El aprendizaje profundo es un subcampo del aprendizaje máquina, que busca clasificar datos mediante algoritmos correlacionales. Se basa en ciertas arquitecturas de redes neuronales, que le permiten jerarquizar la información (visual, auditiva y escrita) mediante una segmentación de patrones categorizados por niveles. Sin embargo, se presenta aún una marcada tendencia hacia los aportes de tipo conceptual, son pocos los resultados y hallazgos que permitan realmente vislumbrar de forma tangible sus beneficios frente a otras tendencias o tecnologías tradicionales. Este estudio se propone comparar el impacto del Big Data en las empresas privadas y los institutos de estadísticas nacionales, a partir de una amplia búsqueda de información especializada.
Cabe aclarar que existen un mayor número de tecnologías que soportan Big Data, tanto libres como propietarias, pero para efectos de este documento se ha acotado de acuerdo con lo anteriormente expuesto y tomando las tecnologías que dieron las bases iniciales al ecosistema Big Data. Big data se ha constituido como un “tópico caliente” que atrae la atención no solo de la industria, sino también de la academia y del Gobierno. Como primera estrategia, se hizo un acercamiento cienciométrico por medio de la herramienta bibliográfica SCOPUS, un índice bibliográfico que contiene una colección representativa, completa y multidisciplinar a nivel mundial.
Diferencias entre big data y data science
Esto ha hecho que cada día se pro duzca un gran volumen de información, donde se ha estimado que en el mundo se producen diariamente 2.5 x 1030 bytes, es decir 2,5 quintillones de datos1. Es importante notar que la manera en que se da la protección a la transferencia de datos y, si es posible, muchas veces al tratamiento que se hace de los mismos, difiere entre un Estado y otro como en el caso de la Unión Europea y los Estados Unidos, que, sin embargo, han accedido a tener un marco regulatorio común para el manejo de los datos (2017, p. 852). El aprendizaje automático abre un sinnúmero de posibilidades de investigación en diversos campos clínicos, donde la COVID-19 ha sido el impulsor de ello. Esto involucra desde los escáneres faciales para identificación de síntomas como la fiebre, wearables para medición y detección de anomalías cardiacas o respiratorias, hasta chatbots que evalúan a un paciente cuando este menciona sus síntomas y, basado en las respuestas dadas, el sistema le indica si debe permanecer en casa, llamar al médico o ir al hospital. 2, si se revisa según el tipo de recurso, se ve una marcada tendencia hacia los artículos de conferencia, con un total de 9.493 resultados. Los artículos científicos muestran 4.824 resultados, mientras que los capítulos de libro y los libros solo despliegan 388 y 88 resultados respectivamente, lo anterior ratifica la etapa naciente en que se encuentra este campo de estudio, puesto que sus bases teóricas apenas se están consolidando.
- Y no solo respecto a la protección de los derechos, sino que también en lo que respecta a las reparaciones por aquellas violaciones que pudieran haberse cometido en contra de los mismos.
- La IA en conjunto con el Big Data han demostrado ser herramientas fundamentales para ayudar al sector salud a detectar y controlar este virus con cierto margen de éxito, permitiendo procesar grandes cantidades de datos estructurados y no estructurados con alto grado de complejidad, que al ser combinados con algoritmos propios de la IA permiten realizar predicciones basado en patrones históricos y bucles de retroalimentación, entre otros.
- Y ya ha logrado incentivar en la comunidad académica y comercial el desarrollo de tecnologías de apoyo que toman los paradigmas base y los emplean en la construcción de soluciones particularizadas a problemas de entornos de investigación y producción reales.
Cabe aclarar que la IA no sustituye al profesional sanitario, por el contrario, es un complemento a su quehacer médico, ayudándole a mejorar la precisión del diagnóstico en un tiempo menor y tomar decisiones mucho más rápido aligerando con ello su carga de trabajo. Otros tipos de desarrollo de aprendizaje profundo, basado en los sistemas de reconocimiento de rostro empleados comúnmente en seguridad física, https://zacatecasonline.com.mx/tendencias/86286-bootcamp-programas-tripleten han sido modificados para detectar si la comunidad está cumpliendo la distancia social reglamentaria. Este software emplea cámaras de video estándar o aquellas dispuestas en una ciudad para videovigilancia, permitiendo monitorear el flujo peatonal en zonas críticas, realizando un reconocimiento sobre la distancia mínima, indicando una alerta a las autoridades si alguien no cumple con la norma.
La complementariedad de Big Data y Data Science
Para proteger la privacidad del paciente, quienes ejecutan el código son un grupo especializado, y luego una vez procesado devuelven los resultados a los investigadores. Este estudio es de especial interés porque permite realizar monitoreos epidemiológicos, caracterizando aquella población que se enferma, pero no ingresa a un hospital, o de aquellos que nunca muestran síntomas. Así, con la investigación empleando bootcamp de programación IA, se logra obtener información predictiva que ayuda a las autoridades sanitarias a tomar cartas sobre el asunto. Por ejemplo, mediante un modelo de ramificación para estimar cuántas personas han sido infectadas, se analiza ADN viral extraído de cada paciente conocido, luego, el modelo utiliza la tasa de mutación para interpolar a cuántas otras personas pasaron el virus en el camino (Li & Ayscue, 2020).
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)