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

Latest Episodes:

Generating SQL Database Queries from Natural Language with Yanshuai Cao - #519 Sep 16, 2021

Show notes

Today we’re joined by Yanshuai Cao, a senior research team lead at Borealis AI. In our conversation with Yanshuai, we explore his work on Turing, their natural language to SQL engine that allows users to get insights from relational databases without having to write code. We do a bit of compare and contrast with the recently released Codex Model from OpenAI, the role that reasoning plays in solving this problem, and how it is implemented in the model. We also talk through various challenges like data augmentation, the complexity of the queries that Turing can produce, and a paper that explores the explainability of this model.

The complete show notes for this episode can be found at twimlai.com/go/519.

Social Commonsense Reasoning with Yejin Choi - #518 Sep 13, 2021

Show notes

Today we’re joined by Yejin Choi, a professor at the University of Washington. We had the pleasure of catching up with Yejin after her keynote interview at the recent Stanford HAI “Foundational Models” workshop. In our conversation, we explore her work at the intersection of natural language generation and common sense reasoning, including how she defines common sense, and what the current state of the world is for that research. We discuss how this could be used for creative storytelling, how transformers could be applied to these tasks, and we dig into the subfields of physical and social common sense reasoning. Finally, we talk through the future of Yejin’s research and the areas that she sees as most promising going forward.

If you enjoyed this episode, check out our conversation on AI Storytelling Systems with Mark Riedl. The complete show notes for today’s episode can be found at twimlai.com/go/518.

Deep Reinforcement Learning for Game Testing at EA with Konrad Tollmar - #517 Sep 09, 2021

Show notes

Today we’re joined by Konrad Tollmar, research director at Electronic Arts and an associate professor at KTH.

In our conversation, we explore his role as the lead of EA’s applied research team SEED and the ways that they’re applying ML/AI across popular franchises like Apex Legends, Madden, and FIFA. We break down a few papers focused on the application of ML to game testing, discussing why deep reinforcement learning is at the top of their research agenda, the differences between training atari games and modern 3D games, using CNNs to detect glitches in games, and of course, Konrad gives us his outlook on the future of ML for games training.

The complete show notes for this episode can be found at twimlai.com/go/517.

Exploring AI 2041 with Kai-Fu Lee - #516 Sep 06, 2021

Show notes

Today we’re joined by Kai-Fu Lee, chairman and CEO of Sinovation Ventures and author of AI 2041: Ten Visions for Our Future.

In AI 2041, Kai-Fu and co-author Chen Qiufan tell the story of how AI could shape our future through a series of 10 “scientific fiction” short stories. In our conversation with Kai-Fu, we explore why he chose 20 years as the time horizon for these stories, and dig into a few of the stories in more detail. We explore the potential for level 5 autonomous driving and what effect that will have on both established and developing nations, the potential outcomes when dealing with job displacement, and his perspective on how the book will be received. We also discuss the potential consequences of autonomous weapons, if we should actually worry about singularity or superintelligence, and the evolution of regulations around AI in 20 years.

We’d love to hear from you! What are your thoughts on any of the stories we discuss in the interview? Will you be checking this book out? Let us know in the comments on the show notes page at twimlai.com/go/516.

Advancing Robotic Brains and Bodies with Daniela Rus - #515 Sep 02, 2021

Show notes

Today we’re joined by Daniela Rus, director of CSAIL & Deputy Dean of Research at MIT.

In our conversation with Daniela, we explore the history of CSAIL, her role as director of one of the most prestigious computer science labs in the world, how she defines robots, and her take on the current AI for robotics landscape. We also discuss some of her recent research interests including soft robotics, adaptive control in autonomous vehicles, and a mini surgeon robot made with sausage casing(?!).

The complete show notes for this episode can be found at twimlai.com/go/515.

