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Una buena gestión de la calidad de datos es el principal factor de éxito en un proceso de integración de datos. Podría ser considerado como el primer paso del proceso de integración y la clave para conseguir que los datos generen rentabilidad.
Las herramientas de business intelligence se basan en gran medida en paneles de control y herramientas analíticas que necesitan integrar datos desde diversas fuentes. Pero siempre, antes de esta integración necesitamos gestionar la calidad de esos datos para asegurarnos que la salida de la herramienta BI sea fiable y nos proporcione ventajas sobre nuestra competencia.
Vista la importancia de un trabajo coordinado y efectivo entre herramientas de integración de datos y de calidad de datos, parece claro que el uso de soluciones líderes puede ser fundamental para garantizar el éxito de estos proyectos.
Veamos algunas razones para implantar este tipo de soluciones líderes.
3 razones para utilizar una herramienta de Integración de datos líder
Los líderes en el mercado de herramientas de integración de datos son compañías que tienen muy clara la enorme relación entre los datos y la integración de aplicaciones y que pueden trabajar con sencillez en implementaciones independientes de la ubicación, las cuales no se limitan sólo a la nube ni a implementaciones locales, sino que pueden desplegarse más allá de una ubicación específica. Además, se adaptan rápidamente para reconocer y diseñar el despliegue de las nuevas y emergentes demandas del mercado, a menudo proporcionando nuevas capacidades funcionales en los productos antes de que exista una demanda, e identificando nuevos tipos de problemas de negocio a los cuales las herramientas de integración de datos pueden aportar un valor significativo. Tienen una presencia en el mercado establecida, un tamaño significativo y una importante presencia multinacional.
Podemos agrupar y enumerar las tres principales razones para utilizar herramientas líderes en integración de datos de la siguiente manera:
- Se adaptan rápidamente a las demandas de funcionalidad del mercado. El desarrollo de productos y la hoja de ruta de un líder en integración de datos abarca diversas capacidades, incluyendo ETL por lotes, integración en tiempo real, compartición y virtualización de datos. La fuerte interoperabilidad y las sinergias entre las herramientas de integración de datos y otras tecnologías de una empresa líder fomentan su uso como un estándar empresarial. El énfasis en el apoyo a la integración de datos tanto digitales como de IoT, iPaaS, preparación de datos de autoservicio, big data, gobernabilidad de datos y seguridad de datos capitaliza las tendencias de la demanda.
- Fuerte apuesta por el data management y roles no técnicos. Las herramientas líderes tienen amplias funcionalidades de usuario no necesariamente técnico para la preparación de datos de autoservicio. Se ofrece tanto en local como en implementaciones cloud o big data, normalmente mediante aplicaciones Data Wizard. Hacen énfasis en funcionalidad colaborativa orientada al usuario empresarial no técnico y a la agilidad de la infraestructura de integración de datos como un estándar empresarial.
- Amplia presencia en el mercado y foco dedicado a la innovación. Las herramientas líderes tienen una amplia red global de partners, revendedores, grandes integradores de sistemas y proveedores de servicios externos que ofrecen un amplio soporte a la implementación. Esto asegura la implementación en prácticamente cualquier ubicación.
3 razones para utilizar una herramienta de Calidad de Datos líder
Los líderes en herramientas de calidad de datos disponen de una gama completa de funciones de calidad de datos, incluyendo perfilado, parsing, normalización, matching, validación y enriquecimiento. Son empresas con una clara comprensión y estrategia para el mercado de calidad de los datos, usan ideas innovadoras y diferenciadoras, y aportan productos innovadores al mercado. Son herramientas que abordan todas las verticales, geografías, dominios de datos y casos de uso. Las capacidades ofrecidas en estos productos incluyen el reconocimiento de problemas de calidad de datos multidominio, opciones de despliegue tales como SaaS, soporte de autoservicio para roles como administradores de información, funcionalidad de preparación de datos para usuarios empresariales, uso de machine learning y algoritmos, soporte de calidad de datos para IoT, soporte para un modelo de gobierno basado en la confianza. Tienen una presencia establecida en el mercado, un tamaño significativo y alta presencia multinacional, ya sea directamente o a través de una empresa matriz.
Podemos agrupar y enumerar las tres principales razones para utilizar herramientas líderes en calidad de datos de la siguiente manera:
- Innovación y estrategia de producto. La estrategia de producto de de los líderes en herramientas de calidad de datos se basan en plataformas de datos inteligentes. Utilizan matching learning, algoritmos y analítica predictiva para abordar escenarios emergentes, como el IOT, el análisis de big data, data governance y el análisis de datos basado en contenido.
- Tecnología empresarial orientada a empresas. Las capacidades de calidad de datos de las herramientas líderes responden a las necesidades de los principales roles empresariales, como el administrador de información y los analistas de datos, a la vez que proporcionan la profundidad y la escalabilidad de la empresa necesarias para los roles técnicos.
- Comprensión del mercado y fuerte estrategia de marketing y ventas. Las herramientas líderes crecen fuertemente, respaldadas por su profunda comprensión del mercado de la calidad de datos y su capacidad de predecir y adaptarse a los cambios del mercado. La comprensión del mercado de estas empresas está altamente correlacionada con su estrategia de ventas y marketing, y con la ejecución del mercado en circuito cerrado.
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Cuando una organización es capaz de ejecutar de manera correcta una estrategia de datos, la agilidad y la capacidad de respuesta de la empresa junto a una clara ventaja competitiva, son los principales beneficios que puedenesperarse.
Por el contrario, aquellas empresas que le dan menos importancia estratégica a sus datos, se encuentran con un panorama de menor crecimiento de ingresos y una competitividad más baja.
Sin embargo, el aspecto más importante de todo esto es entender que la falta de alineación entre TI con el resto de personal de la empresa, en lo que se refiere a estrategias de datos, puede hacerlas fracasar, minimizando el crecimiento esperado.
Las ventajas del uso inteligente de los datos
Utilizar los datos de forma inteligente hace que las empresas aumenten sus ingresos. Es lo que se desprende del informe “The data directive”. Según este informe, el 81% de las empresas de gran crecimiento utilizan los datos mientras que entre las de bajo rendimiento solo el 57% utiliza los datos.
Además, estas empresas de alto rendimiento suelen tener iguales casi todos los elementos de sus procesos de toma de decisiones por lo que es muy probable que estas organizaciones hagan caso a los resultados de esos datos que tienen.
- La mayoría de estas empresas entregan a sus directivos datos en los que poder basar sus decisiones. Lo hace el 92% frente a solo el 35% de las ineficaces.
- La probabilidad de que consideren que su planificación estratégica y sus decisiones están basadas en datos es 12 veces más alta.
- Es el director general quien lleva las iniciativas que tienen relación con los datos en lugar de ser el director de TI.
El problema de TI no alineada
El problema de alineación con iniciativas de datos en el departamento de TI viene más por parte de la plantilla básica de TI que por parte de los directores del departamento. Estos últimos sí ven lo importante que es colaborar con el resto de usuarios de negocio mientras que los trabajadores básicos son bastante menos conscientes de la influencia de los datos en el negocio.
De hecho, los trabajadores de TI están mucho menos predispuestos a colaborar. Eso se demuestra mediante el dato de que solo un 17,2% de los trabajadores no ejecutivos realiza consultas periódicas a líderes sobre estrategias de gestión de datos.
