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artificial","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1454163424":{"id":"flexible-column-container-3073ceef67","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"seo-pillar-hero__content","layout":"SIMPLE","id":"container-7da5c374a2",":items":{"text_copy":{"id":"text-f397877552","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGUÍA FUNDAMENTAL\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-98c8542808","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Inteligencia artificial: guía completa"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-a4ed99e3e5","additionalClasses":"seo-hub-hero__subheadline","text":"\u003Cp\u003ELa IA empresarial ya está en producción, pero la arquitectura, la estrategia de datos y la gobernanza todavía intentan ponerse al día. En esta guía se explica cómo se construyen los sistemas de inteligencia artificial (IA), dónde tienen éxito o fracasan y qué deben tener en cuenta las empresas para gestionarlos de forma eficaz.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-d1f88fa8f5",":items":{"content_chip_copy":{"id":"content-chip-f0025090cd","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer la biografía"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d2c1126d-3244-486b-9007-1d2bc56e1893/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Redactora técnica, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip":{"id":"content-chip-ee4bfbb603","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer la biografía"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--af497c82-10f0-4f45-805e-7f1c4677a824/david.jpg?quality=85&preferwebp=true","alt":"David Gaule","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Editor técnico, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip_copy","content_chip"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["text_copy","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"seo-hub-hero__video","layout":"SIMPLE","id":"container-9cd6d5bd79",":items":{"youtube":{"id":"embed-437ee5d8a9","youtubeVideoId":"-HWNc-Hd90U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_841934424":{"id":"flexible-column-container-6f51c745d5","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"border-top","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container","layout":"SIMPLE","id":"container-02ab1f4336",":items":{"text_894059747":{"id":"text-36122cdf3e","text":"\u003Cp\u003ETemas sobre inteligencia artificial:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-dfe06ecbfa","additionalClasses":"related-topics","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-compliance/\"\u003ECumplimiento de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-ethics/\"\u003EÉtica de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/\"\u003EGobernanza de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/business\"\u003EIA en las empresas\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/\"\u003EAI Observability\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EGestión de riesgos de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ETrazabilidad de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003ETransparencia de la IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003ESesgo algorítmico\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/deep-learning/\"\u003EAprendizaje profundo\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/eu-ai-act/\"\u003ELey de IA de la UE\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/feature-engineering/\"\u003EIngeniería de características\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO/IEC 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003EAprendizaje automático\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/inference/\"\u003EInferencia de ML\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/responsible-ai/\"\u003EIA responsable\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont","flexible_column_cont_1454163424","flexible_column_cont_841934424"],"appliedCssClassNames":"snowflake-container",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],"appliedCssClassNames":"ui-background-05 snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_626207224_":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container_1561483981_602520842":"aem-GridColumn aem-GridColumn--default--12","container_805795510":"aem-GridColumn aem-GridColumn--default--12","container_1561483981":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"hubBody",":items":{"container_805795510":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-cec233b943",":items":{"flexible_column_cont":{"id":"flexible-column-container-04bdd7ef13","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"hub-body","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"container-8d38b3105b",":items":{"text_1521899678":{"id":"text-7a7b70afc7","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDEFINICIÓN DE INTELIGENCIA ARTIFICIAL\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa inteligencia artificial (IA) es el amplio campo de los sistemas capaces de realizar tareas asociadas al razonamiento humano, como el reconocimiento de patrones, la predicción, la comprensión del lenguaje y el apoyo a la toma de decisiones.