Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.
Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.
Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes
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 Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, 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 itself to real-world, human-in-the-loop scenarios.
The complete show notes for this episode can be found at twimlai.com/go/505.
Fairness and Robustness in Federated Learning with Virginia Smith
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 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.
Scaling AI at H&M Group with Errol Koolmeister
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 faced, and the importance of managing the project portfolio, while Errol shares their approach to building infra for a specific product with many products in mind.
Evolving AI Systems Gracefully with Stefano Soatto
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 regression and reprocessing by utilizing existing models, and much more.
The complete show notes for this episode can be found at twimlai.com/go/502.
ML Innovation in Healthcare with Suchi Saria
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 adoption process, where there have been “pockets” of tangible success.
Finally, we explore the state of healthcare data, and of course, we talk about Suchi’s recently announced startup Bayesian Health and their goals in the healthcare space, and an accompanying study that looks at real-time ML inference in an EMR setting.
The complete show notes for this episode can be found at twimlai.com/go/501.
Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar
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 quantization finds its way from research to product, and much more!
After you check out this interview, you can look below for some of the other conversations with researchers mentioned.
The complete show notes for this episode can be found at twimlai.com/go/500.
excellent machine learning perspective
Sam puts lot of attention to every episode. Information is high quality and easy to grasp.
Sam's questions are so spot on. "That's an interesting question" is something you will hear guests say a lot.
Many better AI podcasts out there hosted by scientists doing the dirty work
Difficult to listen unless the guest really knows what he or she’s doing and doesn’t mind the host.