Neural Synthesis of Binaural Speech From Mono Audio with Alexander Richard - #514 Aug 30, 2021

Show notes

Today we’re joined by Alexander Richard, a research scientist at Facebook Reality Labs, and recipient of the ICLR Best Paper Award for his paper “Neural Synthesis of Binaural Speech From Mono Audio.”

We begin our conversation with a look into the charter of Facebook Reality Labs, and Alex’s specific Codec Avatar project, where they’re developing AR/VR for social telepresence (applications like this come to mind). Of course, we dig into the aforementioned paper, discussing the difficulty in improving the quality of audio and the role of dynamic time warping, as well as the challenges of creating this model. Finally, Alex shares his thoughts on 3D rendering for audio, and other future research directions.

The complete show notes for this episode can be found at twimlai.com/go/514.

Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513 Aug 26, 2021

Show notes

Today we’re joined by Alona Fyshe, an assistant professor at the University of Alberta.

We caught up with Alona on the heels of an interesting panel discussion that she participated in, centered around improving AI systems using research about brain activity. In our conversation, we explore the multiple types of brain images that are used in this research, what representations look like in these images, and how we can improve language models without knowing explicitly how the brain understands the language. We also discuss similar experiments that have incorporated vision, the relationship between computer vision models and the representations that language models create, and future projects like applying a reinforcement learning framework to improve language generation.

The complete show notes for this episode can be found at twimlai.com/go/513.

Adaptivity in Machine Learning with Samory Kpotufe - #512 Aug 23, 2021

Show notes

Today we’re joined by Samory Kpotufe, an associate professor at Columbia University and program chair of the 2021 Conference on Learning Theory (COLT).

In our conversation with Samory, we explore his research at the intersection of machine learning, statistics, and learning theory, and his goal of reaching self-tuning, adaptive algorithms. We discuss Samory’s research in transfer learning and other potential procedures that could positively affect transfer, as well as his work understanding unsupervised learning including how clustering could be applied to real-world applications like cybersecurity, IoT (Smart homes, smart city sensors, etc) using methods like dimension reduction, random projection, and others. If you enjoyed this interview, you should definitely check out our conversation with Jelani Nelson on the “Theory of Computation.”

The complete show notes for this episode can be found at https://twimlai.com/go/512.

A Social Scientist’s Perspective on AI with Eric Rice - #511 Aug 19, 2021

Show notes

Today we’re joined by Eric Rice, associate professor at USC, and the co-director of the USC Center for Artificial Intelligence in Society.

Eric is a sociologist by trade, and in our conversation, we explore how he has made extensive inroads within the machine learning community through collaborations with ML academics and researchers. We discuss some of the most important lessons Eric has learned while doing interdisciplinary projects, how the social scientist’s approach to assessment and measurement would be different from a computer scientist's approach to assessing the algorithmic performance of a model.

We specifically explore a few projects he’s worked on including HIV prevention amongst the homeless youth population in LA, a project he spearheaded with former guest Milind Tambe, as well as a project focused on using ML techniques to assist in the identification of people in need of housing resources, and ensuring that they get the best interventions possible.

If you enjoyed this conversation, I encourage you to check out our conversation with Milind Tambe from last year’s TWIMLfest on Why AI Innovation and Social Impact Go Hand in Hand.

The complete show notes for this episode can be found at https://twimlai.com/go/511.

Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510 Aug 16, 2021

Show notes

Today we’re joined by José Miguel Hernández-Lobato, a university lecturer in machine learning at the University of Cambridge. In our conversation with Miguel, we explore his work at the intersection of Bayesian learning and deep learning. We discuss how he’s been applying this to the field of molecular design and discovery via two different methods, with one paper searching for possible chemical reactions, and the other doing the same, but in 3D and in 3D space. We also discuss the challenges of sample efficiency, creating objective functions, and how those manifest themselves in these experiments, and how he integrated the Bayesian approach to RL problems. We also talk through a handful of other papers that Miguel has presented at recent conferences, which are all linked at twimlai.com/go/510.

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