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-f39aabbbfc","text":"\u003Cp\u003ELa mayoría de las organizaciones empresariales no se encuentran al principio de su viaje hacia la IA: tienen modelos en producción, proyectos piloto en marcha y conversaciones sobre gobernanza que todavía intentan seguir el ritmo de la implementación. Ahora los equipos deben decidir cómo elegir la arquitectura de IA, las dependencias de datos y la estrategia de evaluación adecuadas, al tiempo que aplican controles coherentes de gobernanza, seguridad y operaciones a lo largo de todo el ciclo de vida de la IA, a menudo mediante prácticas de \u003Ca href=\"https://www.snowflake.com/es/fundamentals/mlops/\"\u003EMLOps\u003C/a\u003E como los pipelines de implementación, la supervisión y el control de versiones de modelos.\u003C/p\u003E\n\u003Cp\u003EEsto requiere algo más que una estrategia de IA general. Los modelos predictivos, los clasificadores de documentos, las aplicaciones de generación aumentada por recuperación (RAG) y los sistemas de agentes de IA se construyen de forma distinta, fallan de forma diferente y plantean exigencias distintas en cuanto a datos, infraestructura y supervisión. Las decisiones sobre qué construir, qué escalar y qué gobernar deben tener en cuenta esas diferencias sin fragmentar los controles empresariales asociados a ellas.\u003C/p\u003E\n\u003Cp\u003EEsta guía constituye un punto de referencia para esa tarea. Abarca las principales disciplinas de la IA, cómo se entrenan y evalúan los sistemas, dónde las empresas los están implementando de forma eficaz y qué factores de gobernanza, seguridad y datos determinan si una implementación se mantiene con el paso del tiempo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1818028417":{"id":"title-v2-47699f5555","additionalClasses":"anchor title anchor-title--what-is-artificial-intelligence","type":"heading2","lines":["¿Qué es la inteligencia artificial?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-7cece4767b","text":"\u003Cp\u003ELa inteligencia artificial no es una disciplina única, sino un amplio campo formado por numerosas subdisciplinas interconectadas. Por ejemplo, el \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/machine-learning/\"\u003Eaprendizaje automático\u003C/a\u003E (ML) es un subconjunto de la IA: sistemas que aprenden patrones estadísticos a partir de los datos en lugar de seguir reglas explícitas. El aprendizaje profundo es un subconjunto del ML: \u003Ca href=\"https://www.snowflake.com/es/fundamentals/neural-network/\"\u003Eredes neuronales\u003C/a\u003E organizadas en capas que aprenden representaciones de datos complejos de alta dimensionalidad. La IA generativa es un producto del aprendizaje profundo: modelos fundacionales que producen resultados nuevos en lugar de clasificar entradas existentes.\u003C/p\u003E\r\n\u003Cp\u003ECada capa hereda los requisitos de calidad de los datos, infraestructura y evaluación de la capa superior. Una implementación de IA generativa es también una implementación de ML, con todo lo que ello conlleva.\u003C/p\u003E\r\n\u003Cp\u003EEsta disciplina también distingue entre la IA débil (o estrecha) y la IA general (AGI). La IA débil, que describe todos los sistemas de IA actualmente en producción, está optimizada para una tarea o un dominio específicos. Las decisiones empresariales relativas a la IA siempre implican IA débil, así como los marcos de gobernanza, las prácticas de evaluación y los modelos de riesgo que se le aplican. La AGI, un sistema capaz de razonar y aprender en dominios arbitrarios a un nivel humano o superior, sigue siendo un objetivo de investigación.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item_1152526200":{"id":"quote-item-1ae817ea3c","alignment":"left","layout":"simple","quoteSourceName":"Baris Gultekin","quoteSourceTitle":"VP of Product, AI, Snowflake","showQuoteIcon":true,"quote":"La IA abre las puertas a que las organizaciones obtengan información valiosa a partir de datos no estructurados que antes simplemente no podían aprovechar”.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_v2_copy_1903657955":{"id":"title-v2-fe9f9cc55c","additionalClasses":"anchor-title anchor-title--how-does-artificial-intelligence-work","type":"heading2","lines":["¿Cómo funciona la inteligencia artificial?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-739406d846","text":"\u003Cp\u003ELa mayoría de los sistemas de IA siguen el mismo bucle básico: los datos entran, un modelo aprende de ellos y ese modelo se utiliza después para generar predicciones o resultados con datos nuevos. Las tres fases (entrenamiento, inferencia y ajuste fino) conllevan requisitos distintos de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-infrastructure/\"\u003Einfraestructura\u003C/a\u003E y \u003Ca href=\"https://www.snowflake.com/es/data-governance/\"\u003Egobernanza\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003EEl \u003Cb\u003Eentrenamiento\u003C/b\u003E es el proceso de exponer un modelo a grandes cantidades de datos etiquetados o sin etiquetar para que aprenda a reconocer patrones. En los sistemas de aprendizaje profundo, esto suele implicar arquitecturas de transformadores que utilizan mecanismos de atención para aprender qué partes de la entrada son más relevantes para un resultado determinado. Entrenar modelos fundacionales (los grandes modelos base en los que se apoyan la mayoría de las aplicaciones de IA generativa) requiere una capacidad de cómputo a una escala que históricamente solo ha estado al alcance de grandes organizaciones de investigación y proveedores de nube.\u003C/p\u003E\r\n\u003Cp\u003ELa \u003Cb\u003Einferencia\u003C/b\u003E es lo que ocurre cuando se utiliza un modelo entrenado. Un usuario envía una consulta, el modelo genera una respuesta y esa respuesta se devuelve prácticamente en tiempo real. La latencia, el coste y la fiabilidad de la inferencia son cuestiones operativas que cobran importancia a gran escala, especialmente en las aplicaciones orientadas al cliente.