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Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 9 | Future Research Areas

AI 摘要 Self-improving agents utilize test-time and train-time scaling, driven by verifiers and reinforcement learning to enhance reasoning in domains like math and coding. Achieving generalization requires diverse reasoning chains, often generated by multi-agent systems or iterative…

  • LLMs (Large Language Models)
  • Test-time Scaling
  • Train-time Scaling

English摘要 · 转录

Easy German: Learn German with native speakers | Deutsch lernen mit Muttersprachlern

710: Warum lebt Manuel so ordentlich?

AI 摘要 Die persönliche Organisation und das Streben nach Ordnung stehen im Mittelpunkt, wobei die Diskussion die neurobiologischen Grundlagen von ADHS und das Konzept der Neurodiversität beleuchtet. Während feste Systeme für manche Menschen eine notwendige Stütze im Alltag darstellen,…

  • DSGVO
  • Truth Social
  • ADHS

30:59German摘要 · 转录

Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 7 | Self-Improvement and Deep Research Agents

AI 摘要 Improving large language model performance relies on leveraging search within the model's output space to curate high-quality solutions. AlphaCode demonstrated that massive-scale sampling, combined with filtering and clustering, allows models to solve complex competitive…

  • AlphaCode / AlphaCode 2
  • HumanEval
  • Gemini Pro

English摘要 · 转录

The Audit Podcast

IA on AI - Fall 2026 Audit Analytics & AI Conference: Key Takeaways

AI 摘要 The fall 2026 Audit Analytics & AI Conference emphasized practical, production-ready AI applications for internal audit departments. Effective strategies include persona-based reporting, where AI agents tailor communication styles to specific stakeholder preferences, and…

  • Benford's Law
  • Persona-based reporting
  • Shadow IT

19:15English摘要 · 转录

Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 6 | Train Time Scaling/Scaling RL

AI 摘要 Train time scaling enhances AI reasoning capabilities by allowing models to learn from their own outputs, effectively substituting compute for parameter count. This approach leverages verification-rich domains like mathematics and coding to create closed-loop feedback systems.…

  • Train Time Scaling
  • Test Time Scaling
  • AIME (American Invitational Mathematics Examination)

English摘要 · 转录

更多新节目

全部 英文 中文
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    The Manager Track

    摘要00:35:13
  2. Episode 526: Why can't I get promoted so quickly after my last promotion and I got hired as a software engineer but ended up doing ops

    Soft Skills Engineering

    摘要32:38
  3. 20VC: Uber President on The Untold Uber Stories: Travis, China and Self-Driving | Why Autonomy Is Existential | How to Beat DoorDash to #1 in Food with Andrew MacDonald

    The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

    摘要01:06:36
  4. 447. The psychology of PMOS/PCOS ft. Dr Hazel Wallace

    The Psychology of your 20s

    摘要1 h 15 min
  5. Trump envoy in Israel for Gaza peace negotiations

    Global News Podcast

    摘要29 min
  6. 535. What Nobody Will Admit About The Reform Takeover - Rafe Heydel-Mankoo

    Heretics | Andrew Gold

    摘要1 h 8 min
  7. Best of Design Matters: Lulu Miller

    Design Matters with Debbie Millman

    摘要1:22:16
  8. Maria Popova

    Design Matters with Debbie Millman

    摘要1:01:15
  9. We’ve solved many medical mysteries. Where are the cures? | Saloni Dattani

    TED Talks Daily

    摘要12:56
  10. MCP Goes Stateless

    Agentic Conversations (formally mlops.community)

    摘要00:52:27
  11. AI Hype vs. Real Value

    Agentic Conversations (formally mlops.community)

    摘要00:42:37
  12. What Happens When Every Developer Has 20 AI Agents?

    Agentic Conversations (formally mlops.community)

    摘要00:34:37
  13. AI Agents Should Be Treated Like Hackers

    Agentic Conversations (formally mlops.community)

    摘要00:31:35
  14. The Current State of Agentic Retrieval - Qdrant Roundtable

    Agentic Conversations (formally mlops.community)

    摘要00:58:54
  15. Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + Chronon

    Agentic Conversations (formally mlops.community)

    摘要00:51:27
  16. Architecting Modern AI Systems: Platforms, Agents, and Integration

    Agentic Conversations (formally mlops.community)

    摘要00:56:59
  17. Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce

    Agentic Conversations (formally mlops.community)

    摘要01:18:38
  18. How We Cut LLM Latency 70% With TensorRT in Production

    Agentic Conversations (formally mlops.community)

    摘要01:05:20
  19. Operationalizing AI Agents: From Experimentation to Production // Databricks Roundtable

    Agentic Conversations (formally mlops.community)

    摘要01:01:13
  20. The Future of Information Retrieval: From Dense Vectors to Cognitive Search

    Agentic Conversations (formally mlops.community)

    摘要01:02:53
  21. Rethinking Notebooks Powered by AI

    Agentic Conversations (formally mlops.community)

    摘要00:26:13
  22. Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale

    Agentic Conversations (formally mlops.community)

    摘要00:57:24
  23. The Hidden Bottlenecks Slowing Down AI Agents

    Agentic Conversations (formally mlops.community)

    摘要00:47:59
  24. AI Agent Development Tradeoffs You NEED to Know

    Agentic Conversations (formally mlops.community)

    摘要00:57:06
  25. AI Data Engineers - Data Engineering After AI // Vikram Chennai // #309

    Agentic Conversations (formally mlops.community)

    摘要00:49:40
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