Monitoring malicious, user-generated content; contextual AI; adapting to novel evasion attempts: Matar Haller speaks to Jon Krohn about the challenges of identifying, analyzing and flagging malicious information online. In this episode, Matar explains how contextual AI and a “database of evil” can help resolve the multiple challenges of blocking dangerous content across a range of media, even those that are live-streamed.This episode is brought to you by Posit, the open-source data science company, by Anaconda, the world's most popular Python distribution, and by WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• How ActiveFence helps its customers to moderate platform content [05:36]• How ActiveFence finds extreme social media users trying to evade detection [16:32]• How to monitor live-streaming content and analyze it for dangerous material [29:13]• The technologies ActiveFence uses to run its platform [35:54]• Matar’s experience of the Insight Fellows Program (Data Science Fellowship) [40:28]• Leadership opportunities for women in STEM [1:00:41]• Israel’s R&D edge for AI [1:13:19]Additional materials: www.superdatascience.com/683

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