\u003C/p\u003E\r\n\u003Cp\u003EEl \u003Cb\u003Eajuste fino supervisado\u003C/b\u003E adapta un modelo previamente entrenado a un dominio o una tarea específicos utilizando un conjunto de datos más pequeño y especializado. Es una vía habitual para las organizaciones que quieren las capacidades de un modelo fundacional sin entrenarlo desde cero, pero introduce el riesgo de degradación del modelo si los datos del ajuste fino se alejan con el tiempo de las entradas de producción.\u003C/p\u003E\r\n\u003Cp\u003EEl verdadero cuello de botella en la implementación de la IA rara vez es el propio modelo. En una encuesta de \u003Ca href=\"https://www.technologyreview.com/2025/10/23/1125651/redefining-data-engineering-in-the-age-of-ai/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EMIT Technology Review Insights\u003C/a\u003E a 400 altos directivos de datos y tecnología, el 45 % de los encuestados citó la complejidad de la integración y el 40 %, la gobernanza de datos como los mayores retos de las iniciativas de IA. La calidad de los datos, la gobernanza y la infraestructura de cómputo condicionan los resultados con mayor consistencia que la elección del modelo.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_2059046899":{"id":"text-ae3e1373a8","additionalClasses":"callout callout--general","text":"\u003Cul\u003E\n\u003Cli\u003EEl \u003Cb\u003E45 %\u003C/b\u003E de los directivos cita la complejidad de la integración como uno de los principales retos de las iniciativas de IA.\u003C/li\u003E\n\u003Cli\u003EEl \u003Cb\u003E40 %\u003C/b\u003E cita la gobernanza de datos como uno de los principales obstáculos para el éxito de la IA.\u003C/li\u003E\n\u003Cli\u003EEl \u003Cb\u003E37 %\u003C/b\u003E del tiempo de ingeniería de datos se dedica actualmente a proyectos de IA (frente al 19 % en 2023).\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text"},"quote_item":{"id":"quote-item-34a15d57ca","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"Medir el valor que ofrece la IA es fundamental para justificar la inversión en ella, priorizar las aplicaciones y sopesar sus beneficios futuros frente a los posibles riesgos”.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_1948751468":{"id":"text-d10c71ac9a","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EERROR HABITUAL\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELos equipos que se centran en seleccionar modelos o aplicarles ajuste fino antes de construir unos fundamentos sólidos de datos, evaluación y gobernanza suelen encontrarse con problemas en producción difíciles de diagnosticar y solucionar.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_2049864016":{"id":"text-3ba54447ec","text":"\u003Cp\u003E\u003Ci\u003E\u003Cu\u003E\u003C/u\u003EMira un videotutorial sobre cómo empezar con Snowflake Cortex Agents:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube_1156321798":{"id":"embed-65c99875d9","youtubeVideoId":"SDqyB-yWaAE","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_v2_copy":{"id":"title-v2-42032c3b6e","additionalClasses":"anchor-title anchor-title--types-of-ai","type":"heading2","lines":["Tipos de IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_831105251":{"id":"text-4010ca9c68","text":"\u003Cp\u003ELos investigadores suelen clasificar los sistemas de IA en cuatro categorías según sus capacidades de memoria y razonamiento:\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EReactiva → Memoria limitada → Teoría de la mente → Autoconsciente\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EEn la práctica, todos los sistemas actualmente en producción pertenecen a los dos primeros tipos.\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ETipo\u003C/th\u003E\n\u003Cth\u003EDescripción\u003C/th\u003E\n\u003Cth\u003EEjemplo\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EReactiva\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EResponde a las entradas sin memoria de interacciones pasadas\u003C/td\u003E\n\u003Ctd\u003EAlertas de anomalías en tiempo real, filtros de spam\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMemoria limitada\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EUtiliza datos históricos para fundamentar las decisiones actuales\u003C/td\u003E\n\u003Ctd\u003ESistemas de recomendación, modelos de detección de fraude\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ETeoría de la mente\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003EComprendería la intención y el estado emocional humanos\u003C/td\u003E\n\u003Ctd\u003EAún no existe fuera de la investigación\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAutoconsciente\u003C/b\u003E\u003C/td\u003E\n\u003Ctd\u003ETendría conciencia subjetiva y conocimiento de sí misma\u003C/td\u003E\n\u003Ctd\u003ETeórica\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003ELa implicación práctica: la IA actual opera dentro de contextos delimitados. Un modelo que funciona bien en una tarea lo hace porque fue entrenado para esa tarea y evaluado con datos que se asemejan a los que ve en producción. Los sistemas que parecen generalizar suelen ser modelos de memoria limitada muy grandes o sistemas específicos con una amplia cobertura de entrenamiento, no inteligencia general.