#ContextualAI #ArtificialIntelligence #InternetSafety Monitoring malicious, user-generated content; contextual AI; adapting to novel evasion attempts: Matar Haller speaks to @JonKrohnLearns 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 (https://posit.co), by Anaconda, the world's most popular Python distribution (https://superdatascience.com/anaconda), and by WithFeeling.ai (https://withfeeling.ai), the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit https://jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:03:03] How ActiveFence helps its customers to moderate platform content • [00:14:47] How ActiveFence finds extreme social media users trying to evade detection • [00:27:30] How to monitor live-streaming content and analyze it for dangerous material • [00:34:11] The technologies ActiveFence uses to run its platform • [00:38:45] Matar’s experience with the Insight Fellows Program (Data Science Fellowship) • [00:58:59] Leadership opportunities for women in STEM • [01:11:37] Israel’s R&D edge for AI Additional materials: https://www.superdatascience.com/683