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

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This Week in Machine Learning & AI is the most popular podcast of its kind, catering to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These cr…

Sam Charrington


    • Dec 2, 2021 LATEST EPISODE
    • weekdays NEW EPISODES
    • 44m AVG DURATION
    • 558 EPISODES

    Listeners of This Week in Machine Learning & Artificial Intelligence (AI) Podcast that love the show mention: machine learning, ml, ai podcast, interview format, sam's, it's clear, trusted, breadth, ah, applied, discusses, easy to follow, humility, companies, wide variety, data, technical, btw, field, covers.



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    Latest episodes from This Week in Machine Learning & Artificial Intelligence (AI) Podcast

    Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541

    Play Episode Listen Later Dec 2, 2021 45:32

    Today 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 and extract things like tables from a document, the challenges they've faced when dealing with the tables and how they evaluate the performance of models on tables. We also explore how he's handled generalizing across different formats, how fine-tuning has to be in order to be effective, the problems that appear on the NLP side of things, and how deep learning models are being leveraged within the group. The complete show notes for this episode can be found at twimlai.com/go/541

    Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi - #540

    Play Episode Listen Later Nov 29, 2021 49:01

    Today 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 to use a residual-based approach rather than traditional anomaly detection to determine when an anomaly was happening, the challenges of using LSTMs when building these models, the amount of human labeling required to build the models, and much more! The complete show notes for this episode can be found at twimlai.com/go/540

    Building a Deep Tech Startup in NLP with Nasrin Mostafazadeh - #539

    Play Episode Listen Later Nov 24, 2021 51:20

    Today 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 those insights to inform and prioritize their research agenda. We also discuss what advice Nasrin would give to someone thinking about starting a deep tech startup or going from research to product development.  The complete show notes for today's show can be found at twimlai.com/go/539.

    Models for Human-Robot Collaboration with Julie Shah - #538

    Play Episode Listen Later Nov 22, 2021 42:11

    Today 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 doesn't “require a human to be a robot” to work with it. We also discuss work Julie has done on cross-training between humans and robots with the focus on getting them to co-learn how to work together, as well as future projects that she's excited about. The complete show notes for this episode can be found at twimlai.com/go/538.

    Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537

    Play Episode Listen Later Nov 18, 2021 58:01

    Today 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 ground the conversation with real-world examples of these enterprise challenges, including enabling level document discovery at scale using combinations of techniques like deep neural networks and supervised and/or unsupervised learning, and entity extraction and semantic parsing to identify text. Finally, we talk through data augmentation in the context of NLP, and how we enable the humans in-the-loop to generate high-quality data. The complete show notes for this episode can be found at twimlai.com/go/537

    Machine Learning at GSK with Kim Branson - #356

    Play Episode Listen Later Nov 15, 2021 60:36

    Today 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 ML/AI infrastructure team that handles all tooling and engineering problems within their organization. Finally, we explore their recent cancer research collaboration with King's College, which is tasked with understanding the individualized needs of high- and low-risk cancer patients using ML/AI amongst other technologies.  The complete show notes for this episode can be found at twimlai.com/go/536.

    The Benefit of Bottlenecks in Evolving Artificial Intelligence with David Ha - #535

    Play Episode Listen Later Nov 11, 2021 59:04

    Today 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 constraints he's applied to ML systems. We explore abstract generative models and how advanced training agents inside of generative models has become, and quite a few papers including Neuroevolution of self-interpretable agents, World Models and Attention for Reinforcement Learning, and The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. This interview is Nerd Alert certified, so get your notes ready!  PS. David is one of our favorite follows on Twitter (@hardmaru), so check him out and share your thoughts on this interview and his work! The complete show notes for this episode can be found at twimlai.com/go/535

    Facebook Abandons Facial Recognition. Should Everyone Else Follow Suit? With Luke Stark - #534

    Play Episode Listen Later Nov 8, 2021 42:08

    Today 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 people, a practice fundamental to facial recognition, also one that Luke believes is inherently racist at its core.  Finally, briefly discuss the recent wave of hires at the FTC, and the news that broke (mid-recording) announcing that Facebook will be shutting down their facial recognition system and why it's not necessarily the game-changing announcement it seemed on its… face.  The complete show notes for this episode can be found at twimlai.com/go/534.

