Industry 4.0, or the fourth industrial revolution, has called for a merger between automated solutions and smarter, more effective operations through the application of real-time data collection.
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.
Machine learning is a continuance of the perceptions around predictive analytics, except that the AI system is able to make assumptions, test them and learn autonomously.
The proven impact of machine learning models has pushed more investment toward their development. Still there are plenty more gains to be realized.
Every great business has well thought of goals and strategies for the future. Thanks to the AI in your ERP (Enterprise Resource Planning) systems) it will help your users identify "next best actions"
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:
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Allied Vision's compact and light weight Alvium SWIR (short wave infrared) cameras are the ideal choice to build cost-effective OEM systems used in embedded and machine vision applications. The cameras support a spectral range from 400 nm to 1700 nm at high quantum efficiencies. This allows to capture images in both the visible and SWIR spectra with a single camera and enables users to reduce overall system costs! Equipped with Sony's IMX990 and IMX991 SenSWIR InGaAs sensors, Alvium SWIR cameras deliver high image quality and frame rates. This makes them well suited for drones or handheld devices used in various industries such as, agriculture, mining, solar cell inspection or medical.