Y el problema es que se trata del personal que se encarga de administrar los datos y eso afecta a la capacidad de las empresas para conseguir una utilización eficaz de los datos. Algunos estudios adicionales indican que a pesar de que el uso de los datos es una prioridad estratégica para los CIO, cerca del 50% de ellos dice estar cansado de perseguir a sus trabajadores para que se centren más en los clientes y el negocio.
A pesar de esto conviene resaltar que sí existe alineación en cuanto a la idea genérica de que tener una estrategia de datos eficaz puede suponer tener una ventaja respecto a la competencia. También están de acuerdo en que los datos pueden ayudar a conseguir más medios para los usuarios internos y también en que las técnicas que se utilizan para gestionar los datos no son suficientemente adecuadas para satisfacer las necesidades de sus empresas.
Podemos concluir por tanto que parece conveniente formar a los empleados acerca de los efectos que su trabajo puede tener en sus empresas. Y además estos datos parecen indicar que las organizaciones impulsadas por datos coinciden en su diseño en términos como que esta estrategia debe estar unida a KPI concretos de negocio.
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Cuando hablamos de integridad en base de datos nos estamos refiriendo a la completitud, la exactitud y la coherencia del conjunto de datos de una base de datos. Podemos tener una percepción de esta integridad en base de datos cuando vemos que entre dos instancias o entre dos actualizaciones de un registro de datos, no hay ninguna alteración, lo que significa que los datos están intactos y sin cambios.
Créditos fotográficos: JohnDWilliams
Sentamos las bases de la integridad en base de datos durante la fase de diseño de la database, a través del uso de procedimientos y reglas estándar. A partir de ahí, podemos seguir manteniendo la integridad en base de datos mediante el uso de métodos de comprobación de errores y procedimientos de validación.
El concepto de integridad en base de datos garantiza que todos los datos de una base de datos pueden ser rastreados mediante técnicas de trazabilidad, así como conectarse a otros datos. De esta forma se asegura que todo se puede buscar y recuperar.
Tener un sistema de integridad en base de datos único, bien definido y bien controlado aumenta la estabilidad, el rendimiento, la reutilización y facilita el mantenimiento.
Qué es la seguridad de la información y qué tiene que ver con la integridad en base de datos
La seguridad de la información se ocupa de proteger la confidencialidad, disponibilidad e integridad en base de datos de todos los activos de conocimiento de la organización. La forma de lograrlo tiene que ver con:
- Confidencialidad: se trata del aspecto más importante de la seguridad de base de datos. Este objetivo se alcanza a través del La encriptación ha de aplicarse a datos en reposo, pero también a los datos que, por un motivo u otro, se encuentren en tránsito.
- Integridad en base de datos: busca garantizar que sólo las personas autorizadas a ello podrán acceder a información privilegiada de la empresa. La integridad de una base de datos se aplica a través de protocolos de autenticación, políticas internas (como las que impulsan la seguridad de las contraseñas) y un sistema de control de acceso de usuario que define los permisos que determinan quién puede acceder a qué datos. Tampoco puede olvidarse el tomar medidas que ayuden a conseguir que las cuentas no utilizadas queden bloqueadas o sean eliminadas.
- Disponibilidad: hace referencia a la necesidad de que las bases de datos y toda la información que contienen estén listas para su uso. Por una parte, se debe garantizar su funcionalidad y confiabilidad mientras que, por otra, es recomendable planificar los tiempos de inactividad fuera del horario laboral.
Garantizar la integridad en base de datos, así como su disponibilidad y confiabilidad es determinante para el buen funcionamiento del negocio. Sin embargo, la amenaza no da tregua y, a día de hoy, los ataques se multiplican, tanto en frecuencia, como en objetivo. Los piratas informáticos ya no codician sólo los activos informacionales de las grandes corporaciones multinacionales, sino que tienen en su punto de mira a todo tipo de empresas, independientemente de su tamaño, propósito o industria.
Tipos de ataques a la integridad en base de datos
Está claro que el riesgo implícito en este tipo de acciones maliciosas varía de una organización a otra, aunque entre los ataques más comunes se encuentran los que tienen como objetivo:
- Datos personales de clientes, números de tarjetas de crédito y seguridad social.
- Detalles estratégicos del negocio.
- Información financiera de la propia compañía y de sus socios.
- Datos sensibles acerca de los empleados.
Podría decirse que se trata de la mayoría de las bases de datos activas en los directorios de la empresa, al menos, todas las que, de alguna forma, resultan relevantes para el negocio. Precisamente por ello, es necesario mantener sólidas prácticas de seguridad y estrategias de defensa que permitan combatir este tipo de ataques, también en sus versiones más recientes y sofisticadas, como el phisinig, el spear phising, la inyección SQL, el DDos, la amenaza persistente avanzada o el ransomware.
Según la Encuesta de Amenazas de Inyección SQL de Ponemon, “el 65% de las organizaciones encuestadas habían experimentado un exitoso ataque de estas características en el último año”. Entre las causas que podrían haber motivado la vulneración de la integridad en base de datos se encuentran la falta de escaneo de database o su escaneo irregular, un error común en el 47% de los casos.
Se trata de un dato sorprendente, sobre todo si se tiene en cuenta que, el 49% de los encuestados calificaron el nivel de amenaza de una inyección de SQL en su organización con una puntuación de 9 o 10.
Sin embargo, no hace falta ir tan lejos, la autenticación débil es la amenaza más común a la seguridad y la integridad en base de datos. Una misma contraseña usada con fines distintos, compartida entre usuarios, que nunca se actualiza o que resulta obvia facilita el trabajo de un atacante malintencionado en su misión encaminada a robar la identidad de un usuario legítimo. Una vez que conoce esos 8, 10 o 12 dígitos, ya tiene acceso a datos confidenciales, ya tiene a la organización en sus manos.
Mejores prácticas en seguridad que ayudan a garantizar la integridad en base de datos
Una de las formas más efectivas de garantizar la integridad en base de datos es implementando algunas de las mejores prácticas de seguridad. Entre ellas se encuentran las siguientes:
- Recurrir al enmascaramiento de datos o permitir a los usuarios acceder a cierta información sin poder verla ayuda a mantener la confidencialidad incluso en entornos de pruebas.
- Minimizar los extras y limitarse a los servicios, aplicaciones y funcionalidades que realmente son necesarios para asegurar el normal funcionamiento de las operaciones del negocio, de esta forma se reduce el riesgo.
- Asegurarse de que los administradores de la base de datos entiendan la importancia de garantizar su protección.
- Mantener actualizadas las bases de datos y eliminar los componentes desconocidos.
- Recurrir a herramientas como el análisis de código estático, que ayudan a reducir los problemas de inyección de SQL, desbordamiento de búfer y problemas de configuración.
- Hacer copias de seguridad frecuentes y emplear una fuente de alimentación ininterrumpida o SAI que garantice que un corte de energía no causa la pérdida de datos.
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In a world so heavily dependent on technology, it is no surprise that new predictive tech and the ability to finally visualize big data in a fully interactive way are at the forefront of many of our industries today, creating what is fondly referred to as “the analytics bandwagon of 2017”. However, not every one of these industries which are now utilizing big data analytics to their benefit originally seemed to need said data visualization to succeed.