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_565269666":{"id":"title-v2-812e901dca","additionalClasses":"anchor-title anchor-title--core-ai-disciplines","type":"heading2","lines":["Disciplinas fundamentales de la IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1898884637":{"id":"text-602c3d4d26","text":"\u003Cp\u003ELas siguientes disciplinas representan las principales ramas de la IA aplicada. Cada una tiene sus propios métodos, indicadores de referencia y modos de fallo. Para los equipos que crean o evalúan sistemas de IA, entender en qué disciplina se basa una aplicación determinada es el punto de partida para plantear las preguntas adecuadas sobre los requisitos de datos, los criterios de evaluación y el riesgo.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEl aprendizaje automático\u003C/b\u003E entrena modelos estadísticos con datos históricos para clasificar entradas, predecir resultados o detectar patrones. Las decisiones de diseño clave —ingeniería de características, ajuste de hiperparámetros, métodos de ensamblado— implican compromisos entre la complejidad del modelo, la interpretabilidad y la capacidad de generalización. La mayor parte de la analítica predictiva empresarial funciona con ML.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEl aprendizaje profundo\u003C/b\u003E utiliza redes neuronales de muchas capas para aprender representaciones de datos complejos: imágenes, audio, texto y señales de series temporales. Varias técnicas fundamentales, como el descenso de gradiente y la retropropagación, permiten a estas redes aprender características de forma automática en lugar de requerir una ingeniería explícita. El aprendizaje profundo impulsa la mayor parte de lo que actualmente se describe como IA en las aplicaciones de consumo. \u003Ca href=\"https://www.snowflake.com/es/fundamentals/deep-learning/\"\u003EMás información sobre el aprendizaje profundo >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEl procesamiento del lenguaje natural\u003C/b\u003E (PLN) permite a los sistemas interpretar, clasificar y generar lenguaje humano. El PLN moderno se basa en gran medida en modelos de lenguaje basados en transformadores y sustenta aplicaciones que van desde la clasificación de documentos hasta las interfaces conversacionales y la generación de código. \u003Ca href=\"https://www.snowflake.com/es/fundamentals/natural-language-processing/\"\u003EMás información sobre el procesamiento del lenguaje natural >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa visión artificial\u003C/b\u003E aplica el aprendizaje profundo a los datos visuales (imágenes y vídeo) para tareas como la detección de objetos, la clasificación de imágenes, la identificación de defectos y el reconocimiento facial. Está ampliamente implantada en los sectores de fabricación, logística, sanidad y seguridad. \u003Ca href=\"https://www.snowflake.com/es/fundamentals/computer-vision/\"\u003EMás información sobre la visión artificial >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEl aprendizaje por refuerzo\u003C/b\u003E entrena agentes para que realicen acciones en un entorno con el fin de maximizar una señal de recompensa. Es la base de sistemas de juego como AlphaGo y es cada vez más relevante para la robótica, los sistemas autónomos y, de forma modificada, el entrenamiento de modelos de lenguaje mediante el aprendizaje por refuerzo a partir de la retroalimentación humana (RLHF). \u003Ca href=\"https://www.snowflake.com/en/fundamentals/reinforcement-learning/\"\u003EMás información sobre el aprendizaje por refuerzo >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa IA generativa\u003C/b\u003E utiliza grandes modelos fundacionales —a menudo denominados \u003Ca href=\"https://www.snowflake.com/es/fundamentals/large-language-model/\"\u003Elarge language models\u003C/a\u003E (LLM) cuando se aplican a texto— para producir contenido nuevo: texto, código, imágenes, audio y datos sintéticos. Entre sus capacidades clave se incluyen el aprendizaje zero-shot (realizar tareas sin ejemplos de entrenamiento específicos) y el prompting de cadena de pensamiento (guiar a los modelos para que razonen un problema paso a paso). La velocidad de adopción ha superado las prácticas de evaluación en muchas organizaciones, lo que supone un riesgo de gobernanza que aparece en producción antes que en la planificación. \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/generative-ai/\"\u003EMás información sobre IA generativa >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ELa IA multimodal\u003C/b\u003E combina entradas de distintas modalidades (texto, imágenes, audio y vídeo) en un espacio compartido de representaciones vectoriales (embedding), lo que permite que un único modelo razone a través de distintos tipos de datos. Representa la frontera actual del desarrollo de modelos fundacionales y es cada vez más relevante para aplicaciones que necesitan procesar documentos, diagramas y medios no estructurados de forma conjunta.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_2089711109":{"id":"title-v2-9be9e7e38e","additionalClasses":"anchor-title anchor-title--ai-agents-and-agentic-systems","type":"heading2","lines":["Agentes de IA y sistemas de agentes de IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1819706580":{"id":"text-576afeeb28","text":"\u003Cp\u003EUn agente de IA es mucho más capaz que un simple chatbot. Un agente puede planificar en varios pasos, usar herramientas externas, recuperar contexto de fuentes externas y realizar acciones. Por ejemplo, un chatbot de atención al cliente puede responder preguntas, pero un agente puede recibir una consulta, recuperar el historial de la cuenta correspondiente, contrastarlo con una base de conocimientos, redactar una resolución y abrir un ticket de soporte, casi todo de forma autónoma.