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What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct c ...  Show more

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers ...  Show more

In episode nine we talk with George Dahl, of  the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats George!) We learn about how net ...  Show more

Podcast with Gautam Siwach and Jin Vanstee ! Speaker - Elpida Tzortzatos is an IBM Fellow and CTO AI on IBM zSystems. In this Podcast we listen to Elpida's thoughts about driving Artificial Intelligence strategies, associated risks, and Values.We will learn how to drive industry- ...  Show more

In this episode of the SuperDataScience Podcast, I chat with the President and Editor at KDNuggets, Gregory Piatetsky-Shapiro. You will hear about the recent advancements in Data Science, learn how Reinforcement Learning can greatly improve AI capabilities, and learn about the no ...  Show more

In this episode of the SuperDataScience Podcast, I chat with my friend and business partner, Hadelin de Ponteves. You will hear what new exciting things are happening in Hadelin's life now. You will hear some preview of his upcoming presentation at DataScienceGO 2019, which will ...  Show more