In fact, many of these industries may be surprising at first, but the outcome of this use of data analytics has taken them from their original spot in the past and catapulted them into the future of technology in business. With this said, five particular industries have truly grown the most since incorporating data analytics appropriately, and, as data scientists, it is highly important that we recognize this in order to be ahead of the data trends that matter the most. After all, trends are the foundation on which predictive data analytics are based and with which this future is built.
1. Small Business
For years, small business has primarily focused on small amounts of data analysis to succeed and simply learned how to recover after small business failures rather than how to succeed despite the odds. However, with tech giants such as IBM, SAS, and Microsoft offering affordable, cloud-based data-crunching services, it has become easier than ever for small businesses to cross-reference their data with the ever-expanding big data collections gathered through the analysis of social networks, government databases, and usage patterns on mobile devices, among other things.
Although these are all wonderful ways to utilize this data, small companies have also been able to use other sources, such as recorded calls and other forms of consumer behavior analytics, to cross-reference their internal-pricing histories, customer traffic patterns, and purchasing trends. By doing this, companies are able to predict customer needs before they arise, create targeted marketing campaigns, price items accordingly, and remove the human bias from small business in order to become more efficient and less opinion-based.
Furthermore, there are multiple different data analytics resources for small businesses to utilize nowadays that make it even more efficient than ever before. With this said, many small businesses turn to the internet and various sources of big data analytics in order to find their flaws, find the trends they seek, and analyze the aspects of their customer relations necessary to make themselves as efficient and successful as the companies they compare themselves to online. From there, they can finally take the guessing out of running a small business and know exactly what their customer wants and what their competitors offer with very little money invested whatsoever in the process.
2. Education Sector
With the dependency on technology consistently growing, more and more individuals have begun to recognize the advantages of online learning which has led to a tremendous boost in online students worldwide. However, it can often be difficult to analyze the data received from these online classes in order to improve as these students are located all around the world and have different study patterns and success rates. However, it is important that these online courses consistently improve as their onsite counterparts do, which creates a certain disparity in the education world regarding online classes and their success rates over time.
Therefore, it is important that these courses, along with their teachers and respective schools, consistently review any and all data they receive in order to find ways to improve and help students of all learning and success levels. For this, online teachers have begun to analyze time-based analytics, discussion board trends, and individual assignment success rates in order to analyze not only how students work but also when they work, as well as how they feel and interact with other online students.
By doing this, teachers have been able to recognize when to send out assignments based on the days that students are the most successful and engaged, tailor assignments according to what these individuals are successful in or lacking in, and also discuss various subjects with classes based on their opinions of certain assignments likewise in order to help these online students succeed. It also helps teachers disseminate all the information they actually need and learn the information that they need to be successful in the field they hope to soon be a part of. In the end, this allows for educators to improve online courses through data analytics and compile the data for future classes in order to analyze how successful not only the school is but certain generations as well.
3. Insurance Industry
With an industry that is heavily invested in data analytics such as the insurance industry, it comes as no surprise that insurance and technology tend to grow side by side in most cases. In fact, although a large portion of the insurance industry was unaware of just how valuable their analytics were, many insurance companies have begun to provide this information to their counterparts in order to not only help them recognize how implementing analytics for tangible results can increase their business but also to create an industry that is connected and ever-expanding likewise.
Furthermore, by using data analysis to their benefit, these companies are also able to tap into big data analytics and compare their analytics accordingly. Through this process, entrepreneurs can streamline costs, be more targeted with the risks they want to take in business and the demographics and consumers they reach, identify new customers, and predict fraud — which is a major issue in the insurance industry as of late. Therefore, the insurance industry could not only learn how to be more productive and transition their efforts from agents and distribution channels to end customers but also learn how to prevent some of the major data security concerns in their industry through fraud prevention..
This ultimately means that data itself could actually be able to predict its insecurity and oust hackers before they ever have the chance to hack the companies they intend to. On top of this, by gathering data from their companies regarding telematics devices, smart phones, social media, CCTV footage, electoral rolls, credit reports, website analytics, government statistics, and satellite data, they will soon be able to also recognize consumer trends. Not only will this make insurance easier to receive but also make it cheaper for consumers as well, which will help the company to make their guest services more automated and reduce costs of person-to-person interaction. With all of this in mind, it is clear that data analytics have a definitive position in the insurance industry and will continue to improve insurance and its efficiency each and every day as well.
4. Travel Industry
The travel industry is one that is heavily focused on convenience, affordability, and comfort. Because of this, it is highly important that anyone in the travel industry pays close attention to their convenience analytics, as well as their budget analytics of their business, to provide a more affordable option than their competitors and a more convenient one as well.
In fact, by analyzing trends in their competitors reviews, budgets, consumer interactions, and site UX, these companies can provide a more convenient and affordable option that will ultimately attract more people. Furthermore, they can also look into their competitors demographic in comparison to the things that entice said demographic in universal big data so as to know how their competitors are marketing and how they should do so to be more enticing to the relevant demographic.
On top of this, these companies can also create new marketing strategies using predictive analytics trends in order to attract new potential markets likewise. For instance, many companies in the travel industry tend to market themselves to young adventurous demographics. Despite this, a large part of the market is lost as retirees also love to travel to foreign destinations as well. Therefore, by recognizing this trend, these companies can target more than one demographic in an efficient way and not only increase their reach but also their profitability over time.
Furthermore, many travelers have begun to recognize the benefits of medical tourism as well. Medical tourism is when a patient travels outside of the country or state in order to receive medical care. With so many stipulations on medical care now becoming major issues in America, this option has become more and more enticing to individuals dealing with medical issues that are often uninsured or extremely expensive in the states.
Therefore, by analyzing the medical tourism analytics involved in medical travel, travel companies can also find ways to market towards these individuals specifically — in order to open up their company to an entirely new and unique market and their trends before anyone else. Through this, these travel companies can ultimately create a far more profitable and inclusive market which will not only help the travel economy but also promote global travel for all as well.
5. Healthcare Industry
Although medical tourism is definitely one aspect of the healthcare industry being significantly affected by analytics, there are so many more that. By acquiring the right forms of data, can allow the healthcare industry to be able to provide more informed care, predict future outbreaks or disease, and take preventive care to an entirely new level as well.
On top of this, this will ultimately allow healthcare professionals to predict and prevent fraud which will further secure online patient records. With the recent WannaCry ransomware attack attacking the healthcare industry heavily, this could help these hospitals to be able to recognize risks and remove them before they significantly affect their patients.
Furthermore, this could also significantly help rural healthcare initiatives as it would allow for healthcare professionals to monitor patients in real-time in order to help them and also predict conditions even when they are not anywhere near a hospital. Not only would this lower the amount of diseases and deaths in rural America but it would also allow for healthcare professionals to provide affordable healthcare to individuals unable to pay for current insurance prices likewise.
Therefore, by using predictive data analytics in the healthcare world, the industry could ultimately lower the amount of rural and uninsured deaths yearly, as well as provide more informed preventive care as well and remove the risks of data breaches likewise, which would ultimately lead to a more efficient, affordable, and secure healthcare system for all.
In the end, there are many ways that professionals in various industries are currently utilizing big data analytics to their benefit, but the opportunities still remain endless, and, perhaps in time, these analytics could not only help to make our world a more efficient and affordable place but also help us to predict and formulate a brighter future for our children and the world of tomorrow as well.
