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Home Sponsored Editorial

For increased efficiency, deep learning is the future

by Charlotte Pordage
July 30, 2020
in Big Data, Digital Utilities, IOT, Sponsored Editorial
Reading Time: 2 mins read
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Machines are learning to think. Deep learning is a machine learning technique − and the most significant future technology in artificial intelligence. SICK transfers this key technology to its sensors, offering customers added value for greater productivity and flexibility.

Sensors remain the source of information, and integrated software offers a solution for evaluating and communicating network data.

However, the Industry 4.0 trend means there is an urgent need for ‘reformed’ thinking in IT on data complexity. Deep learning is essential, and it’s the path SICK and its customers are taking for modern plant processes.

Deep learning thinks like a human

Deep learning requires algorithms capable of detecting and processing vast, complex amounts of patterns and data. The artificial neural network mimics human thinking and learns from examples.

It learns from experience and learns to adapt to new, updated information. As a result, a range of optimisations are possible today that would have been unthinkable just a few years ago.

Machines and plants, in combination with intelligent data and specialised sensors, can find solutions to the most complex tasks.

Complex algorithms need specialist expertise

The demand is not for a universal solution. Rather, the focus is on solutions tailored to specific use cases. Although modern 2D and 3D cameras are becoming faster and more powerful, their performance is currently restricted by traditional image processing algorithms.

SICK’s deep learning experts work closely with its clients’ process and quality experts to implement customised solutions for different applications and conditions.

This unique expertise forms the basis of simulation training and the development of subsequent deep learning algorithms for SICK’s wide range of sensors.

This partner content is brought to you by SICK. For more information, visit www.sick.com.

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