{"id":78352,"date":"2026-02-03T17:27:38","date_gmt":"2026-02-03T16:27:38","guid":{"rendered":"https:\/\/fhi.nl\/?page_id=78352"},"modified":"2026-02-03T17:27:40","modified_gmt":"2026-02-03T16:27:40","slug":"from-real-to-synthetic-data-unlocking-industrial-ai-by-avoiding-the-black-box-trap","status":"publish","type":"page","link":"https:\/\/fhi.nl\/en\/from-real-to-synthetic-data-unlocking-industrial-ai-by-avoiding-the-black-box-trap\/","title":{"rendered":"From Real to Synthetic Data: Unlocking Industrial AI by Avoiding the Black Box Trap"},"content":{"rendered":"<header id=\"header\" class=\"header header--low header--branch\">\n\n\t\n\t\t\t<div class=\"header__background header__background--graphic\"><\/div>\n\t\n\t<div class=\"container\">\n\t\t<div class=\"header__content\">\n\t\t\t<div class=\"header__first\">\n\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"header__navigation-button\">\n\t\t\t\t\t\t<a class=\"header__navigation-button__arrow\" target=\"_self\" href=\"https:\/\/fhi.nl\/en\/programma-dene\/\">\n\t\t\t\t\t\t\t<svg width=\"56\" height=\"33\" viewbox=\"0 0 56 33\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><rect x=\"56\" y=\"33\" width=\"56\" height=\"33\" rx=\"16.5\" transform=\"rotate(180 56 33)\" fill=\"#2dd881\"\/><path d=\"M22.262 22.456 16.294 16.5l5.968-5.956M39.706 16.5H16.293\" stroke=\"#fff\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg> Back to program\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<h1 class=\"header__title\" >\n\t\t\t\t\tFrom Real to Synthetic Data: Unlocking Industrial AI by Avoiding the Black Box Trap\t\t\t\t<\/h1>\n\n\t\t\t\t<div class=\"header__dots-line\">\n\t\t\t\t\t<svg width=\"431\" height=\"9\" viewbox=\"0 0 431 9\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M430.799 4.192a1.136 1.136 0 1 1-2.272-.001 1.136 1.136 0 0 1 2.272 0Zm-27.272 0a1.135 1.135 0 1 1-2.27 0 1.135 1.135 0 0 1 2.27 0Zm-27.27 0a1.136 1.136 0 1 1-2.272-.001 1.136 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xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><circle cx=\"6.394\" cy=\"6.5\" r=\"6.394\" fill=\"#000\"\/><\/svg><h2 class='image-text-content__title'>11.00 \u2013 11.25<\/h2><\/div>\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"image-text-content__text\"><p>In industrial AI applications and projects, success depends not only on high-quality real data, but equally on the meaningful interpretation of that data at the edge. Not knowing the AI&#039;s interpretation (black box trap) can quickly lead to wrong decisions by misinterpretation and malfunction. By combining real-world datasets with synthetic data for model training and analysis of AI decision processes, it is possible to close critical gaps. On the AI inference side, robustness and efficiency can be achieved with the latest AI acceleration architecture for Edge AI using the &#039;open&#039; RISC-V technology.<\/p>\n<p>This talk illustrates how to leverage both data sources effectively, avoid misinterpretation, and build reliable AI solutions for real-life industrial environments.<\/p>\n<p>Speaker: Syamak Nazary \u2013 Axelera.AI (on behalf of RUTRONIK Elektronische Bauelemente GmbH)<\/p>\n<p><a class=\"header__button button\" href=\"https:\/\/events.fhi.nl\/DesignAutomationEmbeddedSystems-2026\" target=\"_blank\" rel=\"noopener\">Register<\/a><\/p>\n<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":43,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"branches":[13],"events":[361],"secretariat":[],"categories":[],"themes_tax":[],"class_list":["post-78352","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - 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