\u003C/p\u003E\r\n\u003Cp\u003EEsta capacidad es también lo que convierte a los sistemas de agentes de IA en la categoría de mayor riesgo del stack de \u003Ca href=\"https://www.snowflake.com/es/fundamentals/enterprise-ai/\"\u003EIA empresarial\u003C/a\u003E. Los errores se acumulan a lo largo de los pasos, por lo que la supervisión humana debe planificarse antes de la implementación.\u003C/p\u003E\r\n\u003Cp\u003EAdemás, los agentes de IA solo son tan buenos como la información a la que pueden acceder: \u003Ca href=\"https://www.snowflake.com/en/blog/easy-button-context-rich-ai-agents/\"\u003Ela calidad de la recuperación, la gestión del contexto y la fiabilidad de las herramientas\u003C/a\u003E son tan importantes como el propio modelo. Un agente que opera con datos obsoletos, incompletos o mal delimitados produce a gran velocidad errores plausibles y aparentemente fiables.\u003C/p\u003E\r\n\u003Cp\u003ELa IA agéntica es actualmente el área de la IA empresarial que evoluciona más rápido, y los marcos de gobernanza todavía se están poniendo al día. Los equipos que crean sistemas de agentes de IA deben definir el alcance, las vías de escalado y los modos de fallo antes de la implementación, no después. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003EMás información sobre los agentes de IA >\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1848786618":{"id":"title-v2-c39cf064ae","additionalClasses":"anchor-title anchor-title--enterprise-ai-use-cases","type":"heading2","lines":["Casos de uso de la IA empresarial"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_992156203":{"id":"text-0ad1eab580","text":"\u003Cp\u003ELa adopción de la IA empresarial tiende a concentrarse allí donde el flujo de trabajo, las entradas de datos y los criterios de evaluación se comprenden bien. Los siguientes ejemplos se centran en áreas donde la IA ya se está aplicando en producción, en lugar de experimentos especulativos o en fases iniciales.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EEl apoyo a la toma de decisiones\u003C/b\u003E complementa el criterio de analistas y operadores con recomendaciones generadas por modelos, como la puntuación de riesgos, la suscripción de créditos, el apoyo a las decisiones clínicas o la optimización de la cadena de suministro.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ELa automatización\u003C/b\u003E gestiona el trabajo de gran volumen y basado en gran medida en reglas: procesamiento de documentos, clasificación de correos electrónicos, enrutamiento de tickets de soporte, entrada de datos y revisión de código. El valor está en la capacidad de procesamiento y la coherencia, no en la novedad.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ELa generación de contenido\u003C/b\u003E utiliza la IA generativa para producir borradores, resúmenes, traducciones, descripciones de productos y documentación interna a gran escala. Aquí, el control de calidad y la infraestructura de evaluación importan más que la selección del modelo.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ELa personalización\u003C/b\u003E impulsa motores de recomendación, precios dinámicos, marketing individualizado y experiencias de producto adaptativas. Estos sistemas suelen ser las implementaciones de ML más antiguas de una organización y arrastran la mayor deuda técnica histórica.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ELa detección de fraudes y anomalías\u003C/b\u003E utiliza el aprendizaje profundo para identificar patrones que se desvían de la línea de base: fraude financiero, intrusiones en la red, defectos de fabricación y problemas de calidad de los datos.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ELa previsión\u003C/b\u003E aplica modelos de series temporales y ML a la planificación de la demanda, la gestión de la capacidad, las proyecciones financieras y el modelado de riesgos.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EMira esta presentación para entender cómo la IA generativa y la computación acelerada están transformando todos los sectores:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube_1030707130":{"id":"embed-6fc3c615a5","youtubeVideoId":"O8sQxPgw8Ws","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"card_v2":{"id":"card-v2-dbec2979e4","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Historia del cliente: TS Imagine"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/ts-imagine/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer la historia"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3b812ed0-848d-4714-96e8-ce82ea5f8ebb/ts-imagine%25403x.png?quality=85&preferwebp=true","alt":"TSImagine Logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ETS Imagine, una empresa de software de negociación financiera y gestión de riesgo, automatizó la recepción de correos electrónicos y la clasificación de tickets de soporte mediante RAG de Snowflake Cortex AI, recuperando 4000 horas de trabajo manual al año y reduciendo los costes en un 30 %.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_v2_copy_copy":{"id":"title-v2-d70a864ded","additionalClasses":"anchor-title anchor-title--ai-governance-risk-and-security","type":"heading2","lines":["Gobernanza, riesgo y seguridad de la IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110":{"id":"text-a2689d1228","text":"\u003Cp\u003ELa gobernanza de la IA ya no es un ejercicio de planificación para una implementación futura. Muchas organizaciones están gobernando ahora sistemas que ya están en producción, incluidos modelos y aplicaciones creados antes de que los controles empresariales, las estructuras de propiedad y las prácticas de supervisión estuvieran completamente definidos.