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Stanford Online

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

KI-Zusammenfassung 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

EnglishZusammenfassung · Transkript

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

710: Warum lebt Manuel so ordentlich?

KI-Zusammenfassung 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

30:59GermanZusammenfassung · Transkript

Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 8 | Agentic Evaluations and Long Horizon Tasks

KI-Zusammenfassung Agentic evaluations measure the progress of AI models in completing long-horizon, economically valuable tasks. The METR benchmark tracks a seven-month doubling trend in task duration capabilities, yet highlights that high-reliability success remains elusive. Complementing this,…

  • Agentic evaluations
  • SweeBench
  • GDPval

EnglishZusammenfassung · Transkript

Stanford Online

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

KI-Zusammenfassung 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

EnglishZusammenfassung · Transkript

The Audit Podcast

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

KI-Zusammenfassung 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

19:15EnglishZusammenfassung · Transkript

Stanford Online

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

KI-Zusammenfassung 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)

EnglishZusammenfassung · Transkript

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