683: Contextual A.I. for Adapting to Adversaries, with Dr. Matar Haller

<p>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.<br/><br/>This episode is brought to you by <a href='https://posit.co/'>Posit</a>, the open-source data science company, by <a href='https://superdatascience.com/anaconda'>Anaconda</a>, the world&apos;s most popular Python distribution, and by <a href='https://WithFeeling.ai'>WithFeeling.ai</a>, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit <a href='https://jonkrohn.com/podcast'>JonKrohn.com/podcast</a> for sponsorship information.<br/><br/>In this episode you will learn:<br/>• How ActiveFence helps its customers to moderate platform content [05:36]<br/>• How ActiveFence finds extreme social media users trying to evade detection [16:32]<br/>• How to monitor live-streaming content and analyze it for dangerous material [29:13]<br/>• The technologies ActiveFence uses to run its platform [35:54]<br/>• Matar’s experience of the Insight Fellows Program (Data Science Fellowship) [40:28]<br/>• Leadership opportunities for women in STEM [1:00:41]<br/>• Israel’s R&amp;D edge for AI [1:13:19]<br/><br/>Additional materials: <a href='https://www.superdatascience.com/683'>www.superdatascience.com/683</a></p>