#OPENBOX - Data Poisoning and associated Open issues with Antonio Part 1

OPENBOX aims at bringing an easier understanding of open problems that helps in finding solutions for such problems. For the said purpose, I interview researchers and practitioners who have published works on open problems in a variety of areas of Artificial Intelligence and Machine Learning to collect a simplified understanding of these open problems. These are published as podcast series. Ideas emerge when curiosity meets clarity. Here we go with OPENBOX to bring clarity to those curious minds looking to solve real-world problems. This project is done in collaboration with ForHumanity. ForHumanity is a 501(c)(3) nonprofit organization dedicated to minimizing the downside risks of AI and autonomous systems. ForHumanity develops criteria for an independent audit of AI systems. To know more visit https://forhumanity.center/. Today, we have with us Antonio. He is a PhD student at University Ca' Foscari of Venice working in the fields of adversarial machine learning and computer vision. He is expected to join CISPA labs saarbrücken, Germany. He is passionate about machine learning security and closely follows cutting-edge research in this space. He also authored a paper with Kathrin (one of our earlier podcast guests). We are discussing ” Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning” Paper that he co-authored. In this he is speaking about the varied attack vectors and specific open issues in this space --- Send in a voice message: https://podcasters.spotify.com/pod/show/ryan-carrier3/message