Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠⁠⁠ 🤖

Summary

In this espisode we talk about this blog post from MathWorks, which discusses Explainable AI (XAI), a field addressing the "black box" nature of deep learning models. It explores techniques to make AI predictions more transparent, focusing on computer vision tasks like image classification, semantic segmentation, and anomaly detection. The author presents examples using MATLAB tools, highlighting how XAI methods can improve understanding and identify potential model flaws. A checklist is provided to guide the application of XAI, emphasising the importance of considering task specifics, datasets, and available tools. The post concludes by noting that XAI is broader than the showcased visualisation techniques.