\u003C/p\u003E\r\n\u003Cp\u003EEl entorno normativo ha hecho que esta cuestión sea más urgente. La \u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/eu-ai-act/\"\u003ELey de IA de la UE\u003C/a\u003E establece requisitos diferenciados en función del nivel de riesgo: los sistemas utilizados en contratación, crédito, cuerpos de seguridad e infraestructuras críticas están sujetos a las obligaciones más estrictas. El marco \u003Ca href=\"https://csrc.nist.gov/projects/risk-management/about-rmf\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EAI Risk Management Framework de NIST\u003C/a\u003E proporciona una estructura voluntaria, pero ampliamente adoptada, para identificar, evaluar y gestionar el riesgo de la IA a lo largo del ciclo de vida del modelo. Ambos parten de la base de que la gobernanza empieza en el diseño, no en la implementación, lo que significa que las organizaciones que se saltaron ese paso ahora están construyendo de forma retroactiva las estructuras que deberían haber existido desde el principio.\u003C/p\u003E\r\n\u003Cp\u003EEl riesgo de la IA empresarial se divide en tres categorías:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EEl riesgo de los datos\u003C/b\u003E abarca la calidad, el sesgo, la procedencia y la privacidad de los datos de entrenamiento.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEl riesgo del modelo\u003C/b\u003E abarca la precisión, la calibración, la degradación y la interpretabilidad.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEl riesgo operativo\u003C/b\u003E abarca los efectos posteriores de los resultados del modelo en las decisiones, las personas y los sistemas.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EGestionar esos riesgos requiere una evaluación sistemática: procesos definidos para probar el comportamiento del modelo antes de la implementación, monitorizarlo en producción y detectar la degradación antes de que se convierta en un problema empresarial.\u003C/p\u003E\r\n\u003Cp\u003EEl mismo patrón se repite en las implementaciones empresariales: los equipos que invierten pronto en evaluaciones detectan los modos de fallo antes de que lleguen a producción, mientras que los que no lo hacen acaban aplicando ingeniería inversa a los problemas a partir de las quejas de los usuarios.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item_1218174521":{"id":"quote-item-2efe386979","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"La manera de ampliar el uso de la IA de forma eficaz y aumentar el valor que ofrece es mediante la operacionalización y la industrialización. Para ello, es necesario que la tecnología vaya en sintonía con el negocio, coordinar los recursos y normalizar las políticas y los procesos”.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_1896381897":{"id":"text-06e1fbc918","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/es/artificial-intelligence/ai-governance/\"\u003EMás información sobre la gobernanza de la IA \u003E\u003C/a\u003E | \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003EMás información sobre la seguridad de la IA \u003E\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title_v2_copy_copy_1670228814":{"id":"title-v2-2a3611bb06","additionalClasses":"anchor-title anchor-title--the-data-foundation-behind-ai","type":"heading2","lines":["El fundamento de datos detrás de la IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110_19424502":{"id":"text-4ee18767be","text":"\u003Cp\u003ELa calidad de la IA depende de la calidad de los datos. Esta relación se mantiene en todas las disciplinas tratadas en esta guía: un modelo de aprendizaje profundo entrenado con datos no representativos fallará de la misma manera que falla una regresión logística entrenada con datos deficientes, solo que de forma menos evidente.\u003C/p\u003E\n\u003Cp\u003ELos requisitos de datos de la IA moderna van más allá de los conjuntos de datos limpios y etiquetados. Incluyen controles de acceso gobernados (para que los modelos se entrenen con datos que los equipos están autorizados a usar), definiciones de negocio claras (para que las características signifiquen lo mismo en los entornos de entrenamiento y de producción), linaje y procedencia (para que los equipos puedan rastrear el comportamiento de un modelo hasta sus datos de entrada) y una infraestructura de alto rendimiento capaz de servir datos a los modelos en la inferencia sin introducir latencia ni cuellos de botella.\u003C/p\u003E\n\u003Cp\u003ELa realidad operativa es que el trabajo de ingeniería de datos se ha orientado sustancialmente hacia la IA. La encuesta de MIT Technology Review Insights reveló que los ingenieros de datos dedican ahora el 37 % de su tiempo a proyectos de IA, frente al 19 % en 2023, es decir, casi el doble en dos años. Ese cambio está presionando a los equipos de plataformas de datos para que los datos listos para la IA estén disponibles de forma fiable, no solo teóricamente accesibles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_946526351":{"id":"card-v2-1ca5a04e5a","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Historia del cliente: State Street"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/state-street/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer la historia"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d747d8ae-ec39-45c3-b3f3-c4c3f9a7e443/state-street%25403x.png?quality=85&preferwebp=true","alt":"State Street logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EState Street logró multiplicar por 25 la productividad de sus operaciones de datos al implementar la detección de anomalías mediante aprendizaje profundo en Snowflake, con una tasa de detección del 100 % de las excepciones reales y la eliminación del 87 % de los falsos positivos. Los costes laborales de incorporación de clientes se redujeron un 50 %.