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LinkedIn</span></a></li></ul></li></ul> <aside class="tags d-none order-lg-first d-lg-flex flex-wrap gap-2 align-items-center contain-style"> <ul class="nav d-flex flex-wrap gap-2 z-1"><li><a data-linkid="guides.article.tags.glosario" class="btn btn-sm btn-default z-1 link-internal" href="https://www.ionos.mx/digitalguide/tags/glosario/" target="_top">Glosario</a></li><li><a data-linkid="guides.article.tags.inteligencia-artificial" class="btn btn-sm btn-default z-1 link-internal" href="https://www.ionos.mx/digitalguide/tags/inteligencia-artificial/" target="_top">Inteligencia artificial</a></li></ul> </aside> </div> <div class="col-12 col-lg-6 d-none d-md-block mt-2 mt-lg-0" style="display: none"> <figure class="image position-relative overflow-hidden align-center visual-img contain-style my-0 w-100 h-100"> <picture><source media="(min-width: 1680px)" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/9/3/csm_ki-t_20e3b4edcc.webp"/><source media="(min-width: 1400px)" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/9/3/csm_ki-t_2572f9442a.webp"/><source media="(min-width: 1200px)" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/9/3/csm_ki-t_000c3beea0.webp"/><source media="(min-width: 922px)" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/9/3/csm_ki-t_9de0682d57.webp"/><source media="(min-width: 768px)" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/9/3/csm_ki-t_dc580b88c0.webp"/><img src="data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMSAxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgogICAgPHJlY3QgeD0iMCIgeT0iMCIgd2lkdGg9IjEiIGhlaWdodD0iMSIgZmlsbD0idHJhbnNwYXJlbnQiIGZpbGwtcnVsZT0iZXZlbm9kZCIvPgo8L3N2Zz4K" width="1200" height="630" title="Deep learning: el cerebro humano como inspiración" alt="Deep learning: el cerebro humano como inspiración" class="img-fluid d-block mx-auto img-format-landscape w-100 h-100 object-fit-cover rounded-4 border border-1 border-brand-gray-100" data-originalfile="https://www.ionos.mx/digitalguide/fileadmin/DigitalGuide/Teaser/ki-t.jpg"/></picture><span class="media-credits d-flex position-absolute top-0 end-0 start-unset bottom-unset w-auto h-auto fs-sm"><span class="media-artist">whitehoune</span><span class="media-source">Shutterstock</span></span> </figure> </div> </div> </div> </div> <div class="container-fluid container-sm"> <div class="row py-2 py-lg-5 contain-style position-relative gap-4 flex-xl-nowrap"> <section class="content col-12 col-xl-6 col-xxxl-7 contain-style order-2 position-relative overflow-hidden mw-xl-content-area"> <div id="c215836" class="textmedia" data-ctype="textmedia"> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Al hablar con asistentes de voz como el de Google o Siri, ya estamos recolectando los frutos del deep learning o aprendizaje profundo. Sin esta tecnología, estas voces del smartphone y de los altavoces no reconocerían nuestro lenguaje ni, naturalmente, encontrarían tampoco las respuestas adecuadas.</p> <p>El concepto de deep learning en sí no es nuevo, pero en los últimos años esta tecnología se ha desarrollado y se sigue desarrollando a gran velocidad, proporcionando un valor añadido real que se ha colado incluso, <strong>por primera vez, en la vida diaria</strong> de los usuarios. Grandes multinacionales como Google están investigando de forma intensiva el deep learning, y no solo para que lleguemos a tener conversaciones humanas con sus asistentes de voz, sino para realizar tareas de lo más variadas.</p> <p>Aquí te explicamos cómo funciona esta tecnología, cuáles son sus puntos fuertes, sus puntos débiles, y qué ámbitos de aplicación tiene o podría tener.</p> </div> </div> </div> <div id="c215838" class="textmedia" data-ctype="textmedia"> <h2> ¿Qué es el deep learning? </h2> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Para llegar a considerar inteligentes a los ordenadores, las personas les pedimos, principalmente, que puedan <strong>aprender de forma autónoma</strong>. El deep learning es, por lo tanto, una de las ramas de investigación científica en materia de <a href="https://www.ionos.mx/digitalguide/online-marketing/vender-en-internet/que-es-la-inteligencia-artificial/" target="_top" title="¿Qué es la inteligencia artificial?" data-linkid="guides.content.link.int.inteligencia-artificial" class="link-internal">inteligencia artificial</a> o, mejor dicho: es <strong>una rama del aprendizaje automático</strong> o machine learning, uno de los muchos métodos que existen para aplicarlo.