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 popular and emerging programming languages for machine learning, along with the strengths, weaknesses, and approaches offered by Clojure, JavaScript, Julia, Probabilistic Programming, Python, R, Scala, and Swift. We round out the session with an audience Q&A (58:28).
20 Jul 20201h 34min
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 about the coronavirus since it's emergence, and the role of tech in understanding and preventing the spread of the disease. We also explore the broader opportunity for medical applications of AI, the promise of personalized medicine, and how techniques like federated learning can offer more privacy in healthc
16 Jul 202042min
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, we explore her keynote, “Putting the “Machine” Back in Machine Learning: The Case for Hardware-ML Model Co-design” from CVPR 2020. We explore how her research group is focusing on making models more efficient so that they run better on current hardware systems, and how they plan on achieving true co
13 Jul 202045min
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 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.
9 Jul 202040min
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 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
6 Jul 202041min
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 the AI community, including the self-imposed moratorium on facial recognition tech from Amazon, IBM and Microsoft. In our conversation with Deb, we dig into these stories, discussing the origins of Deb’s work on the Gender Shades project, the harms of facial recognition, and much more.
2 Jul 20201h 20min
AI for High-Stakes Decision Making with Hima Lakkaraju - #387
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.
29 Jun 202045min
Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386
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
25 Jun 202046min
