Machine Learning imprescindible para las empresas del futuro

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CLAVES:

  • La tecnología Machine Learning está abriendo nuevas oportunidades para las aplicaciones de software de negocio.
  • Conforme la investigación avanza, esta metodología se vuelve cada vez más atractiva para el mundo de los negocios. Ejemplos de ello son Retention Science, DataRPM o Sony.
  • Disponiendo de tecnología Machine Learning las empresas pueden encontrar al mejor candidato para que trabaje en su empresa.
  • Con la ayuda de un software inteligente, las empresas pueden detectar proactivamente señales que indican que los clientes pueden estar a punto de rescindir la relación contractual y tomar medidas preventivas.
  • El Machine Learning ofrece un enorme potencial para las empresas y el crecimiento económico futuro.

La tecnología Machine Learning está abriendo nuevas oportunidades para las aplicaciones de software de negocio, al permitir a los ordenadores aprender de grandes cantidades de información sin necesidad de ser programados explícitamente.

Lo fundamental ahora es ayudar a las empresas a aprovechar este potencial para resolver una amplia gama de retos de negocio.

La influencia del software inteligente en nuestra vida diaria es cada vez mayor. Como consumidores, vemos esta transformación con desarrollos como asistentes personales, vehículos sin conductor y aplicaciones de domótica. De hecho, todos los sectores económicos se han visto afectados por la revolución provocada por las tecnologías inteligentes.

En este sentido, los sistemas Machine Learning representan un gran avance en el desarrollo de la inteligencia artificial, al imitar la forma en que aprende el cerebro humano (mediante la asignación de significado a la información).

El ordenador puede identificar y categorizar las entradas repetitivas y utilizar la retroalimentación para fortalecer y mejorar su rendimiento. Es un proceso similar a cómo un niño aprende los nombres y la identidad de los animales, haciendo coincidir las palabras con las imágenes; el ordenador, poco a poco, aprende a procesar la información correctamente.

[section label=”¿Por qué ahora?” anchor=”¿Por qué ahora?”]

¿Por qué ahora?

El auge del Machine Learning se debe a la confluencia de tres factores clave:

  • En primer lugar, el acceso a grandes cantidades de datos sobre los cuáles aprender y entrenar al sistema. Datos que pueden provenir de múltiples fuentes como sistemas de negocio con datos de clientes, como fotos, vídeos o comentarios que se comparten en las redes sociales, o como datos que provienen del Internet de las cosas (IoT). Los dispositivos IoT utilizan sensores y conectividad en los objetos para capturar y generar datos y, por lo tanto, a menudo son la fuente de los datos necesarios para permitir los procesos de aprendizaje por parte de las máquinas.
  • En segundo lugar, la evolución de la capacidad de computación para tratar este “Big Data” de forma paralela y a gran velocidad, y por lo tanto, para aprender y tomar decisiones de una manera mucho más eficiente y ágil de lo que era posible hace sólo unos años. “Big Data” es un término genérico bajo el que se incluyen tecnologías capaces de procesar grandes volúmenes de datos de forma rápida y con mayor eficacia que con las bases de datos convencionales.
  • Por último la evolución de los algoritmos que “aprenden” de los datos sin tener que programarse de forma explícita. Un subgrupo particular de Machine Learning se conoce como “aprendizaje profundo” (Deep Learning). Este término describe el uso de un conjunto de algoritmos llamados redes neuronales que toman como modelo el cerebro humano. Los avances en este aprendizaje profundo han impulsado una rápida evolución de las tareas de aprendizaje por parte de las máquinas en los últimos años, en particular el procesamiento del lenguaje y texto, y la interpretación de imágenes y vídeos. Estos sistemas, por ejemplo, llegan a identificar caras o a interpretar el idioma natural a una velocidad y con un grado de acierto que puede superar al de un ser humano.

[section label=”¿Cuáles son los pasos del Machine Learning?” anchor=”¿Cuáles son los pasos del Machine Learning?”]

¿Cuáles son los pasos del Machine Learning?