</p> </div> </div> </div> <div id="c215839" class="textmedia my-4 position-relative d-flex flex-column rounded-4 p-3 border brand-box-bg brand-box-border-color brand-box-color" data-ctype="textmedia"> <div class="h2 has-icon"><svg width="16" height="16"><use xlink:href="#icon-book"></use></svg> Definición: deep learning </div> <div class="ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>El deep learning es una rama del <a href="https://www.ionos.mx/digitalguide/online-marketing/analisis-web/machine-learning-asi-piensan-las-maquinas/" target="_top" title="Machine Learning: así piensan las máquinas" data-linkid="guides.content.link.int.machine-learning" class="link-internal">machine learning</a> o aprendizaje automático, gracias al cual los ordenadores pueden procesar grandes cantidades de datos con la ayuda de redes neuronales inspiradas en el cerebro humano. Las conexiones existentes se van mejorando y ampliando con la nueva información de entrada, de manera que el sistema va aprendiendo sin necesidad de intervención humana y mejora constantemente las decisiones y predicciones que realiza.</p> </div> </div> </div> <div id="c215840" class="textmedia" data-ctype="textmedia"> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Para <strong>procesar la información</strong> se utilizan redes neuronales artificiales formadas por una capa de entrada, una o más capas intermedias (layers) y una capa de salida. Los datos llegan como vector de entrada a la capa de entrada, luego son <strong>ponderados</strong> por neuronas artificiales en las capas intermedias y, finalmente, resultan en un patrón concreto en la capa de salida. Cuantas más capas tenga una red neuronal artificial, más complejas serán las tareas a las que pueda enfrentarse.</p> </div> </div> </div> <div id="c215841" class="textmedia" data-ctype="textmedia"> <h3> Ejemplo: reconocimiento de imágenes </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Supongamos que queremos clasificar fotos según muestren personas, gatos o perros: <strong>todo un desafío para un ordenador</strong>. Naturalmente, al contrario que las personas, un ordenador no es capaz de reconocer imágenes de un vistazo, sino que debe realizar para ello un análisis de ciertas características concretas de la imagen.</p> <p>En el proceso de deep learning se analizan capa a capa los datos primarios introducidos (en este caso, la imagen). En la primera capa de la red neuronal artificial, el sistema comprueba, por ejemplo, qué color tiene cada uno de los píxeles. Cada píxel es procesado entonces por una neurona de la red. En la capa siguiente se identificarán los contornos y esquinas y, en la siguiente, se analizarán características más complejas. </p> <p>Las informaciones que se recaben pasarán a formar parte de un <strong>algoritmo flexible </strong>que se mejora a sí mismo basándose en los resultados que se van transmitiendo de una capa a la siguiente. De esta manera, el ordenador puede decidir, tras haber realizado numerosos análisis, si la imagen muestra un perro, un gato o una persona.</p> <p>Al principio del proceso tiene lugar un training en el que las conclusiones erróneas del ordenador son corregidas por personas para afinar así el algoritmo, el cual, al poco tiempo, será capaz de mejorar su propia capacidad de reconocimiento por sí mismo. <strong>Alterando los enlaces entre las neuronas </strong>y adaptando la ponderación de las variables dentro del algoritmo, ciertas informaciones de entrada (fotos variadas de gatos, por ejemplo) conducirán, cada vez de forma más exacta y con menos fallos, a ciertos resultados (identificación del gato). Cuantas más imágenes se le propongan al sistema, más aprenderá.</p> <p>Para las personas no siempre está claro qué patrones ha identificado el ordenador en el proceso de deep learning para llegar a cierta conclusión, pero el sistema es capaz de <strong>optimizar sus reglas continuamente por sí solo</strong>.</p> </div> </div> </div> <div id="c215842" class="textmedia" data-ctype="textmedia"> <h2> La historia del deep learning </h2> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Como término, deep learning es bastante nuevo: se mencionó por primera vez aproximadamente en el año 2000. No obstante, la idea de usar redes neuronales artificiales para permitir al ordenador tomar decisiones inteligentes tiene ya varias décadas.</p> <p>La <strong>investigación básica</strong> en este ámbito se remonta incluso a los años 40 y las primeras redes neuronales artificiales se desarrollaron en los 80, pero con resultados aún bastante decepcionantes. Una de las razones de tan pobres resultados es que el deep learning, es decir, el aprendizaje autónomo por parte del ordenador, requiere grandes cantidades de datos digitales, de las que entonces aún no se disponía. Hasta la llegada del nuevo milenio no empezó la era de los <a href="https://www.ionos.mx/digitalguide/online-marketing/analisis-web/big-data-nuestra-huella-en-forma-de-datos/" target="_top" title="Big data: nuestra huella en forma de datos" data-linkid="guides.content.link.int.big-data" class="link-internal">big data</a>, que volvieron a reavivar con fuerza el interés de la ciencia y de la economía en el deep learning.