    Building Blocks of Machine Learning at LEGO with Francesc Joan Riera - #533

    Play Episode Listen Later Nov 4, 2021 43:13

    Today 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 weed out bad actors or passive behaviors if their system has a human-in-the-loop component, why they built a feature store as opposed to a traditional database, and challenges they faced along that journey. We also talk through the range of skill sets on their team, the use of MLflow for experimentation, the adoption of AWS for serverless, and so much more! The complete show notes for this episode can be found at twimlai.com/go/534.

    Exploring the FastAI Tooling Ecosystem with Hamel Husain - #532

    Play Episode Listen Later Nov 1, 2021 39:38

    Today 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 some of the foundational pieces of Airbnb's Bighead Platform.  We also spend time exploring Hamel's time working with Jeremy Howard and the team creating fast.ai, how nbdev came about, and how it plans to change the way practitioners interact with traditional jupyter notebooks. Finally, talk through a few more tools in the fast.ai ecosystem, fastpages, fastcore, how these tools interact with Github Actions, and the up and coming ML tools that Hamel is excited about.  The complete show notes for this episode can be found at twimlai.com/go/532.

    Multi-task Learning for Melanoma Detection with Julianna Ianni - #531

    Play Episode Listen Later Oct 28, 2021 37:33

    In 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 Ground Truth”, while talking through how ML aids pathologists in diagnosing Melanoma by building a multitask classifier to distinguish between low-risk and high-risk cases. Finally, we discussed the challenges involved in designing a model that would help in identifying and classifying Melanoma, the results they've achieved, and what the future of this work could look like. The complete show notes for this episode can be found at twimlai.com/go/531.

    House Hunters: Machine Learning at Redfin with Akshat Kaul - #530

    Play Episode Listen Later Oct 26, 2021 43:34

    Today 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 platform, which they've coined “Redeye”, how they've built Redeye to support modeling across the business, and how Akshat thinks about the role of the cloud when building and delivering their platform. Finally, we discuss the impact the pandemic has had on ML at the company, and Akshat's vision for the future of their platform and machine learning at the company more broadly.  The complete show notes for this episode can be found at twimlai.com/go/530.

    Attacking Malware with Adversarial Machine Learning, w/ Edward Raff - #529

    Play Episode Listen Later Oct 21, 2021 47:38

    Today 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 his team explore the use of adversarial transfer attacks and how they're able to lower their success rate by simulating class disparity. Finally, we talk through quite a few future directions for adversarial attacks, including his interest in graph neural networks. The complete show notes for this episode can be found at twimlai.com/go/529.

    Learning to Ponder: Memory in Deep Neural Networks with Andrea Banino - #528

    Play Episode Listen Later Oct 18, 2021 37:12

    Today 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 the inherent complexity of the problem, the impetus and goals of this research, and how PonderNet connects to his memory research.  The complete show notes for this episode can be found at twimlai.com/go/528.

    Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527

    Play Episode Listen Later Oct 14, 2021 42:57

    Take 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 game called NetHack, which is much more rich and complex than the aforementioned environments.   In our conversation with Tim, we explore the ins and outs of using NetHack as a training environment, including how much control a user has when generating each individual game and the challenges he's faced when deploying the agents. We also discuss his work on MiniHack, an environment creation framework and suite of tasks that are based on NetHack, and future directions for this research. The complete show notes for this episode can be found at twimlai.com/go/527.

    Building Technical Communities at Stack Overflow with Prashanth Chandrasekar - #526

    Play Episode Listen Later Oct 11, 2021 40:45

    In 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 of this scale. We also examine where Stack Overflow is in their AI journey, use cases illustrating how they're currently utilizing ML, what their role is in the future of AI-based code generation, what other trends they've picked up on over the last few years, and how they're using those insights to forge the path forward. The complete show notes for this episode can be found at twimlai.com/go/526.

    Deep Learning is Eating 5G. Here's How, w/ Joseph Soriaga - #525

    Play Episode Listen Later Oct 7, 2021 39:38

    Today 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 paper, WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels, explores the use of rf signals to infer what the environment looks like, allowing for estimation of a person's movement.  We also discuss the ability for machine learning and AI to help enable 5G and make it more efficient for these applications, as well as the scenarios that ML would allow for more effective delivery of connected services, and look towards what might be possible in the near future.  The complete show notes for this episode can be found at twimlai.com/go/525.