Las organizaciones que adopten Machine Learning han de seguir los siguientes pasos:

  • Ingerir y preparar grandes volúmenes de datos sobre los que aprender.
  • Analizar estos datos para entrenar el modelo.
  • Aplicar el modelo resultante a una necesidad de negocio.
  • Por último, el paso clave para hablar de Machine Learning, aprender del resultado para mejorar continuamente el modelo de forma autónoma.

Ya no es necesario invertir gran cantidad de horas de expertos para programar el algoritmo perfecto, por ejemplo para reconocer un determinado objeto, puesto que mediante el análisis de enormes cantidades de imágenes el ordenador irá ajustando el algoritmo hasta mejorar el nivel de acierto.

[section label=”El impacto en el mundo de los negocios” anchor=”El impacto en el mundo de los negocios”]

El impacto en el mundo de los negocios

Conforme la investigación avanza, esta metodología se vuelve cada vez más atractiva para el mundo de los negocios.

Por ejemplo, Retention Science lo utiliza para ayudar a empresas de e-Commerce a comprender mejor a cada cliente y mejorar su retención durante sus compañas de marketing.

Otro caso destacado es DataRPM que permite a organizaciones descubrir patrones y anomalías ocultas en sus datos con una interfaz de usuario muy sencilla. Office 365, por ejemplo, utiliza Machine Learning para determinar con una exactitud asombrosa qué email va a leer el usuario y cuáles ignorará.

Combinando Big Data y Machine Learning, estas compañías ofrecen soluciones que con medios tradicionales habría sido muy difícil y costoso proporcionar.

Hace algunos meses, Sony anunció que iba a procurar hacer equipos móviles más personalizados, que aprendieran del usuario y sus hábitos para proveer de una mejor experiencia. La empresa japonesa había estado rezagada en el uso de la tecnología Machine Learning, en comparación con competidores como Samsung y Apple que ya la utilizan. Los nuevos teléfonos de Sony tienen dos funciones particularmente atractivas qué implican el cuidado de la batería y el aprovechamiento de las particularidades de cada dispositivo.

El equipo aprende acerca de los horarios en los que se carga la batería (en la mayoría de los casos se deja conectado toda la noche), y se configura de manera que al alcanzar el 90% de carga, esta se detenga hasta una hora antes del despertar, lo que ayuda a proteger la batería.

Las empresas continuamente deben identificar puntos débiles en sus negocios e implementar cambios operativos para mantener su competitividad. Estas nuevas herramientas dan a los responsables la información que necesitan casi al instante.

[section label=”Encontrar al mejor candidato” anchor=”Encontrar al mejor candidato”]

Encontrar al mejor candidato

Buscar, evaluar y seleccionar a los solicitantes de empleo es una tarea que requiere mucho tiempo. Para identificar a los candidatos más adecuados para cada puesto, los reclutadores tienen que evaluar miles de solicitudes, currículos y perfiles en las redes sociales.

Machine Learning simplifica este proceso al permitir que los departamentos de Recursos Humanos puedan elaborar una lista de candidatos de forma automática. Sus algoritmos peinan todas las solicitudes de empleo por detalles como formación y experiencia, y filtran los candidatos más adecuados para una posición específica. A su vez, el sistema aprende y mejora continuamente los algoritmos en base al resultado.

Esta metodología también permite a los solicitantes de empleo establecer claramente los puestos vacantes que mejor se adaptan a sus capacidades y cualificaciones. El resultado no es sólo un proceso de selección cada vez más eficiente, sino una mano de obra más cualificada y motivada, ya que hay una mayor probabilidad de que los empleados se adapten perfectamente a los trabajos que realizan.

[section label=”Identificación de riesgo de pérdida de clientes” anchor=”Identificación de riesgo de pérdida de clientes”]

Identificación de riesgo de pérdida de clientes

Para las empresas de sectores como el financiero y el de las telecomunicaciones, el coste y los inconvenientes causados por la cancelación de contratos son considerables. En la mayoría de los casos, en el momento en que un cliente se identifica como en riesgo de pérdida y recibe una nueva oferta, ya es demasiado tarde. Sin embargo, las empresas hoy en día ya cuentan con todos los datos que necesitan para evitar que surjan estas situaciones.

