Fairness and Robustness in Federated Learning with Virginia Smith -#504
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Release Date: 07/26/2021
Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we close out our 2021 ICML series joined by Lina Montoya, a postdoctoral researcher at UNC Chapel Hill. In our conversation with Lina, who was an invited speaker at the Neglected Assumptions in Causal Inference Workshop, we explored her work applying Optimal Dynamic Treatment (ODT) to understand which kinds of individuals respond best to specific interventions in the US criminal justice system. We discuss the concept of neglected assumptions and how it connects to ODT rule estimation, as well as a breakdown of the causal roadmap, coined by researchers at UC Berkeley. Finally,...
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Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes - #505
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we continue our ICML series joined by Gustavo Malkomes, a research engineer at Intel via their recent acquisition of SigOpt. In our conversation with Gustavo, we explore his paper , which focuses on a novel algorithmic solution for the iterative model search process. This new algorithm empowers teams to run experiments where they are not optimizing particular metrics but instead identifying parameter configurations that satisfy constraints in the metric space. This allows users to efficiently explore multiple metrics at once in an efficient, informed, and intelligent way that lends...
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Fairness and Robustness in Federated Learning with Virginia Smith -#504
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University. In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. We dig into her paper from ICML, Ditto: Fair and Robust Federated Learning Through Personalization, what fairness means in contrast to AI ethics, the particulars of the failure modes, the relationship between models, and the things being...
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Scaling AI at H&M Group with Errol Koolmeister - #503
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we’re joined by Errol Koolmeister, the head of AI foundation at H&M Group. In our conversation with Errol, we explore H&M’s AI journey, including its wide adoption across the company in 2016, and the various use cases in which it's deployed like fashion forecasting and pricing algorithms. We discuss Errol’s first steps in taking on the challenge of scaling AI broadly at the company, the value-added learning from proof of concepts, and how to align in a sustainable, long-term way. Of course, we dig into the infrastructure and models being used, the biggest challenges...
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Evolving AI Systems Gracefully with Stefano Soatto - #502
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we’re joined by Stefano Soatto, VP of AI applications science at AWS and a professor of computer science at UCLA. Our conversation with Stefano centers on recent research of his called Graceful AI, which focuses on how to make trained systems evolve gracefully. We discuss the broader motivation for this research and the potential dangers or negative effects of constantly retraining ML models in production. We also talk about research into error rate clustering, the importance of model architecture when dealing with problems of model compression, how they’ve solved problems of...
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ML Innovation in Healthcare with Suchi Saria - #501
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we’re joined by Suchi Saria, the founder and CEO of Bayesian Health, the John C. Malone associate professor of computer science, statistics, and health policy, and the director of the machine learning and healthcare lab at Johns Hopkins University. Suchi shares a bit about her journey to working in the intersection of machine learning and healthcare, and how her research has spanned across both medical policy and discovery. We discuss why it has taken so long for machine learning to become accepted and adopted by the healthcare infrastructure and where exactly we stand in the...
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Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we’re joined by a friend of the show Jeff Gehlhaar, VP of technology and the head of AI software platforms at Qualcomm. In our conversation with Jeff, we cover a ton of ground, starting with a bit of exploration around ML compilers, what they are, and their role in solving issues of parallelism. We also dig into the latest additions to the Snapdragon platform, AI Engine Direct, and how it works as a bridge to bring more capabilities across their platform, how benchmarking works in the context of the platform, how the work of other researchers we’ve spoken to on compression and...
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The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we continue our AI in Innovation series joined by Dan Bohus, senior principal researcher at Microsoft Research, and , a principal researcher at Microsoft Research. In this conversation, we use a pair of research projects, Maia Chess and Situated Interaction, to springboard us into a conversation about the evolution of human-AI interaction. We discuss both of these projects individually, as well as the commonalities they have, how themes like understanding the human experience appear in their work, the types of models being used, the various types of data, and the complexity of each...
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Vector Quantization for NN Compression with Julieta Martinez - #498
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we’re joined by , a senior research scientist at recently announced startup Waabi. Julieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk “What do Large-Scale Visual Search and Neural Network Compression have in Common,” which shows that multiple ideas from large-scale visual search can be used to achieve state-of-the-art neural network compression. We explore the commonality between large databases and dealing with high dimensional, many-parameter neural networks, the advantages of using product quantization, and...
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Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Today we continue our CVPR 2021 coverage joined by Claire Monteleoni, an associate professor at the University of Colorado Boulder. We cover quite a bit of ground in our conversation with Claire, including her journey down the path from environmental activist to one of the leading climate informatics researchers in the world. We explore her current research interests, and the available opportunities in applying machine learning to climate informatics, including the interesting position of doing ML from a data-rich environment. Finally, we dig into the evolution of climate...
info_outlineToday we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University.
In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. We dig into her paper from ICML, Ditto: Fair and Robust Federated Learning Through Personalization, what fairness means in contrast to AI ethics, the particulars of the failure modes, the relationship between models, and the things being optimized across devices, and the tradeoffs between fairness and robustness.
We also discuss a second paper, Heterogeneity for the Win: One-Shot Federated Clustering, how the proposed method makes heterogeneity beneficial in data, how the heterogeneity of data is classified, and some applications of FL in an unsupervised setting.
The complete show notes for this episode can be found at twimlai.com/go/504.