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

Today we’re joined by Nishan Subedi, VP of Algorithms at Overstock.com.

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 https://twimlai.com/go/462.

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 https://twimlai.com/go/461. 

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 https://twimlai.com/go/460.

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 https://twimlai.com/go/460.

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.  

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 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.

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.

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 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.