</p> </div> </div> </div> <div id="c215843" class="textmedia" data-ctype="textmedia"> <h2> Puntos fuertes y débiles </h2> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>En comparación con tecnologías anteriores de inteligencia artificial, el deep learning tiene mucha más capacidad de rendimiento, lo cual justifica la gran atención de los medios que ha recibido, así como el interés con el que se investiga en el tema. Sin embargo, para que esta tecnología pueda desarrollar todo su potencial, habrá que lidiar primero con ciertos puntos débiles que presenta.</p> </div> </div> </div> <div id="c215844" class="textmedia" data-ctype="textmedia"> <h3> Puntos fuertes del deep learning </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Uno de los argumentos más importantes a favor del deep learning es la calidad de sus resultados, que superan con creces a los de otras tecnologías, especialmente en el ámbito del <strong>reconocimiento de imágenes</strong> y del <strong>procesamiento de voz</strong>. Suponiendo que se disponga de datos de entrada de calidad para el training, con deep learning pueden realizarse <strong>tareas rutinarias</strong> de forma más rápida y eficiente que si las hiciesen personas, y todo ello sin signos de cansancio ni estancamiento de la calidad. </p> <p>En otras ramas del aprendizaje automático, los desarrolladores analizan los datos primarios y definen regularmente características adicionales que el algoritmo deberá tener en cuenta a la hora de aprender, con el fin de <strong>mejorar así su capacidad de predicción</strong>. En el deep learning, en cambio, el sistema reconoce por sí mismo variables útiles y las integra en su proceso de aprendizaje, de manera que, tras un entrenamiento inicial, es capaz de aprender sin intervención humana. De esta manera, no se necesita personal especializado para el desarrollo de dichas características.</p> <p>Hasta hace poco, para hacer posible el aprendizaje automático había que etiquetar o identificar grandes cantidades de datos a mano: para el reconocimiento de imágenes, por ejemplo, los trabajadores debían asignar en primer lugar la etiqueta de gato o perro a las imágenes. Con el deep learning, el training manual requerido es mucho más breve, lo cual es especialmente relevante porque, si bien las empresas suelen recabar grandes cantidades de datos, solo en pocos casos se trata de <a href="https://www.ionos.mx/digitalguide/paginas-web/creacion-de-paginas-web/los-datos-estructurados-en-la-web-semantica/" target="_top" title="Los datos estructurados en la web semántica" data-linkid="guides.content.link.int.datos-estructurados" class="link-internal">datos estructurados</a> (números de teléfono, direcciones postales, tarjetas de crédito, etc.). En la mayoría de casos, se guardan <strong>datos no estructurados</strong> (imágenes, documentos, correos electrónicos, etc.). A diferencia de otros métodos de machine learning, el deep learning es capaz de analizar datos procedentes de fuentes no estructuradas basándose en la tarea que se le haya asignado. </p> <p>El argumento de que esta tecnología es demasiado costosa como para comercializarse en masa va perdiendo su fuerza: cada vez aparecen más servicios como <a href="https://cloud.google.com/vision/" target="_blank" class="link-external-blank link-external" title="Google Vision" rel="noreferrer noopener noreferrer" data-linkid="guides.content.link.ext.vision-de-google">Vision, de Google</a> o <a href="https://www.ibm.com/watson/products-services" target="_blank" class="link-external-blank link-external" title="IBM Watson" rel="noreferrer noopener noreferrer" data-linkid="guides.content.link.ext.watson-de-ibm">Watson, de IBM</a>, que permiten a las empresas <strong>seguir</strong> <strong>desarrollando redes neuronales ya existentes</strong> en lugar de tener que crearlas desde cero. Gracias a esta posibilidad, en el futuro podremos ver cómo los puntos fuertes del deep learning se desarrollan cada vez más en la práctica empresarial.