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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-465f8ffa4a","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-3fa4cc5be2","text":"\u003Cp\u003EEl aprendizaje profundo (deep learning) es un subconjunto del aprendizaje automático (ML) que aprovecha la potencia de las redes neuronales artificiales para descubrir y modelar automáticamente los complejos patrones ocultos en los datos sin procesar. Se ha convertido en el motor que impulsa los \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-programming-languages/\"\u003Esistemas modernos de inteligencia artificial\u003C/a\u003E (IA) y promueve avances en el reconocimiento de imágenes y el procesamiento del lenguaje natural (PLN), así como en la generación de texto, de forma convincentemente humana, que impulsa los chatbot de IA. El aprendizaje profundo también constituye la base de tecnologías autónomas como los vehículos sin conductor y los robots inteligentes, que procesan flujos de datos de sensores en tiempo real para percibir el mundo y tomar decisiones en fracciones de segundo.\u003C/p\u003E\n\u003Cp\u003EEn esta guía se explica qué es el aprendizaje profundo y por qué es importante, además de analizar sus ventajas y limitaciones.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-1a42353c67","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es el aprendizaje profundo?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2a014e258e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl aprendizaje profundo es un tipo avanzado de \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Eaprendizaje automático\u003C/a\u003E que utiliza redes neuronales de múltiples capas para aprender automáticamente patrones complejos directamente de los \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003Edatos sin procesar\u003C/a\u003E. A diferencia de los algoritmos tradicionales de aprendizaje automático, no requiere que los humanos le indiquen en qué características debe fijarse, como los bordes y los colores de una imagen o patrones de palabras frecuentes en un texto. En su lugar, el aprendizaje profundo se basa en redes con muchas capas de neuronas artificiales que determinan automáticamente cuáles de esas características importan. Este proceso de autoaprendizaje requiere conjuntos de datos de entrenamiento mucho más grandes para garantizar que el \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodelo\u003C/a\u003E entiende de verdad los patrones de los datos y no se limita a memorizarlos. Además, dado que la mayoría de las redes neuronales se basan en decenas de capas de computación distintas, todas realizando cálculos simultáneamente, el aprendizaje profundo también requiere mucha más potencia de cómputo que los algoritmos tradicionales de aprendizaje automático.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_2060034519":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"work","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-d9856e6e22","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Por qué es importante el aprendizaje profundo?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-0a7e45de17","text":"\u003Cp\u003ELa capacidad del aprendizaje profundo para extraer automáticamente patrones significativos de datos no estructurados permite a las empresas automatizar tareas antes imposibles o poco prácticas, como la detección de fraude en tiempo real, el análisis de imágenes médicas y la robótica de almacén. Las organizaciones que dominan el aprendizaje profundo adquieren la capacidad de procesar datos sin explotar, automatizar flujos de trabajo complejos e identificar oportunidades de mercado más rápido que la competencia, lo que lo convierte en un elemento esencial para el posicionamiento estratégico a largo plazo en una economía cada vez más basada en datos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5eec5b4d25","additionalClasses":"headline-decoration","type":"heading2","lines":["Ejemplos y casos de uso de aprendizaje profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-780acfffce","text":"\u003Cp\u003ELos modelos de aprendizaje profundo ya se utilizan en una amplia variedad de sectores. Estos son solo algunos ejemplos:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDetección de fraude en el sector financiero\u003C/h3\u003E\n\u003Cp\u003ELos sistemas de aprendizaje profundo analizan patrones de transacciones en tiempo real para identificar actividades sospechosas que se desvían del comportamiento habitual del cliente. Estos modelos pueden señalar las transacciones de alto riesgo para su revisión o bloquearlas automáticamente, lo que puede ayudar a reducir las pérdidas por fraude y a proteger a los clientes de cargos no autorizados. