La IA débil existe desde hace tiempo, se encuentra en todas las aplicaciones creadas para resolver problemas específicos, tales como reconocer imágenes o conducir un coche. La IA débil es la que habitualmente conocemos como “aprendizaje automático”. Por su parte, la IA fuerte es una máquina hipotética que es capaz de pensar y es consciente de su propia existencia, además, no solo puede resolver las tareas para las que está programada, sino que también puede aprender cosas nuevas.
A menudo, cuando leemos en los medios noticias en las que se habla de inteligencia artificial se anticipan innovaciones que probablemente tardarán todavía décadas en llegar. De hecho, todavía se desconoce cuándo podrá desarrollarse la IA fuerte. Según los estudios de los expertos, no hay una fecha cercana. Por tanto, de lo que podemos hablar es de aprendizaje automático y en nuestro caso, de cómo esta IA o aprendizaje automático está colaborando a la ciberseguridad.
De hecho, gracias al aprendizaje automático, la humanidad no se ha ahogado en una masa gigantesca de información que crece sin parar, y la ciberseguridad tiene mucho que agradecerle. Veamos por qué. En 10 años, la cantidad de programas maliciosos ha crecido 1000 veces: a principios de siglos, analizábamos 300 bits diarios de malware, ahora esa cifra tiene 3 ceros más. Y gracias al aprendizaje y los sistemas automáticos hemos podido mantener el ritmo de detección y prevención de ataques.
A día de hoy, podemos decir que nuestros sistemas automatizados detectan el 99,9% de los ataques. Los algoritmos inteligentes filtran tanto las buenas como las malas aplicaciones, por lo que cada día coleccionamos más de dos millones de archivos diferentes. Y de este total, una quinta parte son maliciosos.
Con este índice de detección parece que los analistas no hicieran nada. Nada de eso. Su papel es esencial a la hora de afinar estos sistemas para hacerlos más efectivos, así como para desarrollar otros nuevos. Así, por ejemplo, tenemos expertos dedicados a descubrir complejos ciberataques reales, que luego transfieren sus nuevos conocimientos y habilidades a nuestros sistemas automatizados, mientras que otros experimentan con diferentes modelos y métodos de aprendizaje automático.
Por tanto, todo el ruido que se genera sobre el aprendizaje automático y la cantidad inmensa de datos en los que se basan las predicciones de IA son básicos para la ciberseguridad digital, ya que cuantos más datos tengamos, más sistemas automatizados desarrollaremos y más valor podremos crear.
Fuente: https://www.interempresas.net
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