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Data science and machine learning have emerged as the keys to unlocking value in enterprise data assets. Unlike traditional business analytics, which focus on known values and past performance, data science aims to identify hidden patterns in order to drive new innovations. Behind these efforts are the programming languages used by data science teams to clean up and prepare data, write and test algorithms, build statistical models, and translate into consumable applications or visualizations. In this regard, Python stands out as the language best suited for all areas of the data science and machine learning framework.
In a recent white paper “Management’s Guide – Unlocking the Power of Data Science & Machine Learning with Python,” ActiveState – the Open Source Language Company – provides a summary of Python’s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights and innovation from big data.
When it comes to which language is best for data science, the short answer is that it depends on the work you are trying to do. Python and R are suited for data science functions, while Java is the standard choice for integrating data science code into large-scale systems like Hadoop. However, Python challenges Java in that respect, and offers additional value as a tool for building web applications. Recently, Go has emerged as an up and coming alternative to the three major languages, but is not yet as well supported as Python.
In practice, data science teams use a combination of languages to play to the strengths of each one, with Python and R used in varying degrees. The guide includes a brief comparison table highlighting each language in the context of data science.
Companies are transforming into 'algorithmic businesses' with Python as the leading language for machine learning. Click To Tweet
Companies are not only maximizing their use of data, but transforming into ‘algorithmic businesses’ with Python as the leading language for machine learning. Whether it’s automatic stock trading, discoveries of new drug treatments, optimized resource production or any number of applications involving speech, text or image recognition, machine and deep learning are becoming the primary competitive advantage in every industry.
In the complete white paper, ActiveState covers:
- Introduction: the Big Data Dilemma
- Python vs. Other Languages
- Data Analysis with Python
- Machine Learning with Python
To learn more about introducing Python into your data science technology stack, download the full white paper.
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Scality, a leader in object and cloud storage, announced the open source launch of its Scality Zenko, a Multi-Cloud Data Controller. The new solution is free to use and embed into developer applications, opening a new world of multi-cloud storage for developers.
Zenko provides a unified interface based on a proven implementation of the Amazon S3 API across clouds. This allows any cloud to be addressed with the same API and access layer, while storing information in their respective native format. For example, any Amazon S3-compliant application can now support Azure Blob Storage without any application modification. Scality’s vision for Zenko is to add data management controls to protect vital business assets, and metadata search to quickly subset large datasets based on simple business descriptors.
We believe that everyone should be in control of their data,” said Giorgio Regni, CTO at Scality. “Our vision for Zenko is simple—bring control and freedom to the developer to unleash a new generation of multi-cloud applications. We welcome anyone who wants to participate and contribute to this vision.”
Zenko builds on the success of the company’s Scality S3 Server, the open-source implementation of the Amazon S3 API, which has experienced more than 600,000 DockerHub pulls since it was introduced in June 2016. Scality is releasing this new code to the open source community under an Apache 2.0 license, so that any developer can use and extend Zenko in their development.
With Zenko, Scality makes it even easier for enterprises of all sizes to quickly and cost-effectively deploy thousands of apps within the Microsoft Azure Cloud and leverage its many advanced services,” said Jurgen Willis, Head of Product for Azure Object Storage at Microsoft Corp. “Data stored with Zenko is stored in Azure Blob Storage native format, so it can easily be processed in the Azure Cloud for maximum scalability.”
Zenko Multi-Cloud Data Controller expands the Scality S3 Server, and includes:
- S3 API – Providing a single API set and 360° access to any cloud. Developers want to have an abstraction layer allowing them the freedom to use any cloud at any time. Scality Zenko provides a single unifying interface using the Amazon S3 API, supporting multi-cloud backend data storage both on-premises and in public cloud services. Zenko is available now for Microsoft Azure Blob Storage, Amazon S3, Scality RING and Docker and will be available soon for other cloud platforms.
- Native format – Data written through Zenko is stored in the native format of the target storage and can be read directly, without the need to go through Zenko. Therefore, data written in Azure Blob Storage or in Amazon S3 can leverage the respective advanced services of these public clouds.
- Backbeat data workflow – A policy-based data management engine used for seamless data replication, data migration services or extended cloud workflow services like cloud analytics and content distribution. This feature will be available in September.
- Clueso metadata search – An Apache Spark-based metadata search for expanded insight to understand data. Clueso makes it easy to interpret petabyte-scale data and easily manipulate it on any cloud to separate high-value information from data noise. It provides the ability to subset data based on key attributes. This feature will be available in September.
Application developers looking for design efficiency and rapid implementation will appreciate the productivity benefits of using Zenko. Today, applications must be rewritten to support each cloud, which reduces productivity and makes the use of multiple clouds expensive. With Zenko, applications are built once and deployed across any cloud service.
Cityzen Data provides a data management platform for collecting, storing, and delivering value from all kinds of sensor data to help customers accelerate progress from sensors to services, primarily for health, sport, wellness, and scientific applications,” said Mathias Herberts, co-founder and CTO at Cityzen Data. “Scality provides our backend storage for this and gives us a single interface for developers to code within any cloud on a common API set. With Scality, we can write an application once and deploy anywhere on any cloud.”
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There are few things social media users love more than flooding their feeds with photos of food. Yet we seldom use these images for much more than a quick scroll on our cellphones. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that analyzing photos like these could help us learn recipes and better understand people’s eating habits. In a new paper the team trained an AI system to look at images of food and be able to predict the ingredients and suggest similar recipes. In experiments the system retrieved the correct recipe 65 percent of the time.
In computer vision, food is mostly neglected because we don’t have the large-scale data sets needed to make predictions,” says Yusuf Aytar, a postdoctoral associate who co-wrote a paper about the system with MIT professor Antonio Torralba. “But seemingly useless photos on social media can actually provide valuable insight into health habits and dietary preferences.”
The paper will be presented later this month at the Computer Vision and Pattern Recognition conference in Honolulu. CSAIL graduate student Nick Hynes was lead author alongside Amaia Salvador of the Polytechnic University of Catalonia in Spain. Co-authors include CSAIL post-doc Javier Marin, as well as scientist Ferda Ofli and research director Ingmar Weber of QCRI.
How it works
The Web has spurred a huge growth of research in the area of classifying food data, but the majority of it has used much smaller datasets, which often leads to major gaps in labeling foods. In 2014 Swiss researchers created the “Food-101” data set and used it to develop an algorithm that could recognize images of food with 50 percent accuracy. Future iterations only improved accuracy to about 80 percent, suggesting that the size of the dataset may be a limiting factor. Even the larger data sets have often been somewhat limited in how well they generalize across populations. A database from the City University in Hong Kong has over 110,000 images and 65,000 recipes, each with ingredient lists and instructions, but only contains Chinese cuisine.
The CSAIL team’s project aims to build off of this work but dramatically expand in scope. Researchers combed websites like All Recipes and Food.com to develop “Recipe1M,” a database of over 1 million recipes which were annotated with information about the ingredients in a wide range of dishes. They then used that data to train a neural network to find patterns and make connections between the food images and the corresponding ingredients and recipes. Given a photo of a food item, the team’s system – which they dubbed Pic2Recipe – could identify ingredients like flour, eggs and butter, and then suggest several recipes that it determined to be similar to images from the database.
