Wednesday, May 8 • 1:00 PM - 1:45 PM
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Location: W315A
Title: The ConnectedAI Approach: Extracting Knowledge While Preserving Data Privacy
Description:
The hype is real. Machine Learning and Artificial Intelligence (AI) do help machine builders and operators significantly improve their operations. This includes an increase in process efficiency and a reduction in machine downtime, energy consumption, and waste. Yet to achieve these results, AI requires large amounts of data from entire fleets of machines, spanning machine design, manufacturing, and operations. This typically requires close collaboration between the machine builders and owners. However, this partnership is not always viable: machine builders guard their machine design while owners try to keep their operations locked away as trade secrets. As a result, extensive use of AI is often limited by the manufacturing firms’ data privacy policies. ConnectedAI uses a technique known as centralized federated learning to achieve the impossible: aggregate knowledge from machine builders and fleets of operational machines without collecting operational data from those machines. It moves the algorithm to the data rather than moving all data to a central place to apply a machine learning algorithm. This reduces the hurdle of data privacy and confidentiality toward the adoption of machine learning algorithms in industrial applications. Join us to understand the vast benefits of ConnectedAI-based solutions in real-world applications. You will also get an overview of the basics of federated learning and how it changes the adoption of AI solutions in scenarios involving sensitive data.
Level: Expo Plus, Expo Premier
Type: AI & ioT
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