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

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

Author: Sam Charrington

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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.

Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
478 Episodes
Today we’re joined by Nishan Subedi, VP of Algorithms at In our conversation with Nishan, we discuss his interesting path to MLOps and how ML/AI is used at Overstock, primarily for search/recommendations and marketing/advertisement use cases. We spend a great deal of time exploring machine learning architecture and architectural patterns, how he perceives the differences between architectural patterns and algorithms, and emergent architectural patterns that standards have not yet been set for. Finally, we discuss how the idea of anti-patterns was innovative in early design pattern thinking and if those concepts are transferable to ML, if architectural patterns will bleed over into organizational patterns and culture, and Nishan introduces us to the concept of Squads within an organizational structure. The complete show notes for this episode can be found at
Today we’re joined by Vered Shwartz, a Postdoctoral Researcher at both the Allen Institute for AI and the Paul G. Allen School of Computer Science & Engineering at the University of Washington. In our conversation with Vered, we explore her NLP research, where she focuses on teaching machines common sense reasoning in natural language. We discuss training using GPT models and the potential use of multimodal reasoning and incorporating images to augment the reasoning capabilities. Finally, we talk through some other noteworthy research in this field, how she deals with biases in the models, and Vered's future plans for incorporating some of the newer techniques into her future research. The complete show notes for this episode can be found at 
Today we’re joined by returning guest and newly appointed Dean of the College of Engineering at The Ohio State University, Ayanna Howard.  Our conversation with Dr. Howard focuses on her recently released book, Sex, Race, and Robots: How to Be Human in the Age of AI, which is an extension of her research on the relationships between humans and robots. We continue to explore this relationship through the themes of socialization introduced in the book, like associating genders to AI and robotic systems and the “self-fulfilling prophecy” that has become search engines.  We also discuss a recurring conversation in the community around AI  being biased because of data versus models and data, and the choices and responsibilities that come with the ethical aspects of building AI systems. Finally, we discuss Dr. Howard’s new role at OSU, how it will affect her research, and what the future holds for the applied AI field.  The complete show notes for this episode can be found at
Today we’re joined by Penousal Machado, Associate Professor and Head of the Computational Design and Visualization Lab in the Center for Informatics at the University of Coimbra.  In our conversation with Penousal, we explore his research in Evolutionary Computation, and how that work coincides with his passion for images and graphics. We also discuss the link between creativity and humanity, and have an interesting sidebar about the philosophy of Sci-Fi in popular culture.  Finally, we dig into Penousals evolutionary machine learning research, primarily in the context of the evolution of various animal species mating habits and practices. The complete show notes for this episode can be found at  
Today we’re joined by Arul Menezes, a Distinguished Engineer at Microsoft.  Arul, a 30 year veteran of Microsoft, manages the machine translation research and products in the Azure Cognitive Services group. In our conversation, we explore the historical evolution of machine translation like breakthroughs in seq2seq and the emergence of transformer models.  We also discuss how they’re using multilingual transfer learning and combining what they’ve learned in translation with pre-trained language models like BERT. Finally, we explore what they’re doing to experience domain-specific improvements in their models, and what excites Arul about the translation architecture going forward.  The complete show notes for this series can be found at
Today we’re joined by Luna Dong, Sr. Principal Scientist at Amazon. In our conversation with Luna, we explore Amazon’s expansive product knowledge graph, and the various roles that machine learning plays throughout it. We also talk through the differences and synergies between the media and retail product knowledge graph use cases and how ML comes into play in search and recommendation use cases. Finally, we explore the similarities to relational databases and efforts to standardize the product knowledge graphs across the company and broadly in the research community. The complete show notes for this episode can be found at