</p> </div> </div> </div> <div id="c215845" class="textmedia" data-ctype="textmedia"> <h4> Resumen de los puntos fuertes </h4> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <ul> <li>Mejores resultados que con otros métodos de machine learning</li> <li>No es necesario desarrollar características ni etiquetar los datos manualmente</li> <li>Realización eficiente de tareas rutinarias sin fluctuaciones de calidad</li> <li>Funcionamiento sin complicaciones con datos no estructurados</li> <li>Cada vez hay más servicios que simplifican el uso de las redes neuronales artificiales</li> </ul> </div> </div> </div> <div id="c215846" class="textmedia" data-ctype="textmedia"> <h3> Puntos débiles del deep learning </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>El deep learning <strong>requiere una enorme capacidad de procesamiento </strong>para establecer conexiones en las redes neuronales y procesar las cantidades de datos necesarias. La potencia de procesamiento necesaria depende en gran medida de la complejidad de la tarea y del tamaño de los datos introducidos. Todo esto encarecía esta tecnología hasta hace poco, hasta el punto de ponerla solo al alcance de la investigación y de algunas multinacionales. </p> <p>En este sentido ha habido muchas mejoras, pero lo que ciertamente no cambiará en el futuro próximo es que las decisiones tomadas por los procesos de deep learning <strong>no siempre son comprensibles para las personas</strong>, al menos no en detalle. La red neuronal sigue siendo una black box, es decir, un sistema cuyo funcionamiento no se llega a conocer en profundidad. Eso la hace inaprovechable para ciertos fines en los que la comprensión de la tecnología en sí es clave.</p> <p>Para que el deep learning funcione, se necesitan <strong>grandes conjuntos de datos de training</strong>. Si no se dispone de ellos, los ordenadores aún no son capaces de dar buenos resultados. Si bien ya se están fundando las primeras bibliotecas de redes neuronales, que facilitan el uso del deep learning para un público más amplio, estos servicios no se pueden aplicar a todos los fines. Por eso, el <strong>desarrollo de algoritmos de aprendizaje</strong> de deep learning sigue requiriendo grandes inversiones de tiempo e incluso podría llegar a requerir más tiempo que otros métodos alternativos.</p> </div> </div> </div> <div id="c215847" class="textmedia" data-ctype="textmedia"> <h4> Resumen de los puntos débiles </h4> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <ul> <li>Requiere una alta potencia de procesamiento.</li> <li>El desarrollo de algoritmos de aprendizaje necesita relativamente mucho tiempo.</li> <li>Se necesitan grandes cantidades de datos de entrada.</li> <li>Se necesitan más datos de entrenamiento que con otros métodos de machine learning.</li> <li>Las decisiones tomadas por el sistema son comprensibles, como mucho, en parte (black box).</li> </ul> </div> </div> </div> <div id="c215848" class="textmedia" data-ctype="textmedia"> <h2> Ámbitos de aplicación del deep learning </h2> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>El deep learning ya se usa en sectores muy diversos y su uso se extenderá aún más en el futuro, hasta llegar a muchos ámbitos de nuestra vida cotidiana.</p> </div> </div> </div> <div id="c215849" class="textmedia" data-ctype="textmedia"> <h3> Experiencia de usuario </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Actualmente se está usando el deep learning para optimizar los llamados chatbots, es decir, las aplicaciones de chat en tiempo real para la atención al cliente. Con el deep learning se pueden mejorar las respuestas del chat a los comentarios de los clientes y reducir así la necesidad de personal en este ámbito.</p> </div> </div> </div> <div id="c215850" class="textmedia" data-ctype="textmedia"> <h3> Asistentes de voz </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Como se ha mencionado, uno de los ejemplos más conocidos de la aplicación de deep learning son los asistentes de voz como Alexa, el asistente de Google o Siri. Gracias a esta tecnología, mejoran su vocabulario y su comprensión lingüística.</p> </div> </div> </div> <div id="c215851" class="textmedia" data-ctype="textmedia"> <h3> Traducciones </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Algunos programas de traducción ya utilizan el deep learning, que les permite, por ejemplo, traducir automáticamente también dialectos o texto en imágenes, algo que hasta ahora no era posible con las aplicaciones de machine learning que solo trabajan con datos estructurados.</p> </div> </div> </div> <div id="c215852" class="textmedia" data-ctype="textmedia"> <h3> Redacción de textos </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Con la ayuda del deep learning, los ordenadores pueden redactar textos que no solo son correctos gramatical y ortográficamente, sino que también pueden imitar el estilo de autores concretos, por ejemplo, siempre que hayan recibido suficiente input para entrenarse. En los primeros intentos en este ámbito, los sistemas de IA crearon artículos de Wikipedia que parecían dignos de Shakespeare.</p> </div> </div> </div> <div id="c215853" class="textmedia" data-ctype="textmedia"> <h3> Seguridad cibernética </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Los sistemas de inteligencia artificial con deep learning, gracias a su aprendizaje autónomo y continuo, funcionan especialmente bien a la hora de identificar irregularidades en la actividad del sistema. De esta manera, pueden avisar de posibles ataques de hackers. Además, puesto que los sistemas también integran material de vídeo, pueden protegerse mejor los lugares que están típicamente en el punto de mira de ataques, como los aeropuertos, ya que el ordenador puede detectar anormalidades en el tráfico habitual del lugar.</p> </div> </div> </div> <div id="c215854" class="textmedia" data-ctype="textmedia"> <h3> Finanzas </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>La habilidad de detectar anomalías resulta especialmente útil en el delicado ámbito de las transacciones financieras. Si se entrena el algoritmo de forma adecuada, pueden evitarse ataques a las redes bancarias y fraudes con tarjetas de crédito mucho mejor que hasta ahora.</p> </div> </div> </div> <div id="c215855" class="textmedia" data-ctype="textmedia"> <h3> Marketing y ventas </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Los sistemas de inteligencia artificial son capaces de realizar análisis de sentimiento o <a href="https://www.ionos.mx/digitalguide/online-marketing/analisis-web/analisis-de-sentimiento/" target="_top" title="Análisis de sentimiento" data-linkid="guides.content.link.int.sentiment-analysis" class="link-internal">sentiment analysis</a> con la ayuda del deep learning, es decir, detectar qué mensajes de los clientes (del chat y del correo electrónico) transmiten indignación, por ejemplo, para transmitirlos prioritariamente al personal encargado.</p> <p>Estos sistemas pueden, además, tomar medidas de forma autónoma para devolver la satisfacción al cliente y evitar que se interrumpa la relación comercial. Mediante la <strong>recolección de información acerca del cliente</strong> en el CRM, los sistemas de deep learning pueden predecir cómo se comportará en el futuro y definir así medidas adecuadas que eviten que deje de contratar los servicios de la empresa, por ejemplo.</p> </div> </div> </div> <div id="c215856" class="textmedia" data-ctype="textmedia"> <h3> Conducción autónoma </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>La idea de un tráfico más seguro gracias a coches que transiten sin conductor sigue siendo una fantasía, pero la tecnología necesaria para ello ya existe y combina diversos algoritmos de deep learning: un algoritmo reconoce, por ejemplo, las señales de tráfico, mientras que otro se especializa en detectar peatones.</p> </div> </div> </div> <div id="c215857" class="textmedia" data-ctype="textmedia"> <h3> Robots industriales </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Los robots con inteligencia artificial basada en deep learning son útiles en muchos sectores industriales. Estos sistemas son capaces de aprender, con tan solo observar a las personas, cómo manejar la maquinaria y cómo optimizarse a sí mismos.</p> </div> </div> </div> <div id="c215858" class="textmedia" data-ctype="textmedia"> <h3> Mantenimiento </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Especialmente en el ámbito del <strong>mantenimiento industrial </strong>existen muchas posibilidades de aplicación de sistemas de deep learning. En instalaciones complejas es necesario controlar continuamente muchos parámetros para garantizar la seguridad. Con deep learning no solo se podría controlar que los sistemas funcionasen sin fallos, sino también crear predicciones de qué unidades de la instalación requieren trabajos de mantenimiento, en lo que se denomina predictive maintenance.</p> </div> </div> </div> <div id="c215859" class="textmedia" data-ctype="textmedia"> <h3> Medicina </h3> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>Los sistemas de deep learning son capaces de analizar imágenes en busca de anomalías de forma mucho más eficaz que el ojo humano más experto. En tomografías computarizadas o radiografías, por ejemplo, la inteligencia artificial permite detectar enfermedades tan prematuramente como nunca se había hecho, lo cual aumenta en gran medida las probabilidades de curación o supervivencia de los pacientes.</p> </div> </div> </div> <div id="c215860" class="textmedia" data-ctype="textmedia"> <h2> Deep learning: un gran potencial, pero no una solución universal </h2> <div class="text-row ce-textpic ce-center ce-above"> <div class="ce-bodytext"> <p>En general, al oír hablar de deep learning, uno tiene a veces la sensación de que se trata del único modo viable de implementar la inteligencia artificial. Sin embargo, y a pesar de que el deep learning realmente da mejores resultados en muchos ámbitos que las tecnologías usadas hasta ahora, el gran potencial de las redes neuronales artificiales aún no se ha explotado del todo, ni mucho menos, y su uso está aún en las fases iniciales.