    Modeling Memory with RNNs and Curriculum Learning w/ Kanaka Rajan - #524

    Play Episode Listen Later Oct 4, 2021 47:08

    Today 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 the biological brain uses?” We also discuss the relationship between memory and dynamically evolving system states, how close we are to understanding how memory actually works, how she uses RNNs for modeling these processes, and what training and data collection looks like. Finally, we touch on her use of curriculum learning (where the task you want a system to learn increases in complexity slowly), and of course, we look ahead at future directions for Kanaka's research.  The complete show notes for this episode can be found at twimlai.com/go/524.

    Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523

    Play Episode Listen Later Sep 30, 2021 40:41

    Today 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 reintroduce the problem that Metaflow was built to solve and discuss some of the unique use cases that Ville has seen since it's release, the relationship between Metaflow and Kubernetes, and the maturity of services like batch and lambdas allowing a complete production ML system to be delivered. Finally, we discuss the degree to which Ville is catering is Outerbounds' efforts to building tools for the MLOps community, and what the future looks like for him and Metaflow.  The complete show notes for this episode can be found at twimlai.com/go/523.

    Delivering Neural Speech Services at Scale with Li Jiang - #522

    Play Episode Listen Later Sep 27, 2021 49:20

    Today 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 quality and the kind of runtime characteristics that you require as a service provider, in the context of engineering and delivering a service at the scale of Azure Speech. Finally, we walk through the data collection process for customizing a voice for TTS, what languages are currently supported, managing the responsibilities of threats like deep fakes, the future for services like these, and much more! The complete show notes for this episode can be found at twimlai.com/go/522.

    AI's Legal and Ethical Implications with Sandra Wachter - #521

    Play Episode Listen Later Sep 23, 2021 49:27

    Today 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 law, as well as a breakdown of counterfactual explanations and how they're created. We also explore why factors like the lack of oversight lead to poor self-regulation, and the conditional demographic disparity test that she helped develop to test bias in models, which was recently adopted by Amazon. The complete show notes for this episode can be found at twimlai.com/go/521.

    Compositional ML and the Future of Software Development with Dillon Erb - #520

    Play Episode Listen Later Sep 20, 2021 41:14

    Today 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 notebooks, and why Paperspace made the choice to pivot towards a more traditional engineering code artifact model after building a popular notebook service. Finally, we talk through their newest release Workflows, an automation and build system for ML applications, which Dillon calls their “most ambitious and comprehensive project yet.” The complete show notes for this episode can be found at twimlai.com/go/520.

    Generating SQL [Database Queries] from Natural Language with Yanshuai Cao - #519

    Play Episode Listen Later Sep 16, 2021 38:28

    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

    Play Episode Listen Later Sep 13, 2021 51:31

    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

    Play Episode Listen Later Sep 9, 2021 40:21

    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

    Play Episode Listen Later Sep 6, 2021 45:42

    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

    Play Episode Listen Later Sep 2, 2021 45:36

    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

    Play Episode Listen Later Aug 30, 2021 46:01

    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

    Play Episode Listen Later Aug 26, 2021 36:25

    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

    Play Episode Listen Later Aug 23, 2021 49:58

    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

    Play Episode Listen Later Aug 19, 2021 43:47

    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

    Play Episode Listen Later Aug 16, 2021 42:27

    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.

    Codex, OpenAI's Automated Code Generation API with Greg Brockman - #509

    Play Episode Listen Later Aug 12, 2021 47:17

    Today 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 could evolve given different types of training data, and how users and practitioners should think about interacting with the API to get the most out of it. We also discuss Copilot, their recent collaboration with Github that is built on Codex, as well as the implications of Codex on coding education, explainability, and broader societal issues like fairness and bias, copyrighting, and jobs.  The complete show notes for this episode can be found at twimlai.com/go/509.

    Spatiotemporal Data Analysis with Rose Yu - #508

    Play Episode Listen Later Aug 9, 2021 31:41

    Today 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 explore their novel neural network design that is focused on non-traditional convolution operators and allows for general symmetry, how we get from these representations to the network architectures that she has developed and another recent paper on deep spatio-temporal models.  The complete show note for this episode can be found at twimlai.com/go/508.

    Parallelism and Acceleration for Large Language Models with Bryan Catanzaro - #507

    Play Episode Listen Later Aug 5, 2021 50:33

    Today 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 discuss the three different kinds of parallelism, tensor parallelism, pipeline parallelism, and data parallelism, that Megatron provides when training models, as well as his work on the Deep Learning Super Sampling project and the role it's playing in the present and future of game development via ray tracing.  The complete show notes for this episode can be found at twimlai.com/go/507.

    Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506

    Play Episode Listen Later Aug 2, 2021 53:50

    Today 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, as well as a breakdown of the causal roadmap, coined by researchers at UC Berkeley.  Finally, Lina talks us through the roadmap while applying the ODT rule problem, how she's applied a “superlearner” algorithm to this problem, how it was trained, and what the future of this research looks like. The complete show notes for this episode can be found at twimlai.com/go/506.

    Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes - #505

    Play Episode Listen Later Jul 29, 2021 48:07

    Today 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 identifying parameter configurations that satisfy constraints in the metric space. This allows users to efficiently explore multiple metrics at once in an efficient, informed, and intelligent way that lends itself to real-world, human-in-the-loop scenarios. The complete show notes for this episode can be found at twimlai.com/go/505.

    Fairness and Robustness in Federated Learning with Virginia Smith -#504

    Play Episode Listen Later Jul 26, 2021 36:20

    Today 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, the particulars of the failure modes, the relationship between models, and the things being optimized across devices, and the tradeoffs between fairness and robustness. We also discuss a second paper, Heterogeneity for the Win: One-Shot Federated Clustering, how the proposed method makes heterogeneity beneficial in data, how the heterogeneity of data is classified, and some applications of FL in an unsupervised setting. The complete show notes for this episode can be found at twimlai.com/go/504.

    Scaling AI at H&M Group with Errol Koolmeister - #503

    Play Episode Listen Later Jul 22, 2021 40:46

    Today 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 course, we dig into the infrastructure and models being used, the biggest challenges faced, and the importance of managing the project portfolio, while Errol shares their approach to building infra for a specific product with many products in mind.

    Evolving AI Systems Gracefully with Stefano Soatto - #502

    Play Episode Listen Later Jul 19, 2021 47:40

    Today 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 architecture when dealing with problems of model compression, how they've solved problems of regression and reprocessing by utilizing existing models, and much more. The complete show notes for this episode can be found at twimlai.com/go/502.

    ML Innovation in Healthcare with Suchi Saria - #501

    Play Episode Listen Later Jul 15, 2021 44:45

    Today 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 become accepted and adopted by the healthcare infrastructure and where exactly we stand in the adoption process, where there have been “pockets” of tangible success.  Finally, we explore the state of healthcare data, and of course, we talk about Suchi's recently announced startup Bayesian Health and their goals in the healthcare space, and an accompanying study that looks at real-time ML inference in an EMR setting. The complete show notes for this episode can be found at twimlai.com/go/501.

    Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500

    Play Episode Listen Later Jul 12, 2021 41:13

    Today 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 context of the platform, how the work of other researchers we've spoken to on compression and quantization finds its way from research to product, and much more!  After you check out this interview, you can look below for some of the other conversations with researchers mentioned.  The complete show notes for this episode can be found at twimlai.com/go/500.

    The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499

    Play Episode Listen Later Jul 8, 2021 46:57

    Today 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 experience appear in their work, the types of models being used, the various types of data, and the complexity of each of their setups.  We explore some of the challenges associated with getting computers to better understand human behavior and interact in ways that are more fluid. Finally, we touch on what excites both Dan and Sid about their respective projects, and what they're excited about for the future.   The complete show notes for this episode can be found at https://twimlai.com/go/499.

    Vector Quantization for NN Compression with Julieta Martinez - #498

    Play Episode Listen Later Jul 5, 2021 39:34

    Today 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 dealing with high dimensional, many-parameter neural networks, the advantages of using product quantization, and how that plays out when using it to compress a neural network.  We also dig into another paper Julieta presented at the conference, Deep Multi-Task Learning for Joint Localization, Perception, and Prediction, which details an architecture that is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently. The complete show notes for this episode can be found at twimlai.com/go/498.

    Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

    Play Episode Listen Later Jul 1, 2021 40:30

    Today 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 doing ML from a data-rich environment.  Finally, we dig into the evolution of climate science-focused events and conferences, as well as the Keynote Claire gave at the EarthVision workshop at CVPR “Deep Unsupervised Learning for Climate Informatics,” which focused on semi- and unsupervised deep learning approaches to studying rare and extreme climate events. The complete show notes for this episode can be found at twimlai.com/go/497.

    Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496

    Play Episode Listen Later Jun 28, 2021 46:15

    Today 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 early exiting framework for efficient video recognition.  The complete show notes for this episode can be found at twimlai.com/go/496.

    Advancing NLP with Project Debater w/ Noam Slonim - #495

    Play Episode Listen Later Jun 24, 2021 51:01

    Today 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 of the underlying capabilities of Debater, including the relationship between systems preparation and training, evidence detection, detecting the quality of arguments, narrative generation, the use of conventional NLP methods like entity linking, and much more. The complete show notes for this episode can be found at twimlai.com/go/495.

    Bringing AI Up to Speed with Autonomous Racing w/ Madhur Behl - #494

    Play Episode Listen Later Jun 21, 2021 50:55

    Today we're joined by Madhur Behl, an Assistant Professor in the department of computer science at the University of Virginia.  In our conversation with Madhur, we explore the super interesting work he's doing at the intersection of autonomous driving, ML/AI, and Motorsports, where he's teaching self-driving cars how to drive in an agile manner. We talk through the differences between traditional self-driving problems and those encountered in a racing environment, the challenges in solving planning, perception, control.  We also discuss their upcoming race at the Indianapolis Motor Speedway, where Madhur and his students will compete for 1 million dollars in the world's first head-to-head fully autonomous race, and how they're preparing for it.

    AI and Society: Past, Present and Future with Eric Horvitz - #493

    Play Episode Listen Later Jun 17, 2021 41:42

    Today we continue our  AI Innovation series joined by Microsoft's Chief Scientific Officer, Eric Horvitz.  In our conversation with Eric, we explore his tenure as AAAI president and his focus on the future of AI and its ethical implications, the scope of the study on the topic, and how drastically the AI and machine learning landscape has changed since 2009. We also discuss Eric's role at Microsoft and the Aether committee that has advised the company on issues of responsible AI since 2017. Finally, we talk through his recent work as a member of the National Security Commission on AI, where he helped commission a 750+ page report on topics including the Future of AI R&D, Building Trustworthy AI systems, civil liberties and privacy, and the challenging area of AI and autonomous weapons.   The complete show notes for this episode can be found at twimlai.com/go/493.

    Agile Applied AI Research with Parvez Ahammad - #492

    Play Episode Listen Later Jun 14, 2021 43:52

    Today we're joined by Parvez Ahammad, head of data science applied research at LinkedIn. In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to identify products that can help in a cross-cutting way across multiple lines of business, quantitative methodologies to identify unintended consequences in experimentation, and navigating the tension between research and applied ML teams in an organization. Finally, we discuss differential privacy, and their recently released GreyKite library, an open-source Python library developed to support forecasting. The complete show note for this episode can be found at twimlai.com/go/492.

    Haptic Intelligence with Katherine J. Kuchenbecker - #491

    Play Episode Listen Later Jun 10, 2021 38:40

    Today we're joined Katherine J. Kuchenbecker, director at the Max Planck Institute for Intelligent Systems and of the haptic intelligence department.  In our conversation, we explore Katherine's research interests, which lie at the intersection of haptics (physical interaction with the world) and machine learning, introducing us to the concept of “haptic intelligence.” We discuss how ML, mainly computer vision, has been integrated to work together with robots, and some of the devices that Katherine's lab is developing to take advantage of this research. We also talk about hugging robots, augmented reality in robotic surgery, and the degree to which she studies human-robot interaction. Finally, Katherine shares with us her passion for mentoring and the importance of diversity and inclusion in robotics and machine learning.  The complete show notes for this episode can be found at twimlai.com/go/491.

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