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Categoría: Modelos de Infografia

Modelo generador de mindmaps e infografias

Este módulo transforma contenido textual estructurado (temas e ideas) en artefactos didácticos reutilizables: mapas conceptuales (mindmaps en Mermaid), infraestructura de infografías (Infographic IR) y derivados como guiones de podcast o prompts para generación de imágenes. Su diseño prioriza determinismo y trazabilidad: cuando falta un LLM, genera salidas coherentes mediante reglas y plantillas; cuando hay LLM,
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Entradas recientes

  • Trust Is a Metric: What an AI Summit Taught Me About Reliable Agents
    Why trust, not capability, is the bottleneck Agents can already do a lot. What keeps them out of production is whether we can trust them. Last week I attended World Summit AI in Amsterdam. Across the sessions I followed (workshops on agent harnesses, talks on agents that improve over time, and panels on hybrid architectures,
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  • Scaling AI models from research breakthroughs to real-world enterprise deployment
    Scaling AI models from research breakthroughs to real-world enterprise deployment involves a complex interplay of technological, organizational, and governance challenges. The transition from research to deployment is not merely a technical endeavor but also involves addressing various forms of friction that can impede the successful integration of AI into enterprise systems. This discussion will explore
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  • De los avances a la infraestructura: qué revela la estrategia de Tencent sobre el futuro de la IA
    Una conversación clave sobre «Scaling AI: From Breakthroughs to Real-World Impact» con Ling Ge (Chief Investment & Strategy Officer EMEA, Tencent) La inteligencia artificial está dejando de ser una promesa de laboratorio para convertirse en infraestructura fundacional: la capa invisible sobre la que operan industrias enteras. Esa es la tesis de una de las sesiones más
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  • Adversarial robustness and safe AI systems
    Adversarial robustness in AI systems refers to the ability of these systems to withstand and function correctly even when faced with adversarial inputs—inputs that are intentionally designed to cause the system to make errors. This is a critical area of research in AI safety, as adversarial attacks can exploit vulnerabilities in AI models, leading to
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  • Reseña: Building Safe AI Systems in an Unsafe World — Fireside Chat de clausura con Zico Kolter
    Sesión: Closing Fireside Chat — Building Safe AI Systems in an Unsafe World Ponente: Zico Kolter — miembro del Consejo de OpenAI; Chief Scientist de Gray Swan AI; catedrático y director del Machine Learning Department, Carnegie Mellon University. Formato: conversación de clausura sobre seguridad técnica y gobernanza de la IA. 1. Marco: quién habla y desde dónde La sesión reúne una
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