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"text_copy_copy_83110_69230348":{"id":"text-b99dc45d39","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/es/product/ai/\"\u003EMás información sobre Snowflake para IA >\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_1534133931":{"id":"title-v2-1b934eddbc","additionalClasses":"anchor-title anchor-title--history-of-ai","type":"heading2","lines":["Historia de la IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy_83110_670475437":{"id":"text-389a44b417","additionalClasses":"callout callout--general","text":"\u003Cdiv\u003E \u003C/div\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E1950\u003C/b\u003E: El pionero de la informática Alan Turing propone el test de Turing como medida de la inteligencia de las máquinas.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E1956\u003C/b\u003E: La conferencia de Dartmouth acuña el término “inteligencia artificial”.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E1997\u003C/b\u003E: Deep Blue de IBM derrota al campeón mundial de ajedrez Garry Kasparov.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E2012\u003C/b\u003E: AlexNet gana la competición ImageNet y el aprendizaje profundo se convierte en el paradigma dominante de la visión artificial.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E2017\u003C/b\u003E: Investigadores de Google presentan la arquitectura de transformadores en el artículo “Attention Is All You Need”.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E2022\u003C/b\u003E: Se lanza ChatGPT y la IA generativa entra en la conversación empresarial generalizada.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E2024+\u003C/b\u003E: Los sistemas de agentes de IA emergen como la siguiente frontera de la implementación empresarial.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_685568339":{"id":"title-v2-2032883324","additionalClasses":"anchor-title anchor-title--next-steps","type":"heading2","lines":["Siguientes pasos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_1142311968":{"id":"text-eb396f48a9","text":"\u003Cp\u003ELa fiabilidad de la IA empresarial depende del sistema operativo que rodea al modelo: métodos de evaluación, acceso a datos gobernados, prácticas de supervisión, estructuras de propiedad y vías de escalado para resultados incorrectos, degradación del modelo, degradación del rendimiento y comportamientos no deseados. Las siguientes guías cubren las disciplinas que ayudan a los sistemas de IA a pasar de implementaciones aisladas a servicios de producción fiables.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_617860334":{"id":"text-9e48fd5dd2","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECONCLUSIÓN CLAVE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa IA empresarial tiene más probabilidades de éxito cuando las organizaciones alinean la arquitectura, los datos, la evaluación y la gobernanza a lo largo de todo el ciclo de vida, reconociendo que los distintos sistemas de IA imponen requisitos diferentes, pero deben operar bajo controles coherentes.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["text_1521899678","text","title_v2_1818028417","text_copy","quote_item_1152526200","title_v2_copy_1903657955","text_copy_copy","text_2059046899","quote_item","text_1948751468","text_2049864016","youtube_1156321798","title_v2_copy","text_copy_copy_831105251","title_v2_565269666","text_1898884637","title_v2_2089711109","text_1819706580","title_v2_1848786618","text_992156203","youtube_1030707130","card_v2","title_v2_copy_copy","text_copy_copy_83110","quote_item_1218174521","text_1896381897","title_v2_copy_copy_1670228814","text_copy_copy_83110_19424502","card_v2_946526351","text_copy_copy_83110_69230348","title_v2_copy_copy_1534133931","text_copy_copy_83110_670475437","title_v2_685568339","text_1142311968","text_617860334"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"container-16f976fd23",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-8245a7e1b9",":items":{"text_943981956_copy_":{"id":"text-ba92bbdecd","additionalClasses":"eyebrow-text","text":"\u003Cp\u003E\u003Cu\u003E\u003C/u\u003EEn esta guía\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"text_copy":{"id":"text-f16c3d0a8a","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-artificial-intelligence\"\u003E¿Qué es la inteligencia artificial?\u003C/li\u003E\n\u003Cli data-anchor=\"how-does-artificial-intelligence-work\"\u003E¿Cómo funciona la inteligencia artificial?