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.

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 [twimlai.com/go/459][1].
[1] https://twimlai.com/go/459

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

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...

Evolution and Intelligence with Penousal Machado - #459

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 twimlai.com/go/458.

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...

Innovating Neural Machine Translation with Arul Menezes - #458

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 [https://twimlai.com/go/457][1].
[1] https://twimlai.com/go/457

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...

Building the Product Knowledge Graph at Amazon with Luna Dong - #457

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 [twimlai.com/go/456][1].
[1] https://twimlai.com/go/456

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...

Towards a Systems-Level Approach to Fair ML with Sarah M. Brown - #456

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 https://twimlai.com/go/455

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...

AI for Digital Health Innovation with Andrew Trister - #455

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 [twimlai.com/go/454][1].
[1] https://twimlai.com/go/454

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,...

System Design for Autonomous Vehicles with Drago Anguelov - #454

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 [https://twimlai.com/go/453][1].
[1] https://twimlai.com/go/453

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...

Building, Adopting, and Maturing LinkedIn's Machine Learning Platform with Ya Xu - #453

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 [twimlai.com/go/452][1].
[1] https://twimlai.com/go/452

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...

Expressive Deep Learning with Magenta DDSP w/ Jesse Engel - #452

Today we’re joined by return guest Francisco Webber, CEO & Co-founder of Cortical.io.
Francisco was originally a guest over 4 years and 400 episodes ago, where we discussed his company Cortical.io, 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 [twimlai.com/go/451][1].
[1] https://twimlai.com/go/451

Today we’re joined by return guest Francisco Webber, CEO & Co-founder of Cortical.io. Francisco was originally a guest over 4 years and 400 episodes ago, where we discussed his company Cortical.io, and their unique approach to natural language...

Semantic Folding for Natural Language Understanding with Francisco Weber - #451

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 [twimlai.com/go/450][1].
[1] https://twimlai.com/go/450

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...

The Future of Autonomous Systems with Gurdeep Pall - #450

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 [twimlai.com/go/449][1].
[1] https://twimlai.com/go/449

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,...

AI for Ecology and Ecosystem Preservation with Bryan Carstens - #449

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 [twimlai.com/go/448][1].
[1] https://twimlai.com/go/448

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...

Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448

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 [twimlai.com/go/447][1].
[1] https://twimlai.com/go/447

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...

A Future of Work for the Invisible Workers in A.I. with Saiph Savage - #447

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’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...

Trends in Graph Machine Learning with Michael Bronstein - #446

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 [twimlai.com/go/445][1].
[1] https://twimlai.com/go/445

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...