Today we’re joined by Sarah Brown, an Assistant Professor of Computer Science at the University of Rhode Island. In our conversation with Sarah, whose research focuses on Fairness in AI, we discuss why a “systems-level” approach is necessary when thinking about ethical and fairness issues in models and algorithms. We also explore Wiggum: a fairness forensics tool, which explores bias and allows for regular auditing of data, as well as her ongoing collaboration with a social psychologist to explore how people perceive ethics and fairness. Finally, we talk through the role of tools in assessing fairness and bias, and the importance of understanding the decisions the tools are making. The complete show notes can be found at
Today we’re joined by Andrew Trister, Deputy Director for Digital Health Innovation at the Bill & Melinda Gates Foundation.  In our conversation with Andrew, we explore some of the AI use cases at the foundation, with the goal of bringing “community-based” healthcare to underserved populations in the global south. We focus on COVID-19 response and improving the accuracy of malaria testing with a bayesian framework and a few others, and the challenges like scaling these systems and building out infrastructure so that communities can begin to support themselves.  We also touch on Andrew's previous work at Apple, where he helped develop what is now known as Research Kit, their ML for health tools that are now seen in apple devices like phones and watches. The complete show notes for this episode can be found at
Today we’re joined by Drago Anguelov, Distinguished Scientist and Head of Research at Waymo.  In our conversation, we explore the state of the autonomous vehicles space broadly and at Waymo, including how AV has improved in the last few years, their focus on level 4 driving, and Drago’s thoughts on the direction of the industry going forward. Drago breaks down their core ML use cases, Perception, Prediction, Planning, and Simulation, and how their work has lead to a fully autonomous vehicle being deployed in Phoenix.  We also discuss the socioeconomic and environmental impact of self-driving cars, a few research papers submitted to NeurIPS 2020, and if the sophistication of AV systems will lend themselves to the development of tomorrow’s enterprise machine learning systems. The complete show notes for this episode can be found at 
Today we’re joined by Ya Xu, head of Data Science at LinkedIn, and TWIMLcon: AI Platforms 2021 Keynote Speaker. We cover a ton of ground with Ya, starting with her experiences prior to becoming Head of DS, as one of the architects of the LinkedIn Platform. We discuss her “three phases” (building, adoption, and maturation) to keep in mind when building out a platform, how to avoid “hero syndrome” early in the process. Finally, we dig into the various tools and platforms that give LinkedIn teams leverage, their organizational structure, as well as the emergence of differential privacy for security use cases and if it's ready for prime time. The complete show notes for this episode can be found at 
Today we’re joined by Jesse Engel, Staff Research Scientist at Google, working on the Magenta Project.  In our conversation with Jesse, we explore the current landscape of creativity AI, and the role Magenta plays in helping express creativity through ML and deep learning. We dig deep into their Differentiable Digital Signal Processing (DDSP) library, which “lets you combine the interpretable structure of classical DSP elements (such as filters, oscillators, reverberation, etc.) with the expressivity of deep learning.” Finally, Jesse walks us through some of the other projects that the Magenta team undertakes, including NLP and language modeling, and what he wants to see come out of the work that he and others are doing in creative AI research. The complete show notes for this episode can be found at 
Today we’re joined by return guest Francisco Webber, CEO & Co-founder of Francisco was originally a guest over 4 years and 400 episodes ago, where we discussed his company, and their unique approach to natural language processing. In this conversation, Francisco gives us an update on Cortical, including their applications and toolkit, including semantic extraction, classifier, and search use cases. We also discuss GPT-3, and how it compares to semantic folding, the unreasonable amount of data needed to train these models, and the difference between the GPT approach and semantic modeling for language understanding. The complete show notes for this episode can be found at
Today we’re joined by Gurdeep Pall, Corporate Vice President at Microsoft. Gurdeep, who we had the pleasure of speaking with on his 31st anniversary at the company, has had a hand in creating quite a few influential projects, including Skype for business (and Teams) and being apart of the first team that shipped wifi as a part of a general-purpose operating system. In our conversation with Gurdeep, we discuss Microsoft’s acquisition of Bonsai and how they fit in the toolchain for creating brains for autonomous systems with “machine teaching,” and other practical applications of machine teaching in autonomous systems. We also explore the challenges of simulation, and how they’ve evolved to make the problems that the physical world brings more tenable. Finally, Gurdeep shares concrete use cases for autonomous systems, and how to get the best ROI on those investments, and of course, what’s next in the very broad space of autonomous systems. The complete show notes for this episode can be found at
