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.
The New DBfication of ML/AI with Arun Kumar
Today we’re joined by Arun Kumarm, an associate professor at UC San Diego. We had the pleasure of catching up with Arun prior to the Workshop on Databases and AI at NeurIPS 2021, where he delivered the talk “The New DBfication of ML/AI.” In our conversation, we explore this “database-ification” of machine learning, a concept analogous to the transformation of relational SQL computation. We discuss the relationship between the ML and database fields and how the merging of the two could have positive outcomes for the end-to-end ML workflow, and a few tools that his team has developed, Cerebro, a tool for reproducible model selection, and SortingHat, a tool for automating data prep, and how tools like these and others affect Arun’s outlook on the future of machine learning platforms and MLOps.
The complete show notes for this episode can be found at twimlai.com/go/553
Building Public Interest Technology with Meredith Broussard
Today we’re joined by Meredith Broussard, an associate professor at NYU & research director at the NYU Alliance for Public Interest Technology. Meredith was a keynote speaker at the recent NeurIPS conference, and we had the pleasure of speaking with her to discuss her talk from the event, and her upcoming book, tentatively titled More Than A Glitch: What Everyone Needs To Know About Making Technology Anti-Racist, Accessible, And Otherwise Useful To All.
In our conversation, we explore Meredith’s work in the field of public interest technology, and her view of the relationship between technology and artificial intelligence. Meredith and Sam talk through real-world scenarios where an emphasis on monitoring bias and responsibility would positively impact outcomes, and how this type of monitoring parallels the infrastructure that many organizations are already building out. Finally, we talk through the main takeaways from Meredith’s NeurIPS talk, and how practitioners can get involved in the work of building and deploying public interest technology.
The complete show notes for this episode can be found at twimlai.com/go/552
A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck
Today we’re joined by Sebastian Bubeck a sr principal research manager at Microsoft, and author of the paper A Universal Law of Robustness via Isoperimetry, a NeurIPS 2021 Outstanding Paper Award recipient. We begin our conversation with Sebastian with a bit of a primer on convex optimization, a topic that hasn’t come up much in previous interviews. We explore the problem that convex optimization is trying to solve, the application of convex optimization to multi-armed bandit problems, metrical task systems and solving the K-server problem. We then dig into Sebastian’s paper, which looks to prove that for a broad class of data distributions and model classes, overparameterization is necessary if one wants to interpolate the data. Finally, we discussed the relationship between the paper and the work being done in the adversarial robustness community.
The complete show notes for this episode can be found at twimlai.com/go/551
Trends in NLP with John Bohannon
Today we’re joined by friend of the show John Bohannon, the director of science at Primer AI, to help us showcase all of the great achievements and accomplishments in NLP in 2021! In our conversation, John shares his two major takeaways from last year, 1) NLP as we know it has changed, and we’re back into the incremental phase of the science, and 2) NLP is “eating” the rest of machine learning. We explore the implications of these two major themes across the discipline, as well as best papers, up and coming startups, great things that did happen, and even a few bad things that didn’t. Finally, we explore what 2022 and beyond will look like for NLP, from multilingual NLP to use cases for the influx of large auto-regressive language models like GPT-3 and others, as well as ethical implications that are reverberating across domains and the changes that have been ushered in in that vein.
The complete show notes for this episode can be found at twimlai.com/go/550
Trends in Computer Vision with Georgia Gkioxari
Happy New Year! We’re excited to kick off 2022 joined by Georgia Gkioxari, a research scientist at Meta AI, to showcase the best advances in the field of computer vision over the past 12 months, and what the future holds for this domain.
Welcome back to AI Rewind!
In our conversation Georgia highlights the emergence of the transformer model in CV research, what kind of performance results we’re seeing vs CNNs, and the immediate impact of NeRF, amongst a host of other great research. We also explore what is ImageNet’s place in the current landscape, and if it's time to make big changes to push the boundaries of what is possible with image, video and even 3D data, with challenges like the Metaverse, amongst others, on the horizon. Finally, we touch on the startups to keep an eye on, the collaborative efforts of software and hardware researchers, and the vibe of the “ImageNet moment” being upon us once again.
The complete show notes for this episode can be found at twimlai.com/go/549
Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik
Today we close out the 2021 NeurIPS series joined by Alison Gopnik, a professor at UC Berkeley and an invited speaker at the Causal Inference & Machine Learning: Why now? Workshop. In our conversation with Alison, we explore the question, “how is it that we can know so much about the world around us from so little information?,” and how her background in psychology, philosophy, and epistemology has guided her along the path to finding this answer through the actions of children. We discuss the role of causality as a means to extract representations of the world and how the “theory theory” came about, and how it was demonstrated to have merit. We also explore the complexity of causal relationships that children are able to deal with and what that can tell us about our current ML models, how the training and inference stages of the ML lifecycle are akin to childhood and adulthood, and much more!
The complete show notes for this episode can be found at twimlai.com/go/548
A premier podcast on AI/ML
I have enjoyed listening to many of the episodes and had fun participating in one
Lots of potential but incompetent host
The guests are amazing and this could be such an amazing podcast for the ML community. Unfortunately, the host is both a poor conversationalist (interviews lack flow, feel disjointed and tortured), and comes to the interviews so poorly informed that he struggles to put follow up questions together or even understand what the guest is saying.
excellent machine learning perspective
Sam puts lot of attention to every episode. Information is high quality and easy to grasp.