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:
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Pleora's Visual Inspection System helps operators detect errors and defects for unique component types, assembly steps, and custom low-run products where automated optical inspection (AOI) is too complex and expensive. The system helps DICA Electronics: Avoid costly, errors as a result of detecting errors at different phases in production, Speed time in detecting the root cause of the in-field errors with Tracking & Reporting apps for traceability, Maintain consistency in training new employees on requirements or with staff any time a new product is added, Easily train the system using 'good product' images for multiple product lines with no programming skills required, Reduce subjective decision-making, especially over a long shift, reducing error-escapes.