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Скачать или смотреть How AI Infrastructure Is Evolving: Models, Flywheels, and the Open Ecosystem

  • Madrona
  • 2025-10-28
  • 292
How AI Infrastructure Is Evolving: Models, Flywheels, and the Open Ecosystem
AI infrastructurelarge language modelsmodel architecturesopen source AIAI flywheelsAI systemsfrontier modelsreinforcement learningAI researchmodel fine-tuningmultimodal AIAI scalingdata qualitycompute infrastructureAI developmentmodel evaluationenterprise AIAI innovationintelligent systemsAI algorithmsAI engineeringAI stackmodel trainingopen ecosystemAI reliabilitymachine learning infrastructureAI Trust
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Описание к видео How AI Infrastructure Is Evolving: Models, Flywheels, and the Open Ecosystem

The infrastructure of reasoning is evolving fast, and the builders behind it are rethinking everything from model evaluation to the physics of AI flywheels. In this 2025 IA Summit discussion, Bloomberg's Dina Bass moderated a conversation with Vijay Karunamurthy (formerly of Scale AI), Noah Smith (Allen Institute for AI), and Jonathan Cohen (Nvidia) on what’s next for model architectures, open ecosystems, and agent reliability.

Vijay reflected on Scale’s work training data for reasoning models and described how “humanities last exam” benchmarks are exposing the limits of today’s models and pointing toward new approaches like parallel search and reasoning at scale.

Noah shared how reinforcement learning with verifiable rewards and higher-quality data are unlocking the next wave of model improvement, emphasizing that “the data always matters more than the algorithm.”

Jonathan explained why NVIDIA considers itself an “AI infrastructure company,” not just a chipmaker, and why building models like Nemotron openly is essential to advancing both scientific progress and practical innovation.

Together, the group discussed the rise of flywheel systems (self-improving AI architectures that learn from usage and feedback) and how they’ll shape the next generation of intelligent agents. As Cohen put it, “The more you use it, the better it gets.”

This conversation offers a rare inside look at how the researchers and engineers driving AI’s core infrastructure are thinking about the road ahead and what it will take to make reasoning systems trustworthy, scalable, and truly adaptive.

Chapters:
00:00 Introduction & Panelist Intros
01:30 Barriers to Enterprise AI Adoption
03:00 Infrastructure & Software Gaps
04:25 Model Refinement & Fine-Tuning
06:30 Reinforcement Learning & Data Quality
08:00 NVIDIA’s Full Stack Approach
09:45 Open vs. Closed Model Debate
12:35 Global Competition & Open Science
14:00 Scientific Discovery & AI Creativity
15:00 Flywheels & Self-Healing Agents
17:00 Mixture of Experts & New Architectures
18:45 The Future: Robotics, Multimodality, and AI-Accelerated Engineering
21:00 Nvidia's AI Mandate

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