Today we’re joined by Bryan Carstens, a professor in the Department of Evolution, Ecology, and Organismal Biology & Head of the Tetrapod Division in the Museum of Biological Diversity at The Ohio State University. In our conversation with Bryan, who comes from a traditional biology background, we cover a ton of ground, including a foundational layer of understanding for the vast known unknowns in species and biodiversity, and how he came to apply machine learning to his lab’s research. We explore a few of his lab’s projects, including applying ML to genetic data to understand the geographic and environmental structure of DNA, what factors keep machine learning from being used more frequently used in biology, and what’s next for his group. The complete show notes for this episode can be found at
Today we’re joined by Jason Gauci, a Software Engineering Manager at Facebook AI. In our conversation with Jason, we explore their Reinforcement Learning platform, Re-Agent (Horizon). We discuss the role of decision making and game theory in the platform and the types of decisions they’re using Re-Agent to make, from ranking and recommendations to their eCommerce marketplace. Jason also walks us through the differences between online/offline and on/off policy model training, and where Re-Agent sits in this spectrum. Finally, we discuss the concept of counterfactual causality, and how they ensure safety in the results of their models. The complete show notes for this episode can be found at
Today we’re joined by Saiph Savage, a Visiting professor at the Human-Computer Interaction Institute at CMU, director of the HCI Lab at WVU, and co-director of the Civic Innovation Lab at UNAM. We caught up with Saiph during NeurIPS where she delivered an insightful invited talk “A Future of Work for the Invisible Workers in A.I.”. In our conversation with Saiph, we gain a better understanding of the “Invisible workers,” or the people doing the work of labeling for machine learning and AI systems, and some of the issues around lack of economic empowerment, emotional trauma, and other issues that arise with these jobs. We discuss ways that we can empower these workers, and push the companies that are employing these workers to do the same. Finally, we discuss Saiph’s participatory design work with rural workers in the global south. The complete show notes for this episode can be found at
Today we’re back with the final episode of AI Rewind joined by Michael Bronstein, a professor at Imperial College London and the Head of Graph Machine Learning at Twitter. In our conversation with Michael, we touch on his thoughts about the year in Machine Learning overall, including GPT-3 and Implicit Neural Representations, but spend a major chunk of time on the sub-field of Graph Machine Learning.  We talk through the application of Graph ML across domains like physics and bioinformatics, and the tools to look out for. Finally, we discuss what Michael thinks is in store for 2021, including graph ml applied to molecule discovery and non-human communication translation.
Today we continue the 2020 AI Rewind series, joined by friend of the show Sameer Singh, an Assistant Professor in the Department of Computer Science at UC Irvine.  We last spoke with Sameer at our Natural Language Processing office hours back at TWIMLfest, and was the perfect person to help us break down 2020 in NLP. Sameer tackles the review in 4 main categories, Massive Language Modeling, Fundamental Problems with Language Models, Practical Vulnerabilities with Language Models, and Evaluation.  We also explore the impact of GPT-3 and Transformer models, the intersection of vision and language models, and the injection of causal thinking and modeling into language models, and much more. The complete show notes for this episode can be found at
AI Rewind continues today as we’re joined by Pavan Turaga, Associate Professor in both the Departments of Arts, Media, and Engineering & Electrical Engineering, and the Interim Director of the School of Arts, Media, and Engineering at Arizona State University. Pavan, who joined us back in June to talk through his work from CVPR ‘20, Invariance, Geometry and Deep Neural Networks, is back to walk us through the trends he’s seen in Computer Vision last year. We explore the revival of physics-based thinking about scenes, differential rendering, the best papers, and where the field is going in the near future. We want to hear from you! Send your thoughts on the year that was 2020 below in the comments, or via Twitter at @samcharrington or @twimlai. The complete show notes for this episode can be found at
Today we kick off our annual AI Rewind series joined by friend of the show Pablo Samuel Castro, a Staff Research Software Developer at Google Brain. Pablo joined us earlier this year for a discussion about Music & AI, and his Geometric Perspective on Reinforcement Learning, as well our RL office hours during the inaugural TWIMLfest. In today’s conversation, we explore some of the latest and greatest RL advancements coming out of the major conferences this year, broken down into a few major themes, Metrics/Representations, Understanding and Evaluating Deep Reinforcement Learning, and RL in the Real World. This was a very fun conversation, and we encourage you to check out all the great papers and other resources available on the show notes page.
Comments (16)

