Latest machine learning publications
Business changes all the time, but advances in today’s technologies have accelerated the pace of change. Machine learning analyses historical data and behaviours to predict patterns and make decisions. It has proved hugely successful in retail for its ability to tailor products and services to customers.
Unsurprisingly, retail banking and machine learning are a perfect combination. Thanks to machine learning, functions such as fraud detection and credit scoring are now automated. Banks also leverage machine learning and predictive analytics to offer their customers a far more personalised user experience, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills.
The Committee on Standards in Public Life says more transparency is needed in the use of AI to ensure public trust around its use Rules around the use of artificial intelligence (AI) in the public sector remain a “work in progress” with “notable deficiencies”, according to a new report. The Committee on Standards in Public… Read More
Almost half a year after acquiring AI networking outfit Mist Systems for a cool $405 million, Juniper Networks has announced the next set of Mist AI integrations into the company’s network and hybrid cloud monitoring platform.
Mist’s cloud-based AI wireless service, WiFI Assurance, will be added to Juniper’s platform as a subscription offering. The service uses network data to make wireless networks more reliable, and centres around an AI-powered virtual network assistant called Marvis that uses dynamic packet capture and machine learning technology to automatically identify, adapt and fix network issues.
As cyber security professionals it’s vital to understand these threats and the various tools and solutions available. Steve Ng, Lead Digital Platform Operations of Mediacorp, identifies today’s most pressing threats and the ways in which we can monitor, mitigate and resolve them.
Over the past few decades, technological advances have enabled telecom service providers to consolidate their network infrastructure. The reduction in equipment size, coupled with an improved ability to bridge larger distances, has also allowed service providers to reduce their data centre footprint.
But with the reduction in data centre sites, the modern facility has also become comparatively supersized and energy hungry, mainly due to the never-ending increase of network traffic. To mitigate this challenge, companies such as Verizon are turning to machine learning and data analytics to improve the energy efficiency of facilities.