Tina Baumgartner

confusion matrix

Confusion Matrix: Addressing Confusion About AI’s Performance

“How do we know how well the AI model performs and how many mistakes it makes?” is a question we often hear from manufacturers interested in our AI-based solution for manufacturing applications such as quality control and predictive maintenance. Model performance is a justified concern, artificial intelligence is still a young discipline and people need

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Synthetic data

A Deep-Dive into Synthetic Data: Definition, Use in Visual Inspection, Advantages and Risks

We live in the age of big data. Whether it is social media platforms, retail and e-commerce, telecommunications, healthcare or manufacturing an estimated 2.5 exabytes – or 2.5 quintillion bytes – of data is generated each and every day. The ability to crunch and make sense of these data was long the limiting factor. However,

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Technology Challenges

4 Technology Challenges You Need to Address when Implementing AI in Manufacturing

In our short series of challenges facing the forward-looking manufacturing professional ready to adopt AI, we have so far talked about data and change management challenges. Now it’s time to talk about technology challenges our customers have encountered and how to address them. Here are the four most relevant ones: Technology Challenge 1: Integration with

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