Machine learning and big data are both very difficult, in practically every way. The necessary data science skills are rare among industry professionals, the data volumes are immense, and the infrastructure required to work on that data is complex.
Manuel Nau for IoT Business News: Oracle is taking the IoT to a new level with the announcement that its IoT cloud service will now incorporate artificial intelligence and machine learning to provide clients with better business data insights than ever.
Ryan Francis for NetworkWorld: Machine learning isn’t confined to science fiction movie plots anymore; it’s fueled the proliferation of technologies that touch our everyday lives, including voice recognition with Siri or Alexa, Facebook auto-tagging photos and recommendations from Amazon and Spotify. And many enterprises are eager to leverage machine learning algorithms to increase the efficiency of their network. In fact, some are already using it to enhance their threat detection and optimize wide area networks.
As with any technology, machine learning could wreak havoc on a network if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat to avoid setting back their operations and turning the c-suite away from implementing this technology. Roman Sinayev, security intelligence software engineer at Juniper Networks, cites ways to avoid the top machine learning missteps. Cont'd...
Louis Columbus for Forbes: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.
Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.
The ten ways machine learning is revolutionizing manufacturing include the following:
Zaber's X-LRQ-DE Series of linear stages have high stiffness, load, and lifetime capabilities in a compact size. The integrated linear encoder combined with stage calibration provides high accuracy positioning over the full travel of the device. At 36 mm high, these stages are excellent for applications where a low profile is required. The X-LRQ-DE's innovative design allows speeds up to 205 mm/s and loads up to 100 kg. Like all Zaber products, the X-LRQ-DE Series is designed for easy set-up and operation.