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Automation of Auxiliary Processes is the Key to the Smart Factory

Market demand is continuously evolving, which is forcing companies to reorganize their existing production strategies. Based on traditional automation systems, plants have looked for ways to reduce downtime, organize a safe work environment using mobile automated guided vehicle (AGV) robots and collaborative robots, and take advantage of the potential of advanced analysis of data acquired within the implemented Internet of Things (IoT) ecosystem.

For the best possible management of real-time manufacturing process, it is crucial to enhance the competence of the IoT ecosystem in the management of auxiliary processes and company resources. According to the concept of Industry 4.0 all production processes should be integrated, monitored and analyzed to achieve the best management based on the actual state of the factory. In this way, technology can inform people of the need for repairs and anticipate possible breakdowns, making downtime  less likely.

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Doing Machine Learning the Right Way

The work of MIT computer scientist Aleksander Madry is fueled by one core mission: “doing machine learning the right way.”

Madry’s research centers largely on making machine learning — a type of artificial intelligence — more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.

“I want society to truly embrace machine learning,” said Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”

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