You can imagine people using this to track their daily nutrition, or to photograph their meal at a restaurant and know what’s needed to cook it at home later,” says Christoph Trattner, an assistant professor at MODUL University Vienna in the New Media Technology Department who was not involved in the paper. “The team’s approach works at a similar level to human judgement, which is remarkable.”
The system did particularly well with desserts like cookies or muffins, since that was a main theme in the database. However, it had difficulty determining ingredients for more ambiguous foods, like sushi rolls and smoothies. It was also often stumped when there similar recipes for the same dishes. For example, there’s dozens of ways to make lasagna, so the team needed to make sure that system wouldn’t “penalize” recipes that are similar when trying to separate those that are different. (One way to solve this was by seeing if the ingredients in each are generally similar before comparing the recipes themselves).
In the future, the team hopes to be able to improve the system so that it can understand food in even more detail. This could mean being able to infer how a food is prepared (i.e. stewed versus diced) or distinguish different variations of foods, like mushrooms or onions. The researchers are also interested in potentially developing the system into a “dinner aide” that could figure out what to cook given a dietary preference and a list of items in the fridge.
This could potentially help people figure out what’s in their food when they don’t have explicit nutritional information,” says Hynes. “For example, if you know what ingredients went into a dish but not the amount, you can take a photo, enter the ingredients, and run the model to find a similar recipe with known quantities, and then use that information to approximate your own meal.”
The project was funded in part by QCRI, as well as the European Regional Development Fund (ERDF) and the Spanish Ministry of Economy, Industry and Competitiveness.
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Here is a question I was asked to discuss at a conference last month: what is Artifical Intelligence (AI)? Instead of trying to answer it, which could take days, I decided to focus on how AI has been defined over the years. Nowadays, most people probably equate AI with deep learning. This has not always been the case as we shall see.
Most people say that AI was first defined as a research field in a 1956 workshop at Dartmouth College. Reality is that is has been defined 6 years earlier by Alan Turing in 1950. Let me cite Wikipedia here:
The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so the result would not depend on the machine's ability to render words as speech. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give.
The test was introduced by Turing in his paper, "Computing Machinery and Intelligence", while working at the University of Manchester (Turing, 1950; p. 460). It opens with the words: "I propose to consider the question, 'Can machines think?'" Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, is one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think".
So, the first definition of AI was about thinking machines. Turing decided to test thinking via a chat.
The definition of AI rapidly evolved to include the ability to perform complex reasoning and planing tasks. Early success in the 50s led prominent researchers to make imprudent predictions about how AI would become a reality in the 60s. The lack of realization of these predictions led to funding cut known as the AI winter in the 70s.
In the early 80s, building on some success for medical diagnosis, AI came back with expert systems. These systems were trying to capture the expertise of humans in various domains, and were implemented as rule based systems. This was the days were AI was focusing on the ability to perform tasks at best human expertise level. Success like IBM Deep Blue beating the chess world champion, Gary Kasparov, in 1997 was the acme of this line of AI research.
Let's contrast this with today's AI. The focus is on perception: can we have systems that recognize what is in a picture, what is in a video, what is said in a sound track? Rapid progress is underway for these tasks thanks to the use of deep learning. Is it AI still? Are we automating human thinking? Reality is we are working on automating tasks that most humans can do without any thinking effort. Yet we see lots of bragging about AI being a reality when all we have is some ability to mimic human perception. I really find it ironic that our definition of intelligence is that of mere perception rather than thinking.
Granted, not all AI work today is about perception. Work on natural language processing (e.g. translation) is a bit closer to reasoning than mere perception tasks described above. Success like IBM Watson at Jeopardy, or Google AlphaGO at Go are two examples of the traditional AI aiming at replicate tasks performed by human experts. The good news (to me at least) is that the progress is so rapid on perception that it will move from a research field to an engineering field in the coming years. We will then see a re-positioning of researchers on other AI related topics such as reasoning and planning. We'll be closer to Turing's initial view of AI.
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Compared to other sectors, like finance and technology, retail can be considered a late adopter of the advantages offered by business intelligence to daily processes. This is a paradox, as the operations in retail are some of the most well adjusted for the insight provided by digital dashboards.
Questions like: “Who is your ideal client?”, “What are the products you should promote?” and “Which items should you sell as a bundle?”, “What is the preferred way of paying?” and “How do your clients engage with your brand?”, can all be answered through a BI platform that integrates point of sale data with demographics and interactions from online interfaces.
Why is Business Intelligence a solution for retail?
The value of BI comes from the evolution of retail companies from organizations based on operations to companies built on innovation. The intermediate stages are consolidation, integration, and optimization. This is a journey from ad-hoc to automation, from naïve to well-defined processes that also gain a predictive dimension. Most retailers are said to be in the integration stage, where the company has enough data to make decisions based on market signals, but the vast majority are not leveraging what they have. Most companies offer three stages of business intelligence consulting, similar to Itransition: monitoring intelligence, analytical intelligence and predictive intelligence, the most important stage.
Business Intelligence is a general name for several applications that can help a company have an integrated overview of vendors, stocks, clients, payments, and marketing. This is a new approach, in contrast to the siloed way of working, specific to the pre-dot com era. A store is like a living organism. Treating each component separately prevents the organization from seeing the bigger picture and the cross-influences that could be used as profit centers.
What are the main BI applications?
Data can show a company how to size their stocks, price products based on demand and create promotions and sales targets to maximize revenue.
Costs, Prices, and Stock Management
Net profit in retail is small, therefore pricing the product to avoid losses and remain competitive is one of the greatest challenges. All costs of doing business should be taken into consideration, including unexpected situations. Decisions are based on scenario planning and making seasonally-influenced decisions.
Stock management is one of the costliest aspects of retail and BI solutions are striving to create the perfect model for optimization based on past purchases and future trends. A great app offers stock analysis, highlights the best-selling products and creates replenishment orders for these while simultaneously advising the managers to cancel orders for the worst-performing products.
Numbers help you get into the mindset of your clients, see their path from learning about your existence to becoming advocates of your quality. You need to understand the correlations between their demographic and sociographic characteristics and the content of their shopping carts. Pinpoint the link between the ads they see and the products they buy. Drill down to find out their whereabouts, payment methods, time spent in the brick and mortar store or the online retail environment. Put all this data together to create product bundles and promotions.
Vendor Management and Evaluation
Without a vendor evaluation, there is no business growth. You need to see per product results, per vendor analysis and take decisions accordingly. A BI solution should consider things like delivery time, client’s satisfaction with the return policy and even brand perception, if applicable.
Sales, Targets and Performance Analysis
In a small neighborhood store, you might want to know what the best-selling brands are and which of the sales assistants are generating more revenue, while in a multi-national corporation you may need to know which branches are meeting their quotas and which are falling short. In fact, these problems are primarily the same; only the scale is different. In this situation, BI is a great tool due to its scalability.
Additional drill-down levels can be added to an existing solution to get to the root of problems. The BI system can be the base of strategic decisions such as the product mix offered or the bonuses and promotions given to sales agents. The numbers from the dashboard also give a great estimate for setting attainable but motivating sales targets, based on the forecasts.
