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49:41ZH
Zhang Xiaojun Podcast· 17 ago 2024Pódcast · 3 voces傅盛:大模型是生产力革命,机器人迎来新的窗口Invitado: 傅盛 · Presenta: 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 StrategyModelos y entrenamientoRobotics+2
2:04:15EN
Cognitive Revolution "How AI Changes Everything"· 14 ago 2024PódcastA parrot cannot paraphrase — and Claude cannot be talked into harmPresenta: 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.Modelos y entrenamientoModel InterpretabilityMultimodal AI+3
2 diapositivas16:06EN
MBMatthew Berman· 9 ago 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.Modelos y entrenamientoAI researchAGI
46:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 1 ago 2024Pódcast · 2 vocesOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedPresenta: 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.Modelos y entrenamientoAGI TimelinesPretraining & midtraining+3
3 diapositivas13:40EN
MBMatthew Berman· 29 jul 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 modelsEvaluacionesModelos y entrenamiento+1
0:58EN
MBMatthew Berman· 11 jul 2024NVIDIA NIM: Containerized AI Inference MicroservicesMatthew Berman · Forward FutureA sponsored overview of NVIDIA’s approach to packaging model inference for local and cloud applications.Infraestructura y despliegueModelos y entrenamiento
1:42:16EN
Cognitive Revolution "How AI Changes Everything"· 4 jul 2024Pódcast267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionPresenta: 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.Modelos y entrenamientoTransformers & attentionInference+3
34:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 27 jun 2024Pódcast · 2 vocesFive Independent Teams Found the Same 10-to-1 Mix That Beats TransformersPresenta: 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.Modelos y entrenamientoTransformers & attentionIA de voz+3
1:54:01EN
Cognitive Revolution "How AI Changes Everything"· 26 jun 2024PódcastA Tiny Network Grew a Salt Crystal the Training Data Never ShowedPresenta: 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.Modelos y entrenamientoAI for scienceFundamentos de ML+3
58:18EN
20VC with Harry Stebbings· 24 jun 2024Pódcast · 2 vocesDavid Luan on Model Scaling, AI Agents and Competition Across the Chip StackInvitado: David Luan · Presenta: Harry StebbingsThe Adept co-founder explains his views on compute, vertical integration, enterprise agents and why human interaction should shape AI product design.AI agentsModelos y entrenamientoSemiconductors+2
1:37:07EN
Cognitive Revolution "How AI Changes Everything"· 22 jun 2024PódcastA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Presenta: 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.Modelos y entrenamientoAI in healthcareMultimodal AI+3
5 diapositivas13:18EN
MBMatthew Berman· 20 jun 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 inferenceInferenceModelos y entrenamiento+2
1:06:04EN
20VC with Harry Stebbings· 12 jun 2024Pódcast · 2 vocesAlexandr Wang on AI Data, Model Development and Building ScaleInvitado: Alexandr Wang · Presenta: Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.Data quality & curationModelos y entrenamientoAI Strategy+2
1:03:12EN
20VC with Harry Stebbings· 5 jun 2024Pódcast · 2 vocesAravind Srinivas on Reasoning, Model Economics and Building PerplexityInvitado: Aravind Srinivas · Presenta: Harry StebbingsThe Perplexity co-founder discusses AI scaling, foundation-model competition, advertising and the challenges of building a search product.Modelos y entrenamientoReasoning & test-time computeAI Strategy+2
1:59:15EN
Cognitive Revolution "How AI Changes Everything"· 25 may 2024PódcastA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI BiologyPresenta: 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.Modelos y entrenamientoAI for scienceComputational Biology+3
58:28EN
Cognitive Revolution "How AI Changes Everything"· 22 may 2024PódcastFrom Jailbreaking ChatGPT to Anthropic’s First Prompt Engineer: How Alex Albert Ships Claude 3Presenta: 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.Modelos y entrenamientoSeguridad y gobernanzaEvaluaciones+3
2 diapositivas12:50EN
MBMatthew Berman· 22 may 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 modelsEvaluacionesModelos y entrenamiento
3 diapositivas13:37EN
MBMatthew Berman· 16 may 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 AIBenchmarksVisión y vídeo+1
Diapositivas27:07EN
MBMatthew Berman· 14 may 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 researchProducto y UXModelos y entrenamiento
4 diapositivas17:12EN
MBMatthew Berman· 30 abr 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.BenchmarksModelos y entrenamientoAgentes de programación+1
3 diapositivas16:28EN
MBMatthew Berman· 23 abr 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 AIBenchmarksModelos y entrenamiento+1
3 diapositivas15:01EN
MBMatthew Berman· 19 abr 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 modelsEvaluacionesModelos y entrenamiento+1
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 13 abr 2024PódcastGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?Presenta: 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.Modelos y entrenamientoScaling LawsSynthetic data+3
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 5 abr 2024PódcastA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens AheadPresenta: 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.Modelos y entrenamientoModel InterpretabilityTransformers & attention+3
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