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

By Sam Charrington

Technology Tech News

What's The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) about?

This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the worlds of machine learning and artificial intelligence. We discuss the latest developments in research, technology, and business and explore interesting projects from across the web.

Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.

4.5

286 ratings

Download our free app to listen on your phone

LAST EPISODE

ML and Epidemiology with El...

07.30.2020

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 ...

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

07.30.2020 ML and Epidemiology with Elaine Nsoesie - #396 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 ...
07.27.2020 Language (Technology) Is Power: Exploring the Inherent Complexity of NLP Systems with Hal Daumé III - #395 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 ...
07.23.2020 Graph ML Research at Twitter with Michael Bronstein - #394 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 ...
07.20.2020 Panel: The Great ML Language (Un)Debate! - #393 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, ...
07.13.2020 The Case for Hardware-ML Model Co-design with Diana Marculescu - #391 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 ...
07.09.2020 Computer Vision for Remote AR with Flora Tasse - #390 Today we conclude our CVPR coverage joined by Flora Tasse, Head of Computer Vision & AI Research at Streem. Flora, a keynote speaker at the AR/VR workshop, walks us through some of the interesting use cases at the intersection of ...
07.06.2020 Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389 Today we're Julian Quiroga, a Computer Vision Team Lead at Genius Sports, to discuss his recent paper “As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States Recognition.” We explore camera setups and angles, detection and ...
07.02.2020 How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388 Today we’re taking a break from our CVPR coverage to bring you this interview with Deb Raji, a Technology Fellow at the AI Now Institute. Recently there have been quite a few major news stories in the AI community, including ...
06.29.2020 AI for High-Stakes Decision Making with Hima Lakkaraju - #387 Today we’re joined by Hima Lakkaraju, an Assistant Professor at Harvard University. At CVPR, Hima was a keynote speaker at the Fair, Data-Efficient and Trusted Computer Vision Workshop, where she spoke on Understanding the Perils of Black Box Explanations. Hima ...
06.25.2020 Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386 We continue our CVPR coverage with today’s guest, Pavan Turaga, Associate Professor at Arizona State University. Pavan gave a keynote presentation at the Differential Geometry in CV and ML Workshop, speaking on Revisiting Invariants with Geometry and Deep Learning. We ...
06.22.2020 Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385 Today we’re joined by Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm. Babak is currently focused on conditional computation, which is the main driver for today’s conversation. We dig into a few papers in great detail including one from this ...
06.18.2020 Machine Learning Commerce at Square with Marsal Gavalda - #384 Today we’re joined by Marsal Gavalda, head of machine learning for the Commerce platform at Square, where he manages the development of machine learning for various tools and platforms, including marketing, appointments, and above all, risk management. We explore how ...
06.15.2020 Cell Exploration with ML at the Allen Institute w/ Jianxu Chen - #383 Today we’re joined by Jianxu Chen, a scientist at the Allen Institute for Cell Science. At the latest GTC conference, Jianxu presented his work on the Allen Cell Explorer Toolkit, an open-source project that allows users to do 3D segmentation ...
06.11.2020 Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382 Today we’re joined by Andreas Madsen, an independent researcher based in Denmark. While we caught up with Andreas to discuss his ICLR spotlight paper, “Neural Arithmetic Units,” we also spend time exploring his experience as an independent researcher, discussing the ...
06.08.2020 2020: A Critical Inflection Point for Responsible AI with Rumman Chowdhury - #381 Today we’re joined by Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture. In our conversation with Rumman, we explored questions like: • Why is now such a critical inflection point in the application of responsible AI? ...
06.04.2020 Panel: Advancing Your Data Science Career During the Pandemic - #380 Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel. In this conversation, we explore ways that Data Scientists and ML/AI practitioners ...
06.02.2020 On George Floyd, Empathy, and the Road Ahead Visit twimlai.com/blacklivesmatter for resources to support organizations pushing for social equity like Black Lives Matter, and groups offering relief for those jailed for exercising their rights to peaceful protest.
05.28.2020 Engineering a Less Artificial Intelligence with Andreas Tolias - #379 Today we’re joined by Andreas Tolias, Professor of Neuroscience at Baylor College of Medicine. We caught up with Andreas to discuss his recent perspective piece, “Engineering a Less Artificial Intelligence,” which explores the shortcomings of state-of-the-art learning algorithms in comparison ...
05.25.2020 Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378 Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. In our conversation, we explore Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which looks at compute-efficient ...
05.21.2020 The Physics of Data with Alpha Lee - #377 Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and ...
05.18.2020 Is Linguistics Missing from NLP Research? w/ Emily M. Bender - #376 Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington. Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making ...
05.14.2020 Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks with Nataniel Ruiz - #375 Today we’re joined by Nataniel Ruiz, a PhD Student at Boston University. We caught up with Nataniel to discuss his paper “Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems.” In our conversation, we discuss the ...
05.11.2020 Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374 Today we’re joined by Sherri Rose, Associate Professor at Harvard Medical School. We cover a lot of ground in our conversation, including the intersection of her research with the current COVID-19 pandemic, the importance of quality in datasets and rigor ...
05.07.2020 The Whys and Hows of Managing Machine Learning Artifacts with Lukas Biewald - #373 Today we’re joined by Lukas Biewald, founder and CEO of Weights & Biases, to discuss their new tool Artifacts, an end to end pipeline tracker. In our conversation, we explore Artifacts’ place in the broader machine learning tooling ecosystem through ...