Trends, Forecasting, and Planning
Even retailers in the 1960s were looking at historical performance. The difference in the smart systems is that you no longer have to wait until the end of the month to do the math and see how the business is doing. A BI system performs in real-time and can dynamically adjust actions to maximize your profit. It’s like constant course correction when sailing to your destination, instead of waiting to see where the waves will take you.
Setting the Right KPIs in Digital Dashboards
Each organization has the possibility of setting their own KPIs, depending on the activity type, strategy and business proposal, but there are a few general guidelines that can be successfully used as defined by Supply Chain Operations Reference (SCOR) Level 1 metrics. These include:
- reliability measured by order fulfillment and delivery performance;
- responsiveness, usually expressed as time;
- flexibility as a combination of vendor agility and product production
- Asset management efficiency.
When it comes to client-related metrics, you can be inspired by the sales funnel approach to select the best metrics. These include passing from one stage to the other: measuring entry point leads, computing conversions rates, the price per conversion, the value of the conversion, the price of the average sale and the time spent in the funnel. Costs of recapturing missed opportunities through re-marketing are also necessary.
Depending on your business model, you are responsible for setting up KPIs to measure the performance of the sales agents.
Where is the retail industry heading?
The retail industry is a mature market, with low net profit rates (1.5%- 5%), where every process optimization and cost cut can mean the difference between survival and being out of business. Business Intelligence offers marketers, financial advisors, and strategists a starting point in their quest for a better understanding of clients’ needs and a better anticipation of the necessary steps to remain relevant.
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We all like to feel as if we have an intuitive sense of what our businesses need to succeed, but the reality is that successful companies rely on big data analytics to continuously understand, measure and improve. With powerful computing available via the cloud, and more tools and services for data collection and analysis than ever before, you can gain the edge over your competition, streamline your operations, connect with the right customers and even develop or refine the right products, using the unprecedented insights from big data. Refine your business strategy by integrating big data analytics into these five business areas:
Process improvement – work smarter
This is often one of the first areas businesses target and think of when it comes to big data analytics. Collecting data on your business process or production invariably exposes inefficiencies and opportunities. While there are often financial gains, resource use or reuse, scheduling and fulfillment/delivery are all areas that benefit from big data analysis.
Product improvement – create smarter
Beyond simply creating efficiencies in your process or production line, big data analytics used correctly will guide and refine your product or service offering. Use big data to gain insight into the market, trends and customer desires, refine your current offerings based on what best serves the company’s mission, direction and wellbeing, and test ideas early and often. This can be at the level of entirely new product lines, or it can be applied simply but effectively to invest in benefits and features of your product or service offering that bring value to your customer, while rolling back those features that bring the least value or don’t recoup their investment. As an added benefit, using big data early and often to develop and refine your product or service offering can help you identify market opportunities and promote the benefits of most interest to your customers, more coherently.
Customer acquisition – market smarter
Your marketing and advertising dollars are money down the drain if you’re not connecting with the right audience. Refine your customer acquisition process by testing every element, every step of the way. Test and drop campaigns, platforms, ads, text and photos that don’t perform well. With powerful analytics tools, you can track levels of engagement with calls to action.
For online properties such as your website, social channels and content marketing resources, dial down to granular elements like a catchphrase, Call to Action button or image. Stock photo websites, such as Dreamstime, can help resource you with high quality images to test for best audience response. Refine your images and messaging with big data analytics to reach the customers who are seeking your services, without wasting energy and resources on other audiences.
Customer retention – grow smarter
It costs five times more to gain a new customer than to retain an existing one. Slash your customer acquisition costs by using big data analytics to track what keeps your customers and what turns them away. Plan regular touch-points to seek out feedback from your customers and ask them what works, what doesn’t and what they really want. Incorporate personalization to create a feeling of connection, greater satisfaction and desire for your products or services – but be sure to include a human touch and don’t rely solely on data. Use analytics to drive direction, but people to build connection. Big data also helps when it comes to functionally achieving the improvements in service or product that your customers want.
Employee retention – collaborate smarter
The use of big data analytics when it comes to improving employee satisfaction can be of great help – but be cautious with overemphasizing data over relationships when it comes to employees or customers. While big data analytics have been touted as an excellent way to identify the right skills for the job, they run the risk of reducing employees to a list of traits and achievements, and alienating potential talent that needs a human point of contact. As in all areas of your business, use big data strategically to search for candidates, test skills and collect aggregate feedback. Support equality and fair workplace policy, but don’t use it as the sole tool in your kit. Incorporate human touch-points in the process to catch opportunities that a machine might miss.
Whatever the size or nature of your business, big data analytics will help you understand opportunities for improvement and to remain competitive. Employ big data strategically to improve your process and service or product, market it better, find and keep the best customers and get the right people on your team.
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The Silicon Valley reverberates of Machine Learning today as Artificial Intelligence (AI) continues to reshape, mold and revolutionize the world. Machine learning is a fragment of AI and a very significant one. It is a subset of AI that has proved to be successful for technology to make headway. Artificial intelligence is based on Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. It is the exploitation of computer functions that are related to human intelligence, such as reasoning, learning and problem-solving.
Where Artificial Intelligence aims to build machines that are capable of intelligent behavior Machine Learning is its application getting computers to work without being unequivocally programmed. It is based on algorithms that need not rely on rules-based programming. Machine learning makes it easier to develop smart and sophisticated software that decreases human effort and saves time. Years of work can be made a matter of minutes and seconds. It is an incredible breakthrough in the field of artificial intelligence.
Although machines have not yet completely taken over humans, they are slowly percolating into our lives and have managed to handle our monotonous, run-of-the-mill jobs as well as provided us with entertainment. Some wonder what will be the future of AI and machine learning, but they are oblivious to the fact that it’s not the future, it’s the present which would perpetuate and bring about more astounding inventions that would modernize and ease our lives. As we venture further into the digital age, our technology makes leaps and strides forward.
I have pooled up the top ten applications of the two which you should know about. You will be amazed when you will find out just many of those are incorporated into your daily life, and you use them!
ROOTING OUT FRAUD
You must be aware of the verification emails and messages you receive from your bank if money has been withdrawn. These are to avoid theft and fraud and save you from loss. AI is used here to monitor any act of fraud. It does so by being able to distinguish between fraudulent and non-fraudulent purchases for which it has been trained. The computer is fed large samples of fraudulent and non-fraudulent purchases and asked to figure out transactions that fall into each category.
The emails are winnowed as well. Gmail has successfully been able to filter 99.9% of the emails that are spam. The Spam filters must continuously learn from signals to catch message and message data to beat the spammers.
Gmail’s categorization of your emails is an attempt whereby it is taught to direct our emails in their respective sections according to how we prioritize them.
AI has been in use since the first video game but no one was introduced to its complexities and effectiveness back then, and it has continued to evolve ever since bringing in changes one is bewildered at.
SOCIAL NETWORKING SITES
Ever wondered at the appearance of suggestions that pop up for tagging every time you put up a picture on Facebook? That’s another of the fascinating aspect of AI-using the machine learning algorithm that mimics the structure of human brain powering facial recognition feature. Your newsfeed is customized according to your likes and ads that are of your interest are displayed.
Pinterest makes use of computer vision which has enabled the automatic identification of objects in images and then recommends similar images. Machine learning aids in spam prevention, searches and discovery, ad performance and email marketing.