</p> <p>Además, el deep learning <strong>no es siempre la mejor solución para los problemas tecnológicos</strong>, sino que hay muchas otras maneras de dotar de inteligencia a los ordenadores. Existen soluciones, por ejemplo, que funcionan también con menores cantidades de datos y que permiten a las personas comprender las decisiones que toma el sistema.</p> <p>Algunos investigadores en materia de IA ven el deep learning como una solución transitoria y están convencidos de que se desarrollarán mejores estrategias que no estén inspiradas en el cerebro humano. La estrategia empresarial de Google en este sentido es un ejemplo de que no se deben ignorar las críticas: en Google, el deep learning es <strong>solo una parte de la estrategia de IA</strong>, de la que también forman parte otros métodos de aprendizaje automático, como el desarrollo de <a href="https://elpais.com/tecnologia/2019/10/24/actualidad/1571917826_234639.html" target="_blank" title="El País. 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Para comprender cómo Alexa o Siri son capaces de responder a tus preguntas o identificar tus gustos para recomendarte música, tienes que entender en qué consisten el aprendizaje…</p><div class="mt-2 minh-5 d-flex flex-nowrap overflow-hidden"><ul class="nav d-flex flex-nowrap card-tags-fade-end w-100 gap-2"><li><span class="btn btn-sm btn-default">Inteligencia artificial</span></li><li><span class="btn btn-sm btn-default">Comparativa</span></li></ul></div><div class="read-more px-0 pb-0 pt-1 m-0 position-relative end-0 text-end lh-1 d-flex align-items-center justify-content-end"><span class="d-inline-block me-1">Leer más</span><svg width="16" height="16" class="icon-arrow-right"><use xlink:href="#icon-arrow-right"></use></svg></div></div></article> </div> <div class="col col-12 col-md-6 col-lg-4"> <article class="card brand-card-bg rounded-4 overflow-hidden contain-style h-100 w-100" id="article-2603"><img src="https://www.ionos.mx/digitalguide/fileadmin/DigitalGuide/Teaser/0xc0000005-t.jpg" width="1200" height="630" srcset="https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_f1aaff0cfd.webp 456w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_4f8f4b9302.webp 430w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_ebaac662c6.webp 370w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_0ac7fb6d96.webp 310w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_d8a227fa5d.webp 348w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_a75d11bc1b.webp 524w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_331aa68105.webp 396w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_39abcca665.webp 304w,https://www.ionos.mx/digitalguide/fileadmin/_processed_/e/0/csm_0xc0000005-t_e4efa63511.webp 304w" sizes="(min-width: 1680px) 456px,(min-width: 1400px) 430px,(min-width: 1200px) 370px,(min-width: 992px) 310px,(min-width: 768px) 348px,(min-width: 576px) 524px,(min-width: 412px) 396px,(min-width: 320px) 304px,(max-width:319px) 304px" title="Supervised learning: un plan de aprendizaje para máquinas" alt="Supervised learning: un plan de aprendizaje para máquinas" loading="lazy" class="img-fluid d-block mx-auto img-format-landscape card-img-top h-auto rounded-top-4" data-originalfile="https://www.ionos.mx/digitalguide/fileadmin/DigitalGuide/Teaser/0xc0000005-t.jpg"><span class="media-credits d-flex position-absolute top-0 end-0 start-unset bottom-unset w-auto h-auto fs-sm"><span class="media-artist">Titima Ongkantong</span><span class="media-source">Shutterstock</span></span><div class="card-body d-flex flex-column p-3"><h3 class="h4 card-title mt-0 mb-3 position-static"><a data-linkid="guides.article.teaser.supervised-learning" class="stretched-link brand-card-color text-decoration-none link-internal" title="Supervised learning: un plan de aprendizaje para máquinas" href="https://www.ionos.mx/digitalguide/online-marketing/marketing-para-motores-de-busqueda/supervised-learning/" target="_top"> Supervised learning: un plan de aprendizaje para máquinas </a></h3><p class="card-text brand-card-color mt-auto mb-0">La inteligencia artificial es la tecnología del futuro. Permitirá a las máquinas resolver problemas por sí mismas, sin necesidad de que intervenga un ser humano. Sin embargo, para que puedan llegar a este nivel, hay que entrenar primero a sus algoritmos. 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