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMantenimiento predictivo en fabricación\u003C/h3\u003E\n\u003Cp\u003EEl aprendizaje profundo analiza datos de sensores de maquinaria industrial —como vibraciones, temperatura y señales acústicas— para identificar señales de alerta de un fallo inminente del equipo. Esta capacidad predictiva permite a los fabricantes programar el mantenimiento durante el tiempo de inactividad planificado; esto reduce drásticamente las interrupciones costosas y alarga la vida útil de los equipos, a la vez que optimiza los costes de mantenimiento.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERecomendaciones personalizadas en retail\u003C/h3\u003E\n\u003Cp\u003ELas plataformas de comercio electrónico usan el aprendizaje profundo para analizar el historial de navegación del cliente, sus patrones de compra y su similitud con otros clientes, lo que les permite recomendar otros productos que podrían interesarle. Al mostrar a los compradores sugerencias más personalizadas, el aprendizaje profundo puede aumentar la interacción de los clientes y, según la implementación y el contexto, podría mejorar las tasas de conversión. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EImágenes médicas y diagnóstico\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo entrenados con millones de imágenes médicas —como radiografías, tomografías computarizadas (TAC), resonancias magnéticas y fotografías de retina— pueden detectar enfermedades como el cáncer, afecciones cardíacas y trastornos oculares. Esta tecnología acelera el diagnóstico, reduce el error humano y ayuda a abordar la escasez global de especialistas médicos en regiones desatendidas. En algunas tareas y estudios muy definidos, los modelos de aprendizaje profundo han mostrado un rendimiento comparable al de los profesionales clínicos; la efectividad en el mundo real depende de la validación, la integración en los flujos de trabajo y la supervisión clínica. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EProcesamiento del lenguaje natural y chatbots\u003C/h3\u003E\n\u003Cp\u003EEl aprendizaje profundo impulsa sistemas de IA conversacional que entienden el lenguaje humano, lo que permite a los chatbots ofrecer atención al cliente, responder preguntas y completar transacciones sin intervención humana. Al aprender de grandes volúmenes de texto y datos conversacionales, estos bots son cada vez más capaces de gestionar consultas complejas y ofrecer respuestas naturales y útiles.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVehículos autónomos y robótica\u003C/h3\u003E\n\u003Cp\u003ELos coches autónomos y los robots dependen del aprendizaje profundo para procesar señales de cámaras, datos LiDAR y flujos de datos de sensores. Esto les permite entender su entorno, detectar obstáculos y tomar decisiones de navegación en tiempo real. La capacidad de percibir el mundo que les rodea permite a los sistemas autónomos adaptarse a variaciones en las condiciones de la carretera, el clima y el comportamiento humano.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReconocimiento de voz y procesamiento de audio\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo convierten las palabras habladas en texto con una precisión notable, lo que impulsa asistentes de voz como Siri y Alexa, así como herramientas de accesibilidad para personas con discapacidad auditiva. Estos sistemas aprenden a manejar diferentes acentos, ruido de fondo y patrones del habla, lo que hace que la interacción por voz sea una interfaz práctica en una amplia variedad de dispositivos y servicios.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-02dfc2d602","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Cómo funciona el aprendizaje profundo?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-95709bf1e0","text":"\u003Cp\u003ELos modelos de aprendizaje profundo se crean mediante redes complejas compuestas por miles de neuronas artificiales (operaciones matemáticas que aprenden patrones automáticamente a partir de ejemplos etiquetados), ajustando millones de parámetros internos mediante ensayo y error hasta que pueden predecir o reconocer con precisión datos nuevos que nunca han visto.\u003C/p\u003E\r\n\u003Cp\u003ECada red se compone de tres partes fundamentales: una capa de entrada en la que se ingieren los datos etiquetados, varias capas ocultas de neuronas que analizan los datos y los refinan progresivamente, y una capa de salida en la que se presenta la predicción final. \u003C/p\u003E\r\n\u003Cp\u003ESupón que quieres entrenar una \u003Ca href=\"https://www.snowflake.com/es/fundamentals/neural-network/\"\u003Ered neuronal\u003C/a\u003E para que reconozca si una foto contiene la imagen de un perro o un gato. Empiezas alimentándola con miles de imágenes etiquetadas como “perro” o “gato” y dejas que la red averigüe por sí sola las diferencias entre ambas.