VIRTUAL PERSONAL ASSISTANTS
Smartphones are equipped with a voice-to-text feature which converts your audio into text. Google uses artificial neural networks to power voice search. Amazon went a step ahead with its Alexa- a virtual assistant that lends a hand in creating a to-do list, order online items, set reminders and answer questions. Echo smart speakers can integrate Alexa into the comfort of your living room, and you can easily throw questions or order food.
Imagine reading a newspaper or a novel while driving or enjoying a delicious, succulent meal. Yes, it is possible. Google’s self-driving car and Tesla’s ‘autopilot’ feature news are rampant. A new algorithm developed by Google allows self-driving cars to learn driving in a similar fashion as humans do, that is by experience!
Although Tesla’s autopilot feature isn’t advanced yet, the cars have already hit the road signaling the breaking in of this technology.
The nightmare of every student, the plagiarism checker is supported by machine learning. Machine learning is capable of detecting plagiarized text which is not even in the database, i.e., content in a foreign language or that which hasn’t been digitized. The similarity function is the algorithmic key which outputs a numeric estimate of how similar two documents are.
To unburden some of the load Robo-readers help is required. Essay grading a laborious task, and it is, for this reason, The Graduate Record Exam (GRE), grades the essay using one human reader and one robot reader called e-Rater. If the results do not match, a third being is brought in to settle the difference. Pairing up of human intelligence and artificial intelligence improves the reliability of the result.
While purchasing things online, you search for an item and can quickly see more of the similar, relevant searches emerge. It is the algorithm which automatically combines multiple fitting searches. Patterns are set that help in the adaptation to and recognition of the client’s needs.
The recommendations you receive about items you might be interested in which others have bought helps increase sales. The personalized recommendations on the homepage, at the bottom of the page and through emails are also artificially generated.
ONLINE CUSTOMER SUPPORT
Many sites give the opportunity to their customers to talk to the customer support service while browsing. But rarely do they provide with an actual human at the other end to walk you through the site or answer your queries. You are actually talking to a rudimentary AI. While some give you a little amount of information, others are capable of extracting the accurate, relevant information from the site.
These chatbots are unable to decipher the way humans communicate, but the rapid advances in natural language processing (NLP) has improved the situation.
WRAPPING IT UP
We have just scratched the surface of AI and machine learning, there is more to dig in and discover. Both AI and machine learning are intertwined and are continuing to touch our lives easing things up and bringing in development. The promising future of the two because of its quick earned popularity and use makes us anticipate of what will come next. Machine learning still has room for improvement because of which it is the buzzword nowadays. The prime focus of all the large technology companies lies on improving it.
The post is by Victoria Ashley, a professional content writer always seeking opportunities to write on. She is associated with a Trucks & Equipment business as a trainer and content analyst!
The post Top 10 Applications of Artificial Intelligence and Machine Learning You Should Know About! appeared first on Big Data Analytics News.
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The ubiquitous influence of Artificial Intelligence and Machine Learning is inescapable. The two terms although virtual and are mostly used interchangeably, are entirely different. Before I dig into the complexities of the two, the best way to give an outline of the two would be to say; Artificial Intelligence bodies the whole concept of machines being able to act intelligently and smartly. Whereas, Machine learning is an approach of Artificial Intelligence or a figment which emphasizes on providing data to the computers which it will analyze and then come up with the best possible solution on its own.
THE JOURNEY OF PROGRESS
The research labs have long been simmering with Artificial Intelligence, and it has been in use for a very long time. For decades now, as the mind has progressed and our understanding has improved, new locks to Artificial Intelligence have been opened, and there is yet more to achieve.
Machine learning stems from the minds of early AI crowd. Artificial Intelligence goes back to the time when Aristotle introduced syllogism. Scientists from Mathematics, Engineering, Psychology, Economics and Political Science opined of creating an artificial brain. In 1930’s and 40’s, Alan Turing, a pioneer in computing formulated techniques that set the ground for Artificial Intelligence today. That is how the magic wand came into being.
The world today is engulfed in Artificial Intelligence. Google, Netflix, Facebook all have revamped themselves with the aid of AI. As something with greater depth and broad scope, of which Machine Learning is just a part, the confusion should be cleared. That is what I am about to do here so that by the time you finish reading, you are able to distinguish between the two.
ARTIFICIAL INTELLIGENCE (AI)
It might not be wrong to interpret Artificial Intelligence as the fusion of humans and machines. AI is an umbrella term, to put it simply, making computers act smart. It is among the major fields of Computer Science that cover robotics, machine learning, expert systems, general intelligence and natural language processing.
Artificial Intelligence can be listed as Applied or General. The more common one is Applied Artificial Intelligence which is used in the designing of systems that cleverly trade stocks and shares. On the other hand, the Generalized AI can handle any task and is the steamy one where advances are being made.
At its infancy, AI has helped in dealing with the mundane daily household chores, such as Vacuum Cleaners, Dishwashers, and Lawn Mowers. The security systems have been developed using AI. The national security system uses data on AI systems, which then presents accurate problems that the nation might face. Crime can be controlled and fought by building criminal profiles.
Artificial intelligence has made its mark in education and learning too. Personalization of tutoring to monitor study pattern of students is achieved by AI. The disabled and elderly have also benefitted from it. Robots have been assisting people from a very long time now. It is used in speech therapy by using voice recognition systems. Artificial Intelligence has even shown its potential in transport. The software programmed cars that have recently been launched would reduce the risk of accidents and traffic jams. The metros and driver-less trains are pretty old now and have proved to be convenient.
AI propelled the development of Machine Learning and has paved the way for further progress.
To best describe Machine Learning you can conclude by saying that it’s a way to achieve Artificial Intelligence.
The prime focus of AI was to establish if-then rules to mimic human knowledge and decision-making. The Expert systems were unable to exploit data and learn from it, which posed a barrier to advancement. They remained within the boundaries laid by programming and cerebral capacity and failed to go beyond. Machine Learning won in replacing the Expert System and breaking through the posed barriers. Machine Learning focuses on constructing algorithms that would learn from data, complete task and make predictions with high statistical accuracy. It is not used in the perusal of the data which is a major factor.
The emergence of the internet and with it a significant amount of data that was generated, stored and needed to be analyzed. The engineers came to a conclusion that instead of teaching computers how to act it would be more efficient and convenient to code them to think like humans. Plugging them into the internet would then open up the passage to the world for them.
Neural Network was the key fashioned to teach the computers to think and understand the world with human perspective but with the speed and accuracy machines are known for. Reading texts and deciding whether it’s a compliment or a complaint, finding out how the genre of music would affect the mood of the listener or composing themes of its own are offered by systems working around Machine Learning and Neural Networks. The idea of communicating with the electronic devices and digital information has also been implanted by science fiction. This has lead to the innovative prospect of Natural Language Processing (NLP), on which work begun and still is being done. With the help of NLP applications, the machines make an attempt to understand human language and then reply accordingly. Machine Learning is used to help the machines to adapt to the nuances of human language and to be able to respond to a particular audience.
The iterative aspect of machine learning has made it possible for models to be exposed to information and then act independently after adapting to its cadences. The past computations help in generating reliable results. The capacity to automatically apply the complex mathematical calculation to Big Data at a faster pace with every passing day is a recent development. The Self-driving cars, online recommendations, customer feedback, and fraud detection are some of the common applications.
