『19: Unlocking Explainable Machine Learning in Manufacturing』のカバーアート

19: Unlocking Explainable Machine Learning in Manufacturing

19: Unlocking Explainable Machine Learning in Manufacturing

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 This month’s episode of the AI Paper Club Podcast welcomes Dr. Diogo Ribeiro, a senior machine learning engineer at Deeper Insights. Diogo presents a research paper he co-developed, focusing on the industrial application of AI, titled "Isolation Forest and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection." The podcast explores the intricacies of combining traditional machine learning models with deep learning techniques to address a critical problem in industrial manufacturing: detecting anomalies in screw tightening processes.

The conversation highlights the importance of explainability in AI, particularly in industrial settings where safety and cost are paramount. The episode also touches on the broader implications of machine learning in AI, contrasting it with the current excitement surrounding generative AI models.

We also extend a special thank you to Diogo and his team of researchers for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://www.mdpi.com/2073-431X/11/4/54.

For more information on all things artificial intelligence, machine learning, and engineering for your business, please visit www.deeperinsights.com or reach out to us at thepaperclub@deeperinsights.com.

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