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Organisations continually strive to strike a balance between increasing equipment efficiency and reducing overall maintenance costs. Asset service engineers and quality departments across industries, such as airlines, oil & gas, pharmaceuticals, telecom, automobiles, and others, are pushed continuously to optimise their planning and operational expenses. As the Industry 4.0 paradigm continues to evolve, the Digital Twins technology provides a pragmatic opportunity to leverage virtual models to also enable impact points such as to predict failures, prescribe actions, and support digital supply chain networks. A digital twin, by definition, offers a seamless convergence of the physical and digital worlds -- creating an ever-evolving digital profile of a physical asset based on its historical and current behaviour. The idea of using digital models to optimise asset efficiency is not new.

Machine learning (ML) is everywhere. Startups, OEMs, and industrial suppliers are investing heavily in developing technology to collect and analyze manufacturing data. Much has been written about ML in manufacturing, but it can still be difficult to understand the different approaches. In manufacturing, the application of wireless sensors and ML offers potential to reduce costs and improve efficiency across the entire organization. Predictive maintenance (PdM) has widely been seen as one of the most promising applications for ML because it gives reliability teams real-time insight into the condition of physical equipment.

"In the past, large volumes of data made us sweat". So said Pari Bajpay, vice president of Next Generation Enablement at CenturyLink, during a presentation titled "Can AI deliver its promise of a cost-effective, improved experience in telecom?" at the TM Forum's recent Digital Transformation World event in Nice. "We didn't have the networking, compute and storage capacity to cope. A lot of the data would be turned off and you would only work on the critical aspects of the data because what you had on the other end of it was humans that could not process such large volumes," noted Bajpay. However, as big data technology has matured, Bajpay and his team at CenturyLink have grappled with the issue and are now leveraging AI to extract more value from their data.

People's lives depend on critical machinery. Traditionally operators relied on regular maintenance and hoped their asset didn't fail ahead of schedule. Now they are being fitted with thousands of sensors to provide early warning of failure. The problem is that often the "high signs" – or warning signs – are second or third or fourth-order problems.

People's lives depend on critical machinery. Traditionally operators relied on regular maintenance and hoped their asset didn't fail ahead of schedule. Now they are being fitted with thousands of sensors to provide early warning of failure. The problem is that often the "high signs" – or warning signs – are second or third or fourth-order problems.

Not many people understand how these unwanted messages are separated from wanted messages. You can't simply filter based on sender address, as new spam addresses can easily be created. The second reason is that spam is often sent from legitimate email accounts hijacked by third parties. The best way to separate spam is to look at the content of the email messages. The most effective techniques to do this are based on machine learning.