Putting AI into action


According to Gartner's IT Glossary, AI is technology that appears to emulate human performance – typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialog with people, and enhancing human cognitive performance. Indeed, AI represents a force of differentiation that will enable service providers to more effectively drive value across the business – from network optimisation and data analytics through to customer care and marketing engagement. AI can keep pace with these demands with solutions (both machine learning and deep learning) that include analytics tools and automation that can systematically respond, operate and improve operational and business support systems. It can take network optimisation to new levels, bringing advanced intelligence to data analytics, while making customer-facing operations and services more effective than ever before.

AI's First Stop: The IoT Edge


Machine learning and other forms of artificial intelligence will likely infiltrate all levels of the IT infrastructure stack, but some architectures will take to it more readily than others. Indeed, with increased data democratization, even long-standing centralized business intelligence platforms are starting to cede ground to smaller, more targeted approaches to data analysis, such as SQL query, predictive data modeling and auto-generated discovery visualization. With NXP's technology, Greengrass can support functions like real-time data gathering and simultaneous management, analysis and storage. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly.



Let's tackle those fears and myths by understanding more realistically what AI can and cannot do to enable business applications: To understand AI in ways that drive business, we must start with something that business is familiar with -- business intelligence (BI). Simply defined, predictive analytics use your existing data to predict data that you don't, or can't, have. Prescriptive analytics in advanced BI can recommend actions to optimize business processes, marketing effectiveness, ad targeting and many other business operations. Machine learning can now train models to produce results that closely match those obtained by human experts.