Using machine learning to detect shifts in government policy

Subscribe: iTunes, Android, Spotify, Stitcher, Google, and RSS.In this episode of the Data Exchange I speak with Weifeng Zhong, Senior Research Fellow at the Mercatus Center at George Mason University. He is the core maintainer of the open source Policy Change Index (PCI), a framework that uses machine learning and NLP to “process and read” large amounts of text to discern government priorities and policies. The initial PCI is focused on major policy shifts in China and uses NLP and machine learning to process and analyze  the People’s Daily.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

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A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].