\u003C/p\u003E\r\n\u003Cp\u003ELa primera capa oculta podría aprender a detectar patrones simples como bordes y esquinas. La segunda capa oculta combina esos bordes en formas como círculos y líneas. Una tercera capa podría reconocer componentes como “orejas puntiagudas” o “nariz húmeda”, y así sucesivamente. Con cada capa, la red desarrolla una comprensión más sofisticada, pasando de píxeles sin procesar a conceptos significativos.\u003C/p\u003E\r\n\u003Cp\u003ELa capa final contiene la predicción de la red: una puntuación de probabilidad que indica si cree que la imagen muestra un canino o un felino. Si la red se equivoca (p. ej., la predicción no coincide con la etiqueta asignada a los datos originales), lo intenta de nuevo automáticamente, dando más peso a algunas característica de la imagen y menos a otras. Después repite este proceso hasta que puede distinguir correctamente entre un perro y un gato con mucha precisión en datos de prueba reservados, en función de la calidad y la diversidad de los datos de entrenamiento y del diseño del modelo. \u003C/p\u003E\r\n\u003Cp\u003EUna red neuronal aprende de sus errores mediante un proceso llamado retropropagación, recorriendo las capas hacia atrás hasta que determina qué características contribuyeron más a la predicción inexacta. Una fórmula matemática conocida como función de pérdida de datos le indica cuánto debe corregir cuando se equivoca. Si un modelo falla por mucho —por ejemplo, si predice con un 95 % de confianza que una foto de un gato en realidad es un perro—, examinará las características que llevaron la predicción en la dirección equivocada y aumentará o reducirá el peso que les asigna. Si falla solo ligeramente (el modelo solo tiene un 51 % de confianza en que es una imagen de un perro), modificará esos pesos de forma menos drástica.\u003C/p\u003E\r\n\u003Cp\u003EPor eso el aprendizaje profundo se ha vuelto tan potente: una vez que configuras este proceso de entrenamiento, descubre automáticamente características y representaciones útiles sin que el usuario tenga que diseñarlas manualmente. La red aprende qué es lo importante. Y, a medida que le proporcionas más datos y más potencia de cómputo, la red puede aprender patrones cada vez más complejos, ampliando los límites de lo que la inteligencia artificial puede lograr.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5f61597c91","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de modelos de aprendizaje profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-005cfe18cd","text":"\u003Cp\u003EHay aproximadamente media docena de arquitecturas distintas de aprendizaje profundo, cada una orientada a tipos específicos de datos y tareas. Estas son las principales.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERedes neuronales convolucionales (CNN)\u003C/h3\u003E\r\n\u003Cp\u003ELas CNN están diseñadas específicamente para procesar datos en forma de cuadrícula, como imágenes, al buscar patrones como bordes, texturas y formas. Dado que las CNN entienden cómo se relacionan los píxeles cercanos entre sí, destacan en tareas de visión artificial como la clasificación de imágenes, la detección de objetos, el reconocimiento facial y el análisis de imágenes médicas. Esto las hace muy eficaces para crear desde aplicaciones de fotos en smartphones que identifican rostros hasta vehículos autónomos que detectan peatones y señales de tráfico.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERedes neuronales recurrentes (RNN)\u003C/h3\u003E\r\n\u003Cp\u003ELas RNN se crean para tareas en las que es importante mantener el orden en que aparecen los datos, como analizar frases en un documento o fotogramas en un vídeo. La capacidad de procesar datos nuevos y, al mismo tiempo, recordar los datos que acaban de analizar hace que las RNN sean útiles para la traducción de idiomas, el reconocimiento de voz y la predicción de series temporales. Aunque las redes de transformadores más recientes las han sustituido en gran medida para muchas tareas lingüísticas, las RNN siguen siendo valiosas cuando se trabaja con flujos continuos de datos, como lecturas de sensores en tiempo real, o cuando los recursos de computación son limitados.