In this Intel on AI podcast episode: Sparse modeling methods can improve the interpretability of predictive AI models and is widely used in academia today. Yet despite advances in the field, issues remain when sparse modeling meets real-life applications. Adrian Sossna, the Chief Marketing Officer at HACARUS, joins the Intel on AI podcast to talk about how the SPECTRO visual software inspection module can make sparse modeling more available. He highlights how SPECTRO enables factory automation by vastly reducing the amount of reclassification needed by human inspectors. Adrian talks about how this enables AI models to be trained faster with less data while achieving accurate results specifically targeted for production of precision parts, metals, plastics and other materials. SPECTRO contains explainability features that allow detection of defects within manufacturing and provides businesses to make improvements in their processes because they have visibility into the detection made by the software. Adrian also talks about how working with the Intel AI Builders program has allowed HACARUS to run SPECTRO on Intel Optimized Python and achieve impressive performance improvements and has been very powerful for HACARUS to deliver a better experience to their customers.

To learn more, visit: hacarus.com

Visit Intel AI Builders at: builders.intel.com/ai