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The Physics of Data with Alpha Lee - #377

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Release Date: 05/21/2020

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Diana Marculescu, Professor of Electrical and Computer Engineering at UT Austin.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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 cases at the intersection of AI, CV, and AR technologies, her current work and the origin of her company Selerio, which was eventually acquired by Streem, the difficulties associated with building 3D mesh environments, extracting metadata from those environments, the challenges of pose estimation and more.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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 States Recognition.” We explore camera setups and angles, detection and localization of figures on the court (players, refs, and of course, the ball), and the role that deep learning plays in the process. We also break down how this work applies to different sports, and the ways that he is looking to improve i

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Hima Lakkaraju, an Assistant Professor at Harvard University. At CVPR, Hima was a keynote speaker at the Fair, Data-Efficient and Trusted Computer Vision Workshop, where she spoke on Understanding the Perils of Black Box Explanations. Hima talks us through her presentation, which focuses on the unreliability of explainability techniques that center perturbations, such as LIME or SHAP, as well as how attacks on these models can be carried out, and what they look like.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

We continue our CVPR coverage with today’s guest, Pavan Turaga, Associate Professor at Arizona State University. Pavan gave a keynote presentation at the Differential Geometry in CV and ML Workshop, speaking on Revisiting Invariants with Geometry and Deep Learning. We go in-depth on Pavan’s research on integrating physics-based principles into computer vision. We also discuss the context of the term “invariant,” and Pavan contextualizes this work in relation to Hinton’s similar Capsule Network res

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Marsal Gavalda, head of machine learning for the Commerce platform at Square, where he manages the development of machine learning for various tools and platforms, including marketing, appointments, and above all, risk management.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Jianxu Chen, a scientist at the Allen Institute for Cell Science.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Andreas Madsen, an independent researcher based in Denmark. While we caught up with Andreas to discuss his ICLR spotlight paper, “Neural Arithmetic Units,” we also spend time exploring his experience as an independent researcher, discussing the difficulties of working with limited resources, the importance of finding peers to collaborate with, and tempering expectations of getting papers accepted to conferences -- something that might take a few tries to get right.

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Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge, and Co-Founder of data-driven drug discovery startup, PostEra. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and physical analysis of machine learning. 

We discuss the similarities and differences between drug discovery and material science, including the parallels in the design test cycle, and the major differences in cost. We also explore the goals associated with uncertainty estimation, why deep networks are easier to optimize than shallow networks, the concept of energy landscape, and how it all fits into his research. We also talk about his startup, PostEra which offers medicinal chemistry as a service powered by machine learning.

The complete show notes for this episode can be found at twimlai.com/talk/377.