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERedes generativas antagónicas (GAN)\u003C/h3\u003E\r\n\u003Cp\u003ELas GAN constan de dos redes neuronales que compiten entre sí: un generador que crea datos sintéticos (como imágenes falsas) y un discriminador que intenta distinguir los datos reales de los falsos. A través de este proceso de entrenamiento antagónico, el generador se vuelve cada vez más hábil para producir resultados realistas; esto hace que las GAN sean potentes para crear imágenes fotorrealistas, generar datos sintéticos de entrenamiento e incluso producir deepfakes. Se han utilizado para crear obras de arte, mejorar imágenes de baja resolución, generar caras realistas de personas que no existen y ayudar a diseñar nuevas moléculas para el descubrimiento de fármacos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERedes de transformadores\u003C/h3\u003E\r\n\u003Cp\u003ELos transformadores revolucionaron el procesamiento del lenguaje natural al usar un “mecanismo de atención” que permite a la red centrarse simultáneamente en las partes más relevantes de la entrada, en lugar de procesar los datos de forma secuencial. Esta arquitectura impulsa los \u003Ca href=\"https://www.snowflake.com/es/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E (LLM) modernos, como GPT y Claude, y les permite comprender el contexto en pasajes largos de texto, generar escritura similar a la humana y realizar tareas como la traducción y el resumen con una precisión sin precedentes. Los transformadores también han demostrado ser eficaces más allá del lenguaje, y las adaptaciones recientes muestran un gran rendimiento en visión artificial e incluso en la predicción de la estructura de las proteínas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAutocodificadores\u003C/h3\u003E\r\n\u003Cp\u003ELos autocodificadores comprimen los datos hasta reducirlos a su característica más esencial y, después, los reconstruyen a partir de esa forma comprimida. Esto los hace útiles para detectar patrones inusuales (es probable que algo que no se pueda reconstruir bien sea anómalo), limpiar datos con ruido y reducir conjuntos de datos complejos a sus elementos principales. La capacidad de detectar rápidamente anomalías en los datos hace que los autocodificadores sean útiles para detectar transacciones de crédito fraudulentas o identificar defectos de producto en líneas de montaje.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_289473020":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-98f5b668fc","additionalClasses":"headline-decoration","type":"heading2","lines":["Principales diferencias entre el ML, el aprendizaje profundo y la IA generativa"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-52a2d24ac5","text":"\u003Cp\u003EEn la actualidad, tres paradigmas de la IA relacionados pero distintos dominan el desarrollo de modelos de inteligencia artificial. Estas son las principales diferencias entre ellos. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAprendizaje automático (ML)\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje automático utilizan algoritmos que aprenden patrones a partir de los datos, pero normalmente requieren que las personas diseñen y extraigan manualmente las características relevantes para que el algoritmo pueda aprender de ellas. Estos sistemas funcionan bien con datos estructurados y tabulares, y con conjuntos de datos relativamente modestos, lo que los hace útiles en aplicaciones como la calificación crediticia, la segmentación de clientes y sistemas de recomendación sencillos. En general, los modelos de aprendizaje automático son más fáciles de interpretar que los de aprendizaje profundo y requieren menos potencia de cómputo para entrenarlos e implementarlos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAprendizaje profundo\u003C/h3\u003E\n\u003Cp\u003EEl aprendizaje profundo utiliza redes neuronales de múltiples capas que descubren automáticamente qué características importan; esto elimina la necesidad de la ingeniería de características manual que requiere el aprendizaje automático tradicional. Estos sistemas funcionan especialmente bien con datos no estructurados, como imágenes, audio y texto, pero requieren grandes conjuntos de datos de entrenamiento (a menudo, millones de ejemplos) y recursos de computación considerables para aprender de forma eficaz. El aprendizaje profundo impulsa aplicaciones que requieren comprender patrones complejos, como el reconocimiento facial, los vehículos autónomos, el diagnóstico de imágenes médicas y los sistemas de reconocimiento de voz.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIA generativa\u003C/h3\u003E\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EIA generativa\u003C/a\u003E es un subconjunto del aprendizaje profundo que, en lugar de clasificar o predecir resultados a partir de datos existentes, está diseñada específicamente para crear contenido nuevo, como texto, imágenes, música, código o vídeo. Entrenar estos sistemas requiere conjuntos de datos realmente masivos (a menudo, miles de millones de ejemplos) mediante arquitecturas como las redes de transformadores y las GAN, que aprenden los patrones y las estructuras subyacentes de los datos de entrenamiento lo bastante bien como para generar resultados nuevos y realistas. La IA generativa es la base de aplicaciones como ChatGPT y Claude (IA conversacional), DALL-E y Midjourney (generación de imágenes), GitHub Copilot (finalización de código) y sistemas que crean datos de entrenamiento sintéticos o contenido personalizado a escala.