Daniel Sierra

Best podcast on machine learning an ai

May 27th

Özgür Yüksel

Thanks a lot for introducing us to the genius of our age. Tremendously inspiring.

Dec 11th

Glory Dey

A very good insightful episode, Maki Moussavi explains the various points in a lucid manner. Truly, we are the captain of our life's ship. We are responsible for our own emotions and actions. Being proactive rather than reactive is the key to success and happiness! I will be reading this book! Thanks for sharing this interesting podcast. Have a great day!

Oct 15th

Glory Dey

I love this channel and all the great podcasts. The topics are very relevant and the speakers are well informed experts so the episodes are very educative. Only request, please change the opening music note of the podcast. It is very unpleasant tune sets a jarring effect right at the beginning. Otherwise all these episodes are very interesting in the field of innovations in Artificial Intelligence and Machine Learning! Regards!

Jun 25th

Billy Bloomer

so smart you can smell it

Jun 14th

raqueeb shaikh

great podcast

May 31st

Loza Boza

Phenomenal discussion. Thank you! Particularly enjoyed the parts on generative models and the link to Daniel Kahneman.

May 20th

simon abdou

Horrible Audio

May 9th

Özgür Yüksel

This is a very realistic and proper episode which explains quantum computing even as alone.

Apr 9th


Hello all, Thanks for podcast Can we combine the two agent learnings from same environment to find the best actions Thanks

Mar 14th

Bhavul Gauri

notes : * Data scientists are not trained to think of money optimisations. plotting cpu usage vs accuracy gives an idea about it. if u increase data 4x as much just to gain 1% increase in accuracy that may not be great because you're using 4 times as much CPU power * a team just decicated to monitoring. i. monitor inputs : should not go beyond a certain range for each feature that you are supposed to have. Nulls ratio shouldn't change by a lot. ii. monitor both business and model metrics. sometimes even if model metrics get better ur business metrics could go low....and this could be the case like better autocompletion makes for low performance spell check OR it could also depend upon other things that have changed. or seasonality. * Data scientists and ML engineers in pairs. ML Engineers get to learn about the model while Data Scientists come up with it. both use same language. ML Engineers make sure it gets scaled up and deployed to production. * Which parameters are somewhat stable no matter how many times you retrain vs what parameters are volatile. the volatile ones could cause drastic changes. so u can reverse engineer this way.

Mar 11th

Abhijeet Gulati

great podcast. do we reference to papers that were discussed by Ganju. good job

Jan 22nd

Khaled Zamer

Super.. very informative. Thanks

Aug 26th

Printing Printing

there is no content lol. Host, please invite real scientists

Jan 1st

James Flint

This is an incredible interview. Dopamine as a correlate of prediction error makes so much sense. Best Twiml talk to date!

Dec 30th

Qanit Al-Syed

conversations drag too much. gets boring. stop the marketing and get to the content

Dec 30th
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