This Week In Machine Learning & Artificial Intelligence (ai) Podcast

Today we continue our ICML series with Elaine Nsoesie, assistant professor at Boston University. In our conversation, we discuss the different ways that machine learning applications can be used to address global health issues, including infectious disease surveillance, and tracking search data for changes in health behavior in African countries. We also discuss COVID-19 epidemiology and the importance of recognizing how the disease is affecting people of different races and economic backgrounds.

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 his research at the intersection of bias, fairness, NLP, and the effects language has on machine learning models, exploring language in two categories as they appear in machine learning models and systems: (1) How we use language to interact with the world, and (2) how we “do” language.

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 new role at Twitter, and some of the research challenges he’s faced, including scalability and working with dynamic graphs. Michael also dives into his work on differential graph modules for graph CNNs, and the various applications of this work.

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).

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

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

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.

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

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.

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.

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.

Visit twimlai.com/blacklivesmatter for resources to support organizations pushing for social equity like Black Lives Matter, and groups offering relief for those jailed for exercising their rights to peaceful protest. 

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.

Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. In our conversation, we explore Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which looks at compute-efficient training strategies for models. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency?