The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
717 episodes - English - Latest episode: about 1 month ago - ★★★★★ - 323 ratingsMachine 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.
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Episodes
re:Invent Roundup 2021 with Bratin Saha - #542
December 06, 2021 18:33 - 42 minutesToday we’re joined by Bratin Saha, vice president and general manager at Amazon. In our conversation with Bratin, we discuss quite a few of the recent ML-focused announcements coming out of last weeks re:Invent conference, including new products like Canvas and Studio Lab, as well as upgrades to existing services like Ground Truth Plus. We explore what no-code environments like the aforementioned Canvas mean for the democratization of ML tooling, and some of the key challenges to delivering i...
Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541
December 02, 2021 16:31 - 45 minutesToday we’re joined by Doug Burdick, a principal research staff member at IBM Research. In a recent interview, Doug’s colleague Yunyao Li joined us to talk through some of the broader enterprise NLP problems she’s working on. One of those problems is making documents machine consumable, especially with the traditionally archival file type, the PDF. That’s where Doug and his team come in. In our conversation, we discuss the multimodal approach they’ve taken to identify, interpret, contextualize...
Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi - #540
November 29, 2021 18:58 - 49 minutesToday we’re joined by Shayan Mortazavi, a data science manager at Accenture. In our conversation with Shayan, we discuss his talk from the recent SigOpt HPC & AI Summit, titled A Novel Framework Predictive Maintenance Using Dl and Reliability Engineering. In the talk, Shayan proposes a novel deep learning-based approach for prognosis prediction of oil and gas plant equipment in an effort to prevent critical damage or failure. We explore the evolution of reliability engineering, the decision ...
Building a Deep Tech Startup in NLP with Nasrin Mostafazadeh - #539
November 24, 2021 17:17 - 51 minutesToday we’re joined by friend-of-the-show Nasrin Mostafazadeh, co-founder of Verneek. Though Verneek is still in stealth, Nasrin was gracious enough to share a bit about the company, including their goal of enabling anyone to make data-informed decisions without the need for a technical background, through the use of innovative human-machine interfaces. In our conversation, we explore the state of AI research in the domains relevant to the problem they’re trying to solve and how they use thos...
Models for Human-Robot Collaboration with Julie Shah - #538
November 22, 2021 19:07 - 42 minutesToday we’re joined by Julie Shah, a professor at the Massachusetts Institute of Technology (MIT). Julie’s work lies at the intersection of aeronautics, astronautics, and robotics, with a specific focus on collaborative and interactive robotics. In our conversation, we explore how robots would achieve the ability to predict what their human collaborators are thinking, what the process of building knowledge into these systems looks like, and her big picture idea of developing a field robot that...
Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537
November 18, 2021 18:29 - 58 minutesToday we’re joined by Yunyao Li, a senior research manager at IBM Research. Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, and if she focuses on solving these problems independent of one another, or through a more unified approach. We then grou...
Machine Learning at GSK with Kim Branson - #356
November 15, 2021 19:30 - 1 hourToday we’re joined by Kim Branson, the SVP and global head of artificial intelligence and machine learning at GSK. We cover a lot of ground in our conversation, starting with a breakdown of GSK’s core pharmaceutical business, and how ML/AI fits into that equation, use cases that appear using genetics data as a data source, including sequential learning for drug discovery. We also explore the 500 billion node knowledge graph Kim’s team built to mine scientific literature, and their “AI Hub”, ...
Machine Learning at GSK with Kim Branson - #536
November 15, 2021 19:30 - 1 hourToday we’re joined by Kim Branson, the SVP and global head of artificial intelligence and machine learning at GSK. We cover a lot of ground in our conversation, starting with a breakdown of GSK’s core pharmaceutical business, and how ML/AI fits into that equation, use cases that appear using genetics data as a data source, including sequential learning for drug discovery. We also explore the 500 billion node knowledge graph Kim’s team built to mine scientific literature, and their “AI Hub”, ...
The Benefit of Bottlenecks in Evolving Artificial Intelligence with David Ha - #535
November 11, 2021 17:57 - 59 minutesToday we’re joined by David Ha, a research scientist at Google. In nature, there are many examples of “bottlenecks”, or constraints, that have shaped our development as a species. Building upon this idea, David posits that these same evolutionary bottlenecks could work when training neural network models as well. In our conversation with David, we cover a TON of ground, including the aforementioned biological inspiration for his work, then digging deeper into the different types of constrain...
Facebook Abandons Facial Recognition. Should Everyone Else Follow Suit? With Luke Stark - #534
November 08, 2021 18:24 - 42 minutesToday we’re joined by Luke Stark, an assistant professor at Western University in London, Ontario. In our conversation with Luke, we explore the existence and use of facial recognition technology, something Luke has been critical of in his work over the past few years, comparing it to plutonium. We discuss Luke’s recent paper, “Physiognomic Artificial Intelligence”, in which he critiques studies that will attempt to use faces and facial expressions and features to make determinations about p...
Building Blocks of Machine Learning at LEGO with Francesc Joan Riera - #533
November 04, 2021 17:05 - 43 minutesToday we’re joined by Francesc Joan Riera, an applied machine learning engineer at The LEGO Group. In our conversation, we explore the ML infrastructure at LEGO, specifically around two use cases, content moderation and user engagement. While content moderation is not a new or novel task, but because their apps and products are marketed towards children, their need for heightened levels of moderation makes it very interesting. We discuss if the moderation system is built specifically to w...
Exploring the FastAI Tooling Ecosystem with Hamel Husain - #532
November 01, 2021 18:33 - 39 minutesToday we’re joined by Hamel Husain, Staff Machine Learning Engineer at GitHub. Over the last few years, Hamel has had the opportunity to work on some of the most popular open source projects in the ML world, including fast.ai, nbdev, fastpages, and fastcore, just to name a few. In our conversation with Hamel, we discuss his journey into Silicon Valley, and how he discovered that the ML tooling and infrastructure weren’t quite as advanced as he’d assumed, and how that led him to help build so...
Multi-task Learning for Melanoma Detection with Julianna Ianni - #531
October 28, 2021 18:50 - 37 minutesIn today’s episode, we are joined by Julianna Ianni, vice president of AI research & development at Proscia. In our conversation, Julianna shares her and her team’s research focused on developing applications that would help make the life of pathologists easier by enabling tasks to quickly and accurately be diagnosed using deep learning and AI. We also explore their paper “A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Grou...
House Hunters: Machine Learning at Redfin with Akshat Kaul - #530
October 26, 2021 06:20 - 44 minutesToday we’re joined by Akshat Kaul, the head of data science and machine learning at Redfin. We’re all familiar with Redfin, but did you know that redfin.com is the largest real estate brokerage site in the US? In our conversation with Akshat, we discuss the history of ML at Redfin and a few of the key use cases that ML is currently being applied to, including recommendations, price estimates, and their “hot homes” feature. We explore their recent foray into building their own internal platfor...
Attacking Malware with Adversarial Machine Learning, w/ Edward Raff - #529
October 21, 2021 16:36 - 47 minutesToday we’re joined by Edward Raff, chief scientist and head of the machine learning research group at Booz Allen Hamilton. Edward’s work sits at the intersection of machine learning and cybersecurity, with a particular interest in malware analysis and detection. In our conversation, we look at the evolution of adversarial ML over the last few years before digging into Edward’s recently released paper, Adversarial Transfer Attacks With Unknown Data and Class Overlap. In this paper, Edward and ...
Learning to Ponder: Memory in Deep Neural Networks with Andrea Banino - #528
October 18, 2021 17:47 - 37 minutesToday we’re joined by Andrea Banino, a research scientist at DeepMind. In our conversation with Andrea, we explore his interest in artificial general intelligence by way of episodic memory, the relationship between memory and intelligence, the challenges of applying memory in the context of neural networks, and how to overcome problems of generalization. We also discuss his work on the PonderNet, a neural network that “budgets” its computational investment in solving a problem, according to...
Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527
October 14, 2021 15:51 - 42 minutesTake our survey at twimlai.com/survey21! Today we’re joined by Tim Rocktäschel, a research scientist at Facebook AI Research and an associate professor at University College London (UCL). Tim’s work focuses on training RL agents in simulated environments, with the goal of these agents being able to generalize to novel situations. Typically, this is done in environments like OpenAI Gym, MuJuCo, or even using Atari games, but these all come with constraints. In Tim’s approach, he utilizes a ...
Building Technical Communities at Stack Overflow with Prashanth Chandrasekar - #526
October 11, 2021 17:58 - 40 minutesIn this special episode of the show, we’re excited to bring you our conversation with Prashanth Chandrasekar, CEO of Stack Overflow. This interview was recorded as a part of the annual Prosus AI Marketplace event. In our discussion with Prashanth, we explore the impact the pandemic has had on Stack Overflow, how they think about community and enable collaboration in over 100 million monthly users from around the world, and some of the challenges they’ve dealt with when managing a community ...
Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525
October 07, 2021 16:21 - 39 minutesToday we’re joined by Joseph Soriaga, a senior director of technology at Qualcomm. In our conversation with Joseph, we focus on a pair of papers that he and his team will be presenting at Globecom later this year. The first, Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking, details the use of deep learning to augment an algorithm to address mismatches in models, allowing for more efficient training and making models more interpretable and predictable. The second p...
Modeling Memory with RNNs and Curriculum Learning w/ Kanaka Rajan - #524
October 04, 2021 16:36 - 47 minutesToday we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation, we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that t...
Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524
October 04, 2021 16:36 - 47 minutesToday we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation, we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that t...
Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523
September 30, 2021 16:15 - 40 minutesToday we’re joined by a friend of the show and return guest Ville Tuulos, CEO and co-founder of Outerbounds. In our previous conversations with Ville, we explored his experience building and deploying the open-source framework, Metaflow, while working at Netflix. Since our last chat, Ville has embarked on a few new journeys, including writing the upcoming book Effective Data Science Infrastructure, and commercializing Metaflow, both of which we dig into quite a bit in this conversation. We ...
Delivering Neural Speech Services at Scale with Li Jiang - #522
September 27, 2021 17:32 - 49 minutesToday we’re joined by Li Jiang, a distinguished engineer at Microsoft working on Azure Speech. In our conversation with Li, we discuss his journey across 27 years at Microsoft, where he’s worked on, among other things, audio and speech recognition technologies. We explore his thoughts on the advancements in speech recognition over the past few years, the challenges, and advantages, of using either end-to-end or hybrid models. We also discuss the trade-offs between delivering accuracy or q...
AI’s Legal and Ethical Implications with Sandra Wachter - #521
September 23, 2021 16:27 - 49 minutesToday we’re joined by Sandra Wacther, an associate professor and senior research fellow at the University of Oxford. Sandra’s work lies at the intersection of law and AI, focused on what she likes to call “algorithmic accountability”. In our conversation, we explore algorithmic accountability in three segments, explainability/transparency, data protection, and bias, fairness and discrimination. We discuss how the thinking around black boxes changes when discussing applying regulation and la...
Compositional ML and the Future of Software Development with Dillon Erb - #520
September 20, 2021 19:46 - 41 minutesToday we’re joined by Dillon Erb, CEO of Paperspace. If you’re not familiar with Dillon, he joined us about a year ago to discuss Machine Learning as a Software Engineering Discipline; we strongly encourage you to check out that interview as well. In our conversation, we explore the idea of compositional AI, and if it is the next frontier in a string of recent game-changing machine learning developments. We also discuss a source of constant back and forth in the community around the role of...
Generating SQL Database Queries from Natural Language with Yanshuai Cao - #519
September 16, 2021 16:32 - 38 minutesToday 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 l...
Social Commonsense Reasoning with Yejin Choi - #518
September 13, 2021 18:01 - 51 minutesToday 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, ho...
Deep Reinforcement Learning for Game Testing at EA with Konrad Tollmar - #517
September 09, 2021 17:35 - 40 minutesToday 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 ...
Exploring AI 2041 with Kai-Fu Lee - #516
September 06, 2021 16:00 - 47 minutesToday 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 wha...
Advancing Robotic Brains and Bodies with Daniela Rus - #515
September 02, 2021 17:43 - 45 minutesToday 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(?!)....
Neural Synthesis of Binaural Speech From Mono Audio with Alexander Richard - #514
August 30, 2021 18:41 - 46 minutesToday 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 ...
Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513
August 26, 2021 17:33 - 36 minutesToday 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 understa...
Adaptivity in Machine Learning with Samory Kpotufe - #512
August 23, 2021 18:27 - 49 minutesToday 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...
A Social Scientist’s Perspective on AI with Eric Rice - #511
August 19, 2021 16:09 - 43 minutesToday 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 m...
Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510
August 16, 2021 17:54 - 42 minutesToday 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...
Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509
August 12, 2021 16:35 - 47 minutesToday we’re joined by return guest Greg Brockman, co-founder and CTO of OpenAI. We had the pleasure of reconnecting with Greg on the heels of the announcement of Codex, OpenAI’s most recent release. Codex is a direct descendant of GPT-3 that allows users to do autocomplete tasks based on all of the publicly available text and code on the internet. In our conversation with Greg, we explore the distinct results Codex sees in comparison to GPT-3, relative to the prompts it's being given, how it ...
Spatiotemporal Data Analysis with Rose Yu - #508
August 09, 2021 18:08 - 32 minutesToday we’re joined by Rose Yu, an assistant professor at the Jacobs School of Engineering at UC San Diego. Rose’s research focuses on advancing machine learning algorithms and methods for analyzing large-scale time-series and spatial-temporal data, then applying those developments to climate, transportation, and other physical sciences. We discuss how Rose incorporates physical knowledge and partial differential equations in these use cases and how symmetries are being exploited. We also ex...
Parallelism and Acceleration for Large Language Models with Bryan Catanzaro - #507
August 05, 2021 17:35 - 50 minutesToday we’re joined by Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. Most folks know Bryan as one of the founders/creators of cuDNN, the accelerated library for deep neural networks. In our conversation, we explore his interest in high-performance computing and its recent overlap with AI, his current work on Megatron, a framework for training giant language models, and the basic approach for distributing a large language model on DGX infrastructure. We also dis...
Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506
August 02, 2021 17:20 - 54 minutesToday we close out our 2021 ICML series joined by Lina Montoya, a postdoctoral researcher at UNC Chapel Hill. In our conversation with Lina, who was an invited speaker at the Neglected Assumptions in Causal Inference Workshop, we explored her work applying Optimal Dynamic Treatment (ODT) to understand which kinds of individuals respond best to specific interventions in the US criminal justice system. We discuss the concept of neglected assumptions and how it connects to ODT rule estimation, ...
Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes - #505
July 29, 2021 18:19 - 50 minutesToday we continue our ICML series joined by Gustavo Malkomes, a research engineer at Intel via their recent acquisition of SigOpt. In our conversation with Gustavo, we explore his paper Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, which focuses on a novel algorithmic solution for the iterative model search process. This new algorithm empowers teams to run experiments where they are not optimizing particular metrics but instead identi...
Fairness and Robustness in Federated Learning with Virginia Smith -#504
July 26, 2021 18:14 - 36 minutesToday we kick off our ICML coverage joined by Virginia Smith, an assistant professor in the Machine Learning Department at Carnegie Mellon University. In our conversation with Virginia, we explore her work on cross-device federated learning applications, including where the distributed learning aspects of FL are relative to the privacy techniques. We dig into her paper from ICML, Ditto: Fair and Robust Federated Learning Through Personalization, what fairness means in contrast to AI ethics, ...
