Pandemic Machine Learning Pitfalls

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 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

I speak with Dan Shiebler who works as a machine learning engineer at Twitter Cortex and at the same time, is doing a Ph.D. on applying category theory in machine learning. We discuss his work at Twitter, the importance of academics, and the future of machine learning. In this e ...  Show more

<span style="color: #333333;">Kevin is a Computer Scientist, part-time lecturer at Paul G. Allen School of Computer Science & Engineering, and research partner at OSAM. Our discussion with Kevin revolves around:
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Andy and Dave discuss the latest in AI news and research, including:

0:57: The Allen Institute for AI and others come together to create a publicly available "COVID-19 Challenges and Directions" search engine, building off of the corpus of COVID-related research.