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

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

Babak is currently focused on conditional computation, which is the main driver for today’s conversation. We dig into a few papers in great detail including one from this year’s CVPR conference, Conditional Channel Gated Networks for Task-Aware Continual Learning, covering how gates are used to drive efficiency and accuracy, while decreasing model size, how this research manifests into actual products, and more!

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.

We explore how they manage their vast portfolio of projects, and how having an ML and technology focus at the outset of the company has contributed to their success, tips and best practices for internal democratization of ML, and much more.

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

At the latest GTC conference, Jianxu presented his work on the Allen Cell Explorer Toolkit, an open-source project that allows users to do 3D segmentation of intracellular structures in fluorescence microscope images at high resolutions, making the images more accessible for data analysis. We discuss three of the major components of the toolkit: the cell image analyzer, the image generator, and the image visualizer

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.

Today we’re joined by Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture. In our conversation with Rumman, we explored questions like: 

• Why is now such a critical inflection point in the application of responsible AI?

• How should engineers and practitioners think about AI ethics and responsible AI?

• Why is AI ethics inherently personal and how can you define your own personal approach?

• Is the implementation of AI governance necessarily authoritarian?

Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel.

In this conversation, we explore ways that Data Scientists and ML/AI practitioners can continue to advance their careers despite current challenges. Our panelists provide concrete tips, advice, and direction for those just starting out, those affected by layoffs, and those just wanting to move forward in their careers.

Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel.

In this conversation, we explore ways that Data Scientists and ML/AI practitioners can continue to advance their careers despite current challenges. Our panelists provide concrete tips, advice, and direction for those just starting out, those affected by layoffs, and those just wanting to move forward in their careers.

Today we’re joined by Andreas Tolias, Professor of Neuroscience at Baylor College of Medicine.

We caught up with Andreas to discuss his recent perspective piece, “Engineering a Less Artificial Intelligence,” which explores the shortcomings of state-of-the-art learning algorithms in comparison to the brain. The paper also offers several ideas about how neuroscience can lead the quest for better inductive biases by providing useful constraints on representations and network architecture.