PodcastGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?Anime : Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.Modèles et entraînementScaling LawsSynthetic data+3
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 5 avr. 2024PodcastA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens AheadAnime : Nathan LabenzNortheastern’s Bau Lab shows that a 40,000-parameter soft prompt can read a model’s future words, and that about ten attention heads carry the task itself.Modèles et entraînementModel InterpretabilityTransformers & attention+3
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