The most significant advent is the image recognition. The algorithms are capable of drawing out results from among thousand and million of images, which has impacted the geospatial industry greatly. Image recognition is though just one area from the many Machine Learning has been able to hit.
Analyzation of bigger and more complex data, at the speed of light and with precision, even on a very larger scale seems fruitful and appealing to the industries and business organizations.
Artificial Intelligence and specifically Machine Learning has a lot in store. It offers the mechanization of the mundane, monotonous tasks as well as promises insight into industrial sector, business sector, and healthcare sector.
Again both Artificial Intelligence and Machine Learning are entirely different; both are being consistently and lucratively sold. Since Artificial Intelligence has always been there, it is seen as something old with the new word Machine Learning taking its place. But then the surface of Artificial Intelligence still needs to be scoured; there is so much more that still needs to come out and revolutionize the human civilization.
Certainly, we are on track to reach that goal and getting nearer with increasing speed. It owes to the light in which we have begun to see Artificial Intelligence with the help of Machine Learning!
The post is by Victoria Ashley, a professional content writer always seeking opportunities to write on. She is associated with a Trucks & Equipment business as a trainer and content analyst!
The post Core Differences between Artificial intelligence and Machine Learning appeared first on Big Data Analytics News.
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The wave of Machine Learning has hit and transformed every sector, affecting the way we take our decisions. The widespread use of Big Data among all the industries has sparked the use of machines to detect patterns and previse future. With multiple complicated territories which Machine Learning has been able to conquer such as data mining, natural language processing (NLP), image recognition, and expert systems, it is said to be the foundation of future civilization.
Machine Learning is a very promising approach of Artificial Intelligence, one which is radically reshaping the present and the future!
MACHINE LEARNING AND ITS WORKING
Machine learning is actually a bottomless pit. It encompasses a lot of things.
The assumption laying the ground for Machine Learning is the analytical solutions that are reached by studying previous data models. It is the process whereby Artificial Intelligence is developed in computers to make them work without being programmed and as efficiently as a human mind but with little or no effort. It makes use of statistical analysis and predictive analysis to carry out the assigned task.
HOW IT WORKS
Three types of techniques are employed by Machine Learning, which trains and predicts the output.
- SUPERVISED MACHINE LEARNING- Input (X) and output (Y) variables are fed to the supervised algorithm which then puts them to use by mapping inputs to the desired outputs. It is called supervised machine learning since it requires human interference for making predictions in the testing data. A number of iterations help in getting the acceptable level of output. Common applications of this procedure are:
- Linear or Logistic regression
- Naive Bayes
- Support Vector Machines
- Discriminant Analysis
- Random Forest
- K-Nearest Neighbors
- UNSUPERVISED MACHINE LEARNING- There is no outcome variable in unsupervised machine learning. The algorithm works out the data using input variable and comes up with a similar structure. It forms classifications and associations in data. The fields where it comes in use are:
- Gaussian Mixture
- Neural Networks
- K-means and Hierarchial clustering
- Apriori algorithm for association rule mining
- REINFORCEMENT LEARNING- Here the machine generates programs, called agents using the process of learning and evolving. The conclusion is drawn from previous results and repercussions and the best method is selected through trial and error.
APPLICATIONS OF MACHINE LEARNING
With all the intensifying hype around Machine Learning, the technology companies are under pressure to exploit it further, and soon, to come up with more if its features and ways in which it can be utilized.
Nevertheless, you will be shocked to discover the diversification of machine learning as well as how much you are already making use of it unknowingly!
Machine learning has proved worthy in many industries globally. Some of the staunch users of ML are:
Machine Learning is a pro at detecting any anomaly. It can flag any malpractice and malfunction in high volume and high-frequency data transfer and communication. Inside trading in stock markets and fraudulent transactions are quickly and efficaciously caught.
Whether it be a language barrier or a matter of text-to-speech and vice verse, all make use of ML.
Visual recognition, Tone Analyzer, chat box, retrieving and ranking the relevant information, and personality insights have been using Machine Learning.
Healthcare organizations make use of Machine Learning. It picks out similar patterns between the patients and diseases. The biometric sensors have been saving lives globally. To determine the effectiveness of treatment in clinical treatments machine learning is employed.
Fraud detection and face recognition first began in the financial sector to catch theft. Since then through further improvisation and method, it has been meticulously working through structured and unstructured data.
In businesses better forecasting plays an intricate and crucial role. The constant and irregular fluctuation makes it difficult to comprehend the demand variability. But now, Machine Learning provides a solution for demand forecasting.
The recommendation and suggestions, market analysis, customer sentiments analyzation, ad ratings, and identification of new markets, Machine Learning has helped the retailers increase their sales and grow their business.
FORECASTS FOR MACHINE LEARNING
In the near future, the world is about to witness tremendous growth in Smart Apps, Virtual Assistants, and substantial use of Artificial Intelligence. The mobile market will escalate by the use of machine learning, and we will soon enter the era of self-driven cars (they have already been launched for testing and trials). Machine Learning is already an incredibly powerful tool which has been solving complicated problems. Although new Machine Learning tools would pop up now and then, the skills required to tune them and jazz them up would forever be in demand.
MACHINE LEARNING A CONTROVERSY?
The disadvantages that would come along with the evolution of Machine Learning are:
- An overwhelmingly automated lifestyle would make the human race vulnerable to threats and misfortunes!
- Things would be robbed of genuineness, and the look and feel of originality would be replaced by fakeness.
- The human resolve and interdependency would be eliminated which is the core of human civilization. Are we prepared to lose ourselves completely? Is it worth being a technology servant?
SCOPE OF MACHINE LEARNING
Regarding job opportunities Machine Learning has a significant role to play, there is no aspect of life where Machine Learning has not left its mark.
As the amount of data proliferates, the need for Engineers and Scientists has increased and will continue to grow. In order to understand and manage the subtleties and pitfalls of Machine Learning, workforce would be required because what seems as well tuned, simple machine is capable of leading you astray from your desired outcomes. Companies and industries heavily rely on Machine Learning, and so you have a great opportunity in the field. The demand for Machine Learning Engineers would continue to grow, and you can get in on the action!
Machine Learning is a harbinger of potential growth of humans and economy. So far now we have just removed the veneer from the surface. There is much more that Machine Learning has yet to achieve and introduce. There is hardly any application for which Machine Learning can not be used for detection and prediction.
Despite the contradictions in views, it is assured that in future, the gap between demand-supply in Data Science and Machine Learning Skills could only be bridged by providing the workforce that can handle Machine Learning’s intricacies, given the benefits of Machine Learning. The businesses would plunge into tap algorithm models that would improve and enhance their operations and customer-facing functions. The algorithms would take the business to whole new levels.
We have already seen how technology has replaced humans in financial market and many other areas for the better, taking off the load of cumbersome and labor-intensive work from human shoulders. It, therefore, wouldn’t be wrong to say that Machine Learning has a bright future ahead that would help humans enter a new modified era. Over time more dramatic evolution would bring in more positive changes.
Do not fear to lose your job! Machine Learning would just change the way you work, and yes it would be more enjoyable and less tiring!
The post is by Victoria Ashley, a professional content writer always seeking opportunities to write on. She is associated with a Trucks & Equipment business as a trainer and content analyst!
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