05.04.2020 Language Modeling and Protein Generation at Salesforce with Richard Socher - #372 Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce. Richard and his team have published quite a few great projects lately, including CTRL: A Conditional Transformer Language Model for Controllable Generation, and ProGen, an AI Protein Generator, ...
04.30.2020 AI Research at JPMorgan Chase with Manuela Veloso - #371 Today we’re joined by Manuela Veloso, Head of AI Research at J.P. Morgan Chase. Since moving from CMU to JP Morgan Chase, Manuela and her team established a set of seven lofty research goals. In this conversation we focus on ...
04.29.2020 Panel: Responsible Data Science in the Fight Against COVID-19 - #370 In this discussion, we explore how data scientists and ML/AI practitioners can responsibly contribute to the fight against coronavirus and COVID-19. Four experts: Rex Douglass, Rob Munro, Lea Shanley, and Gigi Yuen-Reed shared a ton of valuable insight on the ...
04.27.2020 Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369 Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, to discuss his paper “Adversarial Examples Are Not Bugs, They Are Features.” In our conversation, we talk through what we expect these systems to do, vs what they’re ...
04.23.2020 AI for Social Good: Why "Good" isn't Enough with Ben Green - #368 Today we’re joined by Ben Green, PhD Candidate at Harvard and Research Fellow at the AI Now Institute at NYU. Ben’s research is focused on the social and policy impacts of data science, with a focus on algorithmic fairness and ...
04.20.2020 The Evolution of Evolutionary AI with Risto Miikkulainen - #367 Today we’re joined by Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant AI. Risto joined us back on episode #47 to discuss evolutionary algorithms, and today we get an update on the latest on the topic. In our conversation, ...
04.16.2020 Neural Architecture Search and Google’s New AutoML Zero with Quoc Le - #366 Today we’re super excited to share our recent conversation with Quoc Le, a research scientist at Google. Quoc joins us to discuss his work on Google’s AutoML Zero, semi-supervised learning, and the development of Meena, the multi-turn conversational chatbot. This ...
04.13.2020 Automating Electronic Circuit Design with Deep RL w/ Karim Beguir - #365 Today we’re joined by return guest Karim Beguir, Co-Founder and CEO of InstaDeep. In our conversation, we chat with Karim about InstaDeep’s new offering, DeepPCB, an end-to-end platform for automated circuit board design. We discuss challenges and problems with some ...
04.09.2020 Neural Ordinary Differential Equations with David Duvenaud - #364 Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on ...
04.06.2020 The Measure and Mismeasure of Fairness with Sharad Goel - #363 Today we’re joined by Sharad Goel, Assistant Professor at Stanford. Sharad, who also has appointments in the computer science, sociology, and law departments, has spent recent years focused on applying ML to understanding and improving public policy. In our conversation, ...
04.02.2020 Simulating the Future of Traffic with RL w/ Cathy Wu - #362 Today we’re joined by Cathy Wu, Assistant Professor at MIT. We had the pleasure of catching up with Cathy to discuss her work applying RL to mixed autonomy traffic, specifically, understanding the potential impact autonomous vehicles would have on various ...
03.30.2020 Consciousness and COVID-19 with Yoshua Bengio - #361 Today we’re joined by one of, if not the most cited computer scientist in the world, Yoshua Bengio, Professor at the University of Montreal and the Founder and Scientific Director of MILA. We caught up with Yoshua to explore his ...
03.26.2020 Geometry-Aware Neural Rendering with Josh Tobin - #360 Today we’re joined by Josh Tobin, Co-Organizer of the machine learning training program Full Stack Deep Learning. We had the pleasure of sitting down with Josh prior to his presentation of his paper Geometry-Aware Neural Rendering at NeurIPS. Josh's goal ...
03.23.2020 The Third Wave of Robotic Learning with Ken Goldberg - #359 Today we’re joined by Ken Goldberg, professor of engineering at UC Berkeley, focused on robotic learning. In our conversation with Ken, we chat about some of the challenges that arise when working on robotic grasping, including uncertainty in perception, control, ...
03.18.2020 Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358 Today we’re joined by Stefan Lee, an assistant professor at Oregon State University. In our conversation, we focus on his paper ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. We discuss the development and training process for this model, the ...
03.16.2020 Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357 Today we’re joined by Jürgen Schmidhuber, Co-Founder and Chief Scientist of NNAISENSE, the Scientific Director at IDSIA, as well as a Professor of AI at USI and SUPSI in Switzerland. Jürgen’s lab is well known for creating the Long Short-Term ...
03.12.2020 SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356 Beidi Chen is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of attention for their paper, SLIDE: In Defense of ...
03.09.2020 Advancements in Machine Learning with Sergey Levine - #355 Today we're joined by Sergey Levine, an Assistant Professor at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. Sergey and his lab’s recent efforts have been focused on contributing to a future ...
03.05.2020 Secrets of a Kaggle Grandmaster with David Odaibo - #354 Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015, after the second year of his PhD. ...
03.02.2020 NLP for Mapping Physics Research with Matteo Chinazzi - #353 Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach. In addition to predicting the trajectory ...
02.27.2020 Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352 The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenges that Sanmi Koyejo, assistant professor at the University ...
02.24.2020 High-Dimensional Robust Statistics with Ilias Diakonikolas - #351 Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart Noise, recipient of the NeurIPS 2019 Outstanding Paper award. The paper is ...
02.19.2020 How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350 Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being the first to publicly warn ...
02.17.2020 Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349 Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and ...
02.13.2020 Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348 Today we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics, which was the recipient of the Best Paper award at the 2019 Black in AI Workshop ...
02.10.2020 AI for Agriculture and Global Food Security with Nemo Semret - #347 Today we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In ...

Older episodes