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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.

Aug 6, 2020

Today we’re joined by Max Welling, Vice President of Technologies at Qualcomm Netherlands, and Professor at the University of Amsterdam. In case you missed it, Max joined us last year to discuss his work on  Gauge Equivariant CNNs and Generative Models - the 2nd most popular episode of 2019. 

In this conversation, we...

Aug 4, 2020

Today we conclude our 2020 ICML coverage joined by Iordanis Kerenidis, Research Director at Centre National de la Recherche Scientifique (CNRS) in Paris, and Head of Quantum Algorithms at QC Ware.

Iordanis’ research centers around quantum algorithms of machine learning, and was an ICML main conference Keynote speaker...

Jul 30, 2020

Today we continue our ICML series with Elaine Nsoesie, assistant professor at Boston University. 

Elaine presented a keynote talk at the ML for Global Health workshop at ICML 2020, where she shared her research centered around data-driven epidemiology. In our conversation, we discuss the different ways that machine...

Jul 27, 2020

Today we’re joined by Hal Daume III, professor at the University of Maryland, Senior Principal Researcher at Microsoft Research, 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...

Jul 23, 2020

Today we’re excited to be joined by return guest Michael Bronstein, Professor at Imperial College London, and Head of Graph Machine Learning at Twitter. We last spoke with Michael at NeurIPS in 2017 about Geometric Deep Learning

Since then, his research focus has slightly shifted to exploring graph neural networks....