Machine learning is no longer just for smartphones or game shows. Increased data-processing power, the availability of Big Data, the Internet of Things, and improvements in algorithms are convergingto power a renaissance in business intelligence.
Intelligent business processes. Many of today’s business processes are governed by rigid, software based rules. This rules-based approach is limited in its ability to tackle complex processes. Further, these processes often require employees to spend time on boring, highly repetitive work, such as checking invoices and travel expenses for accuracy or going through hundreds or thousands of résumés to fill a position. If we change the rules and let self learning algorithms loose on the data, machine learning could reveal valuable new patterns and solutions that we never knew existed. Meanwhile, employees could be reassigned to more engaging and strategic tasks.
Intelligent infrastructure. Our economy depends on infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas. Machine learning has the potential to discover new signals in the data that could allow for continuous improvement of complex and fast-changing systems. That gives humans more time to apply their creativity (something that machines may never learn to duplicate) to new discoveries and innovation.
Digital assistants and bots. Recent advances in machinelearning technology suggest a future in which robots, machines, and devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, adapt their behavior to different situations, and interact with humans much more closely.
Our devices – already able to react to our voices – will become more interactive, continuously learning assistants to help us with our daily business routines, such as scheduling meetings, translating documents, or analyzing text and data.
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