Exciting discussions with Dr. Phil on industrial machine learning, algorithms, and deep learning!
Dr. Phil and I discussed industrial machine learning, deep learning, and algorithms. We emphasized the importance of understanding the use case and the machines in use before delving into advanced technologies. Not everything that is labeled AI is actually AI. Many other techniques, such as decision trees and support vector machines, are valuable. It's crucial to choose the right approach for specific applications rather than following trends. In traditional industries like composites and plastics, there may be apprehension about adopting AI, but it should be seen as a tool to support human decision-making, not replace it. Utilizing available data can lead to significant efficiency improvements, as demonstrated by a case involving robot utilization.
Dr. Phil emphasized the significance of statistics in decision-making by machines and the diverse tools available for different applications. Applying AI and machine learning in manufacturing is about supporting efficiency, not replacing human workers.
Stay tuned for more insights from our material characterization journey! Overall, the key is to understand the processes and make informed decisions using the available tools and data.
If you want to discuss this in person, then let's meet at JEC Forum DACH 2024 in Stuttgart on October 22-23, 2024 where Dominik Riescher of sensXPERT will be available for meetings.
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