Con la ayuda de un software inteligente, pueden detectar proactivamente señales que indican que los clientes pueden estar a punto de rescindir la relación contractual y tomar medidas preventivas. Consiste en identificar, analizar y comparar todas las fuentes pertinentes de datos del cliente. Las técnicas de Machine Learning se utilizarán para analizar los datos históricos relativos a los clientes y su comportamiento e identificar patrones, que más tarde se compararán con la información de los clientes actuales y sus contratos.

El resultado es una huella digital que permite a una empresa sacar conclusiones acerca de cómo un cliente se comportará en el futuro. La compañía podrá predecir la posible finalización del contrato antes de que suceda y reaccionar ante ella.

Esto son sólo un par de ejemplos, existen muchas más áreas en la empresa que se están transformando gracias al Machine Learning, como la fabricación inteligente, el mantenimiento predictivo, la detección del fraude, los mercados de valores, el análisis de logos y marcas en campañas, el servicio al cliente o la medicina personalizada.

Sin embargo, la incorporación de funciones de aprendizaje por parte de las máquinas en aplicaciones de negocio no es una tarea fácil. Por esta razón, los científicos de datos serán enormemente demandados en el mercado de trabajo durante los próximos años. Su papel será clave para que las empresas saquen el máximo partido a esta tecnología y obtengan ventajas competitivas clave frente a sus competidores.

[section label=”Machine Learning clave para los negocios” anchor=”Machine Learning clave para los negocios”]

Machine Learning clave para los negocios

El Machine Learning ofrece un enorme potencial para las empresas y el crecimiento económico futuro. Esta tecnología proporcionará la base para numerosos nuevos escenarios, posibilidades y modelos de negocio. Supondrá la creación de nuevos puestos de trabajo, un grupo emergente de nuevos profesionales altamente cualificados, los llamados “trabajadores del conocimiento”.

En lo que respecta a los proveedores de aplicaciones de negocio, sin duda se trata de una materia estratégica. Los datos de negocio más importantes del mundo pasan a través de los sistemas de estas empresas y esta metodología de aprendizaje es la clave para extraer todo el potencial que ofrece la información. En los próximos años veremos cómo el Machine Learning se convierte un área clave para la gestión de los negocios.

DATARPM. Machines That Think Like Humans in the Era of Big Data Discovery. [consultado 05-08-2016]. Disponible en: http://www.datarpm.com/resources/machines-that-think-like-humans-in-the-era-of-big-data-discovery/

RESCI. Evaluating Machine Learning Predictions: Customer Churn & CLV [RS Labs]. [consultado 05-08-2016]. Disponible en: http://retentionscience.com/evaluating-machine-learning-predictions-customer-churn-clv-rs-labs/

SONY. Sony Joins Forces with Cogitai to Conduct Research and Development for the Next Wave of Artificial Intelligence. [consultado 05-08-2016]. Disponible en: http://www.sony.net/SonyInfo/News/Press/201605/16-052E/index.html

TICBEAT. Machine Learning, la era de la empresa inteligente. 31 de agosto de 2016 [consultado 05-08-2016]. Disponible en: http://www.ticbeat.com/educacion/machine-learning-la-era-de-la-empresa-inteligente/

MUYCOMPUTER. Intel y la inteligencia artificial, más allá de machine learning. [consultado 05-08-2016]. Disponible en: http://www.muycomputerpro.com/zona-transformacion-digital/intel-inteligencia-artificial-machine-learning/

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    The first 3 subheads start with an action verb advising readers what to do.|
    But then the fourth subhead suddenly changes the format and breaks the flow.
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    This inconsistency may appear fairly innocent, but it’s
    sidetracking to readers. Let’s face it, readers today are info-holics.
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    My recommendations? Note your main points and see if you can add
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    But throwing up old recommendations doesn’t challenge you as an author, nor does it enlighten your audience.

    So put your readers a little espresso for their info-hangover by delivering the unanticipated.

    Numerous short articles have actually been blogged about blogging, but the number of have called you out for being dumb or told you to change your friends ?!

    Jon does just that by knocking you over the head with some hard truth bombs about what it
    takes to make it as a blog writer.

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