\u003C/li\u003E\n\u003Cli data-anchor=\"types-of-ai\"\u003ETipos de IA\u003C/li\u003E\n\u003Cli data-anchor=\"core-ai-disciplines\"\u003EDisciplinas fundamentales de la IA\u003C/li\u003E\n\u003Cli data-anchor=\"ai-agents-and-agentic-systems\"\u003EAgentes de IA y sistemas de agentes de IA\u003C/li\u003E\n\u003Cli data-anchor=\"enterprise-ai-use-cases\"\u003ECasos de uso de la IA empresarial\u003C/li\u003E\n\u003Cli data-anchor=\"ai-governance-risk-and-security\"\u003EGobernanza, riesgo y seguridad de la IA\u003C/li\u003E\n\u003Cli data-anchor=\"the-data-foundation-behind-ai\"\u003EEl fundamento de datos detrás de la IA\u003C/li\u003E\n\u003Cli data-anchor=\"history-of-ai\"\u003EHistoria de la IA\u003C/li\u003E\n\u003Cli data-anchor=\"next-steps\"\u003ESiguientes pasos\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_1561483981":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"topics",":items":{"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont_1969625716":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_470142727":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1605976184":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1090102409":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-6ae449287f",":items":{"flexible_column_cont_470142727":{"id":"flexible-column-container-459d796c73","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-cfef9f45b7",":items":{"title_v2_copy":{"id":"title-v2-1dcc749060","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Preguntas frecuentes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-825405c3b6","text":"\u003Cp\u003ELas preguntas más frecuentes sobre inteligencia artificial, respondidas por expertos de Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-95139428fe",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-e28a43a8c4","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"¿Qué es la IA en términos sencillos?","richText":"\u003Cp\u003ELa IA hace referencia a sistemas de software que realizan tareas asociadas al razonamiento humano: reconocer patrones, hacer predicciones, comprender el lenguaje y generar contenido. En la práctica, la mayoría de los sistemas de IA son herramientas especializadas optimizadas para tareas específicas, más que motores de razonamiento de uso general.\u003C/p\u003E\n"},{"title":"¿Cuál es la diferencia entre la IA y el aprendizaje automático?","richText":"\u003Cp\u003EEl aprendizaje automático (ML) es un subtipo de la IA. La IA es el campo más amplio de sistemas que realizan tareas de razonamiento similares a las humanas. El ML es un enfoque específico de la creación de sistemas de IA que aprende patrones estadísticos a partir de los datos en lugar de seguir reglas programadas de forma explícita.\u003C/p\u003E\n"},{"title":"¿Es ChatGPT inteligencia artificial?","richText":"\u003Cp\u003ESí. ChatGPT es una aplicación de IA generativa basada en grandes modelos de lenguaje (large language models, LLM), que son un producto del aprendizaje profundo, que es un subtipo del aprendizaje automático, que a su vez es un subtipo de la IA. Aunque es muy capaz dentro de su ámbito, no es un sistema de razonamiento general.\u003C/p\u003E\n"},{"title":"¿Cuáles son los principales objetivos de la IA?","richText":"\u003Cp\u003EA nivel empresarial, los objetivos de la IA incluyen mejorar la velocidad, la precisión y la escalabilidad de las decisiones y las operaciones, reducir el trabajo manual y sacar a la luz patrones en los datos que las personas no pueden identificar por sí solas.\u003C/p\u003E\n"},{"title":"¿Para qué se utiliza la IA en las empresas? ","richText":"\u003Cp\u003ELos casos de uso empresariales más frecuentes son el apoyo a la toma de decisiones, la automatización de procesos, la generación de contenido, la personalización, la detección de fraude y anomalías, y las previsiones. El denominador común es aplicar el aprendizaje estadístico a tareas que antes requerían mucho tiempo humano por unidad de producción.\u003C/p\u003E\n"},{"title":"¿Cuál es la diferencia entre el entrenamiento y la inferencia de un modelo de IA?","richText":"\u003Cp\u003EEl entrenamiento es el proceso en el que un \u003Ca href=\"https://www.snowflake.com/es/fundamentals/ai-models/\"\u003Emodelo de IA\u003C/a\u003E aprende patrones a partir de grandes conjuntos de datos ajustando sus parámetros internos. La inferencia se produce cuando el modelo entrenado se utiliza para hacer predicciones o generar resultados con datos nuevos. En resumen, el entrenamiento crea el modelo y la inferencia lo aplica.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"title_v2":{"id":"title-v2-8f53d297a9","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Explorar los recursos sobre IA"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"flexible_column_cont_1605976184":{"id":"flexible-column-container-a3b8f1d3f9","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-7259437abf",":items":{"flexible_column_cont":{"id":"flexible-column-container-5124c44929","propertiesId":"seo-hub-resources","type":"2-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b45546dbbe",":items":{"content_chip":{"id":"content-chip-5d2484d711","tagText":"EBOOK","tagColor":"#71D3DC","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/es/lp/radical-roi-generative-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer más"},"headline":{"id":"title","type":"heading5","lines":["El ROI de la IA generativa y los agentes en 2026"],":type":"snowflake-site/components/title-v2"},"appliedCssClassNames":"snowflake-content-chip-white-bg",":type":"snowflake-site/components/content-chip"},"content_chip_1350384":{"id":"content-chip-3cc81c4539","tagText":"DOCUMENTO TÉCNICO","tagColor":"#FFD9AF","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/white-paper/from-prompt-to-purpose-unlocking-business-value-with-agentic-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leer más"},"headline":{"id":"title","type":"heading5","lines":["Del prompt al propósito: impulsar el valor empresarial con agentes de 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