Causal Models in Practice at Lyft with Sean Taylor - #486

Causal Models in Practice at Lyft with Sean Taylor - #486

Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. The complete show notes for this episode can be found at twimlai.com/go/486.

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Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395

Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395

Today we’re joined by Hal Daume III, professor at the University of Maryland and Co-Chair of the 2020 ICML Conference. We had the pleasure of catching up with Hal ahead of this year's ICML to discuss ...

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Graph ML Research at Twitter with Michael Bronstein - #394

Graph ML Research at Twitter with Michael Bronstein - #394

Today we’re excited to be joined by return guest Michael Bronstein, Head of Graph Machine Learning at Twitter. In our conversation, we discuss the evolution of the graph machine learning space, his ne...

23 Heinä 202055min

Panel: The Great ML Language (Un)Debate! - #393

Panel: The Great ML Language (Un)Debate! - #393

Today we’re excited to bring ‘The Great ML Language (Un)Debate’ to the podcast! In the latest edition of our series of live discussions, we brought together experts and enthusiasts to discuss both pop...

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What the Data Tells Us About COVID-19 with Eric Topol - #392

What the Data Tells Us About COVID-19 with Eric Topol - #392

Today we’re joined by Eric Topol, Director & Founder of the Scripps Research Translational Institute, and author of the book Deep Medicine. We caught up with Eric to talk through what we’ve learned ab...

16 Heinä 202042min

The Case for Hardware-ML Model Co-design	with Diana Marculescu - #391

The Case for Hardware-ML Model Co-design with Diana Marculescu - #391

Today we’re joined by Diana Marculescu, Professor of Electrical and Computer Engineering at UT Austin. We caught up with Diana to discuss her work on hardware-aware machine learning. In particular, ...

13 Heinä 202045min

Computer Vision for Remote AR with Flora Tasse - #390

Computer Vision for Remote AR with Flora Tasse - #390

Today we conclude our CVPR coverage joined by Flora Tasse, Head of Computer Vision & AI Research at Streem. Flora, a keynote speaker at the AR/VR workshop, walks us through some of the interesting use...

9 Heinä 202040min

Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389

Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389

Today we're Julian Quiroga, a Computer Vision Team Lead at Genius Sports, to discuss his recent paper “As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game Sta...

6 Heinä 202041min

How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388

How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388

Today we’re taking a break from our CVPR coverage to bring you this interview with Deb Raji, a Technology Fellow at the AI Now Institute. Recently there have been quite a few major news stories in th...

2 Heinä 20201h 20min

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