Chang Liu Interview – Epsilon Software for Private Machine Learning

In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada. Chang joined me to discuss Georgian’s…

Chris Shallue Interview – Discovering Exoplanets with Deep Learning

In this episode I speak with Chris Shallue, Senior Software Engineer at Google AI, about his project and paper on “Exploring Exoplanets with Deep Learning.” This is a great story. Chris, inspired by a book he was reading, reached out on a whim to a…

Ksenia Konyushkova Interview – Learning Active Learning from Data

In this episode, I speak with Ksenia Konyushkova, Ph.D. student in the CVLab at Ecole Polytechnique Federale de Lausanne in Switzerland. Ksenia and I connected at NIPS in December to discuss her interesting research into ways we might apply machine learning to ease the challenge…

Mike Del Balso Interview – Machine Learning Platforms at Uber with Mike Del Balso

In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion,…

Sriraam Natarajan Interview – Statistical Relational Artificial Intelligence

In this episode, I speak with Sriraam Natarajan, Associate Professor in the Department of Computer Science at UT Dallas. While at NIPS a few months back, Sriraam and I sat down to discuss his work on Statistical Relational Artificial Intelligence. StarAI is the combination of…

Ayanna Howard Interview – Trust in Human-Robot/AI Interactions

In this episode, the third in our Black in AI series, I speak with Ayanna Howard, Chair of the Interactive School of Computing at Georgia Tech. Ayanna joined me for a lively discussion about her work in the field of human-robot interaction. We dig deep…

Interview – A Linear-Time Kernel Goodness-of-Fit Test – NIPS Best Paper ’17 – YouTube

In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is,…

Tuomas Sandholm Interview – Solving Imperfect-Information Games – NIPS ’17 Best Paper – YouTube

In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brown, won a 2017 NIPS Best Paper award for their paper “Safe and Nested Subgame…

Greg Diamos Interview – Accelerating Deep Learning with Mixed Precision Arithmetic – YouTube

In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Greg’s talk focused on some work his team was involved…