Let's Talk About Natural Language Processing

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, ...  Show more

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social ch ...  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

TechStuff listener Nate wanted to know about natural language processing. How can computer scientists teach machines to understand human language and why is it so hard to do? Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for p ...  Show more

A special interview from the Padverb Podcast, in which I discuss my research on language in the brain and thoughts on AI. The discussion covers topics such as backpropagation, how humans acquire language and concepts, how artificial neural networks differ from biological neural n ...  Show more

Hilary and Roger discuss Hilary's Use R! keynote presentation, LLMs and R, and whether R (or any programming language) has a future. Also, a brief preview of Roger's JSM 2024 talk. Show notes: Support us on Patreon Roger Peng Hilary Parker List of NSSD Fellows Get the Not So Stan ...  Show more