The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Latest Episodes:

What NLP Tells Us About COVID-19 and Mental Health with Johannes Eichstaedt - #400 Aug 13, 2020

Show notes

Today we’re joined by Johannes Eichstaedt, an Assistant Professor of Psychology at Stanford University. In our conversation, we explore how Johannes applies his physics background to a career as a computational social scientist, some of the major patterns in the data that emerged over the first few months of lockdown, including mental health, social norms, and political patterns. We also explore how Johannes built the process, and the techniques he’s using to collect, sift through, and understand the da

Human-AI Collaboration for Creativity with Devi Parikh - #399 Aug 10, 2020

Show notes

Today we’re joined by Devi Parikh, Associate Professor at the School of Interactive Computing at Georgia Tech, and research scientist at Facebook AI Research (FAIR). In our conversation, we touch on Devi’s definition of creativity, explore multiple ways that AI could impact the creative process for artists, and help humans become more creative. We investigate tools like casual creator for preference prediction, neuro-symbolic generative art, and visual journaling.

Neural Augmentation for Wireless Communication with Max Welling - #398 Aug 06, 2020

Show notes

Today we’re joined by Max Welling, Vice President of Technologies at Qualcomm Netherlands, and Professor at the University of Amsterdam. In our conversation, we explore Max’s work in neural augmentation, and how it’s being deployed. We also discuss his work with federated learning and incorporating the technology on devices to give users more control over the privacy of their personal data. Max also shares his thoughts on quantum mechanics and the future of quantum neural networks for chip design.

Quantum Machine Learning: The Next Frontier? with Iordanis Kerenidis - #397 Aug 04, 2020

Show notes

Today we're joined by Iordanis Kerenidis, Research Director CNRS Paris and Head of Quantum Algorithms at QC Ware. Iordanis was an ICML main conference Keynote speaker on the topic of Quantum ML, and we focus our conversation on his presentation, exploring the prospects and challenges of quantum machine learning, as well as the field’s history, evolution, and future. We’ll also discuss the foundations of quantum computing, and some of the challenges to consider for breaking into the field.

ML and Epidemiology with Elaine Nsoesie - #396 Jul 30, 2020

Show notes

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.

Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395 Jul 27, 2020

Show notes

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.

Graph ML Research at Twitter with Michael Bronstein - #394 Jul 23, 2020

Show notes

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.

Panel: The Great ML Language (Un)Debate! - #393 Jul 20, 2020

Show notes

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).

What the Data Tells Us About COVID-19 with Eric Topol - #392 Jul 16, 2020

Show notes

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

The Case for Hardware-ML Model Co-design with Diana Marculescu - #391 Jul 13, 2020

Show notes

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

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