\u003C/p\u003E\n\u003Cp\u003EAdemás de estos tres, conviene mencionar otros paradigmas de la IA. La IA clásica (o simbólica) utiliza reglas explícitas, lógica y conocimiento programados por humanos; es el paradigma que emplean los sistemas expertos y los chatbots basados en reglas. En el paradigma del aprendizaje por refuerzo, los agentes de IA interactúan con su entorno y reciben recompensas o penalizaciones, en función de las acciones que realizan. Este modelo se despliega a menudo en sistemas de control robótico y motores de recomendación que aprenden a partir de la interacción del usuario. Los algoritmos evolutivos se inspiran en la evolución biológica y permiten que los modelos mejoren continuamente y se adapten mejor con el tiempo; se usan para resolver problemas como el diseño de redes neuronales o la optimización de la cadena de suministro. La IA neurosimbólica combina redes neuronales (aprendizaje a partir de datos) con razonamiento simbólico (reglas lógicas y conocimiento). Este paradigma emergente apenas está empezando a ver aplicaciones reales para mejorar los diagnósticos médicos y reforzar la ciberseguridad.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container"},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-037917ea1f","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas de los modelos de aprendizaje profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4115788d69","text":"\u003Cp\u003ELos algoritmos de aprendizaje profundo tienen varias ventajas frente a otros paradigmas de IA. Estas son algunos de sus puntos fuertes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESon muy precisos en tareas complejas\u003C/h3\u003E\n\u003Cp\u003EEl aprendizaje profundo puede lograr un rendimiento puntero en determinadas tareas complejas (por ejemplo, clasificación de imágenes y reconocimiento de voz), en función del modelo, los datos y la configuración de evaluación. Los modelos pueden detectar características sutiles y relaciones en los datos que serían casi imposibles de identificar o programar explícitamente para los humanos, como reconocer los primeros signos de una enfermedad en exploraciones médicas o predecir estructuras de proteínas. Esta ventaja en precisión se hace aún más evidente a medida que las tareas se vuelven más complejas, lo que convierte al aprendizaje profundo en el enfoque preferido para problemas que antes superaban las capacidades de los métodos tradicionales.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIdentifican automáticamente las características relevantes de los datos \u003C/h3\u003E\n\u003Cp\u003EA diferencia del aprendizaje automático tradicional, el aprendizaje profundo descubre automáticamente qué características importan sin necesidad de que los expertos del dominio las diseñen y extraigan manualmente. La red aprende representaciones jerárquicas por sí sola: identifica bordes en las primeras capas, los combina en formas en las capas intermedias y reconoce conceptos de alto nivel en las últimas capas. Esta automatización reduce notablemente el tiempo de desarrollo y permite que el aprendizaje profundo aborde problemas en dominios en los que los expertos humanos quizá ni siquiera sepan qué características son relevantes.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEscalan fácilmente con grandes conjuntos de datos\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo mejoran de forma predecible a medida que proporcionas más datos de entrenamiento, mientras que los algoritmos tradicionales de aprendizaje automático suelen estancarse a partir de cierto punto. Esta escalabilidad significa que las organizaciones con acceso a conjuntos de datos masivos pueden mejorar significativamente el rendimiento al invertir en una mayor recopilación de datos y en modelos de mayor tamaño. La relación entre el volumen de datos y el rendimiento genera una ventaja acumulativa para las organizaciones que pueden recopilar y procesar información a escala.