Scaling AI at H&M Group with Errol Koolmeister - #503
July 22, 2021 20:18 - 41 minutesToday we’re joined by Errol Koolmeister, the head of AI foundation at H&M Group. In our conversation with Errol, we explore H&M’s AI journey, including its wide adoption across the company in 2016, and the various use cases in which it's deployed like fashion forecasting and pricing algorithms. We discuss Errol’s first steps in taking on the challenge of scaling AI broadly at the company, the value-added learning from proof of concepts, and how to align in a sustainable, long-term way. Of cou...
Evolving AI Systems Gracefully with Stefano Soatto - #502
July 19, 2021 20:05 - 49 minutesToday we’re joined by Stefano Soatto, VP of AI applications science at AWS and a professor of computer science at UCLA. Our conversation with Stefano centers on recent research of his called Graceful AI, which focuses on how to make trained systems evolve gracefully. We discuss the broader motivation for this research and the potential dangers or negative effects of constantly retraining ML models in production. We also talk about research into error rate clustering, the importance of model...
ML Innovation in Healthcare with Suchi Saria - #501
July 15, 2021 20:32 - 45 minutesToday we’re joined by Suchi Saria, the founder and CEO of Bayesian Health, the John C. Malone associate professor of computer science, statistics, and health policy, and the director of the machine learning and healthcare lab at Johns Hopkins University. Suchi shares a bit about her journey to working in the intersection of machine learning and healthcare, and how her research has spanned across both medical policy and discovery. We discuss why it has taken so long for machine learning to be...
Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500
July 12, 2021 22:25 - 41 minutesToday we’re joined by a friend of the show Jeff Gehlhaar, VP of technology and the head of AI software platforms at Qualcomm. In our conversation with Jeff, we cover a ton of ground, starting with a bit of exploration around ML compilers, what they are, and their role in solving issues of parallelism. We also dig into the latest additions to the Snapdragon platform, AI Engine Direct, and how it works as a bridge to bring more capabilities across their platform, how benchmarking works in the ...
The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499
July 08, 2021 17:38 - 48 minutesToday we continue our AI in Innovation series joined by Dan Bohus, senior principal researcher at Microsoft Research, and Siddhartha Sen, a principal researcher at Microsoft Research. In this conversation, we use a pair of research projects, Maia Chess and Situated Interaction, to springboard us into a conversation about the evolution of human-AI interaction. We discuss both of these projects individually, as well as the commonalities they have, how themes like understanding the human experi...
Vector Quantization for NN Compression with Julieta Martinez - #498
July 05, 2021 16:49 - 41 minutesToday we’re joined by Julieta Martinez, a senior research scientist at recently announced startup Waabi. Julieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk “What do Large-Scale Visual Search and Neural Network Compression have in Common,” which shows that multiple ideas from large-scale visual search can be used to achieve state-of-the-art neural network compression. We explore the commonality between large databases and dealin...
Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497
July 01, 2021 18:31 - 42 minutesToday we continue our CVPR 2021 coverage joined by Claire Monteleoni, an associate professor at the University of Colorado Boulder. We cover quite a bit of ground in our conversation with Claire, including her journey down the path from environmental activist to one of the leading climate informatics researchers in the world. We explore her current research interests, and the available opportunities in applying machine learning to climate informatics, including the interesting position of do...
Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496
June 28, 2021 19:59 - 47 minutesToday we kick off our CVPR coverage joined by Amir Habibian, a senior staff engineer manager at Qualcomm Technologies. In our conversation with Amir, whose research primarily focuses on video perception, we discuss a few papers they presented at the event. We explore the paper Skip-Convolutions for Efficient Video Processing, which looks at training discrete variables to end to end into visual neural networks. We also discuss his work on his FrameExit paper, which proposes a conditional earl...
Advancing NLP with Project Debater w/ Noam Slonim - #495
June 24, 2021 18:27 - 51 minutesToday we’re joined by Noam Slonim, the principal investigator of Project Debater at IBM Research. In our conversation with Noam, we explore the history of Project Debater, the first AI system that can “debate” humans on complex topics. We also dig into the evolution of the project, which is the culmination of 7 years and over 50 research papers, and eventually becoming a Nature cover paper, “An Autonomous Debating System,” which details the system in its entirety. Finally, Noam details many...