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPueden tomar decisiones en tiempo real \u003C/h3\u003E\n\u003Cp\u003EUna vez entrenados, los modelos de aprendizaje profundo pueden procesar información y realizar predicciones con gran rapidez, lo que permite aplicaciones en tiempo real que requieren respuestas instantáneas. Esta velocidad hace que el aprendizaje profundo sea adecuado para vehículos autónomos que deben detectar obstáculos y reaccionar de inmediato, sistemas de detección de fraude que evalúan transacciones a medida que se producen y asistentes de voz que responden a órdenes habladas sin un retraso perceptible. Las optimizaciones modernas de hardware y las técnicas de compresión de modelos siguen mejorando la velocidad de inferencia, ampliando el abanico de aplicaciones en tiempo real.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDestacan en el manejo de datos no estructurados\u003C/h3\u003E\n\u003Cp\u003EEl aprendizaje profundo destaca en el procesamiento de tipos de datos no estructurados que carecen de una organización tabular clara, como imágenes, vídeo, audio, texto y flujos de datos de sensores, con los que los algoritmos tradicionales tienen dificultades. Esta capacidad permite extraer valor del enorme volumen de correos electrónicos, grabaciones de atención al cliente, imágenes de cámaras de seguridad y publicaciones en redes sociales que generan las empresas. Al hacer accesibles para el análisis datos que antes no podían aprovecharse, el aprendizaje profundo permite crear categorías completamente nuevas de aplicaciones y de información.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESe adaptan rápidamente a nuevas tareas\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo entrenados en una tarea a menudo pueden adaptarse a tareas relacionadas con un mínimo de entrenamiento adicional, lo que reduce significativamente la cantidad de datos y el tiempo necesarios para nuevas aplicaciones. Por ejemplo, un modelo entrenado para reconocer objetos cotidianos puede ajustarse para identificar afecciones médicas concretas, usando muchas menos imágenes médicas de las que requeriría entrenarlo desde cero. Esta técnica, conocida como aprendizaje por transferencia, permite a las organizaciones aprovechar modelos existentes como punto de partida, lo que acelera los ciclos de desarrollo y hace que el aprendizaje profundo sea más accesible incluso cuando los datos específicos del dominio son limitados.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAprenden continuamente \u003C/h3\u003E\n\u003Cp\u003ELos sistemas de aprendizaje profundo pueden actualizarse de forma continua con nuevos datos, lo que les permite adaptarse a patrones cambiantes, mejorar la precisión con el tiempo y gestionar condiciones en evolución sin un reentrenamiento completo. Esta capacidad de aprendizaje significa que los modelos implementados en producción pueden mejorar a medida que se enfrentan a más ejemplos del mundo real y adaptarse a cambios en el comportamiento de los usuarios, las condiciones del mercado o los factores ambientales. La capacidad de mejorar gradualmente hace que los sistemas de aprendizaje profundo sean más robustos y sostenibles para su implementación a largo plazo que los sistemas estáticos basados en reglas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"types","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-a3252410a8","additionalClasses":"headline-decoration","type":"heading2","lines":["Limitaciones de los modelos de aprendizaje profundo"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-4727adec48","text":"\u003Cp\u003EAunque son extremadamente útiles en una amplia gama de aplicaciones, los modelos de aprendizaje profundo también plantean enormes desafíos en cuanto a coste, consumo de energía, interpretabilidad y potencial de uso indebido. Estos son los principales inconvenientes del aprendizaje profundo.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENecesitan mucha potencia de cómputo \u003C/h3\u003E\n\u003Cp\u003EEntrenar modelos de aprendizaje profundo requiere una potencia de cómputo considerable y, a menudo, implica el uso de hardware especializado y costoso, como GPU, que puede estar en funcionamiento durante días o semanas. Su consumo energético puede ser enorme: el entrenamiento de modelos grandes puede requerir mucha energía, y las necesidades varían enormemente en función del tamaño del modelo, el hardware y la duración del entrenamiento. El despliegue de modelos para inferencia en tiempo real a escala también exige recursos computacionales continuos e inversión en infraestructura, lo que puede hacer que el aprendizaje profundo sea económicamente inviable para algunas aplicaciones y organizaciones más pequeñas.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERequieren grandes conjuntos de datos etiquetados\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo suelen requerir entre miles y millones de ejemplos de entrenamiento etiquetados para rendir bien, y la creación de estas etiquetas a menudo exige un esfuerzo humano y una experiencia considerables. En dominios especializados, como las imágenes médicas o el diagnóstico de enfermedades raras, donde los expertos deben revisar y anotar manualmente cada ejemplo, obtener suficientes datos etiquetados puede ser extremadamente difícil o costoso. Este requisito de datos genera un problema de arranque en frío, en el que el aprendizaje profundo no puede aplicarse de forma eficaz sin una inversión considerable previa en la recopilación y el etiquetado de datos, lo que deja las aplicaciones avanzadas fuera del alcance de muchas organizaciones que no disponen de ese volumen de datos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPueden ser propensos al sobreajuste\u003C/h3\u003E\n\u003Cp\u003ELos modelos de aprendizaje profundo pueden acabar memorizando los datos de entrenamiento en lugar de aprender a identificar patrones dentro de esos datos. Un modelo sobreajustado funciona extremadamente bien con los ejemplos de entrenamiento, pero falla al encontrarse con situaciones nuevas y ligeramente diferentes, como un sistema de reconocimiento facial que funciona a la perfección en el laboratorio, pero tiene problemas con distintas condiciones de iluminación o ángulos de cámara en producción. Para evitar el sobreajuste, se emplean técnicas como la regularización, el dropout y la validación cruzada, pero incluso con estas medidas los modelos pueden aprender correlaciones espurias que no se mantienen en el mundo real.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESu funcionamiento es opaco \u003C/h3\u003E\n\u003Cp\u003EA menudo es imposible entender por qué exactamente un modelo de aprendizaje profundo hizo una predicción concreta, lo que los hace problemáticos para aplicaciones en las que las explicaciones son legalmente obligatorias o éticamente necesarias. Por ejemplo, un sistema de aprobación de préstamos basado en aprendizaje profundo podría rechazar una solicitud sin poder explicar qué factores motivaron esa decisión, lo que podría infringir las leyes de crédito justo o perpetuar sesgos ocultos. Este “problema de caja negra” crea desafíos en sectores regulados como el de la salud y el financiero; además, dificulta la depuración de los modelos cuando fallan o la verificación de que toman decisiones por los motivos adecuados.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EPlantean importantes preocupaciones éticas\u003C/h3\u003E\n\u003Cp\u003EComo los modelos de aprendizaje profundo aprenden a partir de datos históricos, inevitablemente absorben y amplifican los sesgos presentes en esos datos, lo que puede perpetuar la discriminación en la contratación, la concesión de créditos, la justicia penal y otros ámbitos sensibles. Un sistema de reconocimiento facial entrenado principalmente con rostros de piel más clara tendrá un rendimiento deficiente con personas de piel más oscura, y una herramienta de selección de currículos entrenada con decisiones históricas de contratación puede discriminar a mujeres o minorías. Más allá de los sesgos, el aprendizaje profundo plantea diversas preocupaciones éticas sobre su capacidad para generar deepfakes, su papel en la vigilancia masiva y su uso con sistemas de armas autónomas.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/container"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[],":type":"snowflake-site/components/container"},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[],":type":"snowflake-site/components/container"},"container_copy_2083166428":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy":{"id":"title-v2-fef3472d0b","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-4af8c270c1","text":"\u003Cp\u003EEl aprendizaje profundo ha transformado de manera fundamental la inteligencia artificial, ya que permite que las máquinas aprendan automáticamente patrones complejos a partir de datos sin procesar; esto impulsa capacidades que eran difíciles de lograr con los enfoques tradicionales y promueve avances en sectores que van desde la salud hasta los sistemas autónomos. Las organizaciones que dominan el aprendizaje profundo adquieren la capacidad de extraer valor de grandes cantidades de datos no estructurados, automatizar decisiones sofisticadas a gran escala e identificar oportunidades que permanecen invisibles para los competidores que dependen de métodos convencionales. \u003C/p\u003E\n\u003Cp\u003EEsta tecnología se ha convertido en una infraestructura esencial para la economía moderna. A medida que los datos siguen proliferando y la potencia de cómputo se vuelve más accesible, el dominio del aprendizaje profundo separa cada vez más a los líderes del sector de los rezagados, convirtiéndose en un imperativo estratégico para cualquier organización